• continuous renal replacement therapy
• continuous veno‐venous hemodiafiltration
• cytokine
• high cut‐off membrane
• septic acute kidney injury

• Humans
• Female
• Middle Aged
• Male
• Acute Kidney Injury/therapy
• Acute Kidney Injury/mortality
• Acute Kidney Injury/blood
• Acute Kidney Injury/etiology
• Interleukin-6/blood
• Membranes, Artificial
• Tumor Necrosis Factor-alpha/blood
• Sepsis/complications
• Sepsis/therapy
• Continuous Renal Replacement Therapy/instrumentation
• Continuous Renal Replacement Therapy/methods
• Continuous Renal Replacement Therapy/adverse effects
• Adult
• Treatment Outcome
• Hemodiafiltration/instrumentation
• Hemodiafiltration/methods
• Hemodiafiltration/adverse effects
• Aged

• 2023YFC2411800 National Key Research and Development Program of China
• HX-H2012287 Nikkiso (Shanghai) Co. Ltd
• 2023SCUH0065 "From 0 To 1" Innovation Research Project of Sichuan University
• 2024YFHZ0329 Science and Technology Department of Sichuan Province
• 2020HXFH014 135 Project for Disciplines of Excellence, West China Hospital, Sichuan University

• SOFA
• granzyme B
• lactate
• mortality prediction
• prognosis
• sepsis

• 2022R1F1A1064578 National Research Foundation of Korea

• apache-ii score
• sepsis markers
• sepsis prognosis
• sepsis progression
• systemic inflammatory response syndrome (sirs)

Severe infection often results in bacteremia, which significantly increases mortality rate. Different therapeutic strategies are employed depending on whether the blood-borne infection is Gram-negative (G^-) or Gram-positive (G⁺). However, there is no risk prediction model for assessing whether bacteremia patients are infected with G^- or G⁺ pathogens.

To establish a clinical prediction model to distinguish G^- from G⁺ infection.

A total of 130 patients with positive blood culture admitted to a single intensive care unit were recruited, and Th1 and Th2 cytokine concentrations, routine blood test results, procalcitonin and C-reactive protein concentrations, liver and kidney function test results and coagulation function were compared between G⁺ and G^- groups. Least absolute shrinkage and selection operator (LASSO) regression analysis was employed to optimize the selection of predictive variables by running cyclic coordinate descent and K-fold cross-validation (K = 10). The predictive variables selected by LASSO regression analysis were then included in multivariate logistic regression analysis to establish a prediction model. A nomogram was also constructed based on the prediction model. Calibration chart, receiver operating characteristic curve and decision curve analysis were adopted for validating the prediction model.

Age, plasma interleukin 6 (IL-6) concentration and plasma aspartate aminotransferase concentration were identified from 57 measured variables as potential factors distinguishing G⁺ from G^- infection by LASSO regression analysis. Inclusion of these three variables in a multivariate logistic regression model identified age and IL-6 as significant predictors. In receiver operating characteristic curve analysis, age and IL-6 yielded an area under the curve of 0.761 and distinguished G⁺ from G^- infection with specificity of 0.756 and sensitivity of 0.692. Serum IL-6 and IL-10 levels were upregulated by more than 10-fold from baseline in the G^- bacteremia group but by less than ten-fold in the G⁺ bacteremia group. The calibration curve of the model and Hosmer-Lemeshow test indicated good model fit (P > 0.05). When the decision curve analysis curve indicated a risk threshold probability between 0% and 68%, a nomogram could be applied in clinical settings.

A simple prediction model distinguishing G^- from G⁺ bacteremia can be constructed based on reciprocal association with age and IL-6 level.

• Interleukin 6
• Cytokine
• Bacteremia
• Infection
• Prediction model

• Bacteremia
• Cytokine
• Infection
• Interleukin 6
• Prediction model

• Biomarker
• Canine
• Cytokine
• Endometrium
• Pyometra

• Dogs
• Female
• Animals
• Endometrial Hyperplasia/pathology
• Endometrial Hyperplasia/veterinary
• Pyometra/veterinary
• Pyometra/metabolism
• Cytokines/genetics
• Cytokines/metabolism
• Cyclooxygenase 2/metabolism
• Interleukin-6/metabolism
• Secretory Leukocyte Peptidase Inhibitor/metabolism
• Interleukin-8/metabolism
• Endometrium/metabolism
• Prostaglandins/metabolism
• Dog Diseases/metabolism

Biomarkers of unfavorable tuberculosis treatment outcomes are needed to accelerate new drug and regimen development. Whether plasma cytokine levels can predict unfavorable tuberculosis treatment outcomes is unclear.

We identified and internally validated the association between 20 a-priori selected plasma inflammatory markers and unfavorable treatment outcomes of failure, recurrence and all-cause mortality among adults with drug-sensitive pulmonary tuberculosis in India. We externally validated these findings in two independent cohorts of predominantly diabetic and HIV coinfected tuberculosis patients in India and South Africa, respectively.

Pre-treatment IFN-γ, IL-13 and IL-6 were associated with treatment failure in the discovery analysis. Internal validation confirmed higher pre-treatment IL-6 concentrations among failure cases compared to controls. External validation among predominantly diabetic tuberculosis patients found an association between pre-treatment IL-6 concentrations and subsequent recurrence and death. Similarly, external validation among predominantly HIV coinfected tuberculosis patients found an association between pre-treatment IL-6 concentrations and subsequent treatment failure and death. In a pooled analysis of 363 tuberculosis cases from the Indian and South African validation cohorts, high pre-treatment IL-6 concentrations were associated with higher risk of failure (adjusted odds ratio [aOR]=2.16, 95%CI 1.08-4.33, p=0.02), recurrence (aOR=5.36, 95%CI 2.48-11.57, p<0.001) and death (aOR=4.62, 95%CI 1.95-10.95, p<0.001). Adding baseline IL-6 to a risk-prediction model comprising of low BMI, high smear grade and cavitation improved model performance by 15 percent (C-statistic of 0.66 versus 0.76, p=0.02).

Pre-treatment IL-6 is a biomarker for unfavorable tuberculosis treatment outcomes. Future studies should identify optimal IL-6 concentrations for point-of-care risk prediction.

• Animals
• Arterial Pressure/physiology
• Body Temperature/physiology
• Disease Models, Animal
• Hemodynamics/physiology
• Infections/metabolism
• Infections/microbiology
• Infections/physiopathology
• Kidney/metabolism
• Liver/metabolism
• Male
• Sepsis/metabolism
• Sepsis/microbiology
• Sepsis/physiopathology
• Swine/microbiology
• Uropathogenic Escherichia coli/pathogenicity

• K08 AI128006 NIAID NIH HHS
• R01 GM111400 NIGMS NIH HHS
• R01 GM112799 NIGMS NIH HHS
• R35 GM136312 NIGMS NIH HHS
• National Institute of General Medical Sciences
• National Institute of Allergy and Infectious Diseases

Two-stage exchange is most commonly used for treatment of prosthetic joint infections (PJI) but, this may fail to eradicate infections. C-reactive protein/albumin ratio (CAR) has been used to predict survival and operative success in other surgical subspecialties and so, we assess the association between CAR and reimplantation success during two-stage revision for PJI defined by the Musculoskeletal Infection Society following a primary total hip (THA) or knee (TKA) arthroplasty. From January, 2005 to December, 2015, two institutional databases were queried and patient demographics, antibiotic duration, C-reactive protein, and albumin were collected prior to reimplantation. Two-stage revisions were considered successful if patients were off of antibiotics and did not require a repeat surgery. CAR was available for 79 patients (34 hips and 46 knees) with 61 successful two-stage revisions and 18 failures. The average CAR for patients with successful reimplantation was 1.2 (0.2, 3.0) compared to 1.0 (0.4, 3.2) for treatment failure. However, this was not statistically significant (p=0.766). Therefore, CAR is not applicable in predicting the prognosis of two-stage revisions for PJI in total arthroplasty but other preoperative inflammatory-based prognostic scores should be explored.

The in vivo response to lipopolysaccharide (LPS) occurs rapidly and has profound physiological and metabolic effects. The hypoxia inducible (HIF) transcription factor is an intrinsic and essential part of inflammation, and is induced by LPS. To determine the importance of the HIF response in regulating metabolism following an LPS response, glucose uptake was quantified in a time dependent manner in mice lacking HIF-1α in myeloid cells. We found that deletion of HIF-1α has an acute protective effect on LPS-induced hypoglycemia. Furthermore, reduced glucose uptake was observed in the heart and brown fat, in a time dependent manner, following loss of HIF-1α. To determine the physiological significance of these findings, cardiovascular, body temperature, and blood pressure changes were subsequently quantified in real time using radiotelemetry measurements. These studies reveal the temporal aspects of HIF-1α as a regulator of the metabolic response to acute LPS-induced inflammation.

• HIF-1
• myeloid
• physiological consequences
• sepsis
• temporal dynamics