Biomarkers of sepsis: time for a reappraisal

Development and validation of a prediction model for in-hospital mortality in patients with sepsis

BACKGROUND

Sepsis, a life-threatening condition marked by organ dysfunction due to a dysregulated host response to infection, involves complex physiological and biochemical abnormalities.

AIM

To develop a multivariate model to predict 4-, 6-, and 8-week mortality risks in intensive care units (ICUs).

STUDY DESIGN

A retrospective cohort of 2389 sepsis patients was analysed using data captured by a clinical decision support system. Patients were randomly allocated into training (n = 1673) and validation (n = 716) sets at a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression identified variables incorporated into a multivariate Cox proportional hazards regression model to construct a prognostic nomogram. The area under the receiver operating characteristic curve (AUROC) assessed model accuracy, while performance was evaluated for discrimination, calibration and clinical utility.

RESULTS

A risk score was developed based on 11 independent predictors from 35 initial factors. Key predictors included minimum Acute Physiology and Chronic Health Evaluation II (APACHE II) score as having the greatest impact on prognosis, followed by days of mechanical ventilation, number of vasopressors, maximum and minimum Sequential Organ Failure Assessment (SOFA) scores, infection sources, Gram-positive or Gram-negative bacteria and malignancy. The nomogram demonstrated superior discriminative ability, with AUROC values of 0.882 (95% confidence interval [CI], 0.855-0.909) and 0.851 (95% CI, 0.804-0.899) at 4 weeks; 0.836 (95% CI, 0.798-0.874) and 0.820 (95% CI, 0.761-0.878) at 6 weeks; and 0.843 (95% CI, 0.800-0.887) and 0.794 (95% CI, 0.720-0.867) at 8 weeks for training and validation sets, respectively.

CONCLUSION

A validated nomogram and web-based calculator were developed to predict in-hospital mortality in ICU sepsis patients. Targeting identified risk factors may improve outcomes for critically ill patients.

RELEVANCE TO CLINICAL PRACTICE

The developed prediction model and nomogram offer a tool for assessing in-hospital mortality risk in ICU patients with sepsis, potentially aiding in nursing decisions and resource allocation.

Antibody-free LC-HRMS/MS method for simultaneous quantification of NGAL, CRP and SAA in serum from sepsis patients

Liquid chromatography coupled with high-resolution mass spectrometry (LC-HRMS/MS) is a valuable alternative to ligand-binding assay, enabling specific and accurate quantification of protein biomarkers. We developed a robust antibody-free LC-HRMS/MS method for the multiplex quantification of three sepsis biomarkers in serum: NGAL, CRP and SAA. The method was thoroughly optimized from sample preparation to LC-HRMS/MS analysis, alongside the calibration. Specifically, a modified trichloroacetic acid/isopropanol protein precipitation procedure combined with an optimized Parallel Reaction Monitoring acquisition allowed the quantification of the low abundant NGAL at ng/mL levels. While reference material and reference measurement procedure were available for CRP, no such standards existed for NGAL and SAA. Well-characterized peptide calibrators traceable to the international system of units were developed for NGAL and SAA. The method demonstrated suitable trueness and precision for the quantification of NGAL, CRP, and SAA with coefficients of variation (CV%) ranging from 1.6 % to 22.4 % and bias between -12.6 % and +18.0 %. Successful application to pooled serum samples illustrated the method's effectiveness. Our results pave the way toward the development of reference systems for additional sepsis biomarkers.

Cell population data for early detection of sepsis in patients with suspected infection in the emergency department

OBJECTIVES

Traditional biomarkers used for sepsis diagnosis have limited sensitivity and specificity and, so far, are not recommended for sepsis diagnosis. We aimed to evaluate diagnostic accuracy of XN-9000® hematology analyzer derived cell population data (CPD) for sepsis.

METHODS

We conducted a cross-sectional cohort study on patients admitted to an emergency department (ED) with a suspicion of infection, having a complete blood count with differential (CBC-Diff). CBC-Diff were performed on XN-9000® analyzer (Sysmex, Kobe, Japan). CPD were measured routinely for each CBC-Diff ordered by ED physician. They include: neutrophils-related - Neut-GI and Neut-RI; monocytes-related - Mono-X, Mono-Z, Re-Mono and Mono-Y; IG referring to immature granulocytes; and lymphocytes-related - As-lymp and Re-lymp. Intensive care infection (ICIS) and neutrophile and monocyte (NEMO) scores were calculated using several CPD parameters. Diagnostic performance of each biomarker was computed together with receiver operating characteristic curves for sepsis diagnosis (according to Sepsis-3 definition).

RESULTS

A total of 1,155 patients with a suspicion of infection were included and 230 had sepsis. Median age was 64 years and 49 % were female. Except for lymphocyte count with an area under the receiver operating characteristic (AUROC) of 0.67 (95 % confidential interval 0.63-0.70), the other CPD exhibited modest performances with AUROC under 0.65. The ICIS and NEMO scores had a modest performance with AUROC of 0.56 (0.52-0.61) and 0.55 (0.51-0.59) respectively.

CONCLUSIONS

None of the biomarkers and scores tested demonstrated sufficient diagnostic accuracy to be recommended for routine sepsis screening in the ED.

Exploring METRNL as a novel biomarker in sepsis: diagnostic potential and secretion mechanism

BACKGROUND

Sepsis is a life-threatening condition with a high mortality rate in intensive care unit (ICU). However, rapid and accurate diagnostic criteria are still lacking. This pilot study explored the role of METRNL as a novel biomarker for sepsis by focusing on its diagnostic potential and rapid secretion mechanism.

METHODS

METRNL levels were measured in cell and animal models of sepsis. Serum samples from 107 sepsis patients and 95 non-septic controls in ICU were collected. Diagnostic performance of METRNL, Procalcitonin (PCT) and C-reactive protein (CRP) were assessed using ROC analysis. Endothelial cell-specific Metrnl gene knockout mice (EC-Metrnl-/- mice) were used to identify the source of METRNL secretion. Chemical inhibitors and RNA interference were used to explore the secretion pathways.

RESULTS

In lipopolysaccharide (LPS)-induced cell and mouse models of sepsis, METRNL levels significantly increased in a dose- and time-dependent manner. Similarly, in the cecal ligation and puncture mouse models, serum METRNL levels were elevated over time and correlated with sepsis severity. In animals, serum METRNL increased within 1 h post-modeling, preceding PCT and CRP. Clinically, sepsis patients had significantly higher serum METRNL levels. ROC analysis showed area under the curves [95% confidence intervals] of 0.943 [0.91-0.975] for METRNL, 0.955 [0.929-0.981] for PCT and 0.873 [0.825-0.921] for CRP. At the optimal cutoff value, METRNL (91.6%) exhibited relatively greater diagnostic specificity than PCT (88.4%) and CRP (69.5%). EC-Metrnl-/- reduced majority of serum Metrnl levels in sepsis mouse models. Inhibition of the endoplasmic reticulum-Golgi (ER-Golgi) pathway through chemical inhibitors or RNA interference significantly reduced METRNL levels in the supernatant of sepsis cell models compared to control groups. Similar results were obtained with Toll-like receptor 4 (TLR4) and ERK inhibitors.

CONCLUSIONS

This pilot study demonstrates that METRNL is a novel potential biomarker for sepsis with diagnostic capability comparable to that of PCT. Serum METRNL rapidly increased during the early phase of sepsis. Mechanistically, it mainly originates from the endothelium during sepsis, and TLR4-ERK signaling mediates the rapid secretion of METRNL via the classical ER-Golgi pathway in response to LPS stimulation.

Ferroptosis-related protein biomarkers for diagnosis, differential diagnosis, and short-term mortality in patients with sepsis in the intensive care unit

Background

Sepsis is a disease with high mortality caused by a dysregulated response to infection. Ferroptosis is a newly discovered type of cell death. Ferroptosis-related genes are involved in the occurrence and development of sepsis. However, research on the diagnostic value of ferroptosis-related protein biomarkers in sepsis serum is limited. This study aims to explore the clinical value of Ferroptosis-related proteins in diagnosing sepsis and predicting mortality risk.

Methods

A single-center, prospective, observational study was conducted from January to December 2023, involving 170 sepsis patients, 49 non-septic ICU patients, and 50 healthy individuals. Upon ICU admission, biochemical parameters, GCS, SOFA, and APACHE II scores were recorded, and surplus serum was stored at -80°C for biomarker analysis via ELISA. Diagnostic efficacy was evaluated using ROC curve analysis.

Results

Baseline serum levels of ACSL4, GPX4, PTGS2, CL-11, IL-6, IL-8, PCT, and hs-CRP significantly differed among sepsis, non-septic, and healthy individuals (all p-value < 0.01). ACSL4, GPX4, PTGS2, IL-6, IL-8, PCT, and hs-CRP demonstrated high diagnostic and differential diagnostic performance (AUC: 0.6688 to 0.9945). IL-10 and TNF-α showed good diagnostic performance (AUC = 0.8955 and 0.7657, respectively). ACSL4 (AUC = 0.7127) was associated with predicting sepsis mortality. Serum levels of ACSL4, CL-11, and IL-6 above the cut-off value were associated with shorter survival times. ACSL4 levels were positively correlated with SOFA (Rho = 0.354, p-value < 0.0001), APACHE II (Rho = 0.317, p-value < 0.0001), and septic shock (Rho = 0.274, p-value = 0.003) scores but negatively correlated with the GCS score (Rho = -0.218, p-value = 0.018). GPX4 levels were positively correlated with SOFA (Rho = 0.204, p-value = 0.027) and APACHE II (Rho = 0.233, p-value = 0.011) scores.

Conclusion

ACSL4 and GPX4 have strong diagnostic and differential diagnostic value in sepsis, including the ability to predict 28-day mortality in sepsis patients, and may become new potential serum markers for the diagnostic and differential diagnostic of sepsis.

Prognostic valule of vasoactive drug score, NT-proBNP, and blood lactate level at 6 h post-admission in adult sepsis patients:A single-center, retrospective study

BACKGROUND

Sepsis is a prevalent and critical condition triggered by an exaggerated immune response to specific infectious agents. The classification of the severity of sepsis plays a fundamental role in early identification and structured management, aiding in the prediction of increased risk of mortality. Despite the utilization of numerous biomarkers in sepsis diagnosis and treatment guidance, the early identification and prediction of sepsis still pose significant challenges.

OBJECTIVE

To assess the prognostic value of vasoactive-inotropic score (VIS), N-terminal pro-B-type natriuretic peptide (NT-proBNP), and 6 h blood lactate after admission in adult sepsis patients.

METHODS

177 adult sepsis patients were enrolled in a cross-sectional study. Based on their 28-day outcome upon admission, patients were divided into a death group (n = 52) and a survival group (n = 125). Clinical data from both groups were collected. Univariate analysis and binary logistic regression were employed to identify independent risk factors for poor prognosis in adult sepsis patients. Receiver operating characteristic curve (ROC) analysis was performed to determine the predictive value of VIS, NT-proBNP, and blood lactate level at 6 h after admission for adult sepsis patients. Kaplan-Meier method was utilized to analyze the relationship between VIS, NT-proBNP, blood lactate level at 6 h after admission, and survival prognosis among adult sepsis patients.

RESULTS

1. VIS (OR: 7.117; 95 % CI: 1.648-30.738; P = 0.009), NT-proBNP (OR: 1.296; 95 % CI: 1.026-1.637; P = 0.030), SOFA score (OR:1.232; 95 % CI: 1.031-1.473; P = 0. 02) and blood lactate at 6 h after admission (OR: 3.484; 95 % CI: l.416-8.573; p = 0.007) were independent risk factors for poor prognosis of adult sepsis. 2. VIS, NT-proBNP, and blood lactate at 6 h after admission were positively correlated with SOFA scores (r = 0 0.255, 0.388, and 0.l89 respectively, P < 0.05). 3. ROC curve analysis showed that the area under the curve (AUC) of VIS, NT-proBNP, and blood lactate at 6 h after admission for predicting poor prognosis in adult sepsis patients was 0.673, 0.790, and 0.702 respectively. When the cut-off values were 3.86, l.69,and 3.35, the sensitivity was 40.5 %, 71.8 %, and 59.3 %, the specificity was88.2 %, 65.8 %, and 72.9 %, and the Youden index was 0.288, 0.501, and 0.324, respectively. 3. Kaplan-Meier analysis revealed statistically significant differences in survival prognosis between patients with VIS >1.69 and VIS ≤1.69 (Log-rank χ2 = 18.404, P < 0.001), NT-proBNP >3.86 and NT-proBNP ≤3.86 (Log-rank χ2 = 38.282, P < 0.001), as well as blood lactate >3.35 and blood lactic acid ≤3.35 after a duration of 6 h from admission (Log-rank χ2 = 11.776, P < 0.001).

CONCLUSION

VIS, NT-proBNP, and blood lactic acid levels 6 h post-admission independently contributed to the poor prognosis observed in adult sepsis patients; thus serving as valuable prognostic indicators for this population.

Machine learning-based transcriptmics analysis reveals BMX, GRB10, and GADD45A as crucial biomarkers and therapeutic targets in sepsis

Sepsis is a life-threatening condition characterized by a dysregulated host response to infection, resulting in high mortality rates and complex clinical management. This study leverages transcriptomics and machine learning (ML) to identify critical biomarkers and therapeutic targets in sepsis. Analyzing microarray data from the Gene Expression Omnibus (GEO) datasets GSE28750, GSE26440, GSE13205, and GSE9960, we discovered three pivotal biomarkers that BMX (bone marrow tyrosine kinase gene on chromosome X), GRB10 (growth factor receptor bound protein 10), and GADD45A (growth arrest and DNA damage inducible alpha), exhibiting exceptional diagnostic accuracy (AUC >0.9). Functional enrichment analyses revealed that these genes play key roles in reactive oxygen species metabolism and immune response regulation. Specifically, GADD45A was positively correlated with eosinophils and inversely associated with activated NK cells, CD8 T cells, and activated memory CD4 T cells. BMX showed positive correlations with eosinophils, mast cells, and neutrophils, while GRB10 was linked to eosinophils and M2 macrophages. Additionally, we constructed a comprehensive mRNA-miRNA-lncRNA regulatory network, identifying key interactions that may drive sepsis pathogenesis. Molecular docking and dynamics simulations validated Bendroflumethiazide, Cianidanol, and Hexamidine as promising therapeutic agents targeting these biomarkers. In conclusion, this integrated approach provides profound insights into the molecular mechanisms underlying sepsis, pinpointing BMX, GRB10, and GADD45A as pivotal biomarkers and therapeutic targets. These findings significantly enhance our understanding of sepsis pathophysiology and lay the groundwork for developing personalized diagnostic and therapeutic strategies aimed at improving patient outcomes.

Presepsin is a biomarker that can predict mortality in sepsis patients

OBJECTIVE

Predicting the prognosis of sepsis, a major health problem worldwide, is vital to guide the treatment process accordingly. The aim of this study was to evaluate the ability of presepsin levels to predict mortality in patients with sepsis.

METHODS

The study included 87 intensive care unit patients with sepsis, 30 of whom survived. Complete blood count, blood gas, C-reactive protein, procalcitonin, albumin, and presepsin levels were analyzed. Binary logistic regression and receiver operating characteristic analyses were performed for presepsin levels.

RESULTS

Presepsin levels were higher in non-survivors than in survivors. There was no significant difference in other laboratory parameters. The predictive value of presepsin level on mortality was found to be 78.20%. The cutoff value in the receiver operating characteristic curve graph for presepsin levels is 612.70 pg/mL. The positive predictive value of presepsin levels in terms of mortality is 0.5735, and the negative predictive value is 0.8512. The sensitivity of presepsin levels in terms of mortality is 73.70%, and the specificity is 73.30%. The area under the curve value in the receiver operating characteristic curve plot for mortality for presepsin levels is 0.819.

CONCLUSION

Presepsin levels may predict mortality in patients with sepsis. Presepsin levels above the cutoff value of 612.70 pg/mL may be considered a risk factor for mortality in patients with sepsis.

Rapid Bacterial/Viral Infections Typing Strategy Using a Portable Dual-Channel Electrochemical Biosensor Based on One-Step Assembly of Immunomagnetic Beads

Amidst multiple epidemics, a rapid, sensitive, economical, and portable infection diagnosis strategy is crucial for primary medical care, particularly through the analysis of pathogen sources to determine appropriate antibiotic use. C-reactive protein (CRP) and serum amyloid A (SAA) are host-related biomarkers, and their combined detection can effectively distinguish between bacterial and viral infections, which holds great significance for the diagnosis of unknown pathogens. In this work, a portable dual-channel electrochemical biosensor based on a one-step assembly of immunomagnetic beads was proposed for the on-site combined detection of plasma CRP and SAA, which streamlined the operation and shortened the minimum detection time to less than 3 min. The biosensor exhibited excellent linearity in the detection of 3.125-1250 ng/mL CRP and 31.25-1250 ng/mL SAA, with detection limits of 0.91 and 12 ng/mL, respectively, falling within the clinically relevant reference range. Through simulated sample tests, the biosensor effectively distinguished between bacterial infection, viral infection, and healthy plasma samples. The actual sample tests demonstrated a high correlation and comparable medical value to enzyme-linked immunosorbent assay. Overall, this proposed strategy showed potential to aid in infection diagnosis and enable rapid combined detection of multiple biomarkers.

Erythropoietin as a critical prognostic indicator in ICU patients with sepsis: a prospective observational study

BACKGROUND

Erythropoietin (EPO), a glycoprotein hormone primarily produced in the kidneys, plays pleiotropic roles in hematopoietic and non-hematopoietic system. However, the clinical relevance of circulating EPO in sepsis progression and outcomes remains contentious and requires further elucidation.

METHODS

Participants were categorized into three groups on the basis of EPO tertiles. The primary outcome was 28-day mortality. Multivariate Cox proportional regression analysis and restricted cubic spline regression were employed to evaluate the association between EPO levels and 28-day mortality in sepsis patients. Subgroup analyses were also conducted. Causal mediation analysis was conducted to explore the potential mediating role of EPO in the relationship between lactate and 28-day mortality.

RESULTS

A total of 267 patients (65.17% male) were included in the study. The 28-day and hospital mortality rates were 23.22 and 31.20%, respectively. Multivariate Cox regression revealed significantly higher 28-day and hospital mortality in the highest EPO tertile compared to the lowest (HR 2.93, 95% CI 1.20-7.22; HR 2.47, 95% CI 1.05-5.81, respectively). Restricted cubic spline analysis demonstrated a progressively increasing mortality risk with elevated EPO levels. Subgroup analyses confirmed the consistency and stability of the effect size and direction across different subgroups. Moreover, causal intermediary analysis revealed that the association between lactate and 28-day mortality was partially mediated by EPO, with a mediation ratio of 12.59%.

CONCLUSIONS

Elevated EPO levels in patients with sepsis are correlated with unfavorable prognoses and may function as a prognostic biomarker for adverse outcomes.

Plasma proteomics in pediatric patients with sepsis- hopes and challenges

One of the main causes of morbidity and death in pediatric patients is sepsis. Of the 48.9 million cases of sepsis reported globally, 41.5% involve children under the age of five, with 2.9 million deaths associated with the disease. Clinicians must identify and treat patients at risk of sepsis or septic shock before late-stage organ dysfunction occurs since diagnosing sepsis in young patients is more difficult than in adult patients. As of right now, omics technologies that possess adequate diagnostic sensitivity and specificity can assist in locating biomarkers that indicate how the disease will progress clinically and how the patient will react to treatment. By identifying patients who are at a higher risk of dying or experiencing persistent organ dysfunction, risk stratification based on biomarkers generated from proteomics can enhance prognosis. A potentially helpful method for determining the proteins that serve as biomarkers for sepsis and formulating theories on the pathophysiological mechanisms behind complex sepsis symptoms is plasma proteomics.

Association and mediation between circulating inflammatory proteins and skin fibrosis

Objective

Skin fibrosis is a dermal lesion associated with inflammatory factors. However, the exact causal relationship between circulating inflammatory proteins (CIPs) and skin fibrosis remains unclear. To investigate this potential association and mediated effect, Mendelian randomization (MR) and two-step MR were used.

Methods

Summary statistics from genome-wide association studies (GWAS) were extracted from the GWAS Catalog for CIPs, blood metabolites (BMs), and skin fibrosis. Two-sample MR and reverse MR were conducted to determine the effect of CIPs on skin fibrosis. Two-step MR was then performed to investigate the role of BMs in mediating the effect of CIPs on skin fibrosis. Reverse MR analysis was performed to confirm the unidirectional causality between CIPs and BMs, as well as between BMs and skin fibrosis.

Results

Bidirectional Mendelian randomization revealed negative associations between skin fibrosis and the levels of T-cell surface glycoprotein CD6 isoform (odds ratio [OR] 0.670 [95% confidence interval [CI] 0.472, 0.951], p = 0.025), Delta and Notch-like epidermal growth factor-related receptor (OR 0.779 [95% CI 0.609, 0.998], p = 0.048), and Interleukin-10 receptor subunit beta (OR 0.541 [95% CI 0.332, 0.884], p = 0.014). There was a positive association between skin fibrosis and levels of Fibroblast growth factor 21 (OR 2.276 [95% CI 1.064, 4.870], p = 0.034). Two-step MR showed that Retinol (Vitamin A) to the linoleoyl-arachidonoyl-glycerol ratio (βM 0.108 [95% CI 0.006, 0.210], p = 0.004) and the Cholesterol to linoleoyl-arachidonoyl-glycerol ratio (βM 0.238 [95% CI 0.002, 0.474], p = 0.048) were identified as mediators, which showed evidence of the mediated effect of the levels of Fibroblast growth factor 21 on Keloid through these mediators.

Conclusion

The study presented credible evidence of a causal association between CIPs and skin fibrosis, with BMs potentially acting as a mediator in this association. These findings offer new insights into early screening and prevention of skin fibrosis.

Novel Identification of CD74 as a Biomarker for Diagnosing and Prognosing Sepsis Patients

Purpose

Sepsis, a life-threatening inflammatory condition due to an imbalanced response to infections, has been a major concern. Necroptosis, a newly discovered programmed cell death form, plays a crucial role in various inflammatory diseases. Our study aims to identify necroptosis - related genes (NRGs) and explore their potential for sepsis diagnosis.

Patients and methods

We used weighted gene co-expression network analysis to identify gene modules associated with sepsis. Cox regression and Kaplan-Meier methods were employed to assess the diagnostic and prognostic value of these genes. Single-cell and immune infiltration analyses were carried out to explore the immune environment in sepsis. Plasma CD74 protein levels were quantified in our samples, and relevant clinical data from electronic patient records were analyzed for correlation.

Results

CD74 was identified through the intersection of the hub genes of sepsis and NRGs related modules. Septic patients had lower CD74 expression compared to healthy controls. The CD74-based diagnostic model showed better performance in the training dataset (AUC, 0.79 [95% CI, 0.75-0.84]), was cross-validated in external datasets, and demonstrated better performances than other published diagnostic models. Pathway analysis and single-cell profiling supported further exploration of CD74-related inflammation and immune response in sepsis.

Conclusion

This study presents the first quantitative assessment of human plasma CD74 in sepsis patients. CD74 levels were significantly lower in the sepsis cohort. CD74 warrants further exploration as a potential prognostic and therapeutic target for sepsis.

LINC02363: a potential biomarker for early diagnosis and treatment of sepsis

BACKGROUND

Sepsis remains a leading cause of global morbidity and mortality, yet early diagnosis is hindered by the limited specificity and sensitivity of current biomarkers.

AIM

The aim of this study was to identify lncRNAs that play a key role in sepsis and provide potential biomarkers for the diagnosis and treatment of sepsis.

METHODS

Transcriptomic data from sepsis patients were retrieved from the Chinese National Genebank (CNGBdb). Differential expression analysis identified 2,348 LncRNAs and 5,125 mRNAs (|FC|≥2, FDR < 0.05). Weighted gene co-expression network analysis (WGCNA) and meta-analysis were applied to screen core genes. Gene set enrichment analysis (GSEA) explored functional pathways, while single-cell sequencing and qPCR validated cellular localization and expression patterns.

RESULTS

WGCNA identified three key genes: LINC02363 (LncRNA), DYNLT1, and FCGR1B. Survival and meta-analyses revealed strong correlations between these genes and sepsis outcomes. GSEA highlighted LINC02363's involvement in "herpes simplex virus type 1 infection," "tuberculosis," and ribosome pathways. Single-cell sequencing showed FCGR1B's broad distribution across immune cells, while DYNLT1 localized predominantly in macrophages. qPCR confirmed significant upregulation of LINC02363 (p < 0.01), FCGR1B (p < 0.05), and DYNLT1 (p < 0.05) in sepsis patients compared to controls.

CONCLUSION

LINC02363 may serve as a new biomarker for the diagnosis and treatment of sepsis.

The association of plasma homocysteine levels with short-term mortality in sepsis patients: A meta-analysis

The association between plasma homocysteine (Hcy) levels and short-term mortality in sepsis patients remains unclear. This meta-analysis aimed to clarify this potential relationship. Following PRISMA 2020 and Cochrane Handbook guidelines, we conducted a comprehensive literature search in the PubMed, Embase, and Web of Science databases up to June 24, 2024. We included cohort studies that assessed the association between plasma Hcy levels and all-cause mortality in adult sepsis patients. Standardized mean differences (SMDs) and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using a random-effects model to account for potential heterogeneity. Nine cohort studies involving 771 sepsis patients were included. Overall, no significant difference in plasma Hcy levels was observed between survivors and non-survivors (SMD: -0.23, 95% CI: -0.84 to 0.37, P = 0.45), with substantial heterogeneity (I² = 86%). Subgroup analysis revealed lower plasma Hcy levels among survivors in Chinese patients (SMD: -1.56, 95% CI: -1.98 to -1.13, P < 0.001) but not in non-Asian patients. Plasma Hcy levels were not significantly associated with all-cause mortality (OR per 1-unit increment: 1.03, 95% CI: 0.95 to 1.11, P = 0.51), with notable heterogeneity (I² = 72%). However, a significant association was found in Chinese patients (OR: 1.09, 95% CI: 1.03 to 1.15, P = 0.003), but not in non-Asian patients. In conclusion, plasma Hcy levels were not generally associated with short-term mortality in sepsis patients. However, significant associations were observed in Chinese patients, suggesting potential ethnic differences that warrant further investigation.

Decoy receptor 3 as a prognostic biomarker for sepsis and septic shock according to the Sepsis-3 definitions

Objectives

The present study was conducted to reappraise the prognostic value of decoy receptor 3 (DcR3) for patients with sepsis and septic shock according to the latest Sepsis-3 definitions.

Methods

Subjects suffering from sepsis or septic shock were enrolled within 6 h of admission. The Sequential Organ Failure Assessment (SOFA) score and the plasma levels of DcR3, C-reactive protein, procalcitonin, and interleukin-6 were measured. Group comparisons were made based on the survival status on day 28 after onset. Predictors of mortality were assessed using the Cox proportional hazard models, and survival curves were plotted with the Kaplan-Meier method. Discriminative performances of single and combined indicators were evaluated via the areas under receiver operating characteristic curves.

Results

Among 143 eligible sepsis cases, 77 developed septic shock, and the 28-day mortality rates were 32.2% and 45.5%, respectively. Regardless of the population (all sepsis or septic shock), non-survivors exhibited significantly higher DcR3 levels compared to survivors (median 4.19 vs. 2.64 ng/mL and 4.37 vs. 3.18 ng/mL, respectively; p < 0.001 and p = 0.002, respectively). DcR3 levels were most correlated with organ dysfunction presented by SOFA scores (correlation coefficient = 0.347 and 0.308, respectively; p = 0.001 and 0.016, respectively) but did not differ among the various pathogenic microbes of infection. Multivariate Cox regression identified DcR3 as an independent predictor of mortality [hazard ratio (95% confidence interval): 1.570 (1.048-2.352) and 1.828 (1.047-3.194), respectively; p = 0.029 and 0.034, respectively]. Kaplan-Meier analysis showed that elevated DcR3 concentrations were associated with significantly lower survival rates (p = 0.001 and 0.013, respectively). The areas under receiver operating characteristic curves of DcR3 alone for predicting outcome were superior to that of the other three biomarkers (0.731 and 0.711, respectively) and could be further improved when coupled with SOFA scores (0.803 and 0.784, respectively).

Conclusions

DcR3 is a valuable prognostic biomarker for sepsis and septic shock, offering the potential to predict 28-day mortality in clinical settings.

Study on the diagnostic role of exosome-derived miRNAs in postoperative septic shock and non-septic shock patients

BACKGROUND

Diagnosing septic shock promptly is essential but challenging, especially due to its clinical similarity to non-septic shock. Extracellular vesicle-derived miRNAs may serve as biomarkers to distinguish septic shock from non-septic shock, providing a more accurate diagnostic tool for postsurgical patients. This study aims to identify extracellular vesicle-derived miRNA signatures that differentiate septic shock from non-septic shock in postsurgical patients, potentially improving diagnostic accuracy and clinical decision-making.

METHODS

A multicentre, prospective study was conducted on miRNA profiles in shock patients. Two cohorts were recruited from the Intensive Care Units of two Spanish hospitals: a discovery cohort with 109 patients and a validation cohort with 52 patients. Plasma samples were collected within 24 h of shock diagnosis and subjected to miRNA sequencing. High-throughput sequencing data from the discovery cohort were analysed to identify differentially expressed miRNAs. These findings were validated via qPCR in the validation cohort.

RESULTS

Thirty miRNAs were identified as significantly differentially expressed between septic and non-septic shock patients. Among these, six miRNAs-miR-100-5p, miR-484, miR-10a-5p, miR-148a-3p, miR-342-3p, and miR-451a-demonstrated strong diagnostic capabilities for septic shock. A combination of miR-100-5p, miR-148a-3p, and miR-451a achieved an area under the curve of 0.894, with qPCR validation in the validation cohort yielding an area under the curve of 0.960.

CONCLUSIONS

This study highlights extracellular vesicle-derived miRNAs as promising biomarkers for differentiating septic from non-septic shock. The identified three-miRNA signature has significant potential to enhance septic shock diagnosis, thereby aiding in timely and appropriate treatment for postsurgical patients.

Assessing the Prognostic Accuracy of Red Cell Distribution Width for Predicting Mortality in Sepsis Patients: A Comparative Study With Serum Lactate and the Quick Sequential Organ Failure Assessment (qSOFA) Score

BACKGROUND

Sepsis is a clinical condition characterized by high morbidity and mortality. Several prognostic markers have been evaluated to identify patients with severe sepsis. The red cell distribution width (RDW) is a cost-effective and widely accessible laboratory test to predict mortality across various medical conditions. This research aimed to evaluate the role of RDW as a predictor of mortality in sepsis patients and compare its predictive value with that of serum lactate levels and the qSOFA (quick Sequential Organ Failure Assessment) score.

METHODS

This prospective observational study enrolled sepsis patients admitted to the Medicine Department at the Indira Gandhi Institute of Medical Sciences between March and August 2024. The study evaluated the discriminatory ability of RDW, serum lactate levels, and qSOFA score for predicting mortality by calculating the ROC-AUC (receiver operating characteristic area under the curve).

RESULTS

Out of 100 eligible patients, 97 had been incorporated into the final analysis. The mean RDW was significantly elevated among the non-survivors, and it had been recognized as an independent predictor of 30-day mortality (OR=2.91, (1.56; 5.42), p < 0.001). For predicting 30-day mortality, the AUC values were as follows: RDW at 0.878 (p < 0.001), lactate at 0.719 (p < 0.001), and qSOFA score at 0.837 (p < 0.001). The cutoff value for RDW was established at 14.15%.

CONCLUSION

RDW was significantly associated with 30-day mortality in sepsis patients and was found to be an independent prognostic marker for predicting mortality. Its mortality discriminative ability was comparable to that of the qSOFA score but superior to that of lactate.

Unraveling the immunological landscape and gut microbiome in sepsis: a comprehensive approach to diagnosis and prognosis

BACKGROUND

Comprehensive and in-depth research on the immunophenotype of septic patients remains limited, and effective biomarkers for the diagnosis and treatment of sepsis are urgently needed in clinical practice.

METHODS

Blood samples from 31 septic patients in the Intensive Care Unit (ICU), 25 non-septic ICU patients, and 18 healthy controls were analyzed using flow cytometry for deep immunophenotyping. Metagenomic sequencing was performed in 41 fecal samples, including 13 septic patients, 10 non-septic ICU patients, and 18 healthy controls. Immunophenotype shifts were evaluated using differential expression sliding window analysis, and random forest models were developed for sepsis diagnosis or prognosis prediction.

FINDINGS

Septic patients exhibited decreased proportions of natural killer (NK) cells and plasmacytoid dendritic cells (pDCs) in CD45+ leukocytes compared with non-septic ICU patients and healthy controls. These changes statistically mediated the association of Bacteroides salyersiae with sepsis, suggesting a potential underlying mechanism. A combined diagnostic model incorporating B.salyersia, NK cells in CD45+ leukocytes, and C-reactive protein (CRP) demonstrated high accuracy in distinguishing sepsis from non-sepsis (area under the receiver operating characteristic curve, AUC = 0.950, 95% CI: 0.811-1.000). Immunophenotyping and disease severity analysis identified an Acute Physiology and Chronic Health Evaluation (APACHE) II score threshold of 21, effectively distinguishing mild (n = 19) from severe (n = 12) sepsis. A prognostic model based on the proportion of total lymphocytes, Helper T (Th) 17 cells, CD4+ effector memory T (TEM) cells, and Th1 cells in CD45+ leukocytes achieved robust outcome prediction (AUC = 0.906, 95% CI: 0.732-1.000), with further accuracy improvement when combined with clinical scores (AUC = 0.938, 95% CI: 0.796-1.000).

INTERPRETATION

NK cell subsets within innate immunity exhibit significant diagnostic value for sepsis, particularly when combined with B. salyersiae and CRP. In addition, T cell phenotypes within adaptive immunity are correlated with sepsis severity and may serve as reliable prognostic markers.

FUNDING

This project was supported by the National Key R&D Program of China (2023YFC2307600, 2021YFA1301000), Shanghai Municipal Science and Technology Major Project (2023SHZDZX02, 2017SHZDZX01), Shanghai Municipal Technology Standards Project (23DZ2202600).

CXCL5 as a biomarker for early diagnosis and prognosis of sepsis: A comprehensive clinical evaluation

OBJECTIVES

Sepsis, a critical condition caused by a dysregulated host response to infection, has high morbidity and mortality rates. Timely diagnosis and treatment are vital for improving patient outcomes. This study explores the potential role of CXCL5 in the diagnosis, severity assessment, and prognosis of sepsis.

DESIGN AND METHODS

We included 147 sepsis patients, 50 patients with systemic inflammatory response syndrome (SIRS) and 120 healthy controls. Serum CXCL5 levels, inflammation scores (APACHE II, SOFA), and other laboratory indicators were recorded. Univariate and multivariate logistic regression analyses were conducted to assess the relationship between CXCL5 and sepsis diagnosis, severity, and prognosis. A prognostic nomogram was constructed and evaluated using receiver operator characteristic curves, calibration curves, and clinical decision curves.

RESULTS

Serum CXCL5 levels in sepsis patients were significantly higher than those in patients with SIRS and healthy controls. CXCL5 was identified as a risk factor for sepsis diagnosis. CXCL5 levels were significantly elevated in patients with septic shock (P = 0.04) and in deceased patients compared to survivors (P < 0.001). The prognostic model, incorporating CXCL5, lactate, APACHE II scores, C-reactive protein levels, and respiratory rate, demonstrated high predictive accuracy with an area under the curve of 0.873. Calibration and decision curve analyses demonstrated the model's good predictive performance and potential clinical value.

CONCLUSIONS

Serum CXCL5 concentration is a promising biomarker for enhancing the diagnostic accuracy and prognostic evaluation of sepsis. The constructed multivariate prediction model offers new insights into sepsis prognosis, but its direct application in clinical practice requires further validation.

Sepsis: the evolution of molecular pathogenesis concepts and clinical management

The mortality rate of sepsis is approximately 22.5%, accounting for 19.7% of the total global mortality. Since Lewis Thomas proposed in 1972 that "it is our response that makes the disease (sepsis)" rather than the invading microorganisms, numerous drugs have been developed to suppress the "overwhelming" inflammatory response, but none of them has achieved the desired effect. Continued failure has led investigators to question whether deaths in septic patients are indeed caused by uncontrolled inflammation. Here, we review the history of clinical trials based on evolving concepts of sepsis pathogenesis over the past half century, summarize the factors that led to the failure of these historical drugs and the prerequisites for the success of future drugs, and propose the basic principles of preclinical research to ensure successful clinical translation. The strategy of targeting inflammatory factors are like attempting to eliminate invaders by suppressing the host's armed forces, which is logically untenable. Sepsis may not be that complex; rather, sepsis may be the result of a failure to fight microbes when the force of an invading pathogen overwhelms our defenses. Thus, strengthening the body's defense forces instead of suppressing them may be the correct strategy to overcome sepsis.

Association between pre-ICU statin use and acute kidney injury and in hospital mortality in obese patients with sepsis

BACKGROUND

Acute kidney injury (AKI), a common complication in sepsis, especially for obese patients, is linked to increased morbidity and mortality. Statins, known for their lipid-lowering effects, also exhibit anti-inflammatory and immunomodulatory properties, suggesting potential benefits in sepsis and AKI.

MATERIALS AND METHODS

This retrospective cohort study leveraged data from the MIMIC-IV database. The study population was stratified into survivors and non-survivors based on in-hospital mortality. Demographic data, comorbidities, laboratory parameters, and treatment modalities were extracted. Multivariate logistic regression models were conducted to evaluate the relationship between statin use and outcomes, with adjustments for confounders. Subgroup analyses and propensity score-matching (PSM) were undertaken for further validation.

RESULTS

In this study of 8,921 sepsis patients, pre-ICU statin use was associated with significantly improved outcomes. Univariate analysis showed that statin use reduced the risk of AKI by 69% (OR = 0.31, 95% CI 0.27-0.44, p < 0.001) and decreased in-hospital mortality by 57% (OR = 0.43, 95% CI 0.22-0.54, p < 0.001). Multivariate regression confirmed the robustness of these findings, with fully adjusted models demonstrating a 31% reduction in AKI (OR = 0.67, p < 0.001) and a 22% reduction in mortality (OR = 0.67, p < 0.001). Subgroup and propensity score-matched analyses further supported these associations, showing consistent protective effects across patient subgroups and a reduction in both AKI and mortality.

CONCLUSION

Pre-ICU statin therapy is associated with a lower risk of AKI and improved in-hospital survival among obese patients with sepsis, suggesting its potential role as a protective measure in this high-risk population.

Predictive value of procalcitonin for the therapeutic response of patients with uroseptic shock: a retrospective case-control study

OBJECTIVES

To evaluate the predictive value of procalcitonin (PCT) in assessing the therapeutic response of patients with uroseptic shock.

METHODS

This retrospective case-control study included 220 patients treated for uroseptic shock at Liyang People's Hospital between January 2018 and December 2023. Patients were classified into high-risk (HR) (n = 116) and low-risk (LR) (n = 104) groups based on their Sepsis-related Organ Failure Assessment (SOFA) scores after 14 days of treatment. Demographic, clinical, and laboratory data were collected, and PCT levels were measured using chemiluminescence. Correlation analysis and receiver operating characteristic (ROC) curve analysis were used to assess the predictive value of PCT.

RESULTS

The HR group had significantly higher PCT levels (25.33 ± 5.32 ng/mL) compared to the LR group (18.47 ± 2.88 ng/mL, P < 0.001). Elevated PCT levels were strongly correlated with poor therapeutic response (rho = -0.635, P < 0.001). Other markers, including hypertension (rho = -0.207, P = 0.002), CRP (rho = -0.224, P < 0.001), IL-6 (rho = -0.200, P = 0.003), TNF-α (rho = -0.151, P = 0.025), NEUT% (rho = -0.208, P = 0.002), GGT (rho = -0.160, P = 0.017), and BUN (rho = -0.198, P = 0.003), also showed significant negative correlations with treatment outcome. Conversely, PLT (rho = 0.156, P = 0.021) and the CD4+/CD8+ ratio (rho = 0.242, P < 0.001) were positively correlated with better treatment outcome. ROC analysis revealed an area under the curve (AUC) of 0.867 for PCT, indicating its strong predictive value.

CONCLUSIONS

PCT level is a robust predictor of therapeutic response in uroseptic shock patients and may be integrated into clinical protocols for sepsis management.

A diagnostic model for sepsis using an integrated machine learning framework approach and its therapeutic drug discovery

BACKGROUND

Sepsis remains a life-threatening condition in intensive care units (ICU) with high morbidity and mortality rates. Some biomarkers commonly used in clinic do not have the characteristics of rapid and specific growth and rapid decline after effective treatment. Machine learning has shown great potential in early diagnosis, subtype analysis, accurate treatment and prognosis evaluation of sepsis.

METHODS

Gene expression matrices from GSE13904 and GSE26440 were combined into a training model after quality control and standardization. Then, the intersection genes were obtained by crossing the screened differentially expressed genes (DEGs) and the module genes with the strongest correlation obtained by WGCNA analysis. 113 combined machine learning algorithms to build a diagnosis model. Then the CIBERSORT algorithm is used to analyze the relationship between the change of core gene expression and immune response in sepsis. Construct nomogram, DCA and CIC to further verify the reliability of the diagnosis model. The potential molecular compounds interacting with key genes were searched from the Traditional Chinese Medicine Active Compound Library (TCMACL).

RESULTS

We screened 405 DEGs, including 334 up-regulated and 71 down-regulated genes. The 308 potential genes were obtained by intersection of MEturquoise module genes in WGCNA analysis and DEGs for subsequent machine learning analysis. GO and KEGG enrichment analysis showed that sepsis was mainly related to immune response and bacterial infection. Then 113 combined machine learning algorithms are applied to construct a diagnosis model to screen 22 hub genes. Four four key genes (CD177, GNLY, ANKRD22, and IFIT1) are obtained through further analysis of PPI network constructed by 22 hub genes. Subsequently, the diagnostic model is proved to have good predictive value by nomogram, DCA and CIC. Finally, molecular compounds (Dieckol, Grosvenorine and Tellimagrandin II) were screened out as potential drugs.

CONCLUSION

113 combinated machine learning algorithms screened out four key genes that can distinguish sepsis patients. At the same time, potential therapeutic molecular compounds interacting with key genes genes were screened out by molecular docking.

Elevated plasma soluble lectin-like oxidised low-density lipoprotein receptor 1 as an independent prognostic biomarker in sepsis

BACKGROUND

Soluble lectin-like oxidised low-density lipoprotein receptor 1 (sLOX-1) is overproduced during inflammation, with its expression and release triggered by C-reactive protein (CRP). As CRP levels are typically elevated in sepsis, this study aimed to investigate whether sLOX-1 levels increase in parallel.

METHODS

Plasma sLOX-1 levels of 52 patients with systemic inflammatory response syndrome (SIRS), 45 patients with sepsis, 88 patients with septic shock and 37 controls were measured by ELISA. Associations with CRP, underlying diseases, severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) and bacterial infections were analysed.

RESULTS

Plasma sLOX-1 levels were similarly elevated in patients with SIRS, sepsis, or septic shock compared to controls. Plasma sLOX-1 levels did not differ between male and female controls or patients. Plasma sLOX-1 levels were comparable in patients infected with SARS-CoV-2, Gram-negative bacteria, or Gram-positive bacteria. No association was observed between sLOX-1 levels and underlying liver cirrhosis or pancreatitis. Notably, plasma sLOX-1 levels correlated positively with leukocyte and basophil counts but showed no correlation with CRP or procalcitonin. Of clinical relevance, positive correlations were also found with aspartate aminotransferase (AST) and bilirubin levels. Among the 41 patients who did not survive, sLOX-1, AST, and bilirubin levels were significantly higher compared to those of survivors.

CONCLUSIONS

Plasma levels of sLOX-1 are elevated in patients with SIRS or sepsis and are significantly higher in non-survivors. Of note, they do not correlate with classical inflammatory markers, suggesting that sLOX-1 may function as an independent prognostic biomarker for predicting poor outcomes in patients with SIRS or sepsis.

Predictive value of serum procalcitonin level for the diagnosis of bloodstream infections in hematological patients

OBJECTIVES

Procalcitonin (PCT) is a critical diagnostic biomarker for bacterial infections in patients. Numerous studies have shown that PCT have high sensitivity and specificity for diagnosing bloodstream infection. However, the cut-off value of PCT for the diagnosis of bloodstream infections in patients with hematolgic diseases is still unclear and unreliable.

METHODS

We conducted a retrospective study involving 2299 cases with hematological diseases and who had been performed blood culture and PCT test within 24 h.

RESULTS

For patients with hematological diseases, the serum PCT was slightly elevated in the BSI group. We found that most hematological patients with bloodstream infection were in the stage of severe neutropenia. The main infected strains were Escherichia coli (n = 84, 21%), Klebsiella pneumoniae (n = 61, 15.25%) and Pseudomonas aeruginosa (n = 65, 16.25%), and the increasing trend of PCT level was more obvious in patients infected with Gram-negative bacteria. ROC analysis results showed that the area under the receiver operating characteristic curve for distinguishing bacterial infection from non-bacterial infection was 0.554 (95%CI: 0.522-0.585) with the diagnostic threshold of BSI (PCT > 0.5ng/mL).

CONCLUSIONS

In our study, low PCT levels were found in patients with hematological diseases, and a better cut-off value may be necessary to determine infection in hematology patients.

Exploring the role of inflammatory biomarkers in trigeminal neuralgia

Background

Trigeminal neuralgia (TN) is a severe facial pain disorder with complex etiology. Inflammation has been suggested as a contributing factor to TN pathogenesis. This study investigates the causal relationship between inflammatory biomarkers, including 41 circulating inflammatory cytokines, C-reactive protein (CRP), and procalcitonin (PCT), and TN using Mendelian randomization (MR) analysis.

Methods

A two-sample MR approach was employed using genome-wide association study (GWAS) data from 8293 Finnish individuals for inflammatory cytokines and data from the FinnGen database for TN. Instrumental variables (IVs) were selected based on genome-wide significance and clumping thresholds to avoid linkage disequilibrium. Inverse variance weighting (IVW) was used as the primary method, complemented by MR Egger regression, weighted median, simple mode, and weighted mode methods. Additionally, Bayesian Weighted MR (BWMR) and Multivariable MR (MVMR) were utilized to validate the findings and explore potential confounders.

Results

The present MR analysis identified significant causal associations for three inflammatory cytokines with TN. Stem cell growth factor beta (SCGF-β) (OR = 1.362, 95% CI = 1.049-1.770, p = 0.021) and Interleukin-4 (IL-4) (OR = 1.533, 95% CI = 1.014-2.316, p = 0.043) were positively associated with TN, while Interleukin-16 (IL-16) (OR = 0.720, 95% CI = 0.563-0.921, p = 0.009) had a protective effect. CRP levels were also linked to TN risk (OR = 0.751, 95% CI = 0.593-0.951, p = 0.017). No significant causal effect of PCT on TN was observed. Sensitivity analyses confirmed the robustness of these findings, showing no evidence of horizontal pleiotropy or heterogeneity.

Conclusion

This study highlights specific inflammatory biomarkers that may play pivotal roles in TN pathogenesis. SCGF-β and IL-4 are potential therapeutic targets due to their facilitative effects on TN, while IL-16 could offer protective benefits. CRP's association with TN further supports the involvement of systemic inflammation in this condition. These findings provide novel insights into TN's inflammatory mechanisms, suggesting new avenues for targeted interventions.

Optical Bionanosensors for Sepsis Diagnostics

Sepsis is a global health challenge, characterized by a dysregulated immune response, leading to organ dysfunction and death. Despite advances in medical care, sepsis continues to claim a significant toll on human lives, with mortality rates from 10-25% for sepsis and 30-50% for septic shock, making it a leading cause of death worldwide. Current diagnostic methods rely on clinical signs, laboratory parameters, or microbial cultures and suffer from delays and inaccuracies. Therefore, there is a pressing need for novel diagnostic tools that can rapidly and accurately identify sepsis. This review highlights advances in biosensor development that could ultimately lead to faster and more accurate sepsis diagnostics. The focus is on nanomaterial-based optical approaches that promise rapid diagnostics without the need for large equipment or trained personnel. An overview of sepsis is provided, highlighting potential molecular targets and the challenges they present for assay development. The requirements for an ideal point-of-care test (POC) are discussed, including speed, simplicity, and cost-effectiveness. Different nanomaterials suitable for various optical detection methods are reviewed and innovative nanosensors are discussed for sepsis diagnostics, focusing on chemical design and approaches to increase selectivity by multiplexing.

Comprehensive Sepsis Risk Prediction in Leukemia Using a Random Forest Model and Restricted Cubic Spline Analysis

Background

Sepsis is a severe complication in leukemia patients, contributing to high mortality rates. Identifying early predictors of sepsis is crucial for timely intervention. This study aimed to develop and validate a predictive model for sepsis risk in leukemia patients using machine learning techniques.

Methods

This retrospective study included 4310 leukemia patients admitted to the Affiliated Hospital of Guangdong Medical University from 2005 to 2024, using 70% for training and 30% for validation. Feature selection was performed using univariate logistic regression, LASSO, and the Boruta algorithm, followed by multivariate logistic regression analysis. Seven machine learning models were constructed and evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Shapley additive explanations (SHAP) were applied to interpret the results, and restricted cubic spline (RCS) regression explored the nonlinear relationships between variables and sepsis risk. Furthermore, we examined the interactions among predictors to better understand their potential interrelationships.

Results

The random forest (RF) model outperformed all others, achieving an AUC of 0.765 in the training cohort and 0.700 in the validation cohort. Key predictors of sepsis identified by SHAP analysis included C-reactive protein (CRP), procalcitonin (PCT), neutrophil count (Neut), lymphocyte count (Lymph), thrombin time (TT), red blood cell count (RBC), total bile acid (TBA), and systolic blood pressure (SBP). RCS analysis revealed significant non-linear associations between CPR, PCT, Neut, Lymph, TT, RBC and SBP with sepsis risk. Pairwise correlation analysis further revealed interactions among these variables.

Conclusion

The RF model exhibited robust predictive power for sepsis in leukemia patients, providing clinicians with a valuable tool for early risk assessment and the optimization of treatment strategies.

Sepsis Diagnosis Based on a Parylene Matrix Chip Using LPC16:0 as a Biomarker in Comparison with Colorimetry of Total Phospholipid

For the medical diagnosis of sepsis, it is crucial to differentiate infectious inflammation from noninfectious symptoms to prevent acute aggravation. Herein, a diagnosis for early stage sepsis was performed using LPC 16:0 and total phospholipids as small molecular biomarkers. The measurement of LPC 16:0 was conducted using a parylene matrix chip, which was developed to effectively detect small molecules in laser desorption/ionization mass spectrometry (LDI-MS). Meanwhile, the total phospholipid level was measured using colorimetry, following an enzymatic assay. Next, the two biomarkers were analyzed in serum samples from healthy volunteers, systemic inflammatory response syndrome (SIRS) patients, and sepsis patients. Diagnostic criteria were established based on the biomarker intensities observed in each patient group. After the measurements were conducted, the interference in phospholipid analysis due to hemoglobin contamination was considered. Additionally, the analytical parameters from biomarker detection were statistically interpreted and compared with those of conventional diagnostic standards. Finally, the diagnostic performance of each biomarker was evaluated by analyzing the biomarker levels between patient groups and examining their overlapping extents in box plots to distinguish sepsis from noninfectious inflammatory symptoms.

Prognostic value of HSP27 in 28-day mortality in septic ICU patients: a retrospective cohort study

Background

This study aimed to investigate the association between serum heat shock protein 27 (HSP27) levels and 28-day mortality in patients with sepsis.

Methods

This retrospective study analyzed the clinical data of 76 septic patients admitted to the intensive care unit (ICU). Fifty non-septic ICU patients and 50 healthy individuals served as control groups. Serum HSP27 levels were measured on the day of ICU admission and compared to sepsis severity and survival outcomes.

Results

Median serum HSP27 levels in septic patients (4.70 ng/mL, IQR: 2.10-13.48 ng/mL) were significantly higher than those in both non-septic ICU controls and healthy controls (all p < 0.05). Moreover, non-survivors exhibited significantly higher median HSP27 levels (9.30 ng/mL, IQR: 3.62-25.91 ng/mL) compared to survivors (3.03 ng/mL, IQR: 1.48-7.39 ng/mL, p < 0.05). Multivariate logistic regression analysis confirmed the association between HSP27 levels and 28-day mortality in sepsis patients. Receiver operating characteristic (ROC) curve analysis revealed an area under the curve (AUC) of 0.720 (95% CI: 0.605-0.817, p < 0.001) for HSP27 in predicting sepsis prognosis. Survival analysis demonstrated that patients with high serum HSP27 levels (≥2.61 ng/mL) had a worse prognosis than those with low levels (<2.61 ng/mL).

Conclusion

HSP27 shows potential as a biomarker for the diagnosis and prognosis of sepsis, however, further research is necessary to solidify its clinical utility.

Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients

Background

Sepsis is a life-threatening disease associated with a high mortality rate, emphasizing the need for the exploration of novel models to predict the prognosis of this patient population. This study compared the performance of traditional logistic regression and machine learning models in predicting adult sepsis mortality.

Objective

To develop an optimum model for predicting the mortality of adult sepsis patients based on comparing traditional logistic regression and machine learning methodology.

Methods

Retrospective analysis was conducted on 606 adult sepsis inpatients at our medical center between January 2020 and December 2022, who were randomly divided into training and validation sets in a 7:3 ratio. Traditional logistic regression and machine learning methods were employed to assess the predictive ability of mortality in adult sepsis. Univariate analysis identified independent risk factors for the logistic regression model, while Least Absolute Shrinkage and Selection Operator (LASSO) regression facilitated variable shrinkage and selection for the machine learning model. Among various machine learning models, which included Bagged Tree, Boost Tree, Decision Tree, LightGBM, Naïve Bayes, Nearest Neighbors, Support Vector Machine (SVM), and Random Forest (RF), the one with the maximum area under the curve (AUC) was chosen for model construction. Model validation and comparison with the Sequential Organ Failure Assessment (SOFA) and the Acute Physiology and Chronic Health Evaluation (APACHE) scores were performed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) curves in the validation set.

Results

Univariate analysis was employed to assess 17 variables, namely gender, history of coronary heart disease (CHD), systolic pressure, white blood cell (WBC), neutrophil count (NEUT), lymphocyte count (LYMP), lactic acid, neutrophil-to-lymphocyte ratio (NLR), red blood cell distribution width (RDW), interleukin-6 (IL-6), prothrombin time (PT), international normalized ratio (INR), fibrinogen (FBI), D-dimer, aspartate aminotransferase (AST), total bilirubin (Tbil), and lung infection. Significant differences (p < 0.05) between the survival and non-survival groups were observed for these variables. Utilizing stepwise regression with the "backward" method, independent risk factors, including systolic pressure, lactic acid, NLR, RDW, IL-6, PT, and Tbil, were identified. These factors were then incorporated into a logistic regression model, chosen based on the minimum Akaike Information Criterion (AIC) value (98.65). Machine learning techniques were also applied, and the RF model, demonstrating the maximum Area Under the Curve (AUC) of 0.999, was selected. LASSO regression, employing the lambda.1SE criteria, identified systolic pressure, lactic acid, NEUT, RDW, IL6, INR, and Tbil as variables for constructing the RF model, validated through ten-fold cross-validation. For model validation and comparison with traditional logistic models, SOFA, and APACHE scoring.

Conclusion

Based on deep machine learning principles, the RF model demonstrates advantages over traditional logistic regression models in predicting adult sepsis prognosis. The RF model holds significant potential for clinical surveillance and interventions to enhance outcomes for sepsis patients.

Efficacy of cleaning, disinfection, and sterilization modalities for addressing infectious drug-resistant fungi: a review

This is a timely and important review that focuses on the appropriateness of established cleaning, disinfection, and sterilization methods to safely and effectively address infectious fungal drug-resistant pathogens that can potentially contaminate reusable medical devices used in healthcare environment in order to mitigate the risk of patient infection. The release of the World Health Organization (WHO) fungal priority pathogen list (FPPL) in 2022 highlighted the public health crisis of antimicrobial resistance (AMR) in clinically relevant fungal species. Contamination of medical devices with drug-resistant fungal pathogens (including those on the FPPL) in healthcare is a rare event that is more likely to occur due to cross-transmission arising from lapses in hand hygiene practices. Established disinfection and sterilization methods decontaminate fungal pathogens on single-use and reusable medical devices; however, there are assumptions that reusable devices destined for semi-critical use are appropriately cleaned and do not harbour biofilms that may undermine the ability to effectively decontamination these type devices in healthcare. International standards dictate that manufacturer's instructions for use must provide appropriate guidance to healthcare facilities to meet safe reprocessing expectations that include addressing drug-resistant fungal pathogens. Increased environmental monitoring and vigilance surrounding fungal pathogens in healthcare is advised, including adherence to hand hygiene/aseptic practices and appropriate cleaning encompassing the simplification of reusable device features for 'ease-of-reach'. There are emereging opportunities to promote a more integrated multiactor hub approach to addressing these sophisticated challenges, including future use of artificial intelligence and machine learning for improved diagnostics, monitoring/surveillance (such as healthcare and wastewater-based epidemiology), sterility assurance, and device design. There is a knowledge gap surrounding the occurrence and potential persistence of drug-resistant fungal pathogens harboured in biofilms, particularly for ascertaining efficacy of high-level disinfection for semi-critical use devices.

The expression of MALAT1, plasma brain natriuretic peptide, and Tei index in sepsis-induced myocardial injury

PURPOSE

We sought to investigate the expression of MALAT1, plasma brain natriuretic peptide, and Tei index in sepsis-induced myocardial injury.

METHODS

The current retrospective analysis focused on 146 sepsis patients admitted to our hospital from February 2021 to March 2023. Based on the presence or absence of myocardial injury, the patients were divided into two groups: the sepsis group (n = 80) and the sepsis-induced myocardial injury group (n = 66). Based on the 28-day mortality status of the patients, they were also divided into a survival group of 143 cases and a death group of 3 cases. The study compared the levels of MALAT1, plasma brain natriuretic peptide, and Tei index between the sepsis group and sepsis-induced myocardial injury group, as well as the comparison of two sets of ultrasound indicators. Univariate logistic regression analysis was performed to identify the influencing factors of sepsis-induced myocardial injury, followed by multivariate logistic regression analysis to identify the influencing factors of such condition. MALAT1, plasma brain natriuretic peptide and cardiac Tei index between the survival and death groups were compared and Pearson correlation analysis was conduct to assess their correlations.

RESULTS

In terms of general information, there were no significant differences in gender, age, BMI, mean arterial pressure, systolic pressure, diastolic pressure, respiratory rate, oxygenation index, basic diseases and infection site between the two groups (P > 0.05). However, significant differences were observed in heart rate, SOFA score, and APACHE II score between the two groups (P < 0.05). The levels of MALAT1, plasma brain natriuretic peptide, and Tei index in the sepsis-induced myocardial injury group were significantly higher than those in the sepsis group (P < 0.05). Furthermore, the sepsis-induced myocardial injury group exhibited lower left ventricular end-diastolic diameter and left ventricular end-systolic diameter compared to the sepsis group, along with higher levels of E, E/e', and e', showing significant differences (P < 0.05). The independent variables considered in the analysis included general data, ultrasound indicators with significant differences, as well as MALAT1, plasma brain natriuretic peptide, and Tei index. The dependent variable was sepsis-induced myocardial injury, and univariate logistic regression analysis identified E, E/e', e', MALAT1, plasma brain natriuretic peptide, and Tei index as influencing factors of sepsis-induced myocardial injury. Subsequently, a multivariate logistic regression analysis was conducted with the independent variables set as E, E/e', e', MALAT1, plasma brain natriuretic peptide, and Tei index, and the dependent variable as sepsis-induced myocardial injury. The results indicated that MALAT1, plasma brain natriuretic peptide, and Tei index were influencing factors of sepsis-induced myocardial injury. The levels of MALAT1, plasma brain natriuretic peptide and cardiac Tei index in the death group were significantly higher than those in the survival group (P < 0.05). The Pearson correlation analysis showed that MALAT1, plasma brain natriuretic peptide and cardiac Tei index were correlated with the prognosis of patients with sepsis-induced myocardial injury (P < 0.05).

CONCLUSION

We demonstrated high expression levels of MALAT1, plasma brain natriuretic peptide, and Tei index in patients with sepsis-induced myocardial injury. Ultrasound indicators can effectively contribute to the diagnosis of sepsis-induced myocardial injury. Moreover, MALAT1, plasma brain natriuretic peptide, and Tei index have been identified as influencing factors of sepsis-induced myocardial injury.

Immune Cell-Based versus Albumin-Based Ratios as Outcome Predictors in Critically Ill COVID-19 Patients

Purpose

The aim of the retrospective, single-center study was to assess the prognostic value of immune cell-based and albumin-based ratios regarding lethal outcome in critically ill COVID-19 patients.

Patients and Methods

We analyzed 612 adult critically ill COVID-19 patients admitted to the intensive care unit (ICU) between April 2020 and November 2022. Blood measurement on admission to the ICU encompassed complete blood count (CBC), IL-6, C-reactive protein (CRP), albumin, lactate, lactate dehydrogenase (LDH), serum bicarbonate, arterial base deficit/excess (BD/E), and D-dimer. All the measured and calculated parameters were compared between survivors and nonsurvivors, with the outcome measure being hospital mortality.

Results

Immune cell-based ratios [NLR - Neutrophil-to-Lymphocyte Ratio, MLR - Monocyte-to-Lymphocyte Ratio, PLR - Platelet-to-Lymphocyte Ratio, MPV - Mean Platelet Volume, MPV/PC - Mean Platelet Volume-to-Platelet Count Ratio, Derived (d-)NLR ratio - neutrophil count divided by the result of white blood cell (WBC) count - neutrophil count), N/LP - Neutrophil count x 100/Lymphocyte count x Platelet count, CLR - C-reactive protein (CRP)-to-Lymphocyte Ratio, CPR - CRP-to-Platelet Ratio, LLR - Lactate dehydrogenase (LDH)-to-Lymphocyte Ratio, Systemic Immune Inflammation Index (SII) - platelet x neutrophil/lymphocyte count, Systemic Inflammation Response Index (SIRI) - neutrophil x monocyte/lymphocyte count] were investigated. White blood cell and neutrophil counts were significantly higher, while lymphocyte and platelet counts were significantly lower in nonsurvivors. MPV, MPV/PC, NLR, d-NLR, MLR, N/LP, CRP, LDH, CPR, CLR, LLR, SII, and SIRI values were significantly higher in nonsurvivors. Monocyte count and PLR values did not differ significantly between groups. Albumin-based ratios included CRP-to-Albumin Ratio (CAR), Lactate-to-Albumin Ratio (LAR) and LDH-to-Albumin Ratio (LDH/ALB). All values were significantly higher in nonsurvivors.

Conclusion

The only independent predictor of lethal outcomes at ICU admission is the albumin-based LDH/ALB ratio. Most of the other parameters were moderate, although highly significant predictors of mortality in critically ill COVID-19 patients.

PLASMA PROTEOME, METABOLOME MENDELIAN RANDOMIZATION IDENTIFIES SEPSIS THERAPEUTIC TARGETS

ABSTRACT

Background : The interrelation between the plasma proteome and plasma metabolome with sepsis presents a multifaceted dynamic that necessitates further research to elucidate the underlying causal mechanisms. Methods : Our investigation used public genome-wide association study data to explore the relationships among the plasma proteome, metabolome, and sepsis, considering different sepsis subgroup. Initially, two-sample Mendelian randomization established causal connections between the plasma proteome and metabolome with sepsis. Subsequently, multivariate and iterative Mendelian randomization analyses were performed to understand the complex interactions in plasma during sepsis. The validity of these findings was supported by thorough sensitivity analyses. Result : The study identified 25 plasma proteins that enhance risk and 34 that act as protective agents in sepsis. After P value adjustment (0.05/1306), ICAM5 emerged with a positive correlation to sepsis susceptibility ( P value = 2.14E-05, OR = 1.10, 95% CI = 1.05-1.15), with this significance preserved across three sepsis subgroup examined. Additionally, 29 plasma metabolites were recognized as risk factors, and 15 as protective factors for sepsis outcomes. After P value adjustment (0.05/997), elevated levels of 1,2,3-benzenetriol sulfate (2) was significantly associated with increased sepsis risk ( P value = 3.37E-05, OR = 1.18, 95% CI = 1.09-1.28). Further scrutiny revealed that this plasma metabolite notably augments the abundance of ICAM5 protein ( P value = 3.52E-04, OR = 1.11, 95% CI = 1.04-1.17), devoid of any detected heterogeneity, pleiotropy, or reverse causality. Mediated Mendelian randomization revealed ICAM5 mediated 11.9% of 1,2,3-benzenetriol sulfate (2)'s total effect on sepsis progression. Conclusion : This study details the causal link between the plasma proteome and metabolome with sepsis, highlighting the roles of ICAM5 and 1,2,3-benzenetriol sulfate (2) in sepsis progression, both independently and through crosstalk.

Circulating circular RNAs act as potential novel biomarkers for sepsis secondary to pneumonia: a prospective cohort study

BACKGROUND

Circulating biomarkers for sepsis are lacking, and research on circular RNAs (circRNAs) as potential biomarkers of pneumonia-induced sepsis is limited. This study aims to investigate the diagnostic and prognostic potential of circRNAs in patients with pneumonia-induced sepsis.

METHODS

This prospective cohort study included 40 healthy individuals, 60 patients with pneumonia, and 80 patients with pneumonia-induced sepsis. CircRNAs identified through RNA-sequencing were validated using quantitative real-time polymerase chain reaction (qRT-PCR). Spearman correlation analysis was used to evaluate the associations between circRNAs, inflammatory markers, Sequential Organ Failure Assessment (SOFA) scores, and Acute Physiology and Chronic Health Evaluation II (APACHE II) scores. Receiver operating characteristic (ROC) curves analysis were used to assess the diagnostic performance of circRNAs, while ROC curves and Kaplan-Meier survival analysis were used to evaluate their prognostic value of 28-day mortality.

RESULTS

qRT-PCR confirmed the significant upregulation of Circ-CTD-2281E23.2 and downregulation of Circ-0075723 and Circ-0008679 in sepsis patients. Spearman correlation analysis showed that Circ-CTD-2281E23.2 was positively correlated with inflammatory markers and severity scores, whereas Circ-0075723 and Circ-0008679 were negatively correlated with these parameters. The area under the curve (AUC) values for Circ-CTD-2281E23.2, Circ-0075723, and Circ-0008679 in diagnosing pneumonia-induced sepsis were 0.728, 0.706, and 0.793, respectively. The combination of these circRNAs (AUC=0.846) and the combination with other clinical indicators (AUC=0.990) demostrated enhanced AUC values. The AUC values for Circ-CTD-2281E23.2 and Circ-0075723 in predicting 28-day mortality were 0.664 and 0.765, respectively.

CONCLUSION

This study suggest the additional diagnostic and prognostic value of circRNAs in pneumonia-induced sepsis. Circ-CTD-2281E23.2, Circ-0075723, and Circ-0008679 exhibit diagnostic potential, with Circ-CTD-2281E23.2 and Circ-0075723 showing positive prognostic value for 28-day mortality in sepsis patients.

The Complete Blood Count Sepsis Index Using Monocyte Distribution Width for Early Detection of Sepsis in Patients Without Obvious Signs

OBJECTIVES

Exploiting the complete blood count (CBC) with differential (CBC-diff) for early sepsis detection has practical value for emergency department (ED) care, especially for those without obvious presentations. The objective of this study was to develop the CBC Sepsis Index (CBC-SI) that incorporates monocyte distribution width (MDW) to enhance rapid sepsis screening.

DESIGN

A retrospective observational study.

SETTING

The ED of the University of Kansas Medical Center, United States.

PATIENTS

All adult patients (age 18 or over) presenting to the ED between August 8, 2020, and April 1, 2022, that received a CBC-diff as part of routine clinical care.

INTERVENTIONS

MDW, WBC count, and neutrophil-to-lymphocyte ratio were used to develop a CBC-SI (0 low to 5 high risk) for early sepsis detection. The diagnostic performance of CBC-SI was evaluated for patients with and without obvious early signs of sepsis.

MEASUREMENTS AND MAIN RESULTS

In a cohort of 51,407 ED visits, 1,683 (3.3%) met sepsis criteria; 1,343 (79.8%) septic patients presented with obvious signs and 340 (20.2%) without. The overall area under the curve of the CBC-SI was 0.83 (95% CI, 0.81-0.85). A CBC-SI of greater than or equal to 1 point exhibited a sensitivity of 83.1% (95% CI, 79.9-86.2%) and specificity of 64.8% (95% CI, 64.0-65.5%). Superior performance was observed in the patient subgroup presenting without obvious signs; greater than or equal to 1 point, 81.1% (95% CI, 73.2-88.9%) sensitivity and 69.1% (95% CI, 68.3-69.9%) specificity. Septic patients without obvious signs exhibited delays in antibiotic administration from arrival (median 4.7 vs. 3.4 hr; p < 0.001) and higher rates of ICU admission (43.8% vs. 27.9%; p < 0.001) and in-hospital mortality (14.7% vs. 9.8%; p = 0.011) compared with the septic subgroup presenting with obvious signs.

CONCLUSIONS

The CBC-SI demonstrated strong performance for early sepsis detection. Its performance was best for nonobvious presentations, suggesting highest utility in a subgroup that is most susceptible to delayed interventions and poorer outcomes.

Bacteria and host: what does this mean for sepsis bottleneck?

BACKGROUND

Sepsis is a life-threatening inflammatory condition in which the invading pathogen avoids the host's defense mechanisms and continuously stimulates and damages host cells. Consequently, many immune responses initially triggered for protection become harmful because of the failure to restore homeostasis, resulting in ongoing hyperinflammation and immunosuppression.

METHODS

A literature review was conducted to address bacterial sepsis, describe advances in understanding complex immunological reactions, critically assess diagnostic approaches, and emphasize the importance of studying bacterial bottlenecks in the detection and treatment of sepsis.

RESULTS

Diagnosing sepsis via a single laboratory test is not feasible; therefore, multiple key biomarkers are typically monitored, with a focus on trends rather than absolute values. The immediate interpretation of sepsis-associated clinical signs and symptoms, along with the use of specific and sensitive laboratory tests, is crucial for the survival of patients in the early stages. However, long-term mortality associated with sepsis is now recognized, and alongside the progression of this condition, there is an in vivo selection of adapted pathogens.

CONCLUSION

Bacterial sepsis remains a significant cause of mortality across all ages and societies. While substantial progress has been made in understanding the immunological mechanisms underlying the inflammatory response, there is growing recognition that the ongoing host-pathogen interactions, including the emergence of adapted virulent strains, shape both the acute and long-term outcomes in sepsis. This underscores the urgent need for novel high-throughput diagnostic methods and a shift toward more pre-emptive, rather than reactive, treatment strategies in sepsis care.

The Need for Standardized Guidelines for the Use of Monocyte Distribution Width (MDW) in the Early Diagnosis of Sepsis

Sepsis is a complex and potentially life-threatening syndrome characterized by an abnormal immune response to an infection, which can lead to organ dysfunction, septic shock, and death. Early diagnosis is crucial to improving prognosis and reducing hospital management costs. This narrative review aims to summarize and evaluate the current literature on the role of monocyte distribution width (MDW) as a diagnostic biomarker for sepsis, highlighting its advantages, limitations, and potential clinical applications. MDW measures the volumetric distribution width of monocytes, reflecting monocytic anisocytosis, and is detected using advanced hematological analyzers. In 2019, it was approved by the FDA as a biomarker for sepsis due to its ability to identify systemic inflammatory response at an early stage. Thirty-one studies analyzed by us have shown that an increased MDW value is associated with a higher risk of sepsis and that its combination with clinical parameters (such as qSOFA) and other biomarkers (CRP, PCT) can enhance diagnostic sensitivity and risk stratification capacity. Despite its high sensitivity, MDW has lower specificity compared to more established biomarkers such as procalcitonin, thus requiring a multimodal integration for an accurate diagnosis. The use of MDW in emergency and intensive care settings represents an opportunity to improve early sepsis diagnosis and critical patient management, particularly when combined with other markers and clinical tools. However, further studies are needed to define a universal cut-off and confirm its validity in different clinical contexts and pathological scenarios.

The VDR/FFAR2 axis mitigates sepsis-induced lung injury by suppressing macrophage lipid peroxidation

Sepsis-induced lung injury is a common critical condition in clinical practice, characterized by the accumulation of peroxides and inflammatory damage caused by excessive macrophage activation. Currently, effective treatments for sepsis-induced lung injury are lacking. Short-chain fatty acid receptor FFAR2 serves as an anti-inflammatory biomarker, but its role and mechanism in sepsis-induced lung injury remain unclear. To elucidate the influence and mechanism of FFAR2 on macrophage lipid peroxidation levels in sepsis-induced lung injury, this study conducted bioinformatics analysis and cellular experiments using the THP-1 macrophage cell line. By dual luciferase reporter and chromatin immunoprecipitation-quantitative PCR assays, it is confirmed that the transcription factor VDR upregulates FFAR2 expression in macrophages by binding to the promoter region -1695 ∼ 1525, thereby increasing the expression of iron death negative regulatory molecules and lowering macrophage lipid peroxidation levels. Moreover, both in vitro using THP-1 cells and bone marrow-derived macrophages (BMDMs) and in vivo using an LPS-induced septic mice model experiments revealed that activating the VDR/FFAR2 axis could reduce inflammation-induced macrophage lipid peroxide accumulation and alleviate lung injury in septic mice. This finding highlights the potential of FFAR2 as an immunotherapeutic target for mitigating sepsis-related lung injury.

Pathogenic epitope-specific monoclonal antibody-based immunoassay for accurate diagnosis and monitoring of tetranectin in sepsis

Sepsis is a fatal consequence of compromised host immunity due to widespread infection. Its pathogenesis has recently been found to be associated with tetranectin (TN), a monocyte-produced plasma protein with a critical disease-associated epitope, P5-5. To develop a rapid and simple method for early monitoring of the disease in clinical settings, a purified monoclonal antibody (12F1 mAb) with high affinity for the human TN pathogenic epitope P5-5 was produced in this study. The linear range of the indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) based on the mAb to detect TN-P5-5 was 4.8-312 ng/mL, and the half-maximal inhibitory concentration (IC50) was 26.99 ng/mL, with a limit of detection of 2.4 ng/mL. Furthermore, the average recovery of intra- and inter-assay were 103.253 ± 2.803 % and 107.778 ± 7.490 %, respectively. Importantly, the competitive ELISA method established using 12F1 revealed signals corresponding to disease severity in patients with sepsis. Furthermore, the specific in vivo recognition of a pathogenic epitope by mAbs can be extended to therapeutic applications. Collectively, the development of an epitope-specific mAb against disease-associated proteins could be utilized accurately and quantitatively for diagnosing and monitoring diseases in clinical blood samples.

Diagnostic Value of RDW-Albumin Ratio for the Prediction of Mortality in Sepsis Associated Nonthyroidal Illness Syndrome Patients: A Retrospective Cohort Study

Background

The correlation between RAR is linked to negative outcomes in sepsis, but it remains uncertain if RAR is connected to prognosis in patients with sepsis-related NTIS. So we investigated it in this study.

Methods

Patients with sepsis-associated NTIS admitted to Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, between March 2013 and April 2017 were included in the study. Participants were divided into two groups according to the optimal threshold value for RAR determined by the receiver operating characteristic curve. Cox proportional hazards regression and graphed with Kaplan-Meier curves examined the relationship between RAR and survival in patients with sepsis-associated NTIS. To account for potential confounding variables, a propensity score matching method was conducted to verify the relationship. Subgroup analysis was performed for different sex, age, comorbidities, infection location and other scores.

Results

A total of 328 patients with sepsis-related NTIS were analyzed in our study. The univariate and multivariate regression analysis indicated that RAR was a significant risk factor for 30-day mortality (HR 1.039(1.012, 1.067), p = 0.004). However, subgroup analysis suggested that RAR may not be an independent risk factor for 30-day mortality in sepsis patients with NTIS combined with tumor or urogenital infection. ROC analysis demonstrated that RAR had a high discriminatory ability for predicting 30-day mortality (AUC 0.751, p < 0.001). Kaplan-Meier curve analysis indicated increased 30-day mortality in the higher RAR group. Following PSM, 108 pairs of patients with matched scores were created. The multivariate regression model demonstrated that RAR was an independent factor associated with 30-day mortality risk (HR 1.049 (1.015, 1.085), p = 0.005). ROC analysis revealed that RAR was a strong discriminator for the 30d-mortality (AUC: 0.695, 95% CI: (0.598-0.792)).

Conclusion

A strong correlation was found between RAR and unfavorable clinical results in sepsis-related NTIS, where a greater RAR was linked to increased 30-day and in-hospital death rates.

Machine learning interpretability methods to characterize the importance of hematologic biomarkers in prognosticating patients with suspected infection

OBJECTIVE

To evaluate the effectiveness of Monocyte Distribution Width (MDW) in predicting sepsis outcomes in emergency department (ED) patients compared to other hematologic parameters and vital signs, and to determine whether routine parameters could substitute MDW in machine learning models.

METHODS

We conducted a retrospective analysis of data from 10,229 ED patients admitted to a large regional safety-net hospital in Cleveland, Ohio who had suspected infections and developed sepsis-associated poor outcomes. We developed a new analytical framework consisting of seven data models and an ensemble of high accuracy machine learning (ML) algorithms (accuracy values ranging from 0.83 to 0.90) to predict sepsis-associated poor outcomes (3-day intensive care unit stay or death). Local Interpretable Model-Agnostic Explanation (LIME) and Shapley Additive Value (SHAP) interpretability methods were utilized to assess the contributions of individual hematologic parameters.

RESULTS

The ML interpretability analysis indicated that the predictive value of MDW is significantly reduced when other hematological parameters and vital signs are considered. The results suggest that complete blood count with differential (CBD-DIFF) alongside vital signs can effectively replace MDW in high accuracy machine learning algorithms for screening poor outcome associated with sepsis.

CONCLUSION

MDW, although a newly approved biomarker for sepsis, does not significantly enhance prediction models when combined with routinely available parameters and vital signs. Hospitals, especially those with resource constraints, can rely on existing parameters with high accuracy machine learning models to predict sepsis outcomes effectively, thereby reducing the need for specialized tests like MDW.

Exosomes as novel biomarkers in sepsis and sepsis related organ failure

Sepsis, a severe and life-threatening condition arising from a dysfunctional host response to infection, presents considerable challenges to the health care system and is characterized by high mortality rates and substantial economic costs. Exosomes have garnered attention as potential diagnostic markers because of their capacity to mirror the pathophysiological milieu of sepsis. This discourse reviews the progression of sepsis classification from Sepsis 1.0 to Sepsis 3.0, highlighting the imperative for sensitive and specific biomarkers to facilitate timely diagnosis and optimize patient outcomes. Existing biomarkers, such as procalcitonin (PCT) and C-reactive protein (CRP), exhibit certain limitations, thereby prompting the quest for more dependable diagnostic indicators. Exosomal cargoes, which encompass proteins and miRNAs, present a trove of biomarker candidates, attributable to their stability, pervasive presence, and indicative nature of the disease status. The potential of exosomal biomarkers in the identification of sepsis-induced organ damage, including cardiomyopathy, acute kidney injury, and acute lung injury, is emphasized, as they provide real-time insights into cardiac and renal impairments. Despite promising prospects, hurdles persist in the standardization of exosome extraction and the need for extensive clinical trials to validate their efficacy. The combination of biomarker development and sophisticated exosome detection techniques represents a pioneering strategy in the realm of sepsis diagnosis and management, underscoring the significance of further research and clinical validation.

Crossing Age Boundaries: The Unifying Potential of Presepsin in Sepsis Diagnosis Across Diverse Age Groups

Sepsis is a pervasive condition that affects individuals of all ages, with significant social and economic consequences. The early diagnosis of sepsis is fundamental for establishing appropriate treatment and is based on warning scores and clinical characteristics, with positive microbiological cultures being the gold standard. Research has yet to identify a single biomarker to meet this diagnostic demand. Presepsin is a molecule that has the potential as a biomarker for diagnosing sepsis. In this paper, we present a narrative review of the diagnostic and prognostic performance of presepsin in different age groups. Given its particularities, it is identified that presepsin is a potential biomarker for sepsis at all stages of life.

Dual-Fluorescent Quantum Dot Nanobead-Based Lateral Flow Immunoassay for Simultaneous Detection of C-Reactive Protein and Procalcitonin

Simultaneous detection of C-reactive protein (CRP) and procalcitonin (PCT) at the point of care is crucial for the management of infections in patients with inflammation and in critical care settings. The challenge of detecting high concentrations of CRP alongside low concentrations of PCT in plasma from inflammatory patients has limited the clinical application of multiplexed immunoassays. Herein, we developed a lateral flow immunoassay (LFIA) that employs quantum dot nanobeads (QDNBs) of varying sizes and colors to enable the simultaneous quantification of PCT and CRP in human plasma. To extend the dynamic range of CRP detection, we combined QDNBs with smaller particle sizes with the CRP detection antibodies, thereby increasing the assay's dynamic range and reducing the hook effect. At the same time, the stronger fluorescence emitted by these larger QDNBs, in conjugation with the PCT detection antibodies, allows for the detection of PCT at the nanogram level, meeting the demand for high sensitivity. The results show that this method can detect CRP concentrations from 0.1 to 3 mg/L and PCT with a detection limit of 0.09 ng/mL, which is on par with clinically used methods. By employing this dual-color and dual-size QDNB labeling strategy, we successfully achieved simultaneous detection of CRP with a broad dynamic range and PCT with high sensitivity in a one-step point-of-care rapid test.

Procalcitonin Guided Antibiotic Stewardship

Despite infection and sepsis being a major public health challenge, early detection and timely management are often hindered by several factors. These includes the similarity of clinical presentations between infectious and non-infectious conditisons, as well as limitations of current diagnostic methods such as lengthy turnaround times and low sensitivity. Consequently, there is increasing interest in identifying biomarkers that can quickly and accurately differentiate bacterial sepsis from other inflammatory processes, whether infectious or non-infectious. Procalcitonin has emerged as one of the most extensively studied and utilized biomarkers in managing infection and sepsis, especially within the framework of antibiotic stewardship. This review aims to examine the role of Procalcitonin in guiding antibiotic stewardship. It explores the production and release of procalcitonin and its relevance in the context of infection and sepsis. The discussion focus on the clinical and economic impacts of using procalcitonin to guide the initiation and discontinuation of antibiotics in managing these conditions.

Reagent-free anti-fouling electrochemical immunosensor based on AL-BSA/AuNPs/PANI coating for the point-of-care detection of C-reactive protein in plasma and whole blood

Developing the portable CRP detection technologies that are suitable for point-of-care (POC) and primary care management is of utmost importance, and advancing the electrochemical immunosensors hold promise for POC implementation. Nevertheless, non-specific adsorption of numerous interfering proteins in complex biological media contaminates immunosensors, thereby restricting the reliability in detection efficacy. In this study, a three-dimensional flower-leaf shape amyloid bovine serum albumin/gold nanoparticles/polyaniline (AL-BSA/AuNPs/PANI) coating on the surface of the electrode was developed, which demonstrated strong anti-adsorption properties against bovine serum albumin, plasma, and cells. The immunosensor exhibited a good linear relationship to CRP response, featuring a detection limit of 0.09 μg/mL, consistent with clinical reference range. In addition, the CRP immunosensor demonstrated excellent specificity in other inflammation-related proteins and commendable anti-interference performance for CRP detection in plasma and whole blood tests. Importantly, by combining the development of a USB flash disk-type portable electrochemical workstation with a reagent-free mode, the developed CRP electrochemical immunosensor delivered ideal results in clinical samples. The anti-fouling performance, sensitivity and specificity of the immunosensor, as well as its flexible test modes in clinical samples, provide important scientific basis for developing POC detection technologies of vital biomarkers in complex biological media.