Sepsis is characterized by a complex immune response. This meta-analysis evaluated the clinical effectiveness of intravenous IgM-enriched immunoglobulin (IVIgGM) in patients with sepsis and septic shock. Four databases, PubMed, the Cochrane Library, the ISI Web of Knowledge, and Embase, were systematically searched from inception to June 2018 to update the 2013 edition of the Cochrane review by two investigators, who independently selected studies, extracted relevant data, and evaluated study quality. Data were subjected to a meta-analysis and trial sequential analysis (TSA) for the primary and secondary outcomes. Level of evidence was evaluated using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) scale. Nineteen studies comprising 1530 patients were included in this meta-analysis. Pooled analyses showed that the use of IVIgGM reduced the mortality risk of septic patients (relative risk 0.60; 95% confidence interval [CI] 0.52–0.69, I2 = 0%). TSA showed that IVIgGM had a significant effect on mortality. Additionally, the meta-analysis suggested that use of IVIgGM shortened length of mechanical ventilation (mean difference − 3.16 days; 95% CI − 5.71 to − 0.61 days) and did not shorten length of stay in the intensive care unit (mean difference − 0.38 days; 95% CI − 3.55 to 2.80 days). The GRADE scale showed that the certainty of the body of evidence was low for both benefits and IVIgGM. Administration of IVIgGM to adult septic patients may be associated with reduced mortality. Treatment effects tended to be smaller or less consistent when including only those studies deemed adequate for each indicator. The available evidence is not clearly sufficient to support the widespread use of IVIgGM in the treatment of sepsis. Trial registration PROSPERO registration number: CRD42018084120. Registered on 11 February 2018.

Background: The immune system plays a fundamental role in the pathophysiology of sepsis, and autophagy and autophagy-related molecules are crucial in innate and adaptive immune responses; however, the potential roles of autophagy-related genes (ARGs) in sepsis are not comprehensively understood. Methods: A systematic search was conducted in ArrayExpress and Gene Expression Omnibus (GEO) cohorts from July 2005 to May 2022. Machine learning approaches, including modified Lasso penalized regression, support vector machine, and artificial neural network, were applied to identify hub ARGs, thereby developing a prediction model termed ARG classifier. Diagnostic and prognostic performance of the model was comprehensively analyzed using multi-transcriptome data. Subsequently, we systematically correlated the ARG classifier/hub ARGs with immunological characteristics of multiple aspects, including immune cell infiltration, immune and molecular pathways, cytokine levels, and immune-related genes. Further, we collected clinical specimens to preliminarily investigate ARG expression levels and to assess the diagnostic performance of ARG classifier. Results: A total of ten GEO and three ArrayExpress datasets were included in this study. Based on machine learning algorithms, eight key ARGs (ATG4C, BAX, BIRC5, ERBB2, FKBP1B, HIF1A, NCKAP1, and NFKB1) were integrated to establish ARG classifier. The model exhibited excellent diagnostic values (AUC > 0.85) in multiple datasets and multiple points in time and superiorly distinguished sepsis from other critical illnesses. ARG classifier showed significant correlations with clinical characteristics or endotypes and performed better in predicting mortality (AUC = 0.70) than other clinical characteristics. Additionally, the identified hub ARGs were significantly associated with immune cell infiltration (B, T, NK, dendritic, T regulatory, and myeloid-derived suppressor cells), immune and molecular pathways (inflammation-promoting pathways, HLA, cytolytic activity, apoptosis, type-II IFN response, complement and coagulation cascades), levels of several cytokines (PDGFRB, IL-10, IFNG, and TNF), which indicated that ARG classifier/hub ARGs adequately reflected the immune microenvironment during sepsis. Finally, using clinical specimens, the expression levels of key ARGs in patients with sepsis were found to differ significantly from those of control patients, and ARG classifier exhibited superior diagnostic performance, compared to procalcitonin and C-reactive protein. Conclusion: Collectively, a diagnostic and prognostic model (ARG classifier) based on eight ARGs was developed which may assist clinicians in diagnosis of sepsis and recognizing patient at high risk to guide personalized treatment. Additionally, the ARG classifier effectively reflected the immune microenvironment diversity of sepsis and may facilitate personalized counseling for specific therapy.

Recurrence is still major obstacle to long-term survival in laryngeal squamous cell carcinoma (LSCC). We aimed to establish and validate a nomogram to precisely predict recurrence probability in patients with LSCC. A total of 283 consecutive patients with LSCC received curative-intend surgery between 2011 and 2014 at were enrolled in this study. Subsequently, 283 LSCC patients were randomly assigned to a training cohort (N = 171) and a validation cohort (N = 112) in a 3:2 ratio. According to the results of multivariable Cox regression analysis in the training cohort, we developed a nomogram. The predictive accuracy and discriminative ability of the nomogram were evaluated by calibration curve and concordance index (C-index), and compared with TNM stage system by C-index, receiver operating characteristic (ROC) analysis. Decision curve analysis (DCA) was performed to estimate clinical value of our nomogram. Six independent factors rooted in multivariable analysis of the training cohort to predict recurrence were age, tumor site, smoking, alcohol, N stage and hemoglobin, which were all integrated into the nomogram. The calibration curve for the probability of recurrence presented that the nomogram-based predictions were in good correspondence with actual observations. The C-index of the nomogram was 0.81 (0.75-0.88), and the area under curve (AUC) of nomogram in predicting recurrence free survival (RFS) was 0.894, which were significantly better than traditional TNM stage. Decision curve analysis further affirmed that our nomogram had a larger net benefit than TNM stage. The results were confirmed in the validation cohort. A risk prediction nomogram for patients with LSCC, incorporating readily assessable clinicopathologic variables, generates more accurate estimations of the recurrence probability when compared TNM stage alone, but still needs additional data before being used in clinical implications.

Background: Epilepsy encompasses a group of heterogeneous brain diseases that afflict about 1% of the world’s population. Accumulating evidence shows that the immune system plays a key role in epileptogenesis. Nevertheless, the immune-related mechanisms remain not been precisely understood. Methods: Three epilepsy datasets (GSE16969, GSE32534 and GSE143272) were screened to obtain differentially expressed immune-related genes (DEIRGs). Random forest (RF) and protein–protein interaction (PPI) network were constructed to identify core genes. Another dataset (GSE31718) and 60 clinical samples via quantitative real-time polymerase chain reaction (qRT-PCR) were utilized to validate core genes. Immune cell infiltration score was performed with CIBERSORTx tools and single-sample gene set enrichment analysis (ssGSEA). Gene set variation analysis (GSVA) and ssGSEA were conducted to determine the pathways that are significantly enriched during normal and epilepsy. The correlation between hub genes, immune cells, and enriched molecular pathways was evaluated by Pearson correlation analysis. Results: Based on RF and PPI, 4 DEIRGs (CSF1R, IL6R, TLR2, and TNFRSF1A) were identified as hub genes. Results of qRT-PCR validated that higher expression levels of CSF1R, IL6R, TLR2, and TNFRSF1A in epilepsy samples compared to control sample. Immune infiltration analysis by CIBERSORTx displayed immune signatures that are significantly richer in epilepsy, T cell subsets in particular. Notably, ssGSEA found that Th1 signatures were more abundant in normal tissues; yet Th2 signatures were more abundant in epilepsy tissues. Cytokine cytokine receptor interaction (CCR) was significantly enriched in epilepsy based on multi-transcriptome data. Additionally, hub genes were significantly correlated with score of Th1/Th2 signatures and enrichment score of CCR in multi-transcriptome data. Conclusion: Four IRGs (CSF1R, IL6R, TLR2, and TNFRSF1A) were closely correlated pathogenesis of epilepsy, which may be by impacting CCR and the balance of Th1/Th2 signatures involved in the occurrence of epilepsy. Our data offer compelling insights into the pathogenesis and promising therapeutic targets for epilepsy.

Background: Tissue inhibitor of metalloproteinase 2 (TIMP-2) and insulin-like growth factor binding protein 7 (IGFBP7) are recent promising markers for identification of cardiac surgery-associated acute kidney injury (CSA-AKI). The aim of this study was systematically and quantitatively to evaluate the accuracy of urinary TIMP-2 and IGFBP7 for the diagnosis of CSA-AKI. Methods: Three databases including PubMed, ISI web of knowledge, and Embase were systematically searched from inception to March 2018. Two investigators conducted the processes of literature search study selection, data extraction, and quality evaluation independently. Meta-DiSc and STATA were used for all statistical analyses. Results: A total of 8 studies comprising 552 patients were included in this meta-analysis. Pooled sensitivity and specificity with corresponding 95% confidence intervals (CIs) were 0.79 (95% CI, 0.71-0.86, I ² = 74.2%) and 0.76 (95% CI, 0.72-0.80, I ² = 80.8%), respectively. Pooled positive likelihood ratio (LR), negative LR, and diagnostic odds ratio were 3.49 (95% CI, 2.44-5.00, I ² = 61.5%), 0.31(95% CI, 0.19-0.51, I ² = 51.8%), and 14.89 (95% CI, 7.31-30.32, I ² = 27.9%), respectively. The area under curve estimated by summary receiver operating characteristic was 0.868 (standard error [SE] 0.032) with a Q* value of 0.799 (SE 0.032). Sensitivity analysis demonstrated that one study notably affected the stability of pooled results. One of the subgroups investigated—AKI threshold—could account for partial heterogeneity. Conclusion: Urinary TIMP-2 and IGFBP7 is a helpful biomarker for early diagnosis of CSA-AKI. And, the potential of this biomarker with a broader spectrum of clinical settings may be the focus of future studies.

Background: Extracorporeal membrane oxygenation-induced coagulopathy (ECMO-IC) represents a frequent and severe complication, contributing to oxygenator replacement and unfavorable outcomes. Currently, no reliable machine learning (ML) model exists for early identification. This study comprehensively assesses routine clinical characteristics to develop a reliable, accurate, and explainable ML model for estimating ECMO-IC risk and to identify modifiable factors. Methods: This study included two center cohorts with 266 patients undergoing ECMO from 2015 to 2024. Feature selection utilized the Boruta algorithm, followed by the implementation of a distinctive ML framework incorporating 12 ML algorithms to establish a consensus prediction model (ECMO-IC index). Model and feature variable assessment employed multiple analytical methods: Bootstrapping and fivefold cross-validation, subgroup and interaction analysis, restricted cubic spline (RCS) regression, and threshold effect analysis. Model interpretation and feature quantification relied on the Shapley Additive Explanations (SHAP) methodology for visualization purposes. Results: Through Boruta algorithm selection, 17 characteristics were identified and incorporated into 12 ML methodologies, generating 105 permutations and an optimal algorithm for identifying ECMO-IC. The ECMO-IC index comprising 9 modifiable or nonmodifiable variables, namely platelet (PLT), lactate, systemic immune-inflammation index (SII), K, total protein (TP), shock index (SI), red blood cell volume distribution width (RDWCV), acute physiology and chronic health evaluation II (APACHE II), and Ca, demonstrated strong diagnostic capabilities, achieving a mean area under the curve (AUC) of 0.815 across derivation (AUC = 0.817) and validation (AUC = 0.813) cohorts, along with notable discriminatory power, model fit, and clinical utility. SHAP elucidates the importance of ranking features (PLT, lactate, K, Ca and APACHE II) and visualises global and individual ECMO-IC risk prediction. RCS regression and threshold effect analysis suggested a nonlinear link between model features (PLT: P for nonlinearity = 0.002, SII: P for nonlinearity = 0.001, K: P for nonlinearity = 0.006, Ca: P for nonlinearity = 0.008, and lactate: P for nonlinearity = 0.004) and ECMO-IC, and generated an inflection point for features (PLT = 95 × 10 ⁹ /L, lactate = 5.7 mmol/L, SII = 200, K = 4.4 mmol/L, TP = 45.6 g/L, SI = 0.8, RDWCV = 14%, APACHE II = 15, Ca = 1.03 mmol/L). To provide a more flexible predictive tool, the ECMO-IC model was constructed using a free, publicly available web-based calculator ( https://genglongliu.shinyapps.io/DynNomapp/ ). Conclusion: An optimised explainable ML model (ECMO-IC index) incorporating several modifiable parameters was established and internally validated to deliver an readily available and accurate diagnostic tool for ECMO-IC, with potential applications in ECMO clinical management.