BACKGROUND:Severe fever with thrombocytopenia syndrome (SFTS) is a tick-borne viral illness with high mortality, yet early risk stratification remains challenging. Recent evidence suggests that B-cell dysregulation contributes to disease severity. METHODS:A cohort of 168 patients with confirmed SFTS was retrospectively analyzed. Flow cytometry was used to quantify B-cell subsets in peripheral blood at admission. Laboratory markers, viral load, and B-cell phenotypes were evaluated for prognostic relevance using univariate and multivariate Cox regression analyses. Receiver operating characteristic (ROC) curves and nomogram models were employed to assess predictive value. RESULTS:Deceased patients exhibited significantly higher viral loads, elevated proinflammatory cytokines (interleukin-6 (IL-6), IL-10, and tumor necrosis factor-alpha (TNF-α)), and markers of multiorgan dysfunction. Immunophenotyping revealed a reduction in naïve B-cells (IgD+CD27-), alongside expansion of double negative B-cells (DNBs) (IgD-CD27-) in fatal cases. Furthermore, viral load was positively correlated with inflammatory cytokines and dysfunctional B-cell subsets, suggesting that impaired humoral immunity contributes to persistent hyperinflammation in severe SFTS. Multivariate Cox regression analysis identified higher viral load (HR = 2.193, p  < 0.001), older age (HR = 1.073, p  < 0.001), and increased proportion of DNBs (HR = 1.024, p = 0.035) as independent predictors of 28-day mortality. A combined prognostic model integrating these variables achieved excellent performance (AUC = 0.906, 95% CI: 0.814-0.967), significantly surpassing individual markers and enabling early identification of high-risk SFTS patients. CONCLUSION:This study demonstrates that integrating B-cell subset profiling with laboratory indicators significantly improves early prognostic assessment in SFTS. These findings provide insights into immune-pathological mechanisms and support timely risk stratification and intervention to reduce mortality.

ObjectiveSevere fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne disease characterized by high morbidity and mortality rates. Timely detection and prognosis prediction are critical for implementing effective clinical interventions. This study aimed to develop a binary classification machine learning model utilizing early clinical and laboratory indicators to predict the prognosis of SFTS patients, facilitating early clinical decision-making.MethodsWe conducted a retrospective study including 233 SFTS patients diagnosed from October 2021 to May 2024. Clinical and laboratory data at initial diagnosis were collected and subjected to baseline analysis and correlation analysis to identify significant indicators. Using the area under the receiver operating characteristic curve as an indicator of model performance, select the analytical model among machine learning (LR) models, XGBOOST, LightGBM, and random forest. A binary classification machine learning model for predicting survival outcomes was constructed using a logistic regression algorithm in conjunction with identified metrics. The model's performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV). The model was externally validated using a separate cohort of 73 patients.ResultA total of 233 patients were included in this study, among whom 146 (62.7%) survived and 87 (37.3%) died, with 208 assigned to the internal cohort (177 to the training set and 31 to the test set) and the external validation cohort consisted of 73 patients, including 52 survivors (71.2%) and 21 mortality cases (28.8%). Based on the AUC, the LR model (0.750) is selected, where the values of XGBOOST, LightGBM, and random forest are 0.649, 0.661, and 0.716, respectively. The logistic regression model, incorporating age, lactate dehydrogenase (LDH), albumin, activated partial thromboplastin time (APTT), and platelet count, demonstrated robust predictive accuracy with an AUC of 0.913 (95% CI: 0.821-1.000) for the test set and 0.923 (95% CI: 0.842-1.000) for the external validation set. The model exhibited an accuracy of 0.863, with a sensitivity of 0.952 and an NPV of 0.977. The specificity and PPV were 0.827 and 0.690, respectively. These results indicate that the model maintains robust discriminative performance in an independent cohort, suggesting its potential utility as a screening tool with high sensitivity and favorable NPV.ConclusionThe model constructed using the first diagnostic indicators described above can accurately determine the patient's prognosis, which can help the clinician intervene earlier as well as take the necessary life-saving measures, and has the potential to improve the survival rate of SFTS patients.

Background Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by hepatic steatosis with cardiometabolic disorders. Due to the complicated pathophysiological processes, current therapeutic strategies for MASLD remain limited. Previous studies revealed that miR-320 was a regulator of systemic lipid metabolism with multi-targets. However, whether treatments against miR-320 would be benefit to MASLD was unclear. Methods Mice with MASLD were induced by high-fat diet (HFD) treatment. Tough Decoy or sponge against miR-320 was delivered by recombinant adeno-associated virus (serotype 8) vectors in vivo. Hepatic steatosis and plasma lipids were assessed by histopathology, biochemical assays and LC-MS. Moreover, LC-MS, Western blotting, real-time PCR, immunofluorescence and luciferase reporter were performed to investigate the underlying mechanisms. Results Knockdown of miR-320 attenuated HFD-induced MASLD by alleviating hepatic lipid accumulation and hyperlipidemia. Mechanistically, palmitic acid (PA) combined with oleic acid (OA) treatment promoted the translocation of miR-320 from the cytoplasm into the nucleus of hepatocytes. Especially, increased nuclear miR-320 activated the transcription of APOE by targeting its promoter, which in turn aggravated triglyceride accumulation and secretion in hepatocytes. Conclusions Our study revealed that treatments against miR-320 attenuated hepatic steatosis and hyperlipidemia simultaneously, which might be a potential strategy of MASLD.

Quantification of immunity is a challenge in clinical practice due to the complexity and heterogeneity of immune cells. This study aimed to establish comprehensive reference ranges for immune indicators and characterize immune heterogeneity in healthy adults. A total of 115 healthy adults aged 18–65 years were enrolled. Sixty immune indicators encompassing natural immunity (NK cells, monocytes, dendritic cells, myeloid-derived suppressor cells), cellular immunity (T cells, regulatory T cells, T follicular helper cells, T helper cells), and humoral immunity (B cells), along with nutritional and metabolic indicators, were simultaneously detected. Flow cytometry was used to measure the number, phenotype, and functional subsets of immune cells. Unsupervised k-means clustering was performed to identify immune subtypes. RNA-sequencing was conducted on representative individuals from each cluster for transcriptomic validation. The reference ranges for 60 immune indicators were established, with over half (38/60) exhibiting coefficient of variation > 30

Interstitial lung disease (ILD) is a major cause of morbidity and mortality in idiopathic inflammatory myopathies (IIMs). Although Gender-Age-Physiology (GAP) staging is widely used for prognostic stratification, it relies on pulmonary function testing, underscoring the need for practical markers to facilitate timely risk stratification. We conducted a retrospective cohort study of 47 IIM patients with ILD at Tongji Hospital. ILD was confirmed by baseline high-resolution CT and severity was staged using the GAP index. Patients were categorized as GAP stage I (0–3 points, n = 25) or GAP stages II-III (4–8 points, n = 22). Routine laboratory parameters and anti-MDA5/anti-Ro52 profiles were compared between groups. Multivariable logistic regression was performed with GAP stages II-III (vs GAP stage I) as the outcome to evaluate the association between antibody-defined subgroups and ILD severity. Patients with GAP stages II-III were older than those with GAP stage I (median 58.0 vs 52.0 years, p = 0.030) and had a higher 28-day mortality (27.27

OBJECTIVE:To apply group-based trajectory modeling (GBTM) to longitudinal real world data from patients with systemic lupus erythematosus (SLE) to identify distinct disease activity trajectories and factors associated with 6 month clinical status. METHODS:A total of 91 patients with SLE treated between 2017 and 2024 were included. SLE Disease Activity Index 2000 (SLEDAI-2K) scores were assessed at baseline, 3 months, and 6 months. GBTM was used to characterize longitudinal disease activity patterns. Heatmap clustering analysis was performed to compare clinical laboratory variables and SLEDAI-2K scores across trajectory groups and to further explore their association with 6 month clinical status. RESULTS:Two distinct disease activity trajectories were identified. Class 1, comprising patients with higher initial SLEDAI-2K scores, was predominantly composed of nonremission cases (90.91%). Class 2, characterized by lower initial SLEDAI-2K scores, showed a more balanced distribution of nonremission (46.81%) and remission (53.19%) patients. At 6 months, the remission subgroup had significantly lower SLEDAI-2K scores and lower urinary biomarker levels, including 24 h urinary microalbumin (24 h-UMA), 24 h urinary micrototal protein (24 h-UMTP), UMA, and UMTP, than the nonremission subgroup. CONCLUSIONS:Longitudinal monitoring of SLEDAI-2K trajectories may help identify patients at risk for persistent high disease activity or nonremission at 6 months and those who may benefit from closer monitoring and treatment adjustment. These findings support a stratified management approach in SLE based on trajectory profiles.

BACKGROUND:The interplay between frailty dynamics and the newly defined Cardiovascular-Kidney-Metabolic (CKM) syndrome remains poorly understood. We aimed to quantify the association between frailty transitions and the risk of incident advanced CKM syndrome and identify modifiable drivers of frailty progression. METHODS:Using data from the China Health and Retirement Longitudinal Study (CHARLS), we analyzed 8159 participants cross-sectionally and 3506 longitudinally over four years. Frailty was assessed using a deficit-accumulation index. The primary outcome was prevalent and incident advanced CKM syndrome (Stages 3-4). We employed multivariable regression models to evaluate associations and a machine learning pipeline to identify key predictors of frailty progression. RESULTS:Baseline frailty showed a robust dose-response relationship with prevalent advanced CKM (OR 1.44 per 0.1-unit FI increase; 95% CI 1.38-1.51). Longitudinally, individuals progressing to a frail state had a 40% increased risk of incident advanced CKM compared to stable non-frail peers (OR 1.40; 95% CI 1.01-1.94). Notably, this risk was sex-specific, observing a significant association in men (OR 2.20; 95% CI 1.33-3.64) but not in women. Machine learning identified life satisfaction, smoking status, and sleep duration as the top predictors of frailty progression. CONCLUSIONS:Frailty progression acts as a potent, sex-specific risk amplifier for advanced CKM syndrome. Integrating frailty screening into CKM care and targeting psychosocial well-being-specifically life satisfaction-alongside lifestyle factors may be important strategies to preempt frailty and potentially mitigate cardiovascular-renal-metabolic risks.

Aim: Reliable prognostic tools remain limited for patients with ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI) more than 48 h after symptom onset. This study aimed to develop and externally validate a nomogram based on routinely available in-hospital clinical variables to predict post-discharge adverse outcomes in this population. Methods: We retrospectively analyzed data from Tongji Hospital between June 2019 and August 2022 and identified 198 STEMI patients who underwent delayed PCI as the training cohort. Independent predictors of composite adverse events, defined as all-cause mortality, nonfatal myocardial infarction, and New York Heart Association class IV heart failure, were identified using multivariate Cox proportional hazards regression. A nomogram was subsequently constructed and internally validated using bootstrap resampling. External validation was performed in an independent cohort of 599 patients treated at the Second Hospital of Lanzhou University, with a median follow-up duration of 20 months. Results: Four variables were identified as independent predictors of adverse outcomes and incorporated into the nomogram: (1) heart rate > 83 beats per minute (hazard ratio [HR] 2.786, 95% confidence interval [CI]: 1.226-6.32, P = 0.014); (2) absence of statin therapy (HR 0.213, 95%CI: 0.064-0.71, P = 0.012); (3) intraoperative slow-flow/no-reflow phenomenon (HR 2.889, 95%CI: 1.247-6.69, P = 0.013); and (4) requirement for mechanical ventilation (HR 7.469, 95%CI: 2.57-21.70, P < 0.001). The nomogram demonstrated good discrimination and calibration in the training cohort, with a concordance index of 0.782. External validation confirmed its robust predictive performance. Patients classified as high risk exhibited significantly lower event-free survival compared with those at low risk (P < 0.0001). Conclusion: This validated nomogram, derived from routinely collected clinical variables, provides reliable prediction of adverse outcomes in STEMI patients undergoing delayed PCI and may facilitate individualized risk stratification and optimized post-discharge management.

To analyze whether there were differences in the prevalence and outcomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection between preterm-born and full-term children after lifting the prevention and control measures in China. This survey was conducted in March 2023 after nation-wide lifting of the prevention and control measures for coronavirus disease 2019 (COVID-19) in China. Data were collected from parents of children < 8 years of age across the country through the “Wenjuanxing (Questionnaire Star)” platform. Differences between preterm-born and full-term children in binary outcomes were assessed by binary logistic regression or modified Poisson regression. Models were weighted using entropy balancing. A total of 1981 children with valid questionnaires were included in the analysis, comprising 544 preterm-born children (27.5

OBJECTIVES:To determine the prevalence and clinical associations of rare HEp 2 indirect immunofluorescence ANA patterns and to evaluate their relationships with disease categories, ANA titers, and expression form. METHODS:We retrospectively analyzed 366,524 ANA tests performed between 2018 and 2024 using pattern definitions based on the 2018 ICAP update. Rare patterns were defined as those occurring in less than 1 % of ANA positive cases and represented by more than 30 samples. Clinical diagnoses were categorized as autoimmune, metabolic, infectious, neoplastic, or unclassified. RESULTS:Among 81,860 ANA positive cases (22.3 %), 1,769 (2.2 %) showed rare patterns. The most frequent rare patterns were AC-22 (0.7 %), AC-23 (0.41 %), AC-25 (0.25 %), AC-26 (0.23 %) and AC-29 (0.16 %). AC-29 was largely confined to autoimmune diseases, particularly systemic sclerosis, and was often observed at high titers. For AC-26, higher titers were more frequently observed among autoimmune disease cases, suggesting titer dependent enrichment. By contrast, AC-22 and AC-23 were more frequently observed in metabolic or infectious diseases and were mainly characterized by low titers (≤1:320). Cytoplasmic and mitotic patterns more commonly appeared as mixed patterns and showed broader distributions across disease categories. Among patients with systemic sclerosis exhibiting the AC-29 pattern, 96.2 % were positive for anti-Scl-70 antibodies. CONCLUSIONS:Rare ANA patterns show distinct disease and titer profiles. Recognition of these patterns may enhance diagnostic accuracy when interpreted in conjunction with clinical and serological findings.

INTRODUCTION:Sepsis remains one of the leading causes of death worldwide. Monocytes play a pivotal role in sepsis due to their dual role in both pro-inflammation and immunosuppression. However, the phenotypic markers and developmental characteristics of immunosuppressive monocytic myeloid-derived suppressor cells (M-MDSCs) in sepsis remain largely unknown. OBJECTIVES:This study aimed to investigate the functional heterogeneity of M-MDSCs in sepsis. METHODS:The frequency of M-MDSCs was assessed for the prognosis of sepsis. Single-cell RNA sequencing was conducted on purified HLA-DRhighCD14+ and HLA-DRlowCD14+ monocytes, respectively, from patients with sepsis to study their heterogeneity. RESULTS:We find that the frequency of M-MDSCs, as defined by classical markers, has limited value in the prognosis of sepsis due to their broad heterogeneity. Based on scRNA-seq analysis, M-MDSCs in sepsis are established as HLA-DRlowCD14+ monocytes, which display high expression of RETN and low expression of HLA-DPB1. We further segregate M-MDSCs into five subsets: IL1R2_M-MDSC, THBS1_M-MDSC, S100A_M-MDSC, IFN-sti_M-MDSC, and PPBP_M-MDSC, with the first three subsets accounting for the majority of the population. Pro-inflammatory S100A_M-MDSC dominates early-stage population and is characterized by high expression of S100A. Middle-stage IL1R2_M-MDSC exhibits cytokine production, while THBS1_M-MDSC is associated with TGF-β signaling, revealing concurrent pro-inflammatory and immunosuppressive functions. In the late stage, both THBS1_M-MDSC and IL1R2_M-MDSC display immunosuppressive functions. Furthermore, our data suggest a metabolic shift from oxidative phosphorylation to fatty acid and amino acid biosynthesis as pro-inflammatory monocytes transition into immunosuppressive M-MDSCs. VSIG4, a novel functional marker of immunosuppressive M-MDSCs, is specifically expressed in THBS1_M-MDSC. Subsequently, our preliminary results suggest that the combined detection of surface VSIG4 and IL1R2 on HLA-DRlowCD14+ monocytes shows potential for predicting sepsis outcomes. CONCLUSION:This study illuminates the characteristics of M-MDSCs in sepsis, providing a new direction for disease prognosis.

BACKGROUND:Severe fever with thrombocytopenia syndrome (SFTS) is marked by high case fatality and profound antiviral immune dysregulation, yet the clinical implications of changes in circulating T follicular helper (Tfh) cells remain unclear. METHODS:We enrolled 79 patients with RT-PCR-confirmed SFTSV infection who were hospitalized from May to October 2023 and categorized them as acute-phase survivors (AS) or acute-phase deceased (AD). Frequencies of circulating Tfh subsets (Tfh1, Tfh2, Tfh17, and PD-1+ Tfh) were measured by flow cytometry, and plasma SFTSV RNA was quantified by RT-qPCR. Their relationships with clinical variables and prognosis were analyzed using correlation analysis, ROC analysis, Kaplan-Meier survival analysis, and forward-selection multivariable logistic regression. RESULTS:Compared with survivors, patients in the AD group showed greater inflammatory activation, more pronounced coagulation disturbances, and higher plasma viral loads. While overall circulating Tfh frequencies were elevated in AD patients, subset profiling demonstrated a clear bias toward Tfh2 expansion together with contraction of Tfh1 and Tfh17 populations. Among these subsets, Tfh2 had the best discriminatory value for mortality (AUC = 0.724). Lower Tfh1 and Tfh17 frequencies were associated with poorer 28-day survival. Viral RNA levels were positively related to total Tfh and Tfh2 frequencies and negatively related to Tfh17. After adjustment for age, HLH status, and viral load, Tfh1 remained an independent correlate of acute-phase mortality. CONCLUSIONS:Acute SFTSV infection is associated with substantial remodeling of the circulating Tfh compartment. Expansion of Tfh2 alongside reduction of Tfh1 and Tfh17 was linked to heavier viral burden and worse clinical outcome. The independent association between lower Tfh1 frequency and mortality suggests that Tfh profiling may be useful for early risk assessment and offers mechanistic insight into defective humoral immunity in severe SFTS.

Background:Glycoprotein IIb/IIIa inhibitors are generally reserved for selected high-thrombotic-risk or bailout percutaneous coronary intervention (PCI) scenarios, but their routine value in non-ST-segment elevation acute coronary syndrome (NSTE-ACS) patients with anatomically complex coronary lesions remains uncertain. We evaluated adjunctive tirofiban in patients with left main, chronic total occlusion, bifurcation, ostial, long, or severely calcified lesions, while excluding confirmed intracoronary thrombus, no-reflow, or slow-flow. Methods:This retrospective observational study included 1462 NSTE-ACS patients undergoing PCI with stent implantation. Patients receiving dual antiplatelet therapy (DAPT) with or without tirofiban were compared. The efficacy endpoint was 30-day composite ischemic events, and the safety endpoint was any bleeding. Multivariable adjustment and 1:1 propensity score matching (PSM) were performed. Results:In the overall cohort, tirofiban was not associated with lower ischemic risk (9.35% vs 9.42%; unadjusted OR 0.99, 95% CI 0.68-1.45; adjusted OR 1.02, 95% CI 0.68-1.52). Bleeding was numerically higher but not significant (3.94% vs 3.59%; adjusted OR 1.08, 95% CI 0.58-2.01). After matching, 431 patients per group were retained. Ischemic events (10.21% vs 9.51%; OR 1.08, 95% CI 0.69-1.69) and bleeding events (5.10% vs 3.71%; OR 1.40, 95% CI 0.72-2.69) remained non-significantly higher with tirofiban. No significant subgroup interactions were observed. Conclusion:In this selected non-thrombotic complex-lesion NSTE-ACS cohort, routine adjunctive tirofiban was not associated with improved 30-day ischemic outcomes. Anatomical complexity alone may be insufficient to justify routine "add-on" tirofiban in the absence of thrombotic or bailout indications.

Objective SLE is a chronic autoimmune disease with immune complex deposition in various organs, causing inflammation. The Systemic Lupus Erythematosus Disease Activity Index 2000 assesses disease severity but is subjective. This study aimed to construct a machine learning model based on objective laboratory indicators to assess SLE disease activity.Methods A retrospective study was conducted on 319 patients with SLE, collecting their clinical characteristics and laboratory indicators as model-building indicators. Multiple machine learning algorithms were employed to construct models for assessing SLE disease activity.Results The patients were divided into two cohorts, cohort 1 used as the training set to build the machine learning models and cohort 2 for external validation. Six laboratory indicators, including anti-dsDNA (IFT), quantitative anti-dsDNA, neutrophils, globulin, proteinuria and NK cells, were selected to construct the SLE disease activity evaluation model. The XGBoost model demonstrated superior performance in distinguishing active SLE, with an area under the receiver operating characteristic curve of 0.934, accuracy of 0.925, sensitivity of 0.969, specificity of 0.750 and F1 score of 0.954.Conclusions This pioneering machine learning model, using objective laboratory indicators, enhances clinical feasibility and provides a novel method for assessing SLE disease activity, that may enable timely evaluation of SLE activity, facilitating preparation for treatment and prognosis.

Introduction Lymphocyte compartment undergoes dramatic changes during childhood and adulthood. Changes in lymphocyte subtypes with age, from infancy to senescence, are rare. Methods A total of 364 healthy individuals were included in this study. The population was divided into 2 groups: children and adults. Results The proportion of naive CD4 T cells decreased gradually in the children group (P < .001), and this decrease was significantly negatively correlated with the adult group (P = .008). Conversely, the percentage of memory CD4 T cells increased, with central memory CD4 T cells showing an increase in both groups and effector memory CD4 T cells especially increasing in the children group (P < .001). A similar pattern of changes was observed in naive CD8 T cells, memory CD8 T cells, and CD45RA-positive regulatory T cells. There was a negative correlation between age and the proportion of naive B cells in the children group (P < .001) as well as plasma B cells in the adult group (P < .001). Sex had no influence on the fluctuation of lymphocyte subsets. Furthermore, positive correlations were observed between the expression of T cells and B cells during the developmental process. Discussion The observed trends in the distribution of naive and memory lymphocyte subsets offer valuable insights that can help physicians understand patients’ immune state and assess prognostic conditions.

Severe fever with thrombocytopenia syndrome virus (SFTSV) infection is associated with poor clinical outcomes and defective humoral immunity yet the immunometabolic mechanisms underlying B cell dysfunction remain incompletely defined. Through integrated single-cell RNA sequencing and B cell receptor (BCR) repertoire profiling of peripheral B cells from SFTS patients, we dissected the molecular signatures between survivors and fatal cases. Functional validation and metabolic flux analysis were further performed. Seven transcriptionally distinct B cell subsets were identified. Fatal cases exhibited a marked expansion of CXCR3+Ki-67+CXCR5- extrafollicular plasmablasts, coupled with depletion of naïve and memory B cells. These plasmablasts exhibited hyperactivation of interferon-response genes (IFI27, ISG15), upregulation of inflammatory mediators (S100A8/A9), and contraction of BCR diversity, with skewed usage of λ-light chains. Pseudotime trajectory analysis and metabolic scoring revealed progressive upregulation of oxidative phosphorylation, glycolysis and endoplasmic reticulum stress during terminal differentiation. In fatal cases, B cells exhibited suppressed antigen presentation capacity and impaired immunoglobulin gene expression, alongside heightened oxidative stress and elevated CD39 levels. Furthermore, analysis of SFTSV-infected versus uninfected B cells revealed that infected plasmablasts displayed enhanced inflammatory and migratory features, including upregulated CXCR3 and CCR10 expression, suggesting direct viral modulation of B cell function and trafficking. Our study reveals that dysfunctional, metabolically reprogrammed plasmablasts underlie humoral immune failure in fatal SFTS. These findings provide mechanistic insight into B cell-mediated immunopathogenesis and highlight potential targets for prognostic evaluation and immunomodulatory intervention.

All supplementary figures S1-S8

BackgroundSeptic shock in children is an infectious disease caused by low immunity, and its mortality is very high. Early prediction of the risk of death in children with septic shock is helpful for clinicians to judge the severity of the disease, take active treatment measures, and improve the adverse outcomes of patients. However, the mechanism of death from sepsis in children remains unclear. This study aims to use bioinformatics and machine learning algorithms to identify key genes and pathways associated with fatal sepsis in children, and provide theoretical basis for rational drug use in follow-up TCM treatment.MethodsGene expression profiles were obtained from the GEO database (GSE4607) for 15 blank patients and 14 children with sepsis death. Differentially expressed genes (DEGs) were enriched by GO and KEGG pathways. Construct and visualize protein-protein interaction (PPI) networks to identify candidate genes responsible for fatal sepsis in children. Three kinds of machine learning models were established, and the candidate genes were screened by intersection to obtain the core genes with diagnostic value. ROC curve was drawn for core genes to clarify the diagnostic value of genetic markers.ResultsAnalysis of differences in the preprocessed dataset identified 83 genes, including 78 up-regulated genes and 5 down-regulated genes. 17 candidate genes were screened by protein interaction network analysis. Three machine learning algorithms LASSO, random forest (RF), and support vector machine recursive feature elimination (SVM-RFE) were used to finally screen out three core genes: CD163, MCEMP1 and RETN. CD163, MCEMP1 and RETN may jointly regulate complement and coagulation cascades, toll like receptor signaling pathway, graft versus host disease, type I diabetes mellitus.ConclusionIn this study, three core genes (CD163, MCEMP1 and RETN) that lead to sepsis death in children were screened out, providing a new understanding of the lethal mechanism of sepsis in children and a promising new therapeutic approach.

OBJECTIVES:Systemic lupus erythematosus (SLE) is a complex autoimmune disease that causes severe immune dysfunctions. Recent insights have identified CD8+ T cells expressing inhibitory killer cell immunoglobulin-like receptors (KIRs) as potentially playing a significant role in immune regulation in autoimmune diseases. This study aimed to investigate the role of KIR+CD8+ regulatory T cells in SLE. METHODS:A cross-sectional analysis encompassed 53 individuals with SLE and 42 healthy controls (HC). Analyze lymphocyte subsets and activation effector phenotypes by flow cytometry; assess cytokine secretion and cytotoxic functions of KIR+CD8 + T cells, as well as perform differential gene expression analysis. RESULTS:SLE patients exhibit distinct immunological characteristics, with a significant reduction in lymphocyte absolute counts, alongside a marked increase in the proportions of effector CD4 + T cells, plasmablast, and CD8 + CD28-T cells, yet the levels of regulatory T cells (Treg cells) show no difference compared to those in HC. CD8 + T cells highly express KIRs in SLE, and there is a negative correlation between the levels of CD158e + CD8 + T cells and disease activity. Furthermore, in SLE patients, KIR+CD8 + T cells exhibit elevated PD-1 expression, coupled with a diminished capacity for cytokine secretion, notably gamma-interferon (IFN-γ). These cells demonstrate impaired cytotoxic potential, with downregulation of cytotoxicity-related genes and reduced expression of cytotoxic protein such as perforin. CONCLUSION:KIR+CD8+ T cells, as an essential component of immune regulation, are significantly elevated in SLE, but their perforin expression is reduced, suggesting that its cytotoxic potential may be impaired, leading to impaired immune regulatory functions.

OBJECTIVES:Antiphospholipid syndrome (APS) is an autoimmune disorder characterized by thrombosis and obstetric complications associated with antiphospholipid antibodies (aPLs). This study aimed to compare the diagnostic performance of six commercial assay systems for detecting aCL and aβ2GPI antibodies. METHODS:Sixty-three APS patients, 50 SLE patients, 67 disease controls, and 62 healthy controls were enrolled. aCL and aβ2GPI antibodies of IgA, IgG, and IgM isotypes were measured using six commercial platforms, including three ELISA-based systems and three CLIA-based systems. Inter-assay concordance was compared across all detection platforms, and ROC curve analysis was performed to evaluate and compare their diagnostic performance in APS. RESULTS:Inter-assay concordance varied across platforms, with IgG isotypes showing the highest consistency and IgA exhibiting the lowest agreement. Overall, CLIA-based systems demonstrated superior classification performance compared to ELISA-based methods. The highest area under the curve (AUC) reached 0.811, with sensitivity and specificity up to 0.730 and 0.891, respectively. IgG isotypes demonstrated the best overall performance, while IgA and IgM showed greater variability. The inclusion of IgA modestly improved sensitivity in some systems, although this was sometimes accompanied by decreased specificity. LA-positive patients had higher aPL positivity rates than LA-negative ones, and aPL levels were higher in thrombotic vs. obstetric APS. CONCLUSIONS:Significant variability exists among commercial aPL detection systems. CLIA-based methods provided better consistency and diagnostic accuracy than ELISA. The inclusion of IgA provided additional diagnostic value in identifying APS patients who tested negative for aCL and aβ2GPI of the IgG and IgM isotypes.