High-throughput mass spectrometry maps the sepsis plasma proteome and differences in patient response.

Sepsis, the dysregulated host response to infection causing life-threatening organ dysfunction, is a global health challenge requiring better understanding of pathophysiology and new therapeutic approaches. Here, we applied high-throughput tandem mass spectrometry to delineate the plasma proteome for sepsis and comparator groups (noninfected critical illness, postoperative inflammation, and healthy volunteers) involving 2612 samples (from 1611 patients) and 4553 liquid chromatography-mass spectrometry analyses acquired through a single batch of continuous measurements, with a throughput of 100 samples per day. We show how this scale of data can delineate proteins, pathways, and coexpression modules in sepsis and be integrated with paired leukocyte transcriptomic data (837 samples from n = 649 patients). We mapped the plasma proteomic landscape of the host response in sepsis, including changes over time, and identified features relating to etiology, clinical phenotypes (including organ failures), and severity. This work reveals subphenotypes informative for sepsis response state, disease processes, and outcome; identifies potential biomarkers; and advances opportunities for a precision medicine approach to sepsis.

Relationship between the magnitude of haemoglobin changes and long-term mortality in patients with sepsis: a retrospective cohort study.

BACKGROUND: Sepsis is a common and severe disease with a high mortality rate in intensive care unit (ICU). The hemoglobin (HGB) level is a key parameter for oxygen supply in sepsis. Although HGB is associated with the progression of inflammation in sepsis patients, its role as a marker following sepsis treatment remains unclear. Here, we studied the correlation between early temporal changes in HGB levels and long-term mortality rates in septic patients. METHOD: In this retrospective study of data on patients with sepsis from the Medical Information Mart for Intensive Care (MIMIC) IV database, the outcome was long-term mortality. Patients were divided based on the cut-off of the HGB percentage for receiver operating characteristic (ROC) curve calculation. Kaplan-Meier (KM) survival curves and Cox proportional hazards regression models were used to analyse the associations between groups and outcomes. Propensity score matching (PSM) was used to verify the results. RESULTS: In this study, 2042 patients with sepsis and changes in HGB levels at day 4 after admission compared to day 1 were enrolled and divided into two groups: group 1 (n = 1147) for those with reduction of HGB < 7% and group 2 (n = 895) for those with dropping ≥ 7%. The long-term survival chances of sepsis with less than a 7% reduction in the proportion of HGB at day four were significantly higher than those of patients in the group with a reduction of 7% or more. After adjusting for covariates in the Cox model, the hazard ratios (HRs) with 95% confidence intervals (CIs) for long-term all-cause mortality in the group with a reduction of 7% or more were as follows: 180 days [HR = 1.41, 95% CI (1.22 to 1.63), P < 0.001]; 360 days [HR = 1.37, 95% CI (1.21 to 1.56), P < 0.001]; 540 days [HR = 1.35, 95% CI (1.20 to 1.53), P < 0.001]; 720 days [HR = 1.45, 95% CI (1.29 to 1.64), P < 0.001]. Additionally, the long-term survival rates, using Kaplan-Meier analysis, for the group with a reduction of 7% or more were lower compared to the group with less than 7% reduction at 180 days (54.3% vs. 65.3%, P < 0.001), 360 days (42.3% vs. 50.9%, P < 0.001), 540 days (40.2% vs. 48.6%, P < 0.001), and 720 days (35.5% vs. 46.1%, P < 0.001). The same trend was obtained after using PSM. CONCLUSION: A ≥ 7% decrease in HGB levels on Day 4 after admission was associated with worse long-term prognosis in sepsis patients admitted to the ICU.

Integrated multi-omics and artificial intelligence to explore new neutrophils clusters and potential biomarkers in sepsis with experimental validation.

Background: Sepsis, causing serious organ and tissue damage and even death, has not been fully elucidated. Therefore, understanding the key mechanisms underlying sepsis-associated immune responses would lead to more potential therapeutic strategies. Methods: Single-cell RNA data of 4 sepsis patients and 2 healthy controls in the GSE167363 data set were studied. The pseudotemporal trajectory analyzed neutrophil clusters under sepsis. Using the hdWGCNA method, key gene modules of neutrophils were explored. Multiple machine learning methods were used to screen and validate hub genes for neutrophils. SCENIC was then used to explore transcription factors regulating hub genes. Finally, quantitative reverse transcription-polymerase chain reaction was to validate mRNA expression of hub genes in peripheral blood neutrophils of two mice sepsis models. Results: We discovered two novel neutrophil subtypes with a significant increase under sepsis. These two neutrophil subtypes were enriched in the late state during neutrophils differentiation. The hdWGCNA analysis of neutrophils unveiled that 3 distinct modules (Turquoise, brown, and blue modules) were closely correlated with two neutrophil subtypes. 8 machine learning methods revealed 8 hub genes with high accuracy and robustness (ALPL, ACTB, CD177, GAPDH, SLC25A37, S100A8, S100A9, and STXBP2). The SCENIC analysis revealed that APLP, CD177, GAPDH, S100A9, and STXBP2 were significant associated with various transcriptional factors. Finally, ALPL, CD177, S100A8, S100A9, and STXBP2 significantly up regulated in peripheral blood neutrophils of CLP and LPS-induced sepsis mice models. Conclusions: Our research discovered new clusters of neutrophils in sepsis. These five hub genes provide novel biomarkers targeting neutrophils for the treatment of sepsis.

Postoperative bioactive adrenomedullin is associated with the onset of ARDS and adverse outcomes in patients undergoing open thoracoabdominal aortic surgery.

Cytokine-mediated systemic inflammation after open thoracoabdominal aortic aneurysm (TAAA) repairs plays a pivotal role in disrupting circulatory homeostasis, potentially leading to organ dysfunction. The bioactive form of adrenomedullin (bio-ADM) is a peptide hormone with immunomodulatory and vasomotor effects, making it a potential diagnostic agent in these cases. This retrospective, bicentric study, conducted between January 2019 and December 2022, recruited 36 elective open TAAA repair patients in two German centres. Serum and plasma samples were collected at multiple time points to measure bio-ADM levels. The primary objective was to evaluate the association of bio-ADM levels with the onset of acute respiratory distress syndrome (ARDS), with secondary endpoints focusing on mortality and SIRS-related morbidity. Results showed a significant association between postoperative bio-ADM levels (12-48 h after surgery) and the onset of ARDS (p < .001), prolonged ventilation (p = .015 at 12h after surgery), atrial fibrillation (p < .001), and mortality (p = .05 at 24h). The biomarker was also strongly associated with sepsis (p = .01 at 12 h) and multi-organ dysfunction syndrome (MODS) (p = .02 at 24 h after surgery). The study underscores the potential utility of bio-ADM as a diagnostic tool for identifying patients at risk of postoperative complications following open TAAA repairs.

Circulating bacterial peptides and linked metabolomic signatures are indicative of early mortality in pediatric cirrhosis.

BACKGROUND: Patients with pediatric cirrhosis-sepsis (PC-S) attain early mortality. Plasma bacterial composition, the cognate metabolites, and their contribution to the deterioration of patients with PC-S to early mortality are unknown. We aimed to delineate the plasma metaproteome-metabolome landscape and identify molecular indicators capable of segregating patients with PC-S predisposed to early mortality in plasma, and we further validated the selected metabolite panel in paired 1-drop blood samples using untargeted metaproteomics-metabolomics by UHPLC-HRMS followed by validation using machine-learning algorithms. METHODS: We enrolled 160 patients with liver diseases (cirrhosis-sepsis/nonsepsis [n=110] and noncirrhosis [n=50]) and performed untargeted metaproteomics-metabolomics on a training cohort of 110 patients (Cirrhosis-Sepsis/Nonsepsis, n=70 and noncirrhosis, n=40). The candidate predictors were validated on 2 test cohorts-T1 (plasma test cohort) and T2 (1-drop blood test cohort). Both T1 and T2 had 120 patients each, of which 70 were from the training cohort. RESULTS: Increased levels of tryptophan metabolites and Salmonella enterica and Escherichia coli-associated peptides segregated patients with cirrhosis. Increased levels of deoxyribose-1-phosphate, N5-citryl-d-ornithine, and Herbinix hemicellulolytic and Leifsonia xyli segregated patients with PC-S. MMCN-based integration analysis of WMCNA-WMpCNA identified key microbial-metabolic modules linked to PC-S nonsurvivors. Increased Indican, Staphylobillin, glucose-6-phosphate, 2-octenoylcarnitine, palmitic acid, and guanidoacetic acid along with L. xyli, Mycoplasma genitalium, and Hungateiclostridium thermocellum segregated PC-S nonsurvivors and superseded the liver disease severity indices with high accuracy, sensitivity, and specificity for mortality prediction using random forest machine-learning algorithm. CONCLUSIONS: Our study reveals a novel metabolite signature panel capable of segregating patients with PC-S predisposed to early mortality using as low as 1-drop blood.

Random forest differentiation of Escherichia coli in elderly sepsis using biomarkers and infectious sites.

This study addresses the challenge of accurately diagnosing sepsis subtypes in elderly patients, particularly distinguishing between Escherichia coli (E. coli) and non-E. coli infections. Utilizing machine learning, we conducted a retrospective analysis of 119 elderly sepsis patients, employing a random forest model to evaluate clinical biomarkers and infection sites. The model demonstrated high diagnostic accuracy, with an overall accuracy of 87.5%, and impressive precision and recall rates of 93.3% and 87.5%, respectively. It identified infection sites, platelet distribution width, reduced platelet count, and procalcitonin levels as key predictors. The model achieved an F1 Score of 90.3% and an area under the receiver operating characteristic curve of 88.0%, effectively differentiating between sepsis subtypes. Similarly, logistic regression and least absolute shrinkage and selection operator analysis underscored the significance of infectious sites. This methodology shows promise for enhancing elderly sepsis diagnosis and contributing to the advancement of precision medicine in the field of infectious diseases.

The Value of Systemic Immune-Inflammation Index and T Cell Subsets in the Severity and Prognosis of Sepsis.

Systemic immune-inflammation index (SII) and T cell subsets show involvement in mortality risk in septic patients, and we explored their predictive value in sepsis. Subjects were categorized into the Sepsis (SP)/Septic Shock (SSP)/Septic Shock (SPS) groups. T cell subsets [T-helper (Th)1, Th2, regulatory T cells (Treg), Th17]/platelets (PLT)/neutrophils (NEU)/lymphocytes (LYM)/C-reactive protein (CRP)/procalcitonin (PCT)/interleukin (IL)-4/IL-10/fibrinogen (FIB) were measured by an automatic blood biochemical analyzer/flow cytometry/Countess II FL automatic blood cell analyzer, with SII calculated. The correlations between SII/T cell subsets with Acute Physiology and Chronic Health Evaluation (APACH) II/Sequential Organ Failure Assessment (SOFA) scores and the predictive value of SII/Th1/Th2 for septic diagnosis/prognosis were analyzed using Spearman/ROC curve/Kaplan-Meier. The three groups varied in PLT/NEU/LYM/CRP/PCT/IL-4/IL-10/FIB levels and APACH II/SOFA scores. Compared with the SP group, the other two groups showed elevated APACH II/SOFA scores and SII/Th1/Th2/Th17/Treg levels. SII/Th1/Th2 levels significantly positively correlated with APACH II/SOFA scores. SII/Th1/Th2 levels had high predictive value for septic diagnosis/prognosis, with their combination exhibiting higher predictive value. Septic patients with high SII/Th1/Th2 levels exhibited lower survival rates. Altogether, SII, Th1, and Th2 had good predictive value for the diagnosis and prognosis of patients with varying severity of sepsis, with their high levels increasing mortality in septic patients.

Clinical biomarker profiles reveals gender differences and mortality factors in sepsis.

Background: Sepsis is a major contributor to global morbidity and mortality, affecting millions each year. Notwithstanding the decline in sepsis incidence and mortality over decades, gender disparities in sepsis outcomes persist, with research suggesting higher mortality rates in males. Methods: This retrospective study aims to delineate gender-specific clinical biomarker profiles impacting sepsis progression and mortality by examining sepsis cases and related clinical data from the past three years. Propensity score matching was used to select age-matched healthy controls for comparison. Results: Among 265 sepsis patients, a significantly higher proportion were male (60.8%, P<0.001). While mortality did not significantly differ by gender, deceased patients were significantly older (mean 69 vs 43 years, P=0.003), more likely to have hypertension (54% vs 25%, P=0.019), and had higher SOFA scores (mean ~10 vs 4, P<0.01) compared to survivors. Principal Component Analysis (PCA) showed clear separation between sepsis patients and healthy controls. 48 serum biomarkers were significantly altered in sepsis, with Triiodothyronine, Apolipoprotein A, and Serum cystatin C having the highest diagnostic value by ROC analysis. Gender-stratified comparisons identified male-specific (e.g. AFP, HDLC) and female-specific (e.g. Rheumatoid factor, Interleukin-6) diagnostic biomarkers. Deceased patients significantly differed from survivors, with 22 differentially expressed markers; Antithrombin, Prealbumin, HDL cholesterol, Urea nitrogen and Hydroxybutyrate had the highest diagnostic efficiency for mortality. Conclusion: These findings enhance our understanding of gender disparities in sepsis and may guide future therapeutic strategies. Further research is warranted to validate these biomarker profiles and investigate the molecular mechanisms underlying these gender differences in sepsis outcomes.

Neutrophil-to-lymphocyte Ratio as a Marker for Diagnosis and Prognostication of Sepsis.

PURPOSE: Exploring the ideal marker for early diagnosis and prognosis of sepsis is crucial due to limitations of available sepsis indicators. Hence, we aimed to evaluate the neutrophil-to-lymphocyte ratio (NLR) as a diagnostic and prognostic marker of sepsis. MATERIALS AND METHODS: This prospective case-control study was conducted at a tertiary care teaching public hospital. NLR values among cases and controls were compared for diagnosis. Among cases, serial trends in NLR values, outcome (survival or death), and various parameters [such as Sequential Organ Failure Assessment (SOFA) score, duration of intensive care unit (ICU) stay, etc.] were compared between survivors and nonsurvivors for prognosis. Analysis was performed using MS Excel and PSPP version 1.0.1. RESULTS: A total of 120 patients (60 cases and 60 controls) were analyzed. The NLR among cases was significantly higher (p = 1.31 × 10-16) than in controls. Using binary logistic regression, a high NLR was found to be a statistically significant predictor of sepsis category (p = 2.25 × 10-5). The association of various variables among survivors and nonsurvivors of cases showed statistically significant differences: NLR (p = 5.29 × 10-5), mean = 13.27, interquartile range (IQR) = 5.90, z-value = -4.042), C-reactive protein (CRP) (p = 4.80 × 10-7), mean = 74.40, IQR = 21.30, z-value = -5.034), D-dimer (p = 4.32 × 10-8), mean = 7.09, IQR = 0.88, z-value = -5.477), SOFA score (p = 0.00118, mean = 8.50, IQR = 3.00, z-value = -3.244), and duration of hospital stay (p = 0.03578, mean = 13.45, IQR = 8.00, z-value = -2.099). CONCLUSION: The NLR emerges as a valuable marker for both diagnosis and prognosis in sepsis. Elevated NLR levels aid in diagnosing sepsis at very early stages, and the trend of NLR demonstrates a dynamic course throughout the disease process. Persistently elevated NLR and high NLR values correlate with poor outcomes in sepsis. Additionally, NLR can be correlated with other prognostic markers of sepsis and mortality. Therefore, we recommend the utilization of NLR as a quick, easy, and cost-effective marker for both early diagnosis and regular prognostication of sepsis.

Heparin-binding protein as a biomarker for the diagnosis of sepsis in the intensive care unit: a retrospective cross-sectional study in China.

OBJECTIVES: This study aims to investigate the diagnostic value of heparin-binding protein (HBP) in sepsis and develop a sepsis diagnostic model incorporating HBP with key biomarkers and disease-related scores for rapid, and accurate diagnosis of sepsis in the intensive care unit (ICU). DESIGN: Clinical retrospective cross-sectional study. SETTING: A comprehensive teaching tertiary hospital in China. PARTICIPANTS: Adult patients (aged ≥18 years) who underwent HBP testing or whose blood samples were collected when admitted to the ICU. MAIN OUTCOME MEASURES: HBP, C reactive protein (CRP), procalcitonin (PCT), white blood cell count (WBC), interleukin-6 (IL-6), lactate (LAC), Acute Physiology and Chronic Health Evaluation II (APACHE II) and Sequential Organ Failure Assessment (SOFA) score were recorded. RESULTS: Between March 2019 and December 2021, 326 patients were enrolled in this study. The patients were categorised into a non-infection group (control group), infection group, sepsis group and septic shock group based on the final diagnosis. The HBP levels in the sepsis group and septic shock group were 45.7 and 69.0 ng/mL, respectively, which were significantly higher than those in the control group (18.0 ng/mL) and infection group (24.0 ng/mL) (p<0.001). The area under the curve (AUC) value of HBP for diagnosing sepsis was 0.733, which was lower than those corresponding to PCT, CRP and SOFA but higher than those of IL-6, LAC and APACHE II. Multivariate logistic regression analysis identified HBP, PCT, CRP, IL-6 and SOFA as valuable indicators for diagnosing sepsis. A sepsis diagnostic model was constructed based on these indicators, with an AUC of 0.901, a sensitivity of 79.7% and a specificity of 86.9%. CONCLUSIONS: HBP could serve as a biomarker for the diagnosis of sepsis in the ICU. Compared with single indicators, the sepsis diagnostic model constructed using HBP, PCT, CRP, IL-6 and SOFA further enhanced the diagnostic performance of sepsis.

Elevated Serum KIM-1 in Sepsis Correlates with Kidney Dysfunction and the Severity of Multi-Organ Critical Illness.

The kidney injury molecule (KIM)-1 is shed from proximal tubular cells in acute kidney injury (AKI), relaying tubular epithelial proliferation. Additionally, KIM-1 portends complex immunoregulation and is elevated after exposure to lipopolysaccharides. It thus may represent a biomarker in critical illness, sepsis, and sepsis-associated AKI (SA-AKI). To characterise and compare KIM-1 in these settings, we analysed KIM-1 serum concentrations in 192 critically ill patients admitted to the intensive care unit. Irrespective of kidney dysfunction, KIM-1 serum levels were significantly higher in patients with sepsis compared with other critical illnesses (191.6 vs. 132.2 pg/mL, p = 0.019) and were highest in patients with urogenital sepsis, followed by liver failure. Furthermore, KIM-1 levels were significantly elevated in critically ill patients who developed AKI within 48 h (273.3 vs. 125.8 pg/mL, p = 0.026) or later received renal replacement therapy (RRT) (299.7 vs. 146.3 pg/mL, p < 0.001). KIM-1 correlated with markers of renal function, inflammatory parameters, hematopoietic function, and cholangiocellular injury. Among subcomponents of the SOFA score, KIM-1 was elevated in patients with hyperbilirubinaemia (>2 mg/dL, p < 0.001) and thrombocytopenia (<150/nL, p = 0.018). In univariate and multivariate regression analyses, KIM-1 predicted sepsis, the need for RRT, and multi-organ dysfunction (MOD, SOFA > 12 and APACHE II ≥ 20) on the day of admission, adjusting for relevant comorbidities, bilirubin, and platelet count. Additionally, KIM-1 in multivariate regression was able to predict sepsis in patients without prior (CKD) or present (AKI) kidney injury. Our study suggests that next to its established role as a biomarker in kidney dysfunction, KIM-1 is associated with sepsis, biliary injury, and critical illness severity. It thus may offer aid for risk stratification in these patients.

[Related factors of euthyroid sick syndrome in patients with sepsis].

OBJECTIVE: To evaluate the prevalence of euthyroid sick syndrome (ESS) in sepsis patients and to explore its influencing factors. METHODS: In the study, 365 patients diagnosed with sepsis in the emergency critical care department of Shanghai First People's Hospital from January 2017 to January 2023 were retrospectively enrolled. The patients were divided into ESS and non-ESS groups based on whether the patients were complicated with ESS.Baseline variables and relevant clinical data of the enrolled patients were collected. The prevalence of ESS in sepsis patients and its influencing factors were evaluated by multivariate Logistic regression analysis, and the 30-day survival rates were compared between the two groups. The optimal cutoff value for free triiodothyronine (FT3) was explored to predict death in the patients with sepsis. RESULTS: There were 103 sepsis patients with ESS, accounting for 28.2% of the total cases. The severity of sepsis in ESS group was significantly higher than that in non-ESS group (P < 0.05). The acute physiology and chronic health evaluationⅡ(APACHEⅡ)score and sequential organ failure assessment (SOFA) score of ESS group were significantly higher than those of non-ESS group (P < 0.05). C-reactive protein (CRP), procalcitonin (PCT), serum amyloid A (SAA) and interleukin-6 (IL-6) in ESS group were higher than those in non-ESS group. total cholesterol(TC)and high-density liptein cholesterol(HDL-C)in ESS group were lower than those in non-ESS group, and the differences were statistically significant (P < 0.05).Multivariate Logistic regression analysis showed that PCT, IL-6, CRP, SAA and activated partial thromboplatin time (APTT) were independent risk factors for ESS in the sepsis patients (OR values were 1.105, 1.006, 1.005, 1.009 and 1.033, respectively; 95% CI were 1.044-1.170, 1.001-1.012, 1.001-1.009, 1.005-1.014, 1.004-1.062, respectively, P < 0.05).The 30-day survival rate in ESS group was significantly lower than that in non-ESS group, the Long-rank chi-square test value was 16.611, and the difference was statistically significant (P < 0.05).The receiver operation characteristic area under the curve (AUCROC)of FT3 predicted death in the patients with sepsis was 0.924 (95% CI 0.894-0.954). The serum FT3 cutoff point was 3.705 pmol/L, the specificity was 0.868, and the sensitivity was 0.950. CONCLUSION: In this study, the incidence of ESS in sepsis patients was determined to be 28.2% with poor prognosis. The results showed that PCT, IL-6, CRP, SAA and APTT were independent risk factors for ESS in sepsis patients, while HDL-C was a protective factor (P < 0.05). FT3 is a novel potential biomarker for predicting death in patients with sepsis.

Prognostic value of red blood cell distribution width to albumin ratio for predicting mortality in adult patients meeting sepsis-3 criteria in intensive care units.

BACKGROUND: Patients with sepsis with low albumin levels and high red blood cell distribution width levels have poor prognoses. Red blood cell distribution width to albumin ratio (RAR) has recently attracted attention as an innovative inflammation biomarker. We aimed to explore the association between RAR and the prognosis of patients with sepsis. METHODS: This retrospective observational study included 402 patients meeting the sepsis-3 standards admitted to Yantai Yuhuangding Hospital's intensive care units (ICUs) between January 2020 and December 2022. The relationship between RAR and mortality in patients with sepsis was examined using regression analysis, Kaplan-Meier analyses, and a receiver operating characteristic curve. Subgroup and sensitivity analyses were conducted to assess the results' robustness. RESULTS: RAR, when considered as a continuous variable, was a significant independent in-hospital mortality risk factor (adjusted odds ratio [OR]: 1.383; 95% confidence interval [CI]: 1.164-1.645; P < 0.001). When considering RAR as a categorical variable, the ORs (95% CIs) of hospital mortality for quartile 2 (Q2), Q3, and Q4 compared with Q1 were 1.027 (0.413-2.551), 3.632 (1.579-8.354), and 4.175 (1.625-10.729), respectively, P < 0.001. Similar outcomes were observed for 28- and 90-day mortalities. CONCLUSIONS: RAR may indicate clinical prognosis for patients with sepsis in the ICU, potentially providing a low-cost, easily repeatable, and accessible biomarker for risk categorization for these patients.

Dynamic immune status analysis of peripheral blood mononuclear cells in patients with Klebsiella pneumoniae bloodstream infection sepsis using single-cell RNA sequencing.

Background: Klebsiella pneumoniae is a common Gram-negative bacterium. Blood infection caused by K. pneumoniae is one of the most common causes of human sepsis, which seriously threatens the life of patients. The immune status of peripheral blood mononuclear cells (PBMCs) based on single-cell RNA sequencing (scRNA-seq) in acute stage and recovery stage of sepsis caused by K. pneumoniae bloodstream infection has not been studied. Methods: A total of 13 subjects were included in this study, 3 healthy controls, 7 patients with K. pneumoniae bloodstream infection in the acute stage (4 patients died), and 3 patients in the recovery stage. Peripheral blood of all patients was collected and PBMCs were isolated for scRNA-seq analysis. We studied the changes of PBMCs components, signaling pathways, differential genes, and cytokines in acute and recovery stages. Results: During K. pneumoniae acute infection we observed a decrease in the proportion of T cells, most probably due to apoptosis and the function of T cell subtypes was disorder. The proportion of monocytes increased in acute stage. Although genes related to their phagocytosis function were upregulated, their antigen presentation capacity-associated genes were downregulated. The expression of IL-1ß, IL-18, IFNGR1 and IFNGR2 genes was also increased in monocytes. The proportion of DCs was depleted during the acute stage and did not recover during sepsis recovery. DCs antigen presentation was weakened during the acute stage but recovered fast during the recovery stage. pDCs response to MCP-1 chemokine was weakened, they recovered it quickly during the recovery stage. B cells showed apoptosis both in the acute stage and recovery stage. Their response to complement was weakened, but their antigen presentation function was enhanced. The proportion of NK cells stable during all disease's stages, and the expression of IFN-γ gene was upregulated. Conclusion: The proportion of PBMCs and their immune functions undergo variations throughout the course of the disease, spanning from the acute stage to recovery. These findings provide new insights into the mechanism of PBMCs immune function during K. pneumoniae bloodstream infection sepsis and recovery and sets the basis for further understanding and treatment.

Association between intracellular adenosine triphosphate content of CD4+ T lymphocytes and mortality in sepsis patients: A prospective observational study.

OBJECTIVE: This study aimed to link intracellular adenosine triphosphate content in CD4+ T lymphocytes (CD4+ iATP) with sepsis patient mortality, seeking a new predictive biomarker for outcomes and enhanced management. METHODS: 61 sepsis patients admitted to the Intensive Care Unit between October 2021 and November 2022 were enrolled. iATP levels were gauged using whole blood CD4+ T cells stimulated with mitogen PHA-L. Based on CD4+ iATP levels (<132.24 and ≥132.24 ng/mL), patients were categorized into two groups. The primary endpoint was all-cause mortality. To identify factors associated with mortality, both univariate and multivariate Cox proportional hazard analyses were conducted. RESULTS: Of the patients, 40 had high CD4+ iATP levels (≥132.24 ng/mL) and 21 had low levels (<132.24 ng/mL). In a 28-day follow-up, 21 (34.4%) patients perished. Adjusting for confounders like SOFA score, APACHE II score, lactic acid, and albumin, those with low CD4+ iATP had three- to fivefold higher mortality risk compared to high CD4+ iATP patients (61.9% vs. 20.0%; hazard ratio [95% confidence interval], Model 1: 4.515 [1.276-15.974], p = .019, Model 2: 3.512 [1.197-10.306], p = .022). CD4+ iATP correlated positively with white blood cell and neutrophil counts but not with lymphocytes, CD3, and CD4 counts. CONCLUSIONS: Low CD4+ iATP levels were associated with a higher risk of mortality in sepsis patients. Measurement of CD4+ iATP may serve as a useful tool for identifying patients at a higher risk of mortality and could potentially provide a basis for clinical treatment. Further research is warranted to fully elucidate the underlying mechanisms of this association.

Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine Learning.

A common result of infection is an abnormal immune response, which may be detrimental to the host. To control the infection, the immune system might undergo regulation, therefore producing an excess of either pro-inflammatory or anti-inflammatory pathways that can lead to widespread inflammation, tissue damage, and organ failure. A dysregulated immune response can manifest as changes in differentiated immune cell populations and concentrations of circulating biomarkers. To propose an early diagnostic system that enables differentiation and identifies the severity of immune-dysregulated syndromes, we built an artificial intelligence tool that uses input data from single-cell RNA sequencing. In our results, single-cell transcriptomics successfully distinguished between mild and severe sepsis and COVID-19 infections. Moreover, by interpreting the decision patterns of our classification system, we identified that different immune cells upregulating or downregulating the expression of the genes CD3, CD14, CD16, FOSB, S100A12, and TCRɣδ can accurately differentiate between different degrees of infection. Our research has identified genes of significance that effectively distinguish between infections, offering promising prospects as diagnostic markers and providing potential targets for therapeutic intervention.

Deciphering Abnormal Platelet Subpopulations in COVID-19, Sepsis and Systemic Lupus Erythematosus through Machine Learning and Single-Cell Transcriptomics.

This study focuses on understanding the transcriptional heterogeneity of activated platelets and its impact on diseases such as sepsis, COVID-19, and systemic lupus erythematosus (SLE). Recognizing the limited knowledge in this area, our research aims to dissect the complex transcriptional profiles of activated platelets to aid in developing targeted therapies for abnormal and pathogenic platelet subtypes. We analyzed single-cell transcriptional profiles from 47,977 platelets derived from 413 samples of patients with these diseases, utilizing Deep Neural Network (DNN) and eXtreme Gradient Boosting (XGB) to distinguish transcriptomic signatures predictive of fatal or survival outcomes. Our approach included source data annotations and platelet markers, along with SingleR and Seurat for comprehensive profiling. Additionally, we employed Uniform Manifold Approximation and Projection (UMAP) for effective dimensionality reduction and visualization, aiding in the identification of various platelet subtypes and their relation to disease severity and patient outcomes. Our results highlighted distinct platelet subpopulations that correlate with disease severity, revealing that changes in platelet transcription patterns can intensify endotheliopathy, increasing the risk of coagulation in fatal cases. Moreover, these changes may impact lymphocyte function, indicating a more extensive role for platelets in inflammatory and immune responses. This study identifies crucial biomarkers of platelet heterogeneity in serious health conditions, paving the way for innovative therapeutic approaches targeting platelet activation, which could improve patient outcomes in diseases characterized by altered platelet function.

The elevated lactate dehydrogenase to albumin ratio is a risk factor for developing sepsis-associated acute kidney injury: a single-center retrospective study.

BACKGROUND: There is no evidence to determine the association between the lactate dehydrogenase to albumin ratio (LAR) and the development of sepsis-associated acute kidney injury (SAKI). We aimed to investigate the predictive impact of LAR for SAKI in patients with sepsis. METHODS: A total of 4,087 patients with sepsis from the Medical Information Mart for Intensive Care IV (MIMIC IV) database were included. Logistic regression analysis was used to identify the association between LAR and the risk of developing SAKI, and the relationship was visualized using restricted cubic spline (RCS). The clinical predictive value of LAR was evaluated by ROC curve analysis. Subgroup analysis was used to search for interactive factors. RESULTS: The LAR level was markedly increased in the SAKI group (p < 0.001). There was a positive linear association between LAR and the risk of developing SAKI (p for nonlinearity = 0.867). Logistic regression analysis showed an independent predictive value of LAR for developing SAKI. The LAR had moderate clinical value, with an AUC of 0.644. Chronic kidney disease (CKD) was identified as an independent interactive factor. The predictive value of LAR for the development of SAKI disappeared in those with a history of CKD but remained in those without CKD. CONCLUSIONS: Elevated LAR 12 h before and after the diagnosis of sepsis is an independent risk factor for the development of SAKI in patients with sepsis. Chronic comorbidities, especially the history of CKD, should be taken into account when using LAR to predict the development of AKI in patients with sepsis.

Cell-free DNA in patients with sepsis: long term trajectory and association with 28-day mortality and sepsis-associated acute kidney injury.

Introduction: Outcome-prediction in patients with sepsis is challenging and currently relies on the serial measurement of many parameters. Standard diagnostic tools, such as serum creatinine (SCr), lack sensitivity and specificity for acute kidney injury (AKI). Circulating cell-free DNA (cfDNA), which can be obtained from liquid biopsies, can potentially contribute to the quantification of tissue damage and the prediction of sepsis mortality and sepsis-associated AKI (SA-AKI). Methods: We investigated the clinical significance of cfDNA levels as a predictor of 28-day mortality, the occurrence of SA-AKI and the initiation of renal replacement therapy (RRT) in patients with sepsis. Furthermore, we investigated the long-term course of cfDNA levels in sepsis survivors at 6 and 12 months after sepsis onset. Specifically, we measured mitochondrial DNA (mitochondrially encoded NADH-ubiquinone oxidoreductase chain 1, mt-ND1, and mitochondrially encoded cytochrome C oxidase subunit III, mt-CO3) and nuclear DNA (nuclear ribosomal protein S18, n-Rps18) in 81 healthy controls and all available samples of 150 intensive care unit patients with sepsis obtained at 3 ± 1 days, 7 ± 1 days, 6 ± 2 months and 12 ± 2 months after sepsis onset. Results: Our analysis revealed that, at day 3, patients with sepsis had elevated levels of cfDNA (mt-ND1, and n-Rps18, all p<0.001) which decreased after the acute phase of sepsis. 28-day non-survivors of sepsis (16%) had higher levels of cfDNA (all p<0.05) compared with 28-day survivors (84%). Patients with SA-AKI had higher levels of cfDNA compared to patients without AKI (all p<0.05). Cell-free DNA was also significantly increased in patients requiring RRT (all p<0.05). All parameters improved the AUC for SCr in predicting RRT (AUC=0.88) as well as APACHE II in predicting mortality (AUC=0.86). Conclusion: In summary, cfDNA could potentially improve risk prediction models for mortality, SA-AKI and RRT in patients with sepsis. The predictive value of cfDNA, even with a single measurement at the onset of sepsis, could offer a significant advantage over conventional diagnostic methods that require repeated measurements or a baseline value for risk assessment. Considering that our data show that cfDNA levels decrease after the first insult, future studies could investigate cfDNA as a "memoryless" marker and thus bring further innovation to the complex field of SA-AKI diagnostics.

Expression of FoxO3a in sepsis mice and its association with lymphocyte apoptosis.

This study investigated forkhead box O3a (FoxO3a) expression in peripheral blood of sepsis mice and its correlation with lymphocyte apoptosis. Sixty male C57 mice were randomly assigned to sham, model, and intervention groups. Sepsis was induced via cecal ligation in the model and intervention groups, while sham mice underwent similar procedures excluding cecal ligation. Apoptosis proteins in lymphocytes were assessed by Western blotting, reactive oxygen species (ROS) levels by 2,7-Dichlorodi-hydrofluorescein diacetate (DCFH-DA), and serum interleukin-1ß (IL-1ß) and IL-10 content. The model group exhibited elevated mortality, increased lymphocyte apoptosis, higher Caspase3 expression, and lower Bcl-2/Bax ratio compared to sham and intervention groups. Additionally, the model group displayed decreased serum IL-10, elevated IL-1ß, heightened lymphocytic ROS, reduced FoxO3a expression, and increased levels of p-FoxO3a, p-PI3K, and p-Akt compared to sham. In sepsis mice, inhibited FoxO3a signaling in lymphocytes leads to enhanced apoptosis, elevated ROS, and immune cell dysfunction, contributing to increased mortality.