|
1
|
Bicanin Ilic M, Nikolic Turnic T, Ilic I, Nikolov A, Mujkovic S, Rakic D, Jovic N, Arsenijevic N, Mitrovic S, Spasojevic M, Savic J, Mihajlovic K, Jeremic N, Joksimovic Jovic J, Pindovic B, Balovic G, Dimitrijevic A. SARS-CoV-2 Infection and Its Association with Maternal and Fetal Redox Status and Outcomes: A Prospective Clinical Study. J Clin Med 2025; 14:1555. [PMID: 40095482 PMCID: PMC11899921 DOI: 10.3390/jcm14051555] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2024] [Revised: 02/09/2025] [Accepted: 02/11/2025] [Indexed: 03/19/2025] Open
Abstract
Background: The impact of the SARS-CoV-2 viral infection during pregnancy on the fetus can be direct-transmitted through the placenta-and indirect-creating unfavorable conditions for the development of the fetus because of inflammation, micro-thrombosis, and hypercoagulation. Our study aimed to determine the types and frequency of pathohistological changes in placental tissue in SARS-CoV-2-positive pregnant women and to examine the possible role of oxidative stress in the prognosis of the delivery and its maternal and fetal complications. Methods: This prospective clinical study included 50 pregnant women divided into two groups, SARS-CoV-2 positive (COVID-19 group) and SARS-CoV-2 negative (control group), from who we collected demographic, clinical, obstetric, biochemical and pathologic data. Data about the newborn characteristics were also collected, which included anamnestic, clinical, and biochemical data. Results: The values of the superoxide anion radical and index of lipid peroxidation were significantly different in mothers concerning the presence of the SARS-CoV-2 infection, while the levels of the nitric oxide, index of lipid peroxidation, reduced glutathione, and superoxide dismutase were significantly different in the newborns depending on the SARS-CoV-2 infection. Newborn characteristics were similar between groups except for concentrations of IgM antibody. The incidence of pathohistological changes of the FVM type in the COVID-19 group of pregnant women was 46%, while in the control group, the incidence was 18%. Conclusions: This study confirmed the significant impact of the SARS-CoV-2 viral infection on maternal and fetal biochemical parameters and oxidative stress-mediated placental dysfunction. Future studies should be performed with more participants and follow-up neonatal development.
Collapse
Affiliation(s)
- Marija Bicanin Ilic
- Faculty of Medical Sciences, Department of Gynecology and Obstetrics, University of Kragujevac, 34000 Kragujevac, Serbia; (A.N.); (S.M.); (D.R.); (N.J.); (N.A.); (A.D.)
- Clinic of Gynecology and Obstetrics, University Clinical Center Kragujevac, 34000 Kragujevac, Serbia
| | - Tamara Nikolic Turnic
- Faculty of Medical Sciences, Department of Pharmacy, University of Kragujevac, 34000 Kragujevac, Serbia; (T.N.T.); (K.M.); (N.J.); (B.P.)
- N.A. Semashko Public Health and Healthcare Department, F.F. Erismann Institute of Public Health, I.M. Sechenov First Moscow State Medical University (Sechenov University), 119435 Moscow, Russia
- Center of Excellence for Redox Balance Research in Cardiovascular and Metabolic Disorders, 34000 Kragujevac, Serbia;
| | - Igor Ilic
- Department of Radiology, University Clinical Center Kragujevac, 34000 Kragujevac, Serbia
| | - Aleksandar Nikolov
- Faculty of Medical Sciences, Department of Gynecology and Obstetrics, University of Kragujevac, 34000 Kragujevac, Serbia; (A.N.); (S.M.); (D.R.); (N.J.); (N.A.); (A.D.)
- Clinic of Gynecology and Obstetrics, University Clinical Center Kragujevac, 34000 Kragujevac, Serbia
| | - Srdjan Mujkovic
- Faculty of Medical Sciences, Department of Gynecology and Obstetrics, University of Kragujevac, 34000 Kragujevac, Serbia; (A.N.); (S.M.); (D.R.); (N.J.); (N.A.); (A.D.)
- Clinic of Gynecology and Obstetrics, University Clinical Center Kragujevac, 34000 Kragujevac, Serbia
| | - Dejana Rakic
- Faculty of Medical Sciences, Department of Gynecology and Obstetrics, University of Kragujevac, 34000 Kragujevac, Serbia; (A.N.); (S.M.); (D.R.); (N.J.); (N.A.); (A.D.)
- Clinic of Gynecology and Obstetrics, University Clinical Center Kragujevac, 34000 Kragujevac, Serbia
| | - Nikola Jovic
- Faculty of Medical Sciences, Department of Gynecology and Obstetrics, University of Kragujevac, 34000 Kragujevac, Serbia; (A.N.); (S.M.); (D.R.); (N.J.); (N.A.); (A.D.)
- Clinic of Gynecology and Obstetrics, University Clinical Center Kragujevac, 34000 Kragujevac, Serbia
| | - Neda Arsenijevic
- Faculty of Medical Sciences, Department of Gynecology and Obstetrics, University of Kragujevac, 34000 Kragujevac, Serbia; (A.N.); (S.M.); (D.R.); (N.J.); (N.A.); (A.D.)
- Clinic of Gynecology and Obstetrics, University Clinical Center Kragujevac, 34000 Kragujevac, Serbia
| | - Slobodanka Mitrovic
- Faculty of Medical Sciences, Department of Pathology, University of Kragujevac, 34000 Kragujevac, Serbia; (S.M.); (M.S.); (J.S.)
- Department of Pathology, University Clinical Center Kragujevac, 34000 Kragujevac, Serbia
| | - Marija Spasojevic
- Faculty of Medical Sciences, Department of Pathology, University of Kragujevac, 34000 Kragujevac, Serbia; (S.M.); (M.S.); (J.S.)
- Department of Pathology, University Clinical Center Kragujevac, 34000 Kragujevac, Serbia
| | - Jelena Savic
- Faculty of Medical Sciences, Department of Pathology, University of Kragujevac, 34000 Kragujevac, Serbia; (S.M.); (M.S.); (J.S.)
- Department of Pathology, University Clinical Center Kragujevac, 34000 Kragujevac, Serbia
| | - Katarina Mihajlovic
- Faculty of Medical Sciences, Department of Pharmacy, University of Kragujevac, 34000 Kragujevac, Serbia; (T.N.T.); (K.M.); (N.J.); (B.P.)
- Center of Excellence for Redox Balance Research in Cardiovascular and Metabolic Disorders, 34000 Kragujevac, Serbia;
| | - Nevena Jeremic
- Faculty of Medical Sciences, Department of Pharmacy, University of Kragujevac, 34000 Kragujevac, Serbia; (T.N.T.); (K.M.); (N.J.); (B.P.)
- Center of Excellence for Redox Balance Research in Cardiovascular and Metabolic Disorders, 34000 Kragujevac, Serbia;
- Federal State Autonomous Educational Institution of Higher Education I.M. Sechenov First, Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University), 119991 Moscow, Russia
| | - Jovana Joksimovic Jovic
- Center of Excellence for Redox Balance Research in Cardiovascular and Metabolic Disorders, 34000 Kragujevac, Serbia;
- Faculty of Medical Sciences, Department of Physiology, University of Kragujevac, 34000 Kragujevac, Serbia
| | - Bozidar Pindovic
- Faculty of Medical Sciences, Department of Pharmacy, University of Kragujevac, 34000 Kragujevac, Serbia; (T.N.T.); (K.M.); (N.J.); (B.P.)
- Center of Excellence for Redox Balance Research in Cardiovascular and Metabolic Disorders, 34000 Kragujevac, Serbia;
| | - Goran Balovic
- Faculty of Medical Sciences, Department of Surgery, University of Kragujevac, 34000 Kragujevac, Serbia;
- Center of Pediatric Surgery, University Clinical Center Kragujevac, 34000 Kragujevac, Serbia
| | - Aleksandra Dimitrijevic
- Faculty of Medical Sciences, Department of Gynecology and Obstetrics, University of Kragujevac, 34000 Kragujevac, Serbia; (A.N.); (S.M.); (D.R.); (N.J.); (N.A.); (A.D.)
- Clinic of Gynecology and Obstetrics, University Clinical Center Kragujevac, 34000 Kragujevac, Serbia
| |
Collapse
|
|
2
|
Ahmad IS, Dai J, Xie Y, Liang X. Deep learning models for CT image classification: a comprehensive literature review. Quant Imaging Med Surg 2025; 15:962-1011. [PMID: 39838987 PMCID: PMC11744119 DOI: 10.21037/qims-24-1400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2024] [Accepted: 10/18/2024] [Indexed: 01/23/2025]
Abstract
Background and Objective Computed tomography (CT) imaging plays a crucial role in the early detection and diagnosis of life-threatening diseases, particularly in respiratory illnesses and oncology. The rapid advancement of deep learning (DL) has revolutionized CT image analysis, enhancing diagnostic accuracy and efficiency. This review explores the impact of advanced DL methodologies in CT imaging, with a particular focus on their applications in coronavirus disease 2019 (COVID-19) detection and lung nodule classification. Methods A comprehensive literature search was conducted, examining the evolution of DL architectures in medical imaging from conventional convolutional neural networks (CNNs) to sophisticated foundational models (FMs). We reviewed publications from major databases, focusing on developments in CT image analysis using DL from 2013 to 2023. Our search criteria included all types of articles, with a focus on peer-reviewed research papers and review articles in English. Key Content and Findings The review reveals that DL, particularly advanced architectures like FMs, has transformed CT image analysis by streamlining interpretation processes and enhancing diagnostic capabilities. We found significant advancements in addressing global health challenges, especially during the COVID-19 pandemic, and in ongoing efforts for lung cancer screening. The review also addresses technical challenges in CT image analysis, including data variability, the need for large high-quality datasets, and computational demands. Innovative strategies such as transfer learning, data augmentation, and distributed computing are explored as solutions to these challenges. Conclusions This review underscores the pivotal role of DL in advancing CT image analysis, particularly for COVID-19 and lung nodule detection. The integration of DL models into clinical workflows shows promising potential to enhance diagnostic accuracy and efficiency. However, challenges remain in areas of interpretability, validation, and regulatory compliance. The review advocates for continued research, interdisciplinary collaboration, and ethical considerations as DL technologies become integral to clinical practice. While traditional imaging techniques remain vital, the integration of DL represents a significant advancement in medical diagnostics, with far-reaching implications for future research, clinical practice, and healthcare policy.
Collapse
Affiliation(s)
- Isah Salim Ahmad
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Jingjing Dai
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yaoqin Xie
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Xiaokun Liang
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
- University of Chinese Academy of Sciences, Beijing, China
| |
Collapse
|
|
3
|
Melkonian AK, Hakobyan GV. Evaluation of the therapeutic action of original antiviral drug in SARS-CoV-2. Biotechnol Appl Biochem 2024; 71:1057-1069. [PMID: 38710664 DOI: 10.1002/bab.2597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 04/23/2024] [Indexed: 05/08/2024]
Abstract
Purpose of this article is to study the possible direct antiviral effect of "Armenikum" on SARS-CoV-2, conduct an in vitro study on the SARS-CoV-2 encephalomocarditis virus, and an in vivo study on the Syrian hamster model. Human coronavirus SARS-CoV-2 (delta strain) was used as the virus. Two groups of four-specimen hamsters were used to study the therapeutic activity of the drug during 48 h after infecting. One group of hamsters served as positive control and was infected with the virus at a similar dose as experimental one and was used as a control of pathology induced by the viral infection till the end of the experiment. Another group of hamsters (four of them) was injected physiological solution and was used as a control. The Syrian hamsters underwent a clinical blood test and computed tomography. "Armenikum" in the form of an injection has a significant antiviral effect on the human coronavirus SARS-CoV-2, credibly reducing the titers of the virus and the time of its elimination from the Syrian hamsters, significantly mitigating the viral infection. "Armenikum" in the form of an injection drug almost completely removes the pathological effect of the virus in the lungs of the hamsters.
Collapse
Affiliation(s)
| | - Gagik V Hakobyan
- Department of Oral and Maxillofacial Surgery, University of Yerevan State Medical University, Yerevan, Armenia
| |
Collapse
|
|
4
|
Islam N, Mohsin ASM, Choudhury SH, Shaer TP, Islam MA, Sadat O, Taz NH. COVID-19 and Pneumonia detection and web deployment from CT scan and X-ray images using deep learning. PLoS One 2024; 19:e0302413. [PMID: 38976703 PMCID: PMC11230556 DOI: 10.1371/journal.pone.0302413] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 04/03/2024] [Indexed: 07/10/2024] Open
Abstract
During the COVID-19 pandemic, pneumonia was the leading cause of respiratory failure and death. In addition to SARS-COV-2, it can be caused by several other bacterial and viral agents. Even today, variants of SARS-COV-2 are endemic and COVID-19 cases are common in many places. The symptoms of COVID-19 are highly diverse and robust, ranging from invisible to severe respiratory failure. Current detection methods for the disease are time-consuming and expensive with low accuracy and precision. To address such situations, we have designed a framework for COVID-19 and Pneumonia detection using multiple deep learning algorithms further accompanied by a deployment scheme. In this study, we have utilized four prominent deep learning models, which are VGG-19, ResNet-50, Inception V3 and Xception, on two separate datasets of CT scan and X-ray images (COVID/Non-COVID) to identify the best models for the detection of COVID-19. We achieved accuracies ranging from 86% to 99% depending on the model and dataset. To further validate our findings, we have applied the four distinct models on two more supplementary datasets of X-ray images of bacterial pneumonia and viral pneumonia. Additionally, we have implemented a flask app to visualize the outcome of our framework to show the identified COVID and Non-COVID images. The findings of this study will be helpful to develop an AI-driven automated tool for the cost effective and faster detection and better management of COVID-19 patients.
Collapse
Affiliation(s)
- Nahid Islam
- Department of Electrical and Electronics Engineering, Nanotechnology, IoT and Machine Learning Research Group, BRAC University, Dhaka, Bangladesh
| | - Abu S M Mohsin
- Department of Electrical and Electronics Engineering, Nanotechnology, IoT and Machine Learning Research Group, BRAC University, Dhaka, Bangladesh
| | - Shadab Hafiz Choudhury
- Department of Electrical and Electronics Engineering, Nanotechnology, IoT and Machine Learning Research Group, BRAC University, Dhaka, Bangladesh
| | - Tazwar Prodhan Shaer
- Department of Electrical and Electronics Engineering, Nanotechnology, IoT and Machine Learning Research Group, BRAC University, Dhaka, Bangladesh
| | - Md Adnan Islam
- Department of Electrical and Electronics Engineering, Nanotechnology, IoT and Machine Learning Research Group, BRAC University, Dhaka, Bangladesh
| | - Omar Sadat
- Department of Electrical and Electronics Engineering, Nanotechnology, IoT and Machine Learning Research Group, BRAC University, Dhaka, Bangladesh
| | - Nahid Hossain Taz
- Department of Electrical and Electronics Engineering, Nanotechnology, IoT and Machine Learning Research Group, BRAC University, Dhaka, Bangladesh
| |
Collapse
|
|
5
|
Fuente-Moreno M, Garcia-Terol C, Domínguez-Salas S, Rubio-Valera M, Motrico E. Maternity care changes and postpartum mental health during the COVID-19 pandemic: a Spanish cross-sectional study. J Reprod Infant Psychol 2024; 42:753-768. [PMID: 36710435 DOI: 10.1080/02646838.2023.2171375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 01/17/2023] [Indexed: 01/31/2023]
Abstract
BACKGROUND The coronavirus disease 2019 (COVID-19) pandemic led to abrupt changes in maternity care, but the impact of these changes has not yet been deeply evaluated. This study aimed to assess the impact of the unexpected changes in maternity care due to the COVID-19 pandemic on postpartum mental health (depression, anxiety and posttraumatic stress disorder). METHODS A cross-sectional, web-based study was conducted in Spain during the second half of 2020. The eligibility criteria were women≥18 years with a child≤6 months. The Edinburgh Postnatal Depression Scale (EPDS), the Generalized Anxiety Disorder-7 Screener (GAD-7) and a subset of the PTSD checklist (PCL-5) were used to assess postpartum mental health. Information regarding sociodemographic characteristics and maternity care changes was collected, and multivariate regression models were used. RESULTS Among 1781 participants, 29.3% and 33% had clinically significant depressive and anxiety symptoms, respectively. The most prevalent unexpected changes reported were related to the exclusion of supportive relatives during birth and postpartum. Changes reported during birth showed a minor association with PTSD symptomatology, and those that occurred during the postpartum period were associated with clinical depression, anxiety and PTSD symptoms. CONCLUSIONS The unexpected changes in maternity care due to the COVID-19 pandemic, especially those that occurred during the postpartum period, increased the risk of mental health problems.
Collapse
Affiliation(s)
- Marina Fuente-Moreno
- Health Technology Assessment in Primary Care and Mental Health (PRISMA) Research Group, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain
- Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, Barcelona, Spain
- Red de Investigación en Cronicidad, Atención Primaria y Promoción de la Salud (RICAPPS), Madrid, Spain
| | - Clara Garcia-Terol
- Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, Barcelona, Spain
- Parc Sanitari Sant Joan de Déu,Sant Boi de Llobregat, Spain
| | | | - María Rubio-Valera
- Health Technology Assessment in Primary Care and Mental Health (PRISMA) Research Group, Institut de Recerca Sant Joan de Déu, Esplugues de Llobregat, Barcelona, Spain
- Parc Sanitari Sant Joan de Déu,Sant Boi de Llobregat, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
| | - Emma Motrico
- Department of Psychology, Universidad Loyola Andalucía, Seville, Spain
| |
Collapse
|
|
6
|
Oraya DB, Militante SKN, Dans LF, Lozada MCH, Valle AOS, Cabaluna ITG. Chest CT Scan Findings in Children with COVID-19: A Systematic Review. ACTA MEDICA PHILIPPINA 2024; 58:110-128. [PMID: 38882921 PMCID: PMC11168958 DOI: 10.47895/amp.v58i7.6385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
Objectives To gather, summarize, and appraise the available evidence on: 1) the accuracy of chest CT scan in diagnosing COVID-19 among children, and 2) the characteristic chest CT scan findings associated with COVID-19 pneumonia in children. Methods We comprehensively searched databases (MEDLINE, COCHRANE), clinical trial registries, bibliographic lists of selected studies, and unpublished data for relevant studies. Guide questions from the Painless Evidence Based Medicine and the National Institutes of Health Quality Assessment Tools were used to assess study quality. Results A poor quality study showed 86.0% (95% CI 73.8, 93.0) sensitivity and 75.9% (95% CI 67.1, 83.0) specificity of chest CT scan in diagnosing COVID-19 in children. Thirty-nine observational studies describing chest CT scan in children with COVID-19 showed abnormal findings in 717 of 1028 study subjects. Common chest CT scan findings in this population include: 1) ground glass opacities, patchy shadows, and consolidation, 2) lower lobe involvement, and 3) unilateral lung lesions. Conclusion Studies which investigate the accuracy of chest CT scan in the diagnosis of COVID-19 in children are limited by heterogeneous populations and small sample sizes. While chest CT scan findings such as patchy shadows, ground glass opacities, and consolidation are common in children with COVID-19, these may be similar to the imaging findings of other respiratory viral illnesses.
Collapse
Affiliation(s)
- Denisa B Oraya
- Department of Pediatrics, Philippine General Hospital, University of the Philippines Manila
| | | | - Leonila F Dans
- Division of Rheumatology, Department of Pediatrics, Philippine General Hospital, University of the Philippines Manila
| | - Maria Cristina H Lozada
- Division of Pulmonology, Department of Pediatrics, Philippine General Hospital, University of the Philippines Manila
| | - Andrea Orel S Valle
- Division of Cardiology, Department of Pediatrics, Philippine General Hospital, University of the Philippines Manila
| | - Ian Theodore G Cabaluna
- Department of Clinical Epidemiology, College of Medicine, University of the Philippines Manila
| |
Collapse
|
|
7
|
Yi J, Chen L, Meng X, Chen Y. The impact of gestational weeks of Coronavirus disease 2019 (COVID-19) infection on perinatal outcomes. Reprod Health 2024; 21:31. [PMID: 38433197 PMCID: PMC10910700 DOI: 10.1186/s12978-024-01762-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 02/24/2024] [Indexed: 03/05/2024] Open
Abstract
BACKGROUND To evaluate the relationship between coronavirus disease 2019 (COVID-19) infection at different time points during pregnancy and perinatal outcomes. METHODS This retrospective study included 611 women who hospitalized for delivery between December 7 and April 30, 2023. Based on the different pregnancy weeks infected with COVID-19, the participants were divided into four groups: Group 1 (14-27+6 weeks gestation), Group 2 (28-36+6 weeks gestation), Group 3 (37-39+6 weeks gestation), and Group 4 (≥ 40 weeks gestation). Data including maternal demographic characteristics, clinical profiles, and perinatal outcomes were analyzed. RESULTS There were no significant differences in maternal demographic characteristics among the four groups (P > 0.05). Compared to Groups 3 and 4, a higher rate of fever was noted in Groups 1 and 2 (P < 0.05). The frequency of preeclampsia and gestational diabetes mellitus showed a decreasing trend as pregnancy progressing (P < 0.05). Preterm delivery and neonatal intensive care unit admission were more frequently observed in Groups 1 and 2 than in Groups 3 and 4 (P < 0.05). Multivariate logistic regression analysis demonstrated that the timing of gestation in which COVID-19 was infected was not associated with preterm delivery and neonatal intensive care unit admission (P > 0.05), whereas gestational age at COVID-19 infection was negatively associated with the occurrence of preeclampsia and gestational diabetes mellitus (P < 0.05). CONCLUSIONS Gestational age at COVID-19 infection is a simple parameter that predicts adverse perinatal outcomes to aid clinicians in determining to provide early enhanced prenatal care and increased monitoring to reduce maternal complications.
Collapse
Affiliation(s)
- Jiao Yi
- Department of Obstetrics and Gynecology, Maternal and Child Health Care Hospital Affiliated With Anhui Medical University, Anhui Maternal and Child Health Care Hospital, No 15 Yimin Street, Hefei, 230000, China.
| | - Lei Chen
- Department of Obstetrics and Gynecology, Maternal and Child Health Care Hospital Affiliated With Anhui Medical University, Anhui Maternal and Child Health Care Hospital, No 15 Yimin Street, Hefei, 230000, China
| | - Xianglian Meng
- Department of Obstetrics and Gynecology, Maternal and Child Health Care Hospital Affiliated With Anhui Medical University, Anhui Maternal and Child Health Care Hospital, No 15 Yimin Street, Hefei, 230000, China
| | - Yi Chen
- Department of Obstetrics and Gynecology, Maternal and Child Health Care Hospital Affiliated With Anhui Medical University, Anhui Maternal and Child Health Care Hospital, No 15 Yimin Street, Hefei, 230000, China
| |
Collapse
|
|
8
|
Kleinwechter HJ, Weber KS, Liedtke TP, Schäfer-Graf U, Groten T, Rüdiger M, Pecks U. COVID-19, Pregnancy, and Diabetes Mellitus. Z Geburtshilfe Neonatol 2024; 228:17-31. [PMID: 37918833 DOI: 10.1055/a-2180-7715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
During the severe acute respiratory distress virus coronavirus type 2 (SARS-CoV-2) pandemic, many women were infected during their pregnancies. The SARS-CoV-2-induced coronavirus disease 19 (COVID-19) has an impact on maternal health and pregnancy outcomes; peripartum and perinatal morbidity and mortality are increased. Pregnancy is considered a risk factor for severe COVID-19 course. Additional risk factors during pregnancy are diabetes mellitus, gestational diabetes mellitus (GDM), and obesity. Systemic inflammation can lead to severe metabolic dysregulation with ketoacidosis. The endocrine pancreas is a target organ for SARS-CoV-2 and the fetal risk depends on inflammation of the placenta. Up to now there is no evidence that SARS-CoV-2 infection during pregnancy leads to permanent diabetes in mothers or their offspring via triggering autoimmunity or beta cell destruction. The frequently observed increased prevalence of GDM compared to the years before the pandemic is most likely due to changed lifestyle during lockdown. Furthermore, severe COVID-19 may be associated with the development of GDM due to worsening of glucose tolerance. Vaccination with a mRNA vaccine is safe and highly effective to prevent infection and to reduce hospitalization. Registries support offering evidence-based recommendations on vaccination for pregnant women. Even with the current omicron virus variant, there are increased risks for symptomatic and unvaccinated pregnant women.
Collapse
Affiliation(s)
| | | | | | - Ute Schäfer-Graf
- Department of Obstetrics, Berlin Diabetes Center for Pregnant Women, St. Joseph Hospital, Berlin, Germany
| | - Tanja Groten
- Department of Obstetrics, Competence Center for Diabetic Women, Jena University Hospital, Jena, Germany
| | - Mario Rüdiger
- Saxony Center for Fetal-Neonatal Health, University Hospital Carl Gustav Carus, Dresden, Germany
| | - Ulrich Pecks
- Department of Obstetrics and Gynecology, University Hospital of Schleswig-Holstein, Campus Kiel, Kiel, Germany
- Department of Obstetrics, University Hospital Würzburg, Maternal Health and Midwifery Science, Würzburg, Germany
| |
Collapse
|
|
9
|
Feghali JA, Russo RA, Mamou A, Lorentz A, Cantarinha A, Bellin MF, Meyrignac O. Image quality assessment in low-dose COVID-19 chest CT examinations. Acta Radiol 2024; 65:3-13. [PMID: 36744376 PMCID: PMC9905706 DOI: 10.1177/02841851231153797] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 12/21/2022] [Indexed: 02/07/2023]
Abstract
BACKGROUND Low-dose thoracic protocols were developed massively during the COVID-19 outbreak. PURPOSE To study the impact on image quality (IQ) and the diagnosis reliability of COVID-19 low-dose chest computed tomography (CT) protocols. MATERIAL AND METHODS COVID-19 low-dose protocols were implemented on third- and second-generation CT scanners considering two body mass index (BMI) subgroups (<25 kg/m2 and >25 kg/m2). Contrast-to-noise ratios (CNR) were compared with a Catphan phantom. Next, two radiologists retrospectively assessed IQ for 243 CT patients using a 5-point Linkert scale for general IQ and diagnostic criteria. Kappa score and Wilcoxon rank sum tests were used to compare IQ score and CTDIvol between radiologists, protocols, and scanner models. RESULTS In vitro analysis of Catphan inserts showed in majority significantly decreased CNR for the low dose versus standard acquisition protocols on both CT scanners. However, in vivo, there was no impact on the diagnosis: sensitivity and specificity were ≥0.8 for all protocols and CT scanners. The third-generation scanner involved a significantly lower dose compared to the second-generation scanner (CTDIvol of 1.8 vs. 2.6 mGy for BMI <25 kg/m2 and 3.3 vs. 4.6 mGy for BMI >25 kg/m2). Still, the third-generation scanner showed a significantly higher IQ with the low-dose protocol compared to the second-generation scanner (30.9 vs. 28.1 for BMI <25 kg/m2 and 29.9 vs. 27.8 for BMI >25 kg/m2). Finally, the two radiologists had good global inter-reader agreement (kappa ≥0.6) for general IQ. CONCLUSION Low-dose protocols provided sufficient IQ independently of BMI subgroups and CT models without any impact on diagnosis reliability.
Collapse
Affiliation(s)
- Joelle A Feghali
- Diagnostic and Interventional Radiology Department, AP-HP Paris Saclay University, Bicêtre Hospital, Le Kremlin-Bicêtre, Le Kremlin Bicêtre, France
| | - Roberta A Russo
- Diagnostic and Interventional Radiology Department, AP-HP Paris Saclay University, Bicêtre Hospital, Le Kremlin-Bicêtre, Le Kremlin Bicêtre, France
| | - Adel Mamou
- Diagnostic and Interventional Radiology Department, AP-HP Paris Saclay University, Bicêtre Hospital, Le Kremlin-Bicêtre, Le Kremlin Bicêtre, France
| | - Axel Lorentz
- Diagnostic and Interventional Radiology Department, AP-HP Paris Saclay University, Bicêtre Hospital, Le Kremlin-Bicêtre, Le Kremlin Bicêtre, France
| | - Alfredo Cantarinha
- Diagnostic and Interventional Radiology Department, AP-HP Paris Saclay University, Bicêtre Hospital, Le Kremlin-Bicêtre, Le Kremlin Bicêtre, France
| | - Marie-France Bellin
- Diagnostic and Interventional Radiology Department, AP-HP Paris Saclay University, Bicêtre Hospital, Le Kremlin-Bicêtre, Le Kremlin Bicêtre, France
- Faculty of Medicine, Paris-Saclay University, Le Kremlin-Bicêtre, France
- Laboratoire d'Imagerie Biomédicale Multimodale (BioMaps), Université Paris-Saclay, CEA, CNRS, Inserm, Service Hospitalier Frédéric Joliot, Orsay, France
| | - Olivier Meyrignac
- Diagnostic and Interventional Radiology Department, AP-HP Paris Saclay University, Bicêtre Hospital, Le Kremlin-Bicêtre, Le Kremlin Bicêtre, France
- Faculty of Medicine, Paris-Saclay University, Le Kremlin-Bicêtre, France
- Laboratoire d'Imagerie Biomédicale Multimodale (BioMaps), Université Paris-Saclay, CEA, CNRS, Inserm, Service Hospitalier Frédéric Joliot, Orsay, France
| |
Collapse
|
|
10
|
Saelim J, Kritsaneepaiboon S, Charoonratana V, Khantee P. Radiographic patterns and severity scoring of COVID-19 pneumonia in children: a retrospective study. BMC Med Imaging 2023; 23:199. [PMID: 38036961 PMCID: PMC10691029 DOI: 10.1186/s12880-023-01154-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 11/13/2023] [Indexed: 12/02/2023] Open
Abstract
BACKGROUND Chest radiography (CXR) is an adjunct tool in treatment planning and monitoring of the disease course of COVID-19 pneumonia. The purpose of the study was to describe the radiographic patterns and severity scores of abnormal CXR findings in children diagnosed with COVID-19 pneumonia. METHODS This retrospective study included children with confirmed COVID-19 by reverse transcriptase-polymerase chain reaction test who underwent CXR at the arrival. The CXR findings were reviewed, and modified radiographic scoring was assessed. RESULTS The number of abnormal CXR findings was 106 of 976 (10.9%). Ground-glass opacity (GGO) was commonly found in children aged > 9 years (19/26, 73.1%), whereas peribronchial thickening was predominantly found in children aged < 5 years (25/54, 46.3%). Overall, the most common radiographic finding was peribronchial thickening (54/106, 51%). The lower lung zone (56/106, 52.8%) was the most common affected area, and there was neither peripheral nor perihilar predominance (84/106, 79.2%). Regarding the severity of COVID-19 pneumonia based on abnormal CXR findings, 81 of 106 cases (76.4%) had mild lung abnormalities. Moderate and severe lung abnormalities were found in 21 (19.8%) and 4 (3.8%) cases, respectively. While there were no significant differences in the radiographic severity scores among the various pediatric age groups, there were significant disparities in severity scores in the initial CXR and medical treatments. CONCLUSIONS This study clarified the age distribution of radiographic features across the pediatric population. GGO was commonly found in children aged > 9 years, whereas peribronchial thickening was predominant in children aged < 5 years. The lower lung zone was the most common affected area, and the high severity lung scores required more medical treatments and oxygen support.
Collapse
Affiliation(s)
- Jumlong Saelim
- Department of Radiology, Faculty of Medicine, Prince of Songkla University, Hat Yai, 90110, Thailand
- Department of Radiology, Hatyai Hospital, Hat Yai, 90110, Thailand
| | - Supika Kritsaneepaiboon
- Department of Radiology, Faculty of Medicine, Prince of Songkla University, Hat Yai, 90110, Thailand.
| | - Vorawan Charoonratana
- Department of Radiology, Faculty of Medicine, Prince of Songkla University, Hat Yai, 90110, Thailand
| | - Puttichart Khantee
- Division of Infectious Diseases, Department of Pediatrics, Faculty of Medicine, Prince of Songkla University, Hat Yai, 90110, Thailand
| |
Collapse
|
|
11
|
Hernández-García M, Solito C, Pavón Ortiz A, Arguedas Casamayor N, Melé-Casas M, Pons-Tomàs G, F. de Sevilla M, Pino R, Launes C, Guitart C, Girona-Alarcón M, Jordan I, García-García JJ. Characteristics and Risk Factors Associated with SARS-CoV-2 Pneumonias in Hospitalized Pediatric Patients: A Pilot Study. CHILDREN (BASEL, SWITZERLAND) 2023; 10:1703. [PMID: 37892366 PMCID: PMC10605629 DOI: 10.3390/children10101703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 10/11/2023] [Accepted: 10/16/2023] [Indexed: 10/29/2023]
Abstract
SARS-CoV-2 pneumonia in children has a lower incidence and severity compared to adults. Risk factors are adolescence and comorbidities. Our aims were to describe the characteristics of children admitted with SARS-CoV-2 pneumonia, identify risk factors associated with severity and compare the cases according to the variant of SARS-CoV-2. This was a descriptive and retrospective study, including patients aged 0-18 years hospitalized in a tertiary-care hospital between 1 March 2020 and 1 March 2022. Epidemiological, clinical, diagnostic and therapeutic data were analyzed. Forty-four patients were admitted; twenty-six (59%) were male and twenty-seven (61%) were older than 12 years. Thirty-six (82%) had comorbidities, the most frequent of which were obesity and asthma. Seven (15.9%) patients required high-flow oxygen, eleven (25%) non-invasive ventilation and four (9.1%) conventional mechanical ventilation. In critically ill patients, higher levels of anemia, lymphopenia, procalcitonin, lactate dehydrogenase (LDH) and hypoalbuminemia and lower levels of HDL-cholesterol were detected (all p < 0.05). Prematurity (p = 0.022) was associated with intensive care unit admission. Patients were younger during the Omicron wave (p < 0.01); no variant was associated with greater severity. In conclusion, pediatric patients with a history of prematurity or with anemia, lymphopenia, elevated procalcitonin, elevated LDH levels, hypoalbuminemia and low HDL-cholesterol levels may require admission and present more severe forms. Apart from age, no notable differences between SARS-CoV-2 variant periods were found.
Collapse
Affiliation(s)
- María Hernández-García
- Paediatrics Department, Hospital Sant Joan de Déu Barcelona, 08950 Barcelona, Spain; (M.H.-G.); (C.S.); (A.P.O.); (N.A.C.); (M.M.-C.); (G.P.-T.); (M.F.d.S.); (R.P.); (C.L.); (J.J.G.-G.)
- Infectious Diseases and Microbiome, Institut de Recerca Sant Joan de Déu (IRSJD), 08950 Barcelona, Spain; (C.G.); (M.G.-A.)
| | - Claudia Solito
- Paediatrics Department, Hospital Sant Joan de Déu Barcelona, 08950 Barcelona, Spain; (M.H.-G.); (C.S.); (A.P.O.); (N.A.C.); (M.M.-C.); (G.P.-T.); (M.F.d.S.); (R.P.); (C.L.); (J.J.G.-G.)
| | - Alba Pavón Ortiz
- Paediatrics Department, Hospital Sant Joan de Déu Barcelona, 08950 Barcelona, Spain; (M.H.-G.); (C.S.); (A.P.O.); (N.A.C.); (M.M.-C.); (G.P.-T.); (M.F.d.S.); (R.P.); (C.L.); (J.J.G.-G.)
| | - Noelia Arguedas Casamayor
- Paediatrics Department, Hospital Sant Joan de Déu Barcelona, 08950 Barcelona, Spain; (M.H.-G.); (C.S.); (A.P.O.); (N.A.C.); (M.M.-C.); (G.P.-T.); (M.F.d.S.); (R.P.); (C.L.); (J.J.G.-G.)
| | - Maria Melé-Casas
- Paediatrics Department, Hospital Sant Joan de Déu Barcelona, 08950 Barcelona, Spain; (M.H.-G.); (C.S.); (A.P.O.); (N.A.C.); (M.M.-C.); (G.P.-T.); (M.F.d.S.); (R.P.); (C.L.); (J.J.G.-G.)
- Infectious Diseases and Microbiome, Institut de Recerca Sant Joan de Déu (IRSJD), 08950 Barcelona, Spain; (C.G.); (M.G.-A.)
| | - Gemma Pons-Tomàs
- Paediatrics Department, Hospital Sant Joan de Déu Barcelona, 08950 Barcelona, Spain; (M.H.-G.); (C.S.); (A.P.O.); (N.A.C.); (M.M.-C.); (G.P.-T.); (M.F.d.S.); (R.P.); (C.L.); (J.J.G.-G.)
- Infectious Diseases and Microbiome, Institut de Recerca Sant Joan de Déu (IRSJD), 08950 Barcelona, Spain; (C.G.); (M.G.-A.)
| | - Mariona F. de Sevilla
- Paediatrics Department, Hospital Sant Joan de Déu Barcelona, 08950 Barcelona, Spain; (M.H.-G.); (C.S.); (A.P.O.); (N.A.C.); (M.M.-C.); (G.P.-T.); (M.F.d.S.); (R.P.); (C.L.); (J.J.G.-G.)
- Infectious Diseases and Microbiome, Institut de Recerca Sant Joan de Déu (IRSJD), 08950 Barcelona, Spain; (C.G.); (M.G.-A.)
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, 08036 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBER-ESP), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Rosa Pino
- Paediatrics Department, Hospital Sant Joan de Déu Barcelona, 08950 Barcelona, Spain; (M.H.-G.); (C.S.); (A.P.O.); (N.A.C.); (M.M.-C.); (G.P.-T.); (M.F.d.S.); (R.P.); (C.L.); (J.J.G.-G.)
| | - Cristian Launes
- Paediatrics Department, Hospital Sant Joan de Déu Barcelona, 08950 Barcelona, Spain; (M.H.-G.); (C.S.); (A.P.O.); (N.A.C.); (M.M.-C.); (G.P.-T.); (M.F.d.S.); (R.P.); (C.L.); (J.J.G.-G.)
- Infectious Diseases and Microbiome, Institut de Recerca Sant Joan de Déu (IRSJD), 08950 Barcelona, Spain; (C.G.); (M.G.-A.)
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, 08036 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBER-ESP), Instituto de Salud Carlos III, 28029 Madrid, Spain
| | - Carmina Guitart
- Infectious Diseases and Microbiome, Institut de Recerca Sant Joan de Déu (IRSJD), 08950 Barcelona, Spain; (C.G.); (M.G.-A.)
- Paediatric Intensive Care Unit, Hospital Sant Joan de Déu Barcelona, 08950 Barcelona, Spain
| | - Mònica Girona-Alarcón
- Infectious Diseases and Microbiome, Institut de Recerca Sant Joan de Déu (IRSJD), 08950 Barcelona, Spain; (C.G.); (M.G.-A.)
- Paediatric Intensive Care Unit, Hospital Sant Joan de Déu Barcelona, 08950 Barcelona, Spain
| | - Iolanda Jordan
- Infectious Diseases and Microbiome, Institut de Recerca Sant Joan de Déu (IRSJD), 08950 Barcelona, Spain; (C.G.); (M.G.-A.)
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, 08036 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBER-ESP), Instituto de Salud Carlos III, 28029 Madrid, Spain
- Paediatric Intensive Care Unit, Hospital Sant Joan de Déu Barcelona, 08950 Barcelona, Spain
| | - Juan José García-García
- Paediatrics Department, Hospital Sant Joan de Déu Barcelona, 08950 Barcelona, Spain; (M.H.-G.); (C.S.); (A.P.O.); (N.A.C.); (M.M.-C.); (G.P.-T.); (M.F.d.S.); (R.P.); (C.L.); (J.J.G.-G.)
- Infectious Diseases and Microbiome, Institut de Recerca Sant Joan de Déu (IRSJD), 08950 Barcelona, Spain; (C.G.); (M.G.-A.)
- Faculty of Medicine and Health Sciences, Universitat de Barcelona, 08036 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBER-ESP), Instituto de Salud Carlos III, 28029 Madrid, Spain
| |
Collapse
|
|
12
|
Ahmed IS, Tapponi SL, Widatallah ME, Alakkad YM, Haider M. Unmasking the enigma: An in-depth analysis of COVID-19 impact on the pediatric population. J Infect Public Health 2023; 16:1346-1360. [PMID: 37433256 PMCID: PMC10299956 DOI: 10.1016/j.jiph.2023.06.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 06/18/2023] [Accepted: 06/21/2023] [Indexed: 07/13/2023] Open
Abstract
OBJECTIVES COVID-19, caused by the novel coronavirus, has had a profound and wide-reaching impact on individuals of all age groups across the globe, including children. This review article aims to provide a comprehensive analysis of COVID-19 in children, covering essential topics such as epidemiology, transmission, pathogenesis, clinical features, risk factors, diagnosis, treatment, vaccination, and others. By delving into the current understanding of the disease and addressing the challenges that lie ahead, this article seeks to shed light on the unique considerations surrounding COVID-19 in children and contribute to a deeper comprehension of this global health crisis affecting our youngest population. METHODS A comprehensive literature search was conducted to gather the most recent and relevant information regarding COVID-19 in children. Multiple renowned databases, including MEDLINE, PubMed, Scopus, as well as authoritative sources such as the World Health Organization (WHO), the U.S. Food and Drug Administration (FDA), the European Medicines Agency (EMA), and the National Institutes of Health (NIH) websites and others were thoroughly searched. The search included articles, guidelines, reports, clinical trials results and expert opinions published within the past three years, ensuring the inclusion of the latest research findings on COVID-19 in children. Several relevant keywords, including "COVID-19," "SARS-CoV-2," "children," "pediatrics," and related terms were used to maximize the scope of the search and retrieve a comprehensive set of articles. RESULTS AND CONCLUSION Three years since the onset of the COVID-19 pandemic, our understanding of its impact on children has evolved, but many questions remain unanswered. While SAR-CoV-2 generally leads to mild illness in children, the occurrence of severe cases and the potential for long-term effects cannot be overlooked. Efforts to comprehensively study COVID-19 in children must continue to improve preventive strategies, identify high-risk populations, and ensure optimal management. By unraveling the enigma surrounding COVID-19 in children, we can strive towards safeguarding their health and well-being in the face of future global health challenges.
Collapse
Affiliation(s)
- Iman Saad Ahmed
- Department of Pharmaceutics & Pharmaceutical Technology, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates; Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates.
| | - Sara Luay Tapponi
- Department of Pharmaceutics & Pharmaceutical Technology, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates
| | - Marwa Eltahir Widatallah
- Department of Pharmaceutics & Pharmaceutical Technology, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates
| | - Yumna Mohamed Alakkad
- Department of Pharmaceutics & Pharmaceutical Technology, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates
| | - Mohamed Haider
- Department of Pharmaceutics & Pharmaceutical Technology, College of Pharmacy, University of Sharjah, Sharjah 27272, United Arab Emirates; Research Institute for Medical and Health Sciences, University of Sharjah, Sharjah 27272, United Arab Emirates
| |
Collapse
|
|
13
|
López-Morales H, Canet-Juric L, Del-Valle MV, Sosa JM, López MC, Urquijo S. Prenatal anxiety during the pandemic context is related to neurodevelopment of 6-month-old babies. Eur J Pediatr 2023; 182:4213-4226. [PMID: 37452845 DOI: 10.1007/s00431-023-05112-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 06/27/2023] [Accepted: 07/08/2023] [Indexed: 07/18/2023]
Abstract
Prenatal anxiety and depression in pandemic context could introduce changes in the fetal developmental trajectories that, ultimately, could alter the adaptive behaviors of the offspring, potentially affecting, for example, general neurodevelopment. The sample consisted of 105 mother-child dyads, recruited between March and May 2020. The dyads were evaluated longitudinally, prenatally and postnatally (6 months). The Pandemic Impact Questionnaire, the State-Trait Anxiety Inventory, and the Beck-II Depression Inventory were used to assess indicators of maternal anxiety and depression, respectively. Regarding the babies, their mothers responded to Age and Stages: 3, which assesses different dimensions of early neurodevelopment, in addition to a closed questionnaire to identify sociodemographic and maternal and child health variables. A series of mediation models were tested to examine the association between prenatal psychopathology/negative experiences of the pandemic and neurodevelopment. The results indicated that the negative experiences of the pandemic were indirectly associated with the socio-individual and fine motor neurodevelopment of the offspring, through maternal anxiety symptoms, during the third trimester, which functioned as a mediator. Conclusions: This study provides evidence on the mediating effects of maternal anxiety on infant neurodevelopment in contexts of early adversity. It is important to point out the need to implement public health policies that allow a timely evaluation of neurodevelopmental variables during early childhood, which can implement early interventions to reduce the risks associated with these deficits. What is Known: • Effects of maternal mental health have been reported, effects on child neurodevelopment, in motor, cognitive, linguistic and socio-emotional dimensions. • Contexts of early adversity have been associated with maternal mental health and offspring development. What is New: • The context of pandemic adversity caused by COVID-19 is associated with motor and socio-individual neurodevelopment, mediated by maternal prenatal anxiety.
Collapse
Affiliation(s)
- Hernán López-Morales
- Instituto de Psicología Básica Aplicada y Tecnología (IPSIBAT), Mar del Plata, Argentina.
- Universidad Nacional de Mar del Plata (UNMDP), Mar del Plata, Argentina.
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina.
- Escuela Superior de Medicina, Universidad Nacional de Mar del Plata, Mar del Plata, Argentina.
| | - Lorena Canet-Juric
- Instituto de Psicología Básica Aplicada y Tecnología (IPSIBAT), Mar del Plata, Argentina
- Universidad Nacional de Mar del Plata (UNMDP), Mar del Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Macarena Verónica Del-Valle
- Instituto de Psicología Básica Aplicada y Tecnología (IPSIBAT), Mar del Plata, Argentina
- Universidad Nacional de Mar del Plata (UNMDP), Mar del Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| | - Julieta Mariel Sosa
- Instituto de Psicología Básica Aplicada y Tecnología (IPSIBAT), Mar del Plata, Argentina
- Universidad Nacional de Mar del Plata (UNMDP), Mar del Plata, Argentina
| | - Marcela Carolina López
- Instituto de Psicología Básica Aplicada y Tecnología (IPSIBAT), Mar del Plata, Argentina
- Universidad Nacional de Mar del Plata (UNMDP), Mar del Plata, Argentina
| | - Sebastián Urquijo
- Instituto de Psicología Básica Aplicada y Tecnología (IPSIBAT), Mar del Plata, Argentina
- Universidad Nacional de Mar del Plata (UNMDP), Mar del Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina
| |
Collapse
|
|
14
|
Al-kuraishy HM, Al-Gareeb AI, Albezrah NKA, Bahaa HA, El-Bouseary MM, Alexiou A, Al-Ziyadi SH, Batiha GES. Pregnancy and COVID-19: high or low risk of vertical transmission. Clin Exp Med 2023; 23:957-967. [PMID: 36251144 PMCID: PMC9574177 DOI: 10.1007/s10238-022-00907-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 09/25/2022] [Indexed: 11/03/2022]
Abstract
Coronavirus disease 19 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome 2 (SARS-CoV-2). Throughout the pandemic, evidence on the effects of COVID-19 during pregnancy has been inadequate due to the limited number of studies published. Therefore, the objective of this systematic review was to evaluate current literature regarding the effects of COVID-19 during pregnancy and establish pregnancy outcomes and vertical and perinatal transmission during pregnancy. Multiple databases were searched, including Embase, Medline, Web of Science, Scopus, and Cochrane Central Register of Control Clinical Trials, using the following keywords: [Pregnancy] AND [COVID-19 OR SARS-CoV-2 OR nCoV-19] OR [Perinatal transmission, Vertical transmission (VT), Pregnancy complications], [Pregnancy] AND [Hyperinflammation OR Cytokine storm]. We excluded in vitro and experimental studies, but also ex-vivo and animal study methods. To exclude the risk of bias during data collection and interpretation, all included studies were peer-reviewed publications. This review is estimated to tabulate the study intervention characteristics and compare them against the planned groups for each synthesis. Our findings showed that pregnant women are commonly susceptible to respiratory viral infections and severe pneumonia due to physiological immune suppression and pregnancy-induced changes. VT of SARS-CoV-2 infection during pregnancy is associated with a great deal of controversy and conflict. However, there is still no robust clinical evidence of VT. Furthermore, the clinical presentation and management of COVID-19 during pregnancy are nearly identical to those of non-pregnant women. Finally, chloroquine and remdesivir are the only two drugs evaluated as adequate for the management of COVID-19 during pregnancy.
Collapse
Affiliation(s)
- Hayder M. Al-kuraishy
- Department of Clinical Pharmacology and Medicine, College of Medicine, AL-Mustansiriyah University, Baghdad, Iraq
| | - Ali I. Al-Gareeb
- Department of Clinical Pharmacology and Medicine, College of Medicine, AL-Mustansiriyah University, Baghdad, Iraq
| | | | - Haitham Ahmed Bahaa
- Department of Obstetrics and Gynaecology, Faculty of Medicine, Minia University, Minia, Egypt
| | - Maisra M. El-Bouseary
- Department of Pharmaceutical Microbiology, Faculty of Pharmacy, Tanta University, Tanta, Egypt
| | - Athanasios Alexiou
- Department of Science and Engineering, Novel Global Community Educational Foundation, Hebersham, Australia
- AFNP Med Austria, Vienna, Austria
| | - Shatha Hallal Al-Ziyadi
- Saudi Board Certified in Obstetrics & Gynecology, Assistant Professor at Taif University, Taif, Saudi Arabia
| | - Gaber El-Saber Batiha
- Department of Pharmacology and Therapeutics, Faculty of Veterinary Medicine, Damanhour University, Damanhour, 22511 AlBeheira Egypt
| |
Collapse
|
|
15
|
Utilizing CNN-LSTM techniques for the enhancement of medical systems. ALEXANDRIA ENGINEERING JOURNAL 2023; 72:323-338. [PMCID: PMC10105249 DOI: 10.1016/j.aej.2023.04.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/30/2023] [Accepted: 04/05/2023] [Indexed: 04/04/2024]
Abstract
COVID-19 is one of the most chronic and serious infections of recent years due to its worldwide spread. Determining who was genuinely affected when the disease spreads more widely is challenging. More than 60% of affected individuals report having a dry cough. In many recent studies, diagnostic models were developed using coughing and other breathing sounds. With the development of technology, body sounds are now collected using digital techniques for respiratory and cardiovascular tests. Early research on identifying COVID-19 utilizing speech and diagnosing signs yielded encouraging findings. The gathering of extensive, multi-group, airborne acoustical sound data is used in the developed framework to conduct an efficient assessment to test for COVID-19. An effective classification model is created to assess COVID-19 utilizing deep learning methods. The MIT-Covid-19 dataset is used as the input, and the Weiner filter is used for pre-processing. Following feature extraction done by Mel-frequency cepstral coefficients, the classification is performed using the CNN-LSTM approach. The study compared the performance of the developed framework with other techniques such as CNN, GRU, and LSTM. Study results revealed that CNN-LSTM outperformed other existing approaches by 97.7%.
Collapse
|
|
16
|
López-Morales H, Del-Valle MV, López MC, Andrés ML, García MJ, Canet-Juric L, Urquijo S. Maternal anxiety, exposure to the COVID-19 pandemic and socioemotional development of offspring. JOURNAL OF APPLIED DEVELOPMENTAL PSYCHOLOGY 2023; 86:101517. [PMID: 36748034 PMCID: PMC9892320 DOI: 10.1016/j.appdev.2023.101517] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 01/20/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023]
Abstract
The COVID-19 pandemic context may predispose mothers to increased maternal psychopathology, which may be associated with offspring socioemotional development. The aim of this study is to analyze the relationships between prenatal anxiety and depression and exposure to the COVID-19 pandemic with offspring socioemotional development, controlling for postnatal anxiety and depression. A total of 105 mother-child dyads were assessed in pre- and postnatal periods. Questionnaires were used to assess the impact of the pandemic, indicators of psychopathology, and the socioemotional development of the offspring. Results suggest that negative pandemic experiences are indirectly associated with offspring socioemotional development via prenatal maternal anxiety symptomatology and after controlling for postnatal anxiety and depression. These indicators predispose to emotional deficits and increase the risks of psychopathological and neurodevelopmental disorders. It is important to adopt health policies that provide timely assessment of development in early childhood to reduce the risks associated with these deficits.
Collapse
Affiliation(s)
- Hernán López-Morales
- Instituto de Psicología Básica Aplicada y Tecnología (IPSIBAT), Mar del Plata, Argentina
- Universidad Nacional de Mar del Plata (UNMDP), Mar del Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
- Escuela Superior de Medicina, Universidad Nacional de Mar del Plata, Argentina
| | - Macarena Verónica Del-Valle
- Instituto de Psicología Básica Aplicada y Tecnología (IPSIBAT), Mar del Plata, Argentina
- Universidad Nacional de Mar del Plata (UNMDP), Mar del Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
| | - Marcela Carolina López
- Instituto de Psicología Básica Aplicada y Tecnología (IPSIBAT), Mar del Plata, Argentina
- Universidad Nacional de Mar del Plata (UNMDP), Mar del Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
| | - María Laura Andrés
- Instituto de Psicología Básica Aplicada y Tecnología (IPSIBAT), Mar del Plata, Argentina
- Universidad Nacional de Mar del Plata (UNMDP), Mar del Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
| | - Matías Jonás García
- Instituto de Psicología Básica Aplicada y Tecnología (IPSIBAT), Mar del Plata, Argentina
- Universidad Nacional de Mar del Plata (UNMDP), Mar del Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
| | - Lorena Canet-Juric
- Instituto de Psicología Básica Aplicada y Tecnología (IPSIBAT), Mar del Plata, Argentina
- Universidad Nacional de Mar del Plata (UNMDP), Mar del Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
| | - Sebastián Urquijo
- Instituto de Psicología Básica Aplicada y Tecnología (IPSIBAT), Mar del Plata, Argentina
- Universidad Nacional de Mar del Plata (UNMDP), Mar del Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Argentina
| |
Collapse
|
|
17
|
Kanumuri C, Chodavarapu RM. GUI Enabled Optimized Approach of CNN for Automatic Diagnosis of COVID-19 Using Radiograph Images. NEW GENERATION COMPUTING 2023; 41:213-224. [PMID: 37229178 PMCID: PMC10010635 DOI: 10.1007/s00354-023-00212-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Accepted: 02/23/2023] [Indexed: 05/27/2023]
Abstract
World Health Organization (WHO) proclaimed the Corona virus (COVID-19) as a pandemic, since it contaminated billions of individuals and killed lakhs. The spread along with the severity of the disease plays a key role in early detection and classification to reduce the rapid spread as the variants are changing. COVID-19 could be categorized as a pneumonia infection. Bacterial pneumonia, fungal pneumonia, viral pneumonia, etc., are the classifications of several forms of pneumonia, which are subcategorized into more than 20 forms and COVID-19 will come under viral pneumonia. The wrong prediction of any of these can mislead humans into improper treatment, which leads to a matter of life. From the radiograph that is X-ray images, diagnosis of all these forms can be possible. For detecting these disease classes, the proposed method will employ a deep learning (DL) technique. Early detection of the COVID-19 is possible with this model; hence, the spread of the disease is minimized by isolating the patients. For execution, a graphical user interface (GUI) provides more flexibility. The proposed model, which is a GUI approach, is trained with 21 types of pneumonia radiographs by a convolutional neural network (CNN) trained on Image Net and adjusts them to act as feature extractors for the Radiograph images. Next, the CNNs are combined with united AI strategies. For the classification of COVID-19 detection, several approaches are proposed in which those approaches are concerned with COVID-19, pneumonia, and healthy patients only. In classifying more than 20 types of pneumonia infections, the proposed model attained an accuracy of 92%. Likewise, COVID-19 images are effectively distinguished from the other pneumonia images of radiographs.
Collapse
Affiliation(s)
- Chalapathiraju Kanumuri
- Electronics and Communication Engineering, S.R.K.R Engineering College, Bhimavaram, Andhra Pradesh India
| | - Renu Madhavi Chodavarapu
- Electronics and Instrumentation Engineering, RV College of Engineering, Bangalore, Karnataka India
| |
Collapse
|
|
18
|
Eken S. A topic-based hierarchical publish/subscribe messaging middleware for COVID-19 detection in X-ray image and its metadata. Soft comput 2023; 27:2645-2655. [PMID: 33100897 PMCID: PMC7570402 DOI: 10.1007/s00500-020-05387-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Putting real-time medical data processing applications into practice comes with some challenges such as scalability and performance. Processing medical images from different collaborators is an example of such applications, in which chest X-ray data are processed to extract knowledge. It is not easy to process data and get the required information in real time using central processing techniques when data get very large in size. In this paper, real-time data are filtered and forwarded to the right processing node by using the proposed topic-based hierarchical publish/subscribe messaging middleware in the distributed scalable network of collaborating computation nodes instead of classical approaches of centralized computation. This enables processing streaming medical data in near real time and makes a warning system possible. End users have the capability of filtering/searching. The returned search results can be images (COVID-19 or non-COVID-19) and their meta-data are gender and age. Here, COVID-19 is detected using a novel capsule network-based model from chest X-ray images. This middleware allows for a smaller search space as well as shorter times for obtaining search results.
Collapse
Affiliation(s)
- Süleyman Eken
- grid.411105.00000 0001 0691 9040Department of Information Systems Engineering, Kocaeli University, 41001 Kocaeli, Turkey
| |
Collapse
|
|
19
|
Aljohani MA, Albalawi FM, Albalawi BM, Alghamdi SS, Alghamdi EH, Almahl AA, Alagoul HA, Alamori AM, Mobarki AY, Hadi IM, Asiri MA, Dighriri IM. Consequences of SARS-CoV-2 Infection in Pregnant Women and Their Infants: A Systematic Review. Cureus 2022; 14:e32787. [PMID: 36694500 PMCID: PMC9857045 DOI: 10.7759/cureus.32787] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/21/2022] [Indexed: 12/24/2022] Open
Abstract
Coronavirus disease 2019 (COVID-19) is a worldwide health problem, particularly for pregnant women. This review assesses the effects of COVID-19 on pregnant women and their infants. A systematic search was performed of studies published on PubMed, Web of Science, Google Scholar, and Embase from January 2020 to January 2021, without restriction by language. This review included 27 studies (22 from China, one from the United States, one from Honduras, one from Italy, one from Iran, and one from Spain), which cumulatively evaluated 386 pregnant women with clinically confirmed COVID-19 and their 334 newborns. Of the 386 pregnant women, 356 had already delivered their infants, four had medical abortions at the time of research, 28 were still pregnant, and two died from COVID-19 before they were able to give birth. Cesarean sections were performed on 71% of pregnant women with COVID-19 to give birth. Fever and cough were common symptoms among women. Premature rupture of membranes, distress, and preterm birth were pregnancy complications. Low birth weight and a short gestational age were common outcomes for newborns. The common laboratory findings among pregnant women were lymphopenia, leukocytosis, and elevated levels of C-reactive protein. Chest computed tomography revealed abnormal viral lung changes in 73.3% of women. Eleven infants tested positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. There was no evidence of vertical transmission. Most infants were observed to have lymphopenia and thrombocytopenia. The clinical features of pregnant women were found to be similar to those of generally infected patients. There is evidence of adverse pregnancy and neonatal outcomes caused by COVID-19.
Collapse
Affiliation(s)
- Mohammed A Aljohani
- Department of Laboratory and Blood Bank, Alwajh General Hospital, Tabuk, SAU
| | - Fahad M Albalawi
- Department of Laboratory and Blood Bank, Alwajh General Hospital, Tabuk, SAU
| | - Bader M Albalawi
- Department of Laboratory and Blood Bank, Alwajh General Hospital, Tabuk, SAU
| | - Sameer S Alghamdi
- Department of Laboratory, Laboratories and Blood Bank Administration, Taif, SAU
| | - Essam H Alghamdi
- Department of Laboratory, Laboratories and Blood Bank Administration, Taif, SAU
| | - Ali A Almahl
- Department of Laboratory, Dammam Regional Laboratory and Blood Bank, Dammam, SAU
| | - Hassan A Alagoul
- Department of Laboratory, Dammam Regional Laboratory and Blood Bank, Dammam, SAU
| | - Ahmed M Alamori
- Department of Laboratory and Blood Bank, King Fahad Hospital, Jeddah, SAU
| | - Ahmed Y Mobarki
- Department of Molecular Biology, Regional Laboratory and Central Blood Bank, Abha, SAU
| | | | | | | |
Collapse
|
|
20
|
Shmakov RG, Prikhodko A, Polushkina E, Shmakova E, Pyregov A, Bychenko V, Priputnevich TV, Dolgushin GO, Yarotskaya E, Pekarev O, Bolibok N, Degtyarev D, Sukhikh GT. Clinical course of novel COVID-19 infection in pregnant women. J Matern Fetal Neonatal Med 2022; 35:4431-4437. [PMID: 33249969 PMCID: PMC7711745 DOI: 10.1080/14767058.2020.1850683] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2020] [Accepted: 11/10/2020] [Indexed: 12/27/2022]
Abstract
OBJECTIVES Evaluation of clinical course of COVID-19 during pregnancy and maternal and perinatal outcomes of this pregnancy. METHODS 66 women with polymerase chain reaction (PCR) - confirmed SARS-CoV-2 and their 42 neonates were included in the prospective observational study. Demographic, epidemiological, clinical, laboratory and instrumental data of pregnancy, delivery, postpartum period, including pharmacotherapy and neonatal outcomes were analyzed. RESULTS 15 (22.7%) women were asymptomatic, 25 (38%) had mild disease, while moderate and severe forms were detected in 20 (30.2%) and 6 (9.1%) cases, respectively. Additional oxygenation was required in 6 (9%) cases: 4 (6%) received CPAP therapy and 2 (3%) - mechanical ventilation. Main clinical symptoms were cough (51.5%), anosmia (34.9%), and hyperthermia (33.3%). Laboratory changes included increased levels of lactate dehydrogenase (LDH), creatinine, d-dimer, and C-reactive protein (CRP), anemia, and leukopenia. All pregnant women received low molecular weight heparin and interferon alfa-2b according to the National clinical recommendations. Antimicrobial drugs included Amoxicillin/Clavulanic acid (46%) and macrolides (28%) or carbapenems in severe cases of disease. Spontaneous abortion was reported in 6.1% of cases. Eight preterm (19%) and 34 term deliveries (81%) occurred. The mean weight of neonates was (3283 ± 477) g, 1- and 5-min Apgar score was (7.8 ± 0.6) and (8.7 ± 0.5), respectively. No cases of neonatal COVID-19 infection were reported. CONCLUSIONS Mostly, the manifestations of COVID-19 were mild. However, 9% of cases were severe, and could contribute to preterm delivery or maternal morbidity. Main predictors of severe COVID-19 course in pregnant women were a decrease in the levels of erythrocytes and lymphocytes and increase in the levels of alanine aminotransferase and CRP. Elimination of the virus in pregnant women required more time due to altered immunity. No evidence of vertical transmission during pregnancy and delivery was found. However, the possibility of this cannot be excluded.
Collapse
Affiliation(s)
- Roman G. Shmakov
- Institute of Obstetrics, National Medical Research Center for Obstetrics, Gynecology and Perinatology Ministry of Healthcare of Russian Federation, Moscow, Russia
| | - Andrey Prikhodko
- Department of Maternity, Institute of Obstetrics, National Medical Research Center for Obstetrics, Gynecology and Perinatology Ministry of Healthcare of Russian Federation, Moscow, Russia
| | - Evgeniya Polushkina
- Department of Maternity, Institute of Obstetrics, National Medical Research Center for Obstetrics, Gynecology and Perinatology Ministry of Healthcare of Russian Federation, Moscow, Russia
| | - Elena Shmakova
- National Medical Research Center for Obstetrics, Gynecology and Perinatology Ministry of Healthcare of Russian Federation, Moscow, Russia
| | - Aleksey Pyregov
- Institute of Anesthesiology and Intensive Care National Medical Research Center for Obstetrics, Gynecology and Perinatology Ministry of Healthcare of Russian Federation, Moscow, Russia
| | - Vladimir Bychenko
- Department of Radiology, National Medical Research Center for Obstetrics, Gynecology and Perinatology Ministry of Healthcare of Russian Federation, Moscow, Russia
| | - Tatyana V. Priputnevich
- Department of Microbiology and Clinical Pharmacology and Epidemiology National Medical Research Center for Obstetrics, Gynecology and Perinatology Ministry of Healthcare of Russian Federation, Moscow, Russia
| | - Grigory O. Dolgushin
- National Medical Research Center for Obstetrics, Gynecology and Perinatology Ministry of Healthcare of Russian Federation, Moscow, Russia
| | - Ekaterina Yarotskaya
- Department of International Cooperation National Medical Research Center for Obstetrics, Gynecology and Perinatology Ministry of Healthcare of Russian Federation, Moscow, Russia
| | - Oleg Pekarev
- Institute of Obstetrics, National Medical Research Center for Obstetrics, Gynecology and Perinatology Ministry of Healthcare of Russian Federation, Moscow, Russia
| | - Nikolai Bolibok
- National Medical Research Center for Obstetrics, Gynecology and Perinatology Ministry of Healthcare of Russian Federation, Moscow, Russia
| | - Dmitriy Degtyarev
- National Medical Research Center for Obstetrics, Gynecology and Perinatology Ministry of Healthcare of Russian Federation, Moscow, Russia
| | - Gennady T. Sukhikh
- National Medical Research Center for Obstetrics, Gynecology and Perinatology Ministry of Healthcare of Russian Federation, Moscow, Russia
| |
Collapse
|
|
21
|
Farhan FS, Nori W, Al Kadir ITA, Hameed BH. Can Fetal Heart Lie? Intrapartum CTG Changes in COVID-19 Mothers. J Obstet Gynaecol India 2022; 72:479-484. [PMID: 35634476 PMCID: PMC9128777 DOI: 10.1007/s13224-022-01663-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2021] [Accepted: 05/01/2022] [Indexed: 02/08/2023] Open
Abstract
BACKGROUND COVID-19 infection has raised multiple concerns in pregnant mothers; many questioned the risk of vertical transmission and the implication on the feto-maternal outcome. Cardiotocogrm (CTG) is the principal method to observe intrapartum fetal well-being. This paper aims to verify intrapartum CTG changes seen in seropositive COVID-19 mothers versus healthy controls and looks into their relation to subsequent delivery mode and neonatal outcome. METHODS A case-control study recruited 90 pregnant women at the labor word of AL Yarmouk Teaching Hospital. All were term pregnancy admitted for delivery. They were grouped into 2: seropositive COVID-19 confirmed by real-time RT-PCR test (30/90) and healthy controls (60/90). We recorded their demographic criteria, laboratory results, CTG changes, delivery mode, and indication. RESULTS COVID-19 cases showed significantly higher pulse rate, temperature, and leukocyte counts. Cesarian deliveries (CS) were higher in cases versus healthy controls (70 % vs. 53.3 %) and P = 0.45. Analysis of the CS indications showed that abnormal fetal heart tracing accounts for 33.3 % versus 15.6 % (P-value = 0.015) for cases versus healthy controls. 60 % of COVID-19 cases exhibited abnormal CTG changes versus 19.4 % in healthy controls. These changes were primarily fetal tachycardia and reduced variabilities. CONCLUSIONS The higher incidence of abnormal CTG in COVID-19 cases, alongside infection signs and symptoms, underlies the exaggerated inflammatory reactions inside the pregnant mother. These inflammatory reactions are the main causes of CTG changes and higher CS rates. Therefore, obstetricians are advised to optimize the maternal condition to rectify reactive CTG changes rather than proceeding into urgent CS. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1007/s13224-022-01663-6.
Collapse
Affiliation(s)
- Fatin Shallal Farhan
- Department of Gynaecology And Obstetrics, Mustansiriyah University \ College of Medicine, Baghdad, Iraq
| | - Wassan Nori
- Department of Gynaecology And Obstetrics, Mustansiriyah University \ College of Medicine, Baghdad, Iraq
| | | | - Ban Hadi Hameed
- Department of Gynaecology And Obstetrics, Mustansiriyah University \ College of Medicine, Baghdad, Iraq
| |
Collapse
|
|
22
|
Azarkish F, Rigiyousefabadi S, Janghorban R, Aminifard M, Bozorgzadeh S, Zahirnia M, Golmohammadi MS, Mirtalaie E, Pirak A, Kashani ZA. Treatment of Hydatidiform Mole Suspected to COVID 19. Adv Biomed Res 2022; 11:105. [PMID: 36660755 PMCID: PMC9843593 DOI: 10.4103/abr.abr_142_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 12/28/2021] [Accepted: 01/04/2022] [Indexed: 01/21/2023] Open
Abstract
The aim of this study was to report a case of the treatment of hydatidiform mole in Coronavirus pandemic in Iranshahr. A 17-year-old primiparous woman with gestational age of 14 weeks presented with unilateral leg swelling and sudden abdominal distension beginning in the night before referring to the health center. In the abdominal examination of the patient by a healthcare provider, the baby's heartbeat was not heard and a mismatch was observed between gestational age and fundal height, which corresponded to approximately 24 weeks of gestation. She was conscious and pale with hematuria and uterine contractions. After inserting two IV lines, the patient immediately underwent monitoring and was visited by a gynecologist. Complete molar pregnancy was diagnosed with an enlarged heterogeneous uterus 180 cm × 90 cm in size and containing 170 mm × 80 mm cysts. The treatment began with vancomycin AMP, hydrocortisone AMP, oseltamivir CAP 75 mg, kaletra CAP 200 mg, and meropenem AMP.
Collapse
Affiliation(s)
- Fatemeh Azarkish
- Tropical and Communicable Diseases Research Center, Iranshahr University of Medical Sciences, Iranshahr, Iran,Department of Midwifery, Iranshahr University of Medical Sciences, Iranshahr, Iran
| | - Saeedeh Rigiyousefabadi
- Tropical and Communicable Diseases Research Center, Iranshahr University of Medical Sciences, Iranshahr, Iran,Department of Midwifery, Iranshahr University of Medical Sciences, Iranshahr, Iran,Address for correspondence: Dr. Saeedeh Rigiyousefabadi, Tropical and Communicable Diseases Research Center, Department of Midwifery, Iranshahr University of Medical Sciences, Iranshahr, Iran. E-mail:
| | - Roksana Janghorban
- Department of Midwifery, School of Nursing and Midwifery, Maternal – Fetal Medicine Research Center, Shiraz University of Medical Sciences, Shiraz, Iran
| | | | - Shirin Bozorgzadeh
- Department of Obstetrics and Gynecology, Iranshahr University of Medical Sciences, Iranshahr, Iran
| | - Mahya Zahirnia
- Department of Infectious Diseases, Iranshahr University of Medical Sciences, Iranshahr, Iran
| | | | - Elahe Mirtalaie
- Department of Internal Medicine, Iranshahr University of Medical Sciences, Iranshahr, Iran
| | - Arezoo Pirak
- Department of Midwifery, Iranshahr University of Medical Sciences, Iranshahr, Iran
| | | |
Collapse
|
|
23
|
Diwakar M, Singh P, Swarup C, Bajal E, Jindal M, Ravi V, Singh KU, Singh T. Noise Suppression and Edge Preservation for Low-Dose COVID-19 CT Images Using NLM and Method Noise Thresholding in Shearlet Domain. Diagnostics (Basel) 2022; 12:diagnostics12112766. [PMID: 36428826 PMCID: PMC9689094 DOI: 10.3390/diagnostics12112766] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/09/2022] [Accepted: 11/09/2022] [Indexed: 11/16/2022] Open
Abstract
In the COVID-19 era, it may be possible to detect COVID-19 by detecting lesions in scans, i.e., ground-glass opacity, consolidation, nodules, reticulation, or thickened interlobular septa, and lesion distribution, but it becomes difficult at the early stages due to embryonic lesion growth and the restricted use of high dose X-ray detection. Therefore, it may be possible for a patient who may or may not be infected with coronavirus to consider using high-dose X-rays, but it may cause more risks. Conclusively, using low-dose X-rays to produce CT scans and then adding a rigorous denoising algorithm to the scans is the best way to protect patients from side effects or a high dose X-ray when diagnosing coronavirus involvement early. Hence, this paper proposed a denoising scheme using an NLM filter and method noise thresholding concept in the shearlet domain for noisy COVID CT images. Low-dose COVID CT images can be further utilized. The results and comparative analysis showed that, in most cases, the proposed method gives better outcomes than existing ones.
Collapse
Affiliation(s)
- Manoj Diwakar
- Computer Science and Engineering Department, Graphic Era Deemed to be University, Dehradun 248007, India
| | - Prabhishek Singh
- School of Computer Science Engineering and Technology, Bennett University, Greater Noida 201310, India
| | - Chetan Swarup
- Department of Basic Science, College of Science and Theoretical Studies, Saudi Electronic University, Riyadh-Male Campus, Riyadh 13316, Saudi Arabia
- Correspondence:
| | - Eshan Bajal
- Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Noida 201303, India
| | - Muskan Jindal
- Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Noida 201303, India
| | - Vinayakumar Ravi
- Center for Artificial Intelligence, Prince Mohammad Bin Fahd University, Khobar 34754, Saudi Arabia
| | - Kamred Udham Singh
- Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan 701, Taiwan
| | - Teekam Singh
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India
| |
Collapse
|
|
24
|
Sava CN, Bodog TM, Niulas LR, Iuhas AR, Marinau CP, Negrut N, Balmos AB, Pasca B, Roman NA, Delia Nistor-Cseppento C. Biomarker Changes in Pediatric Patients With COVID-19: A Retrospective Study from a Single Center Database. In Vivo 2022; 36:2813-2822. [PMID: 36309348 PMCID: PMC9677791 DOI: 10.21873/invivo.13019] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 10/07/2022] [Accepted: 10/11/2022] [Indexed: 11/25/2022]
Abstract
BACKGROUND/AIM The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for generating a global pandemic with deadly consequences and life changes worldwide. With the appearance of the new variants of the virus, clinical manifestations have been reported in the pediatric population, some with severe evolution. The aim of this study was to identify the laboratory parameters necessary to establish an effective therapy. PATIENTS AND METHODS In the period from August 2020 to September 2021, 234 pediatric patients met the inclusion criteria and were selected for the study. After confirming the COVID-19 diagnosis, laboratory parameters were analyzed and compared to the severity of the illness. RESULTS Thrombocytopenia (p<0.001), leukocytosis (p<0.001), and lymphopenia (p<0.001) correlated with the severity of the disease. Also, D-dimer values were closely monitored due to the high association of this parameter with an unsatisfactory prognosis and a severe form of the disease. CONCLUSION The D-dimer values and complete blood count are useful parameters in COVID-19 evaluation in children.
Collapse
Affiliation(s)
- Cristian Nicolae Sava
- Department of Medical Disciplines, Faculty of Medicine and Pharmacy, University of Oradea, Oradea, Romania
- Department of Pediatrics, County Clinical Emergency Hospital of Oradea, Oradea, Romania
| | | | - Larisa Roxana Niulas
- Department of Medical Disciplines, Faculty of Medicine and Pharmacy, University of Oradea, Oradea, Romania
- Department of Pediatrics, County Clinical Emergency Hospital of Oradea, Oradea, Romania
| | - Alin Remus Iuhas
- Department of Medical Disciplines, Faculty of Medicine and Pharmacy, University of Oradea, Oradea, Romania
- Department of Pediatrics, County Clinical Emergency Hospital of Oradea, Oradea, Romania
| | - Cristian Phillip Marinau
- Department of Medical Disciplines, Faculty of Medicine and Pharmacy, University of Oradea, Oradea, Romania
- Department of Pediatrics, County Clinical Emergency Hospital of Oradea, Oradea, Romania
| | - Nicoleta Negrut
- Department of Psycho-neuroscience and Recovery, Faculty of Medicine and Pharmacy, University of Oradea, Oradea, Romania;
| | - Andreea Bianca Balmos
- Department of Medical Disciplines, Faculty of Medicine and Pharmacy, University of Oradea, Oradea, Romania;
- Department of Pediatrics, County Clinical Emergency Hospital of Oradea, Oradea, Romania
| | - Bianca Pasca
- Department of Pharmacy, Faculty of Medicine and Pharmacy, University of Oradea, Oradea, Romania
| | | | - Carmen Delia Nistor-Cseppento
- Department of Psycho-neuroscience and Recovery, Faculty of Medicine and Pharmacy, University of Oradea, Oradea, Romania
| |
Collapse
|
|
25
|
Clinical and Laboratory Characteristics of Pediatric COVID-19 Population—A Bibliometric Analysis. J Clin Med 2022; 11:jcm11205987. [PMID: 36294306 PMCID: PMC9605229 DOI: 10.3390/jcm11205987] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/17/2022] [Accepted: 10/09/2022] [Indexed: 11/16/2022] Open
Abstract
The literature on the COVID-19 landscape has rapidly expanded in the pandemic period. The current study undertakes a bibliometric analysis of research in the topic of the clinical and laboratory characteristics of pediatric COVID-19 cases. Our aim is to perform a comprehensive bibliometric review of current research trends and patterns of this research domain. Publications retrieved from the Web of Science Core Collection and VOSviewer were used for analysis and network visualization. We analyzed geographical distribution and temporal trends, collaboration and citation patterns of authors, institutions, and countries, and core research themes from co-occurrence of keywords and terms. The analysis showed that contributions in the research field were from 302 publications, 1104 institutions, 62 countries, and 172 journals. Many publications were authored by American and Chinese authors, and many were published in the Pediatric Infectious Disease Journal, Pediatric Pulmonology, and Frontiers in Pediatrics. The top cited and co-cited journals were the New England Journal of Medicine, Nature, JAMA, Lancet Infectious Diseases, and BMJ. The network visualization maps of keywords and terms offered a global overview of the clinical and laboratory characteristics of pediatric COVID-19 patients. The bibliometric profile of the researched domain, based on analyzing a large collection of publications/data, could (i) enrich the researchers and non-researchers understanding of the field existing patterns and trends, and (ii) be useful in clinical practice (diagnostic and management) and public health policy.
Collapse
|
|
26
|
Jangam E, Annavarapu CSR, Barreto AAD. A multi-class classification framework for disease screening and disease diagnosis of COVID-19 from chest X-ray images. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 82:14367-14401. [PMID: 36157353 PMCID: PMC9490695 DOI: 10.1007/s11042-022-13710-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 05/05/2022] [Accepted: 08/24/2022] [Indexed: 06/16/2023]
Abstract
To accurately diagnose multiple lung diseases from chest X-rays, the critical aspect is to identify lung diseases with high sensitivity and specificity. This study proposed a novel multi-class classification framework that minimises either false positives or false negatives that is useful in computer aided diagnosis or computer aided detection respectively. To minimise false positives or false negatives, we generated respective stacked ensemble from pre-trained models and fully connected layers using selection metric and systematic method. The diversity of base classifiers was based on diverse set of false positives or false negatives generated. The proposed multi-class framework was evaluated on two chest X-ray datasets, and the performance was compared with the existing models and base classifiers. Moreover, we used LIME (Local Interpretable Model-agnostic Explanations) to locate the regions focused by the multi-class classification framework.
Collapse
Affiliation(s)
- Ebenezer Jangam
- Department of Information Technology, Velagapudi Ramakrishna Siddhartha Engineering College, Vijayawada, Andhra Pradesh India
- Department of Computer Science Engineering, Indian Institute of Technology(ISM), Dhanbad, Jharkhand India
| | | | | |
Collapse
|
|
27
|
Cao H, Baranova A, Wei X, Wang C, Zhang F. Bidirectional causal associations between type 2 diabetes and COVID-19. J Med Virol 2022; 95:e28100. [PMID: 36029131 PMCID: PMC9538258 DOI: 10.1002/jmv.28100] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 08/22/2022] [Accepted: 08/25/2022] [Indexed: 01/11/2023]
Abstract
Observational studies have reported high comorbidity between type 2 diabetes (T2D) and severe COVID-19. However, the causality between T2D and COVID-19 has yet to be validated. We performed genetic correlation and Mendelian randomization (MR) analyses to assess genetic relationships and potential causal associations between T2D and three COVID-19 outcomes (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2] infection, COVID-19 hospitalization, and critical COVID-19). Molecular pathways connecting SARS-CoV-2 and COVID-19 were reconstructed to extract insights into the potential mechanisms underlying the connection. We identified a high genetic overlap between T2D and each COVID-19 outcome (genetic correlations 0.21-0.28). The MR analyses indicated that genetic liability to T2D confers a causal effect on hospitalized COVID-19 (odds ratio 1.08, 95% confidence interval [CI] 1.04-1.12) and critical COVID-19 (1.09, 1.03-1.16), while genetic liability to SARS-CoV-2 infection exerts a causal effect on T2D (1.25, 1.00-1.56). There was suggestive evidence that T2D was associated with an increased risk for SARS-CoV-2 infection (1.02, 1.00-1.03), while critical COVID-19 (1.06, 1.00-1.13) and hospitalized COVID-19 (1.09, 0.99-1.19) were associated with an increased risk for T2D. Pathway analysis identified a panel of immunity-related genes that may mediate the links between T2D and COVID-19 at the molecular level. Our study provides robust support for the bidirectional causal associations between T2D and COVID-19. T2D may contribute to amplifying the severity of COVID-19, while the liability to COVID-19 may increase the risk for T2D.
Collapse
Affiliation(s)
- Hongbao Cao
- School of Systems BiologyGeorge Mason UniversityManassasVirginiaUSA
| | - Ancha Baranova
- School of Systems BiologyGeorge Mason UniversityManassasVirginiaUSA,Research Centre for Medical GeneticsMoscowRussia
| | - Xuejuan Wei
- Fengtai District Fangzhuang Community Health Service Center in BeijingBeijingChina
| | - Chun Wang
- Department of Medical PsychologyThe Affiliated Brain Hospital of Nanjing Medical UniversityNanjingChina
| | - Fuquan Zhang
- Department of PsychiatryThe Affiliated Brain Hospital of Nanjing Medical UniversityNanjingChina,Institute of NeuropsychiatryThe Affiliated Brain Hospital of Nanjing Medical UniversityNanjingChina
| |
Collapse
|
|
28
|
Honarmandpour F, Jahangirimehr A, Tahmasbi M, Khalighi A, Honarmandpour A. Follow-up the severity of abnormalities diagnosed in chest CT imaging of COVID-19 patients: A cross-sectional study. Health Sci Rep 2022; 5:e818. [PMID: 36110344 PMCID: PMC9466357 DOI: 10.1002/hsr2.818] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 08/15/2022] [Accepted: 08/15/2022] [Indexed: 01/08/2023] Open
Abstract
Background and Aims This study aimed to evaluate the severity of diagnosed lung abnormalities of coronavirus disease 2019 (COVID-19) patients based on the pre-and postrecovery follow-up chest computed tomography (CT) scan findings done at regular intervals. Methods This cross-sectional study was performed in three phases. The severity of lung abnormalities was recorded and compared based on the initial and follow-up chest CT findings carried out pre-and at regular intervals (3 and 6 months) of postrecovery of COVID-19 patients. Statistical data analysis was conducted using SPSS-Version 26. Pearson Chi-square test was used to analyze the results. p-value < 0.05 was considered statistically significant. Results Regarding the initial chest CT findings, although ground-glass opacity (GGO) was observed as the most common lung lesion, almost all the evaluated COVID-19 patients had multiple lung lesions and involvements, especially with more involvement of the lower lobes. concerning the frequency of lung lesions and involvements in all phases of the study, almost no statistically significant differences were observed between male and female COVID-19 patients and different age groups. However, older age groups had relatively more lung abnormalities due to Covid-19 based on initial CT images which take more time to be eliminated. Lung abnormalities of Covid-19 patients decreased significantly during the follow ups based on chest CT findings at different study phases. Conclusion According to evaluated pre- and post-recovery chest CT scans, the frequency of lung lesions and lung involvement distribution decreased significantly in COVID-19 patients, 3 and 6 months after recovery, and most of the recovered patients had no lung lesions or involvement anymore.
Collapse
Affiliation(s)
| | - Azam Jahangirimehr
- Department of BiostatisticsShoushtar Faculty of Medical SciencesShoushtarIran
| | - Marziyeh Tahmasbi
- Department of Radiology Technology, School of Allied Medical SciencesAhvaz Jundishapur University of Medical SciencesAhvazIran
| | - Azam Khalighi
- Emergency Medicine, Shoushtar Faculty of Medical SciencesShoushtarIran
| | - Azam Honarmandpour
- Department of MidwiferyShoushtar Faculty of Medical SciencesShoushtarIran
| |
Collapse
|
|
29
|
Ibiwoye O, Hill JE, Thomson G. The impacts of Covid-19 on perinatal mental health - Part 1. THE PRACTISING MIDWIFE 2022; 25:26-30. [PMID: 39040130 PMCID: PMC7616272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Subscribe] [Scholar Register] [Indexed: 07/24/2024]
Affiliation(s)
| | - J E Hill
- University of Central Lancashire
| | | |
Collapse
|
|
30
|
Retrospective assessment of the association between co-morbid disease burden and biochemical parameters in hospitalized hypertensive COVID-19 patients. JOURNAL OF SURGERY AND MEDICINE 2022. [DOI: 10.28982/josam.1089604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Background/Aim: Hypertension (HT) was examined as a risk factor affecting the progression of the 2019 novel coronavirus disease (COVID-19). In COVID-19 patients, it can be found in many co-morbid diseases, along with hypertension. It is not clear whether the co-morbid burden of the disease affects the prognosis in hypertensive COVID-19 patients and which biochemical parameters may be indicative of this. Therefore, this study was designed to determine the effect of co-morbid disease burden on biochemical parameters in hospitalized hypertensive COVID-19 patients.
Methods: After receiving approval from the University Ethics Committee, demographic, clinical, radiological, and laboratory data of 250 hospitalized hypertensive COVID-19 patients between May 2020 and Sept 2020 were screened. Patients with missing records and unclear history of hypertension drug use were excluded from the study. A total of 215 patients were included in the study. Patients were divided into four groups according to the co-morbidity status: (1) HT alone (Group HT0), (2) HT+ Diabetes Mellitus (DM) (Group HTDM1), (3) HT+one co-morbidity exclude DM (Group HT2), and (4) HT+at least two co-morbidities (Group HT3).
Results: We analyzed the data of 105 female and 110 male patients. Of the 215 patients whose data were evaluated in this study, 15 patients died. Two hundred people were discharged with recovery. The mortality rate was 7%. Of the hypertension patients, 34.9% had DM, 32.6% had coronary artery disease (CAD), 30.2% had chronic obstructive pulmonary disease (COPD), 16.3% had heart failure (HF), 23.3% had chronic kidney failure (CKD), and 9.3% had cerebrovascular disease (CVD). Twenty-five percent were smokers. Urea, creatinine, direct bilirubin (DBil), and Troponin-I values were significantly higher in the Group HT3 compared to the Group HT0, Group HTDM1, and Group HT2 (P < 0.001, P < 0.001, P < 0.001, P = 0.002 respectively). Glomerular filtration rate (GFR) and albümin levels were significantly lower in Group HT3 than in Group HT0, Group HTDM1, and Group HT2 (P < 0.001 and P < 0.001, respectively). The logistic regression model was statistically significant (χ2(7) = 69.088 and P < 0.001); advanced age, decrease in GFR and plateletcrit (PCT) levels, and increase in D-dimer and DBil levels were observed as predictive parameters of mortality in all hospitalized COVID-19 HT patients.
Conclusion: We determined that SARS-CoV-2 pneumonia patients with HT plus at least two co-morbidities were more serious than other patient groups in terms of organ damage and biochemical variables. In our study, we observed an increase in urea, creatinine, D-dimer, Dbil, and Troponin-I values and a decrease in GFR and albumin values as the co-morbidity burden increased in hypertensive COVID-19 patients. However, a decrease in GFR and hemogram PCT levels and an increase in D-dimer and DBil levels could be risk factors for mortality.
Collapse
|
|
31
|
Vaz A, Pedrazzani BM, Ledesma JA, Yagui A, Schelin HR. Effect of lateral decubitus acquisition in accuracy and lung severity estimation of chest computed tomography in children with suspected COVID-19. EINSTEIN-SAO PAULO 2022; 20:eAO0061. [PMID: 35894371 PMCID: PMC9299577 DOI: 10.31744/einstein_journal/2022ao0061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 05/18/2022] [Indexed: 11/05/2022] Open
|
|
32
|
Emin Sahin M. Deep learning-based approach for detecting COVID-19 in chest X-rays. Biomed Signal Process Control 2022; 78:103977. [PMID: 35855833 PMCID: PMC9279305 DOI: 10.1016/j.bspc.2022.103977] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 06/16/2022] [Accepted: 07/11/2022] [Indexed: 12/15/2022]
Abstract
Today, 2019 Coronavirus (COVID-19) infections are a major health concern worldwide. Therefore, detecting COVID-19 in X-ray images is crucial for diagnosis, evaluation, and treatment. Furthermore, expressing diagnostic uncertainty in a report is a challenging duty but unavoidable task for radiologists. This study proposes a novel CNN (Convolutional Neural Network) model for automatic COVID-19 identification utilizing chest X-ray images. The proposed CNN model is designed to be a reliable diagnostic tool for two-class categorization (COVID and Normal). In addition to the proposed model, different architectures, including the pre-trained MobileNetv2 and ResNet50 models, are evaluated for this COVID-19 dataset (13,824 X-ray images) and our suggested model is compared to these existing COVID-19 detection algorithms in terms of accuracy. Experimental results show that our proposed model identifies patients with COVID-19 disease with 96.71 percent accuracy, 91.89 percent F1-score. Our proposed approach CNN’s experimental results show that it outperforms the most advanced algorithms currently available. This model can assist clinicians in making informed judgments on how to diagnose COVID-19, as well as make test kits more accessible.
Collapse
Affiliation(s)
- M Emin Sahin
- Department of Computer Engineering, Yozgat Bozok University, Turkey
| |
Collapse
|
|
33
|
The impact of coronavirus on reproduction: contraceptive access, pregnancy rates, pregnancy delay, and the role of vaccination. F&S REVIEWS 2022; 3:190-200. [PMID: 35663280 PMCID: PMC9150907 DOI: 10.1016/j.xfnr.2022.05.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 05/20/2022] [Accepted: 05/25/2022] [Indexed: 11/28/2022]
Abstract
It is important to closely examine trends in reproduction during a pandemic because it provides not only the foundation for an improved future response but also crucial insights regarding the disparate impact across different races and socioeconomic classes. The coronavirus disease 2019 pandemic is a prime example of the impact a pandemic can have on a nation’s reproductive health. Contraception and abortion access became more difficult with more barriers to access, likely contributing to increasing unintended pregnancy rates. Underrepresented minorities and vulnerable populations were disproportionately affected by the virus on their reproductive health as well as by the virus itself. As the first ever messenger ribonucleic acid vaccine in conjunction with the lack of inclusion of pregnant and peripartum women in initial studies and conflicting and misinformation on social media, the initial role of the coronavirus disease 2019 vaccine in women of reproductive age was unclear. Further research inclusive of this group of women has led to the consensus by major medical societies to recommend vaccination of women regardless of pregnancy or lactating status. Examining these topics in depth will lead to the development of strategies that can be employed to mitigate the negative effects on reproductive health during the current pandemic and can also be applied to future strategic plans to prevent similar negative outcomes.
Collapse
|
|
34
|
Omar M, Youssef MR, Trinh LN, Attia AS, Elshazli RM, Jardak CL, Farhoud AS, Hussein MH, Shihabi A, Elnahla A, Zora G, Abdelgawad M, Munshi R, Aboueisha M, Toraih EA, Fawzy MS, Kandil E. Excess of cesarean births in pregnant women with COVID-19: A meta-analysis. Birth 2022; 49:179-193. [PMID: 34997608 DOI: 10.1111/birt.12609] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 12/18/2021] [Indexed: 12/22/2022]
Abstract
BACKGROUND Studies have suggested that cesarean birth in pregnant women with COVID-19 may decrease maternal adverse events and perinatal transmission. This systematic review aimed to evaluate variations in clinical presentation, laboratory findings, and maternal/neonatal outcomes in COVID-19 patients who delivered vaginally versus via cesarean. METHODS A comprehensive search following PRISMA guidelines was performed for studies published up to May 23, 2020, using PubMed, Web of Science, Scopus, Embase, Cochrane, Science Direct, and clinicaltrials.gov. Original retrospective and prospective studies, case reports, or case series with sufficient data for estimating the association of COVID-19 with different pregnancy outcomes with no language restriction and published in peer-reviewed journals were included. Pooled mean and arcsine transformation proportions were applied. Next, a two-arm meta-analysis was performed comparing the perinatal outcomes between the study groups. RESULTS Forty-two studies with a total of 602 pregnant women with COVID-19 were included. The mean age was 31.8 years. Subgroup analysis showed that Americans had the lowest gestational age (mean = 32.7, 95%CI = 27.0-38.4, P < 0.001) and the highest incidence of maternal ICU admission (95%CI = 0.45%-2.20, P < 0.001) of all nationalities in the study. There was no significant difference in perinatal complications, premature rupture of membrane, placenta previa/accreta, or gestational hypertension/pre-eclampsia between women who delivered vaginally versus by cesarean. Importantly, there were also no significant differences in maternal or neonatal outcomes. CONCLUSION Vaginal delivery was not associated with worse maternal or neonatal outcomes when compared with cesarean. The decision to pursue a cesarean birth should be based on standard indications, not COVID-19 status.
Collapse
Affiliation(s)
- Mahmoud Omar
- Division of Endocrine and Oncologic Surgery, Department of Surgery, School of Medicine, Tulane University, New Orleans, Louisiana, USA
| | - Mohanad R Youssef
- Division of Endocrine and Oncologic Surgery, Department of Surgery, School of Medicine, Tulane University, New Orleans, Louisiana, USA
| | - Lily N Trinh
- School of Medicine, Tulane University, New Orleans, Louisiana, USA
| | - Abdallah S Attia
- Division of Endocrine and Oncologic Surgery, Department of Surgery, School of Medicine, Tulane University, New Orleans, Louisiana, USA
| | - Rami M Elshazli
- Department of Biochemistry and Molecular Genetics, Faculty of Physical Therapy, Horus University - Egypt, New Damietta, Egypt
| | | | - Ashraf S Farhoud
- Department of Surgery, School of Medicine, Tulane University, New Orleans, Louisina, USA
| | - Mohammad H Hussein
- Division of Endocrine and Oncologic Surgery, Department of Surgery, School of Medicine, Tulane University, New Orleans, Louisiana, USA
| | - Areej Shihabi
- Division of Endocrine and Oncologic Surgery, Department of Surgery, School of Medicine, Tulane University, New Orleans, Louisiana, USA
| | - Ahmed Elnahla
- Division of Endocrine and Oncologic Surgery, Department of Surgery, School of Medicine, Tulane University, New Orleans, Louisiana, USA
| | - Ghassan Zora
- Division of Endocrine and Oncologic Surgery, Department of Surgery, School of Medicine, Tulane University, New Orleans, Louisiana, USA
| | | | - Ruhul Munshi
- Division of Endocrine and Oncologic Surgery, Department of Surgery, School of Medicine, Tulane University, New Orleans, Louisiana, USA
| | - Mohamed Aboueisha
- Department of Surgery, School of Medicine, Tulane University, New Orleans, Louisina, USA.,Department of Otolaryngology-Head and Neck Surgery, Faculty of Medicine, Suez Canal University, Ismailia, Egypt
| | - Eman A Toraih
- Division of Endocrine and Oncologic Surgery, Department of Surgery, School of Medicine, Tulane University, New Orleans, Louisiana, USA.,Genetics Unit, Department of Histology and Cell Biology, Faculty of Medicine, Suez Canal University, Ismailia, Egypt
| | - Manal S Fawzy
- Department of Medical Biochemistry and Molecular Biology, Faculty of Medicine, Suez Canal University, Ismailia, Egypt.,Department of Biochemistry, Faculty of Medicine, Northern Border University, Arar, Saudi Arabia
| | - Emad Kandil
- Division of Endocrine and Oncologic Surgery, Department of Surgery, School of Medicine, Tulane University, New Orleans, Louisiana, USA
| |
Collapse
|
|
35
|
Ilieva E, Boyapati A, Chervenkov L, Gulinac M, Borisov J, Genova K, Velikova T. Imaging related to underlying immunological and pathological processes in COVID-19. World J Clin Infect Dis 2022; 12:1-19. [DOI: 10.5495/wjcid.v12.i1.1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 11/09/2021] [Accepted: 03/07/2022] [Indexed: 02/06/2023] Open
Abstract
The introduction of coronavirus disease-2019 (COVID-19) as a global pandemic has contributed to overall morbidity and mortality. With a focus on understanding the immunology and pathophysiology of the disease, these features can be linked with the respective findings of imaging studies. Thus, the constellation between clinical presentation, histological, laboratory, immunological, and imaging results is crucial for the proper management of patients. The purpose of this article is to examine the role of imaging during the particular stages of severe acute respiratory syndrome coronavirus 2 infection – asymptomatic stage, typical and atypical COVID-19 pneumonia, acute respiratory distress syndrome, multiorgan failure, and thrombosis. The use of imaging methods to assess the severity and duration of changes is crucial in patients with COVID-19. Radiography and computed tomography are among the methods that allow accurate characterization of changes.
Collapse
Affiliation(s)
- Elena Ilieva
- Department of Diagnostic Imaging, University Emergency Hospital (UMHATEM) "N. I. Pirogov”, Sofia 1606, Bulgaria
| | - Alexandra Boyapati
- Department of Diagnostic Imaging, University Emergency Hospital (UMHATEM) "N. I. Pirogov”, Sofia 1606, Bulgaria
| | - Lyubomir Chervenkov
- Department of Diagnostic Imaging, Medical University, Plovdiv, University Hospital "St George", Plovdiv 4000, Bulgaria
| | - Milena Gulinac
- Department of General and Clinical Pathology, Medical University, Plovdiv, University Hospital "St George", Plovdiv 4000, Bulgaria
| | - Jordan Borisov
- Department of Diagnostic Imaging, MBAL-Dobrich” AD, Dobrich 9300, Bulgaria
| | - Kamelia Genova
- Department of Diagnostic Imaging, University Emergency Hospital (UMHATEM) "N. I. Pirogov”, Sofia 1606, Bulgaria
| | - Tsvetelina Velikova
- Department of Clinical Immunology, University Hospital “Lozenetz”, Sofia 1407, Bulgaria
- Medical Faculty, Sofia University “St. Kliment Ohridski”, Sofia 1407, Bulgaria
| |
Collapse
|
|
36
|
Goyal LD, Garg P, Verma M, Kaur N, Bakshi D, Arora J. Effect of restrictions imposed due to COVID-19 pandemic on the antenatal care and pregnancy outcomes: a prospective observational study from rural North India. BMJ Open 2022; 12:e059701. [PMID: 35387835 PMCID: PMC8987212 DOI: 10.1136/bmjopen-2021-059701] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 03/02/2022] [Indexed: 01/02/2023] Open
Abstract
OBJECTIVES To assess the difficulties faced by the pregnant women in seeking appropriate antenatal care due to the restrictions imposed during the COVID-19 pandemic; assess the difficulties encountered during delivery and postpartum period; the suitability of the teleconsultation services offered; effect of COVID-19 infection on pregnancy outcomes and the effect of restrictions on the nutrition profile of the pregnant women. DESIGN Prospective observational study. SETTING AND PARTICIPANTS We included 1374 pregnant women from the rural areas of three districts of Punjab, India registered at government health centres before the implementation of lockdown due to the COVID-19 pandemic on 24 March 2020. PRIMARY AND SECONDARY OUTCOME MEASURES The primary outcome was the difficulties faced by the women during their pregnancies due to restrictions imposed during the lockdown. The secondary outcomes included the effect of COVID-19 infections on pregnancy outcomes, satisfaction from the telemedicine services and restrictions on the nutrition profile of the pregnant women. RESULTS One-third of the women (38.4%) considered their last pregnancy unplanned. Women faced difficulties due to the restrictions in getting adequate nutrition (76.5%), accessing transportation facilities (35.4%), consultations from doctors (22.4%) or getting an ultrasonography scan (48.7%). One-fifth (21.9%) of women could not access safe abortion services. Only 3.6% of respondents ever took any teleconsultation services offered by the government. Most of them felt unsatisfied compared with routine visits (77.5%). COVID-19-infected women were primarily asymptomatic (76.1%), but there was a high incidence of preterm birth (42.8%). Frontline workers could visit 64.3% of the women in the postpartum period despite restrictions. CONCLUSIONS Lockdown compromised the antenatal care in our study area while the frontline workers attempted to minimise the inconvenience. Telemedicine services did not prove to be of many benefits to pregnant women and should only work as a supplement to the existing protocols of antenatal care.
Collapse
Affiliation(s)
- Lajya Devi Goyal
- Department of Obstetrics and Gynaecology, All India Institute of Medical Sciences-Bathinda, Bathinda, Punjab, India
| | - Priyanka Garg
- Department of Obstetrics and Gynaecology, All India Institute of Medical Sciences-Bathinda, Bathinda, Punjab, India
| | - Madhur Verma
- Department of Community and Family Medicine, All India Institute of Medical Sciences-Bathinda, Bathinda, Punjab, India
| | - Navdeep Kaur
- Department of Obstetrics and Gynaecology, All India Institute of Medical Sciences-Bathinda, Bathinda, Punjab, India
| | - Dapinder Bakshi
- Punjab State Council for Science and Technology, Chandigarh, India
| | - Jatinder Arora
- Punjab State Council for Science and Technology, Chandigarh, India
| |
Collapse
|
|
37
|
Toapanta-Pinta PC, Vasco-Toapanta CS, Herrera-Tasiguano AE, Verdesoto-Jácome CA, Páez-Pástor MJ, Vasco-Morales S. COVID 19 in pregnant women and neonates: Clinical characteristics and laboratory and imaging findings. An overview of systematic reviews. REVISTA DE LA FACULTAD DE MEDICINA 2022. [DOI: 10.15446/revfacmed.v71n1.97588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022] Open
Abstract
Introduction: SARS-CoV-2 infection in the perinatal period may be associated with an increased risk of morbidity and mortality in both the mother and the neonate.
Objective: To describe the clinical characteristics and, laboratory and imaging findings in pregnant women with COVID-19 and their newborns.
Materials and methods: We searched PubMed, Scopus, Web of Science, and Cochrane databases for systematic reviews published between February 1, 2020, and May 30, 2021, describing clinical characteristics and laboratory and imaging (chest) findings in pregnant women with COVID-19 and their newborns; there were no language restrictions. Data were reanalyzed by means of Bayesian meta-analysis using Markov Chain Monte Carlo methods. The study protocol is registered in PROSPERO under code CRD42020178329.
Results: Six systematic reviews were retrieved (for a total of 617 primary studies). A narrative synthesis of the proportions of signs, symptoms, and imaging and laboratory findings of both mothers and neonates was performed. The Odds ratios (OR) between pregnant women with and without COVID-19 were as follows: fetal well-being involvement: 1.9 (95%CI:1.09-3.63); stillbirth: 1.73 (95%CI:1.01-2.94); preterm birth: 1.77 (95%CI:1.25-2.61); maternal admission to the intensive care unit (ICU): 6.75 (95%CI:1-31.19). Regarding symptomatology, the following OR was obtained for myalgia between pregnant women and non-pregnant women with COVID-19: 0.67 (95% CI:0.51-0.93).
Conclusions: Cough, fever, dyspnea, and myalgia are the most common symptoms in pregnant women with COVID-19; in addition, there is a higher risk of admission to the ICU. Regarding complementary testing, the most frequent alterations are lymphopenia and the evidence of lesions in chest imaging studies. The presence of COVID-19 in pregnant women is associated with premature birth. It seems that SARS-CoV-2 infection in neonates is not serious and the risk of vertical transmission is low, since no data about congenital malformations attributable to the virus were found.
Collapse
|
|
38
|
Almaghrabi R, Shaiba LA, Babic I, Abdelbaky M, Aljuhani SI, Omer M, Abdelmaksoud HA, Abdulghani S, Hadid A, Arafah MA, Omar Ali NM, Alamir A, Alateah S, Salem HAB, Alrumaihi AM, Bukhari M, Aljubab R, AlSaud N, Alhetheel AF, Somily AM, Albarrag AM, Alahdal HM, Sonbol H, Alnemri A, Alzamil F. Possible vertical transmission of corona virus disease 19 (COVID-19) from infected pregnant mothers to neonates: a multicenter study. J Matern Fetal Neonatal Med 2022; 35:9558-9567. [PMID: 35282749 DOI: 10.1080/14767058.2022.2047926] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
INTRODUCTION Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly contagious with various possible routes of transmission, resulting in high mortality globally. Controversy exists regarding the vertical transmission of the SARS-CoV-2 infection to fetuses of COVID-19-infected women. The aim of this study was to investigate the possibility of the vertical transmission of SARS-CoV-2 from COVID-19-infected mothers to their neonates. MATERIALS AND METHODS We prospectively collected demographical and clinical characteristics of 31 COVID-19 positive pregnant women and their neonates. All mothers and neonates were tested for SARS-CoV-2 infection using the real-time polymerase chain reaction on nasopharyngeal swabs and breast milk samples. Antenatal and placental abnormalities were ultrasonically and histopathologically examined. In cord blood samples, the immunoglobins (Ig) M and IgG were estimated qualitatively. RESULTS The women's mean age and gestational age were 31 years and 38 weeks, respectively, with 58% undergoing an elective cesarean section. Gestational diabetes was reported in 29% of cases, 64.5% of women were medically free and only 16.12% were symptomatic. A normal antenatal ultrasound was observed in 77.42% of cases. Nine cord blood samples were positive for IgG. Villous infarction (24%), villous agglutination, and chorangiosis (51%), accelerated villous maturation (21%) and reduced and hypercoiling were reported for 6.97% of the umbilical cords. Three newborns had possible vertical transmission of SARS-CoV-2 infection, of which, two were preterm and IUFD. The third neonate was born full-term, admitted to NICU and later discharged in good health. CONCLUSION Our findings support the possibility of the direct vertical transmission of the SARS-CoV-2 infection to neonates from infected mothers. Further studies with a larger sample size are required to validate the current findings.
Collapse
Affiliation(s)
- Rana Almaghrabi
- Pediatric Department, Prince Sultan Military Medical City (PSMMC), Riyadh, Saudi Arabia
| | - Lana A Shaiba
- Pediatric Department, College of medicine, King Saud University, Riyadh, Saudi Arabia.,Neonatal Intensive Care Unit, King Saud University Medical City, Riyadh, Saudi Arabia
| | - Inas Babic
- Prince Sultan Military Medical City (PSMMC), Riyadh, Saudi Arabia
| | - Mona Abdelbaky
- Registrar Obstetrics and Gynecology, Prince Sultan Military Medical City (PSMMC), Riyadh, Saudi Arabia
| | - Sana Ibrahim Aljuhani
- Consultant of Obstetrics and Gynecologist, Prince Sultan Military Medical City (PSMMC), Riyadh, Saudi Arabia
| | - Magdy Omer
- Registrar Neonatal Intensive Care Unit, Prince Sultan Military Medical City (PSMMC), Riyadh, Saudi Arabia
| | | | - Sahar Abdulghani
- Department of Obstetrics and Gynecology, King Khalid University Medical City, Riyadh, Saudi Arabia.,Department of Pathology, King Saud University, Riyadh, Saudi Arabia
| | - Adnan Hadid
- Pediatric Department, College of medicine, King Saud University, Riyadh, Saudi Arabia.,Neonatal Intensive Care Unit, King Saud University Medical City, Riyadh, Saudi Arabia
| | - Maria A Arafah
- Department of Pathology, King Saud University, Riyadh, Saudi Arabia
| | - Nagoud Mohamed Omar Ali
- Department of Pediatrics, College of Medicine, Majmaah University, Al Majma'ah, Saudi Arabia
| | - Abdulrahman Alamir
- Clinical Scientist, Molecular Microbiology, Prince Sultan Military Medical City (PSMMC), Riyadh, Saudi Arabia
| | - Souad Alateah
- Medical Laboratory Scientific Officer (MLSO), Riyadh, Saudi Arabia
| | - Howaida A Bin Salem
- Medical Laboratory Scientific Officer (MLSO), Riyadh, Saudi Arabia.,Department of Pathology and Laboratory Medicine (Microbiology), College of Medicine, King Saud University, King Saud University Medical City, Riyadh, Saudi Arabia
| | - Ahmed Muhammed Alrumaihi
- Department of Pathology and Laboratory Medicine (Microbiology), College of Medicine, King Saud University, King Saud University Medical City, Riyadh, Saudi Arabia
| | - Mahdyah Bukhari
- Department of Pathology, King Saud University, Riyadh, Saudi Arabia
| | - Reem Aljubab
- Department of Pathology, King Saud University, Riyadh, Saudi Arabia
| | - Nora AlSaud
- Pediatric Department, College of medicine, King Saud University, Riyadh, Saudi Arabia
| | - Abdulkarim F Alhetheel
- Department of Pathology and Laboratory Medicine (Microbiology), College of Medicine, King Saud University, King Saud University Medical City, Riyadh, Saudi Arabia
| | - Ali M Somily
- Department of Pathology and Laboratory Medicine (Microbiology), College of Medicine, King Saud University, King Saud University Medical City, Riyadh, Saudi Arabia
| | - Ahmed M Albarrag
- Department of Pathology and Laboratory Medicine (Microbiology), College of Medicine, King Saud University, King Saud University Medical City, Riyadh, Saudi Arabia
| | - Hadil Mohammad Alahdal
- Department of Pediatric Infectious Disease, King Saud University Medical City, Riyadh, Saudi Arabia
| | - Hana Sonbol
- Biology Department, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Abdulrahman Alnemri
- Pediatric Department, College of medicine, King Saud University, Riyadh, Saudi Arabia.,Neonatal Intensive Care Unit, King Saud University Medical City, Riyadh, Saudi Arabia
| | - Fahad Alzamil
- Pediatric Department, College of medicine, King Saud University, Riyadh, Saudi Arabia.,Department of Pediatric Infectious Disease, King Saud University Medical City, Riyadh, Saudi Arabia
| |
Collapse
|
|
39
|
Rana A, Singh H, Mavuduru R, Pattanaik S, Rana PS. Quantifying prognosis severity of COVID-19 patients from deep learning based analysis of CT chest images. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:18129-18153. [PMID: 35282403 PMCID: PMC8901869 DOI: 10.1007/s11042-022-12214-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 01/04/2022] [Accepted: 01/10/2022] [Indexed: 05/28/2023]
Abstract
The COVID-19 pandemic has affected all the countries in the world with its droplet spread mode. The colossal amount of cases has strained all the healthcare systems due to the serious nature of infections especially for people with comorbidities. A very high specificity Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) test is the principal technique in use for diagnosing the COVID-19 patients. Also, CT scans have helped medical professionals in patient severity estimation & progression tracking of COVID-19 virus. In study we present our own extensible COVID-19 viral infection tracking prognosis technique. It uses annotated dataset of CT chest scan slice images created with the help of medical professionals. The annotated dataset contains bounding box coordinates of different features for COVID-19 detection like ground glass opacities, crazy paving pattern, consolidations, lesions etc. We qualitatively identify the severity of the patient for later prognosis stages in our study to assist medical staff for patient prioritization. First we detected COVID-19 positive patients with pre-trained Siamese Neural Network (SNN) which obtained 87.6% accuracy, 87.1% F1-Score & 95.1% AUC scores. These metrics were achieved after removal of 40% quantitatively highly similar images from the COVID-CT dataset. This reduced dataset was further medically annotated with COVID-19 features for bounding box detection. After this we assigned severity scores to detected COVID-19 features and calculated the cumulative severity score for COVID-19 patients. For qualitative patient prioritization with prognosis clinical assistance information, we finally converted this score into a multi-classification problem which obtained 47% weighted-average F1-score.
Collapse
Affiliation(s)
- Ashish Rana
- Department of Computer Science and Engineering, TIET, Patiala, Punjab India
| | - Harpreet Singh
- Department of Computer Science and Engineering, TIET, Patiala, Punjab India
| | | | - Smita Pattanaik
- Department of Urology and Pharmacology, PGIMER, Chandigarh, India
| | | |
Collapse
|
|
40
|
Nair AV, Ramanathan S, Venugopalan P. Chest imaging in pregnant patients with COVID-19: Recommendations, justification, and optimization. Acta Radiol Open 2022; 11:20584601221077394. [PMID: 35284094 PMCID: PMC8905047 DOI: 10.1177/20584601221077394] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Accepted: 01/16/2022] [Indexed: 01/11/2023] Open
Abstract
Evaluation of COVID-19 related complication is challenging in pregnancy, due to concerns about ionizing radiation risk to mother and the fetus. Although there are instances when diagnostic imaging is clinically warranted for COVID-19 evaluation despite the minimal risks of radiation exposure, often there are concerns raised by the patients and sometimes by the attending physicians. This article reviews the current recommendations on indications of chest imaging in pregnant patients with COVID-19, the dose optimization strategies, and the risks related to imaging exposure during pregnancy. In clinical practice, these imaging strategies are key in addressing the complex obstetrical complications associated with COVID-19 pneumonia.
Collapse
Affiliation(s)
| | - Subramaniyan Ramanathan
- Department of Clinical Imaging, Al-Wakra Hospital, Hamad Medical Corporation, Doha, Qatar
- Department of Radiology, Weill Cornell Medicine, Doha, Qatar
| | - Prasanna Venugopalan
- Department of Obstetrics and Gynaecology, Travancore Medical College, Kollam, Kerala, India
| |
Collapse
|
|
41
|
Nillmani, Jain PK, Sharma N, Kalra MK, Viskovic K, Saba L, Suri JS. Four Types of Multiclass Frameworks for Pneumonia Classification and Its Validation in X-ray Scans Using Seven Types of Deep Learning Artificial Intelligence Models. Diagnostics (Basel) 2022; 12:652. [PMID: 35328205 PMCID: PMC8946935 DOI: 10.3390/diagnostics12030652] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/04/2022] [Accepted: 03/04/2022] [Indexed: 12/31/2022] Open
Abstract
Background and Motivation: The novel coronavirus causing COVID-19 is exceptionally contagious, highly mutative, decimating human health and life, as well as the global economy, by consistent evolution of new pernicious variants and outbreaks. The reverse transcriptase polymerase chain reaction currently used for diagnosis has major limitations. Furthermore, the multiclass lung classification X-ray systems having viral, bacterial, and tubercular classes—including COVID-19—are not reliable. Thus, there is a need for a robust, fast, cost-effective, and easily available diagnostic method. Method: Artificial intelligence (AI) has been shown to revolutionize all walks of life, particularly medical imaging. This study proposes a deep learning AI-based automatic multiclass detection and classification of pneumonia from chest X-ray images that are readily available and highly cost-effective. The study has designed and applied seven highly efficient pre-trained convolutional neural networks—namely, VGG16, VGG19, DenseNet201, Xception, InceptionV3, NasnetMobile, and ResNet152—for classification of up to five classes of pneumonia. Results: The database consisted of 18,603 scans with two, three, and five classes. The best results were using DenseNet201, VGG16, and VGG16, respectively having accuracies of 99.84%, 96.7%, 92.67%; sensitivity of 99.84%, 96.63%, 92.70%; specificity of 99.84, 96.63%, 92.41%; and AUC of 1.0, 0.97, 0.92 (p < 0.0001 for all), respectively. Our system outperformed existing methods by 1.2% for the five-class model. The online system takes <1 s while demonstrating reliability and stability. Conclusions: Deep learning AI is a powerful paradigm for multiclass pneumonia classification.
Collapse
Affiliation(s)
- Nillmani
- School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India; (N.); (P.K.J.); (N.S.)
| | - Pankaj K. Jain
- School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India; (N.); (P.K.J.); (N.S.)
| | - Neeraj Sharma
- School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India; (N.); (P.K.J.); (N.S.)
| | - Mannudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02115, USA;
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 10015 Cagliari, Italy;
| | - Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint, Roseville, CA 95661, USA
- Knowledge Engineering Center, Global Biomedical Technologies, Inc., Roseville, CA 95661, USA
| |
Collapse
|
|
42
|
Ruiz-Roman R, Martinez-Perez C, Gil Prados I, Cristóbal I, Sánchez-Tena MÁ. COVID-19 and Pregnancy: Citation Network Analysis and Evidence Synthesis. JMIR Pediatr Parent 2022; 5:e29189. [PMID: 35044301 PMCID: PMC8989383 DOI: 10.2196/29189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 12/22/2021] [Accepted: 01/03/2022] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND COVID-19 spread quickly around the world shortly after the first outbreaks of the new coronavirus disease at the end of December 2019, affecting all populations, including pregnant women. OBJECTIVE The aim of this study was to analyze the relationship between different publications on COVID-19 in pregnancy and their authors through citation networks, as well as to identify the research areas and to determine the publication that has been the most highly cited. METHODS The search for publications was carried out through the Web of Science database using terms such as "pregnancy," "SARS-CoV-2," "pregnant," and "COVID-19" for the period between January and December 2020. Citation Network Explorer software was used for publication analysis and VOSviewer software was used to construct the figures. This approach enabled an in-depth network analysis to visualize the connections between the related elements and explain their network structure. RESULTS A total of 1330 publications and 5531 citation networks were identified in the search, with July being the month with the largest number of publications, and the United States, China, and England as the countries with the greatest number of publications. The most cited publication was "Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women: a retrospective review of medical records" by Chen and colleagues, which was published in March 2020. Six groups identified as being close in the citation network reflect multidisciplinary research, including clinical characteristics and outcomes in pregnancy, vertical transmission, delivery mode, and psychological impacts of the pandemic on pregnant women. CONCLUSIONS Thousands of articles on COVID-19 have been published in several journals since the disease first emerged. Identifying relevant publications and obtaining a global view of the main papers published on COVID-19 and pregnancy can lead to a better understanding of the topic. With the accumulation of scientific knowledge, we now know that the clinical features of COVID-19 during pregnancy are generally similar to those of infected nonpregnant women. There is a small increase in frequency of preterm birth and cesarean birth, related to severe maternal illness. Vaccination for all pregnant women is recommended. Several agents are being evaluated for the treatment of COVID-19, but with minimal or no information on safety in pregnancy. These results could form the basis for further research. Future bibliometric and scientometric studies on COVID-19 should provide updated information to analyze other relevant indicators in this field.
Collapse
Affiliation(s)
- Rebeca Ruiz-Roman
- Department of Gynecology and Obstetrics, Hospital Clínico San Carlos, Madrid, Spain
| | | | - Inés Gil Prados
- Department of Gynecology and Obstetrics, Hospital Clínico San Carlos, Madrid, Spain
| | - Ignacio Cristóbal
- Department of Gynecology and Obstetrics, Hospital Clínico San Carlos, Madrid, Spain.,Faculty of Medicine, Universidad Francisco de Vitoria, Madrid, Spain
| | - Miguel Ángel Sánchez-Tena
- Instituto Superior de Educação e Ciências, Lisboa, Portugal.,Department of Optometry and Vision, Faculty of Optics and Optometry, Universidad Complutense de Madrid, Madrid, Spain
| |
Collapse
|
|
43
|
Thamer E, Al-Rawaf S. Hematological Changes and Pregnancy Outcome in COVID-19 Pregnant Patients: A Case–Control Study. Open Access Maced J Med Sci 2022. [DOI: 10.3889/oamjms.2022.8632] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
Background: Viral infections during pregnancy are associated with adverse maternal as well as fetal outcomes such as higher rates of miscarriage, perinatal mortality, restriction of fetal growth and preterm delivery. Aim of the study: to explore maternal outcomes and hematological alterations in a sample of Iraqi pregnant women. Patients and methods: The current cross sectional study was carried out in Obstetric department in Al Imamain Al-Kadhimain Medical City, Baghdad, Iraq, including a total of 55 full term pregnant women who were grouped into 25 women with SARS-Cov-2 and 30 control pregnant women, starting from January 2021 through December 2021. Results: Fever was the most common symptom, which was reported in 12 cases (48.0 %), and it was followed by cough that was seen in 9 cases (36.0 %). Gastrointestinal symptoms in the form of nausea, vomiting and diarrhea were seen in 2 cases (8.0 %). Leaking liquor was seen in a single case of COVID-19 women. Pregnancy induced hypertension, antepartum hemorrhage and diabetes mellitus were seen in 2 cases of COVID-19 women for each event; with no significant difference between groups. The rate of cesarean section was higher in COVID-19 group in comparison with control group, 19 (76.0 %) versus 13 (43.3 %), respectively and the difference was significant (p = 0.014). Conclusion: COVID-19 at time of pregnancy is accompanied by higher rate of cesarean section because of fetal distress with no significant increase in fetal or maternal mortality rates and the main hematological changes are leukopenia and lymphopenia.
Collapse
|
|
44
|
Shah PM, Ullah H, Ullah R, Shah D, Wang Y, Islam SU, Gani A, Rodrigues JJPC. DC-GAN-based synthetic X-ray images augmentation for increasing the performance of EfficientNet for COVID-19 detection. EXPERT SYSTEMS 2022; 39:e12823. [PMID: 34898799 PMCID: PMC8646497 DOI: 10.1111/exsy.12823] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 06/30/2021] [Accepted: 08/13/2021] [Indexed: 06/14/2023]
Abstract
Currently, many deep learning models are being used to classify COVID-19 and normal cases from chest X-rays. However, the available data (X-rays) for COVID-19 is limited to train a robust deep-learning model. Researchers have used data augmentation techniques to tackle this issue by increasing the numbers of samples through flipping, translation, and rotation. However, by adopting this strategy, the model compromises for the learning of high-dimensional features for a given problem. Hence, there are high chances of overfitting. In this paper, we used deep-convolutional generative adversarial networks algorithm to address this issue, which generates synthetic images for all the classes (Normal, Pneumonia, and COVID-19). To validate whether the generated images are accurate, we used the k-mean clustering technique with three clusters (Normal, Pneumonia, and COVID-19). We only selected the X-ray images classified in the correct clusters for training. In this way, we formed a synthetic dataset with three classes. The generated dataset was then fed to The EfficientNetB4 for training. The experiments achieved promising results of 95% in terms of area under the curve (AUC). To validate that our network has learned discriminated features associated with lung in the X-rays, we used the Grad-CAM technique to visualize the underlying pattern, which leads the network to its final decision.
Collapse
Affiliation(s)
- Pir Masoom Shah
- School of Computer ScienceWuhan UniversityWuhanChina
- Department of Computer ScienceBacha Khan UniversityCharsaddaPakistan
| | - Hamid Ullah
- Department of Computer ScienceKohat University of Science and TechnologyKohatPakistan
| | - Rahim Ullah
- Department of Computer ScienceUniversity of MalakandMalakandPakistan
| | - Dilawar Shah
- Department of Computer ScienceBacha Khan UniversityCharsaddaPakistan
| | - Yulin Wang
- School of Computer ScienceWuhan UniversityWuhanChina
| | - Saif ul Islam
- Department of Computer ScienceKICSIT, Institute of Space TechnologyIslamabadPakistan
| | - Abdullah Gani
- Faculty of Computing and InformaticsUniversity Malaysia SabahLabuanMalaysia
| | | |
Collapse
|
|
45
|
Mubarak AS, Serte S, Al‐Turjman F, Ameen ZS, Ozsoz M. Local binary pattern and deep learning feature extraction fusion for COVID-19 detection on computed tomography images. EXPERT SYSTEMS 2022; 39:e12842. [PMID: 34898796 PMCID: PMC8646483 DOI: 10.1111/exsy.12842] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 09/09/2021] [Indexed: 06/14/2023]
Abstract
The deadly coronavirus virus (COVID-19) was confirmed as a pandemic by the World Health Organization (WHO) in December 2019. It is important to identify suspected patients as early as possible in order to control the spread of the virus, improve the efficacy of medical treatment, and, as a result, lower the mortality rate. The adopted method of detecting COVID-19 is the reverse-transcription polymerase chain reaction (RT-PCR), the process is affected by a scarcity of RT-PCR kits as well as its complexities. Medical imaging using machine learning and deep learning has proved to be one of the most efficient methods of detecting respiratory diseases, but to train machine learning features needs to be extracted manually, and in deep learning, efficiency is affected by deep learning architecture and low data. In this study, handcrafted local binary pattern (LBP) and automatic seven deep learning models extracted features were used to train support vector machines (SVM) and K-nearest neighbour (KNN) classifiers, to improve the performance of the classifier, a concatenated LBP and deep learning feature was proposed to train the KNN and SVM, based on the performance criteria, the models VGG-19 + LBP achieved the highest accuracy of 99.4%. The SVM and KNN classifiers trained on the hybrid feature outperform the state of the art model. This shows that the proposed feature can improve the performance of the classifiers in detecting COVID-19.
Collapse
Affiliation(s)
- Auwalu Saleh Mubarak
- Department of Electrical and Electronics EngineeringNear East UniversityMersinTurkey
| | - Sertan Serte
- Department of Electrical and Electronics EngineeringNear East UniversityMersinTurkey
| | - Fadi Al‐Turjman
- Department of Artificial Intelligence, Research Center for AI and IoTNear East UniversityMersinTurkey
| | | | - Mehmet Ozsoz
- Department of Biomedical EngineeringNear East UniversityMersinTurkey
| |
Collapse
|
|
46
|
Christophers B, Marin BG, Oliva R, Powell WT, Savage TJ, Michelow IC. Trends in clinical presentation of children with COVID-19: a systematic review of individual participant data. Pediatr Res 2022; 91:494-501. [PMID: 32942286 PMCID: PMC7965792 DOI: 10.1038/s41390-020-01161-3] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Revised: 07/27/2020] [Accepted: 08/20/2020] [Indexed: 12/17/2022]
Abstract
BACKGROUND There are sparse patient-level data available for children with novel coronavirus disease (COVID-19). Therefore, there is an urgent need for an updated systematic literature review that analyzes individual children rather than aggregated data in broad age groups. METHODS Six databases (MEDLINE, Scopus, Web of Science, CINAHL, Google Scholar, medRxiv) were searched for studies indexed from January 1 to May 15, 2020, with MeSH terms: children, pediatrics, COVID-19, SARS-CoV-2. 1241 records were identified, of which only unique papers in English with individual patient information and documented COVID-19 testing were included. This review of 22 eligible studies followed Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data guidelines. RESULTS A total of 123 patients from five countries were identified. 46% were females. The median age was 5 years (IQR = 8). At presentation, 62% had a fever, 32% had a cough, 58% had a single symptom, and 21% were asymptomatic. Abnormal chest imaging was seen in 62% (65/105) of imaged and 76.9% (20/26) of asymptomatic children. A minority of children had elevated platelets, CRP, lactate dehydrogenase, and D-dimer. CONCLUSION Data from this independent participant data systematic review revealed that the majority of children with COVID-19 presented with either no symptoms or a single, non-respiratory symptom. IMPACT This systematic review revealed that the majority of children with COVID-19 presented with either no symptoms or a single, non-respiratory symptom. By using an independent participant data approach, this analysis underscores the challenge of diagnosing COVID-19 in pediatric patients due to the wide variety of symptoms and seemingly poor correlation of imaging findings with symptomatic disease. The data presented from individual patients from case series or cohort studies add more granularity to the current description of pediatric COVID-19.
Collapse
Affiliation(s)
- Briana Christophers
- Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD Program, New York, NY, USA.
| | | | - Rocío Oliva
- Warren Alpert Medical School of Brown University, Providence, Rhode Island
| | - Weston T. Powell
- Division of Pulmonary and Sleep Medicine, Seattle Children’s Hospital, Seattle, WA,Department of Pediatrics, University of Washington, Seattle, WA
| | - Timothy J. Savage
- Division of Infectious Diseases, Department of Pediatrics, Boston Children’s Hospital, MA,Harvard Medical School, Boston, MA
| | - Ian C. Michelow
- Warren Alpert Medical School of Brown University, Providence, Rhode Island,Department of Pediatrics, Division of Infectious Diseases and Center for International Health Research, Rhode Island Hospital, Providence, Rhode Island
| |
Collapse
|
|
47
|
Tangudu VSK, Kakarla J, Venkateswarlu IB. COVID-19 detection from chest x-ray using MobileNet and residual separable convolution block. Soft comput 2022; 26:2197-2208. [PMID: 35106060 PMCID: PMC8794607 DOI: 10.1007/s00500-021-06579-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/10/2021] [Indexed: 10/27/2022]
Abstract
A newly emerged coronavirus disease affects the social and economical life of the world. This virus mainly infects the respiratory system and spreads with airborne communication. Several countries witness the serious consequences of the COVID-19 pandemic. Early detection of COVID-19 infection is the critical step to survive a patient from death. The chest radiography examination is the fast and cost-effective way for COVID-19 detection. Several researchers have been motivated to automate COVID-19 detection and diagnosis process using chest x-ray images. However, existing models employ deep networks and are suffering from high training time. This work presents transfer learning and residual separable convolution block for COVID-19 detection. The proposed model utilizes pre-trained MobileNet for binary image classification. The proposed residual separable convolution block has improved the performance of basic MobileNet. Two publicly available datasets COVID5K, and COVIDRD have considered for the evaluation of the proposed model. Our proposed model exhibits superior performance than existing state-of-art and pre-trained models with 99% accuracy on both datasets. We have achieved similar performance on noisy datasets. Moreover, the proposed model outperforms existing pre-trained models with less training time and competitive performance than basic MobileNet. Further, our model is suitable for mobile applications as it uses fewer parameters and lesser training time.
Collapse
Affiliation(s)
| | - Jagadeesh Kakarla
- Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram, Chennai, India
| | | |
Collapse
|
|
48
|
Paudyal P, Katuwal N, Rawal S. COVID-19 among Pregnant Women Delivering in a Tertiary Care Center: A Descriptive Cross-sectional Study. JNMA J Nepal Med Assoc 2022; 60:1-5. [PMID: 35199679 PMCID: PMC9157656 DOI: 10.31729/jnma.6768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Accepted: 01/17/2022] [Indexed: 11/01/2022] Open
Abstract
INTRODUCTION Coronavirus Disease 2019 pandemic is raging across the world and has affected pregnant women as well. There is limited information regarding COVID-19 in pregnant women. The study aimed to find the prevalence of COVID-19 among all pregnant women who delivered during the study period in a tertiary care center. METHODS This was a descriptive cross-sectional study conducted in a tertiary care center from 16th August to 15th November 2020 after obtaining ethical clearance from the Institutional Review Committee of a tertiary care center. All the women who delivered in the hospital during the study period were enrolled and they were subjected to COVID-19 Reverse Transcriptase Polymerase Chain Reaction test. A total of 667 samples were taken using convenience sampling technique. Data were analyzed using the Statistical Package for the Social Sciences version 24 software. Point estimate at 95% Confidence Interval was calculated along with frequency and proportion for binary data. RESULTS Among 667 pregnant women, the prevalence of COVID-19 was 47 (7.05%) (5.10-8.99 at 95% Confidence Interval). Though the majority of women were asymptomatic 40 (85.1%), 5 (10.64%) developed mild disease, 1 (2.12%) each had severe and critical COVID-19 pneumonia. CONCLUSIONS The prevalence of COVID-19 among pregnant women delivering in our center is similar to other studies done in similar settings. In our study, we found that the majority of women had been asymptomatic and were diagnosed on routine testing. Hence, it is important to test all pregnant women before delivery for Coronavirus Disease 2019 irrespective of the presence or absence of symptoms.
Collapse
Affiliation(s)
- Pooja Paudyal
- Department of Obstetrics and Gynecology, Tribhuvan University Teaching Hospital, Maharajgunj Medical Campus, Institute of Medicine, Kathmandu, Nepal
| | - Neeta Katuwal
- Department of Obstetrics and Gynecology, Tribhuvan University Teaching Hospital, Maharajgunj Medical Campus, Institute of Medicine, Kathmandu, Nepal
| | - Suniti Rawal
- Department of Obstetrics and Gynecology, Tribhuvan University Teaching Hospital, Maharajgunj Medical Campus, Institute of Medicine, Kathmandu, Nepal
| |
Collapse
|
|
49
|
Chepur SV, Alekseeva II, Vladimirova OO, Myasnikov VA, Tyunin MA, Ilinskii NS, Nikishin AS, Shevchenko VA, Smirnova AV. [Specific features of the pathology of the respiratory system in SARS-CoV-2 (Coronaviridae: Coronavirinae: Betacoronavirus: Sarbecovirus) infected Syrian hamsters (Mesocricetus auratus)]. Vopr Virusol 2022; 66:442-451. [PMID: 35019251 DOI: 10.36233/0507-4088-63] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Indexed: 11/05/2022]
Abstract
INTRODUCTION Verification of histological changes in respiratory system using Syrian (golden) hamsters (Mesocricetus auratus) as experimental model is an important task for preclinical studies of drugs intended for prevention and treatment of the novel coronavirus infection COVID-19.The aim of this work was to study pathological changes of pulmonary tissue in SARS-CoV-2 (Coronaviridae: Coronavirinae: Betacoronavirus; Sarbecovirus) experimental infection in Syrian hamsters. MATERIAL AND METHODS Male Syrian hamsters weighting 80-100 g were infected by intranasal administration of culture SARS-CoV-2 at dose 4 × 104 TCID50/ml (TCID is tissue culture infectious dose). Animals were euthanatized on 3, 7 and 14 days after infection, with gravimetric registration. The viral load in lungs was measured using the polymerase chain reaction (PCR). Right lung and trachea tissues were stained with hematoxylin-eosin and according to Mallory. RESULTS AND DISCUSSION The highest viral replicative activity in lungs was determined 3 days after the infection. After 7 days, on a background of the decrease of the viral load in lungs, a pathologically significant increase of the organ's gravimetric parameters was observed. Within 3 to 14 days post-infection, the lung histologic pattern had been showing the development of inflammation with a succession of infiltrative-proliferative, edematousmacrophagal and fibroblastic changes. It was found that initial changes in respiratory epithelium can proceed without paranecrotic interstitial inflammation, while in the formation of multiple lung parenchyma lesions, damage to the epithelium of bronchioles and acinar ducts can be secondary. The appearance of epithelioid large-cell metaplastic epithelium, forming pseudoacinar structures, was noted as a pathomorphological feature specific to SARS-CoV-2 infection in Syrian hamsters. CONCLUSION As a result of the study, the specific features of the pathology of the respiratory system in SARSCoV-2 infected Syrian hamsters were described. These findings are of practical importance as reference data that can be used for preclinical studies to assess the effectiveness of vaccines and potential drugs.
Collapse
Affiliation(s)
- S V Chepur
- FSBI «State Research Testing Institute of Military Medicine» of the Ministry of Defense of the Russian Federation
| | - I I Alekseeva
- FSBI «State Research Testing Institute of Military Medicine» of the Ministry of Defense of the Russian Federation
| | - O O Vladimirova
- FSBI «State Research Testing Institute of Military Medicine» of the Ministry of Defense of the Russian Federation
| | - V A Myasnikov
- FSBI «State Research Testing Institute of Military Medicine» of the Ministry of Defense of the Russian Federation
| | - M A Tyunin
- FSBI «State Research Testing Institute of Military Medicine» of the Ministry of Defense of the Russian Federation
| | - N S Ilinskii
- FSBI «State Research Testing Institute of Military Medicine» of the Ministry of Defense of the Russian Federation
| | - A S Nikishin
- FSBI «State Research Testing Institute of Military Medicine» of the Ministry of Defense of the Russian Federation
| | - V A Shevchenko
- FSBI «State Research Testing Institute of Military Medicine» of the Ministry of Defense of the Russian Federation
| | - A V Smirnova
- FSBI «State Research Testing Institute of Military Medicine» of the Ministry of Defense of the Russian Federation
| |
Collapse
|
|
50
|
Venkataraman M, Basker N, Prakash L. Lung involvement in COVID-19 positive pregnant women and their outcomes – A clinical and imaging based retrospective case study. JOURNAL OF OBSTETRIC ANAESTHESIA AND CRITICAL CARE 2022. [DOI: 10.4103/joacc.joacc_32_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
|