Sepsis, characterized as a systemic inflammatory response triggered by pathogen invasion, represents a continuum that may progress from mild systemic infection to severe sepsis, potentially culminating in septic shock and multiple organ dysfunction syndrome. A pivotal element in the pathogenesis and progression of sepsis involves the significant disruption of oncological metabolic networks, where cells within the pathological milieu exhibit metabolic functions that diverge from their healthy counterparts. Among these, purine metabolism plays a crucial role in nucleic acid synthesis. However, the contribution of Purine Metabolism Genes (PMGs) to the defense mechanisms against sepsis remains inadequately explored. Leveraging bioinformatics, this study aimed to identify and substantiate potential PMGs implicated in sepsis. The approach encompassed a differential expression analysis across a pool of 75 candidate PMGs. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were employed to assess the biological significance and pathways associated with these genes. Additionally, Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) methodologies were implemented to identify key hub genes and evaluate the diagnostic potential of nine selected PMGs in sepsis identification. The study also examined the correlation between these hub PMGs and related genes, with validation conducted through expression level analysis using the GSE13904 and GSE65682 datasets. The study identified twelve PMGs correlated with sepsis, namely AK9, ENTPD3, NUDT16, GMPR2, PKM, RRM2B, POLR2J, POLE3, ADCY3, ADCY4, ADSSL1, and AMPD1. Functional analysis revealed their involvement in critical processes such as purine nucleotide and ribose phosphate metabolism. The diagnostic capability of these PMGs to effectively differentiate sepsis cases underscored their potential as biomarkers. This research elucidates twelve PMGs associated with sepsis, providing valuable insights into novel biomarkers for this condition and facilitating the monitoring of its progression. These findings highlight the significance of purine metabolism in sepsis pathogenesis and open avenues for further investigation into therapeutic targets.

To investigate the correlation between serum levels of Heart-type fatty acid binding protein (H-FABP), Soluble Triggering Receptor Expressed on Myeloid Cells 1 (sTREM-1), and High mobility group box 1 protein (HMGB1) with disease severity, and their prognostic value in sepsis.

A retrospective analysis was conducted using the clinical data from 86 sepsis patients admitted to West China Hospital of Sichuan University between June 2021 and December 2023, and these cases constituted the observation group. In addition, clinical data from 80 healthy individuals who underwent medical examinations at our hospital during the same period served as the control group. Serum levels of H-FABP, sTREM-1, and HMGB1 were measured in both groups. Based on disease severity, the patients were categorized into mild (n=42), severe (n=28), and shock (n=16) groups. The Acute Physiology and Chronic Health Evaluation II (APACHE II) score was used to assess the patients' condition. Follow-up evaluations showed that 60 patients survived and 26 died.

Serum levels of H-FABP, sTREM-1, and HMGB1 were significantly higher in the observation group compared to the control group (all P<0.05). Among the sepsis patients, the severe and shock groups exhibited significantly elevated levels of H-FABP, sTREM-1, and HMGB1 compared to the mild group, with the shock group showing the highest levels (all P<0.05). The levels of H-FABP, sTREM-1, and HMGB1 were positively correlated with APACHE II scores (r=0.760, r=0.715, r=0.709, all P<0.001). Furthermore, the levels of these biomarkers were significantly higher in patients who died than in survivors (all P<0.05). The AUCs of H-FABP, sTREM-1, and HMGB1 for predicting prognosis were 0.786, 0.790, and 0.781, respectively. Their combined prediction yielded an AUC of 0.834. Log-rank test showed that the survival time of patients with different expression levels of sTREM-1 (<856.50 pg/ml, ≥856.50 pg/ml) and HMGB1 (<395.80 ng/ml, ≥395.80 ng/ml) were significantly different (P<0.05).

Serum levels of H-FABP, sTREM-1, and HMGB1 are elevated in sepsis patients and closely associated with the disease severity, making them valuable biomarkers for monitoring the severity of sepsis. Combined detection of serum H-FABP, sTREM-1, and HMGB1 shows promising prognostic value in sepsis, with lower levels of sTREM-1 and HMGB1 linked to improved survival.

Background/Objectives: Sepsis and septic shock remain significant contributors to high early mortality rates among patients admitted to the emergency department (ED). The objective of this study was to identify among newer biomarkers those with the highest sensitivity in early mortality prediction. Methods: This prospective, unicentric, observational study enrolled 47 adult patients admitted to the ED between November 2020 and December 2022. This study monitored the kinetics of the older and newer biomarkers, including azurocidin (AZU1), soluble triggering receptor expressed on myeloid cells (sTREM), soluble urokinase-type plasminogen activator receptor (suPAR), high-sensitivity C-reactive protein (hsCRP), procalcitonin (PCT), and interleukin-6 (IL-6), and their capacity in predicting mortality. Results: SuPAR showed the most significant predictive utility for early prognosis of mortality in the ED, with an area under the curve (AUC) of 0.813 (95% CI: 0.672 to 0.912), a cutoff value > 8168 ng/mL, sensitivity of 75%, and specificity of 81.48% (p < 0.001). IL-6 and PCT showed comparable prognostic accuracy, whereas hsCRP and AZU1 demonstrated lower predictive performance. Conclusions: In our study, suPAR, IL-6, and PCT showed good predictive value for short-term mortality in sepsis and septic shock patients.

A prospective observational cohort study of COVID-19 patients in a single Emergency Department (ED) showed that sTREM-1- and IL-6-based algorithms were highly predictive of adverse outcome (Van Singer et al. J Allergy Clin Immunol 2021). We aim to validate the performance of these algorithms at ED presentation.

This multicentric prospective observational study of PCR-confirmed COVID-19 adult patients was conducted in the ED of three Swiss hospitals. Data of the three centers were retrospectively completed and merged. We determined the predictive accuracy of the sTREM-1-based algorithm for 30-day intubation/mortality. We also determined the performance of the IL-6-based algorithm using data from one center for 30-day oxygen requirement.

373 patients were included in the validation cohort, 139 (37%) in Lausanne, 93 (25%) in St.Gallen and 141 (38%) in EOC. Overall, 18% (93/373) patients died or were intubated by day 30. In Lausanne, 66% (92/139) patients required oxygen by day 30. The predictive accuracy of sTREM-1 and IL-6 were similar compared to the derivation cohort. The sTREM-1-based algorithm confirmed excellent sensitivity (90% versus 100% in the derivation cohort) and negative predictive value (94% versus 100%) for 30-day intubation/mortality. The IL-6-based algorithm performance was acceptable with a sensitivity of 85% versus 98% in the derivation cohort and a negative predictive value of 60% versus 92%.

The sTREM-1 algorithm demonstrated good reproducibility. A prospective randomized controlled trial, comparing outcomes with and without the algorithm, is necessary to assess its safety and impact on hospital and ICU admission rates. The IL-6 algorithm showed acceptable validity in a single center and need additional validation before widespread implementation.

The central role of inflammatory progression in the development of Coronavirus disease 2019 (COVID-19), especially in severe cases, is indisputable. However, the role of some novel inflammatory biomarkers in the prognosis of COVID-19 remains controversial.

To assess the effect of some novel inflammatory biomarkers in the occurrence and prognosis of COVID-19.

We systematically retrieved the studies related to COVID-19 and the inflammatory biomarkers of interest. The data of each biomarker in different groups were extracted, then were categorized and pooled. The standardized mean difference was chosen as an effect size measure to compare the difference between groups.

A total of 90 studies with 12,059 participants were included in this study. We found higher levels of endocan, PTX3, suPAR, sRAGE, galectin-3, and monocyte distribution width (MDW) in the COVID-19 positive groups compared to the COVID-19 negative groups. No significant differences for suPAR and galectin-3 were detected between the severe group and mild/moderate group of COVID-19. In addition, the deaths usually had higher levels of PTX3, sCD14-ST, suPAR, and MDW at admission compared to the survivors. Furthermore, patients with higher levels of endocan, galectin-3, sCD14-ST, suPAR, and MDW usually developed poorer comprehensive clinical prognoses.

In summary, this meta-analysis provides the most up-to-date and comprehensive evidence for the role of the mentioned novel inflammatory biomarkers in the prognosis of COVID-19, especially in evaluating death and other poor prognoses, with most biomarkers showing a better discriminatory ability.

Growth differentiation factor (GDF)-15 is a member of a transforming growth factor-β cytokine superfamily that regulates metabolism and is released in response to inflammation, hypoxia and tissue injury. It has evolved as one of the most potent cytokines for predicting the severity of infections and inflammatory conditions, such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection.

To investigate the utility of GDF-15 in predicting the severity of SARS-CoV-2 infection.

PubMed, Reference Citation Analysis, CNKI, and Goggle Scholar were explored by using related MeSH keywords and data such as the first author's name, study duration, type and place of study, sample size and subgroups of participants if any, serum/plasma GDF- 15 level in pg/mL, area under the curve and cut-off value in receiver operating characteristic analysis, method of measurement of GDF-15, and the main conclusion were extracted.

In all studies, the baseline GDF-15 level was elevated in SARS-CoV-2-infected patients, and it was significantly associated with severity, hypoxemia, viral load, and worse clinical consequences. In addition, GDF-15 levels were correlated with C-reactive protein, D-dimer, ferritin and procalcitonin, and it had superior discriminatory ability to detect severity and in-hospital mortality of SARS-CoV-2 infection. Hence, GDF-15 might be used to predict the severity and prognosis of hospitalized patients with SARS-CoV-2.

Serial estimation of GDF-15 levels in hospitalized patients with SARS-CoV-2 infection appeared to have useful prognostic value and GDF-15 can be considered a clinically prominent sepsis biomarker for SARS-CoV-2 infection.

In the last years, biomarkers of infection, such as the soluble urokinase plasminogen activator receptor (suPAR), have been extensively studied as potential diagnostic and prognostic biomarkers in the intensive care unit (ICU). In this study, we investigated whether this biomarker can be used in COVID-19 and non-COVID-19 septic patients for mortality prediction. Serum suPAR levels were measured in 79 non-COVID-19 critically ill patients upon sepsis (within 6 h), and on admission in 95 COVID-19 patients (66 critical and 29 moderate/severe). The non-COVID-19 septic patients were matched for age, sex, and disease severity, while the site of infection was the respiratory system. On admission, COVID-19 patients presented with higher suPAR levels, compared to non-COVID-19 septic patients (p < 0.01). More importantly, suPAR measured upon sepsis could not differentiate survivors from non-survivors (p > 0.05), as opposed to suPAR measured on admission in COVID-19 survivors and non-survivors (p < 0.0001). By the generated ROC curve, the prognostic value of suPAR in COVID-19 was 0.81, at a cut-off value of 6.3 ng/mL (p < 0.0001). suPAR measured early (within 24 h) after hospital admission seems like a specific and sensitive mortality risk predictor in COVID-19 patients. On the contrary, suPAR measured at sepsis diagnosis in non-COVID-19 critically ill patients, does not seem to be a prognostic factor of mortality.