BackgroundNon-criteria antiphospholipid antibodies (aPLs) may be associated with adverse obstetric outcomes in patients who do not meet conventional antiphospholipid syndrome (APS) classification criteria. Their clinical relevance in fetal growth restriction (FGR) remains incompletely defined.MethodsThis retrospective cohort study included 104 pregnant women with FGR or related adverse obstetric presentations who underwent comprehensive testing for 26 solid-phase aPL markers, together with lupus anticoagulant assessment. Criteria-aPL positivity was defined as positivity for lupus anticoagulant, anticardiolipin IgG/IgM, or anti-β2-glycoprotein I IgG/IgM. IgA aCL and IgA anti-β2GPI were measured as exploratory markers but were not included in criteria-aPL classification. Patients were categorized according to criteria and non-criteria aPL status. Hierarchical clustering was performed using the 26 solid-phase aPL markers. Continuous outcomes were compared using Kruskal–Wallis or Wilcoxon rank-sum tests, and categorical outcomes using Fisher’s exact test. Birth-weight analyses were restricted to live births. Treatment status was assessed using Fisher’s exact test with Haldane–Anscombe correction for odds ratio estimation when appropriate.ResultsAmong live births, gestational age at delivery and birth weight differed significantly across antibody-profile groups. Patients positive for both criteria and non-criteria aPLs had the lowest median gestational age and birth weight, 36.43 weeks (IQR, 34.57–38.00) and 2.15 kg (IQR, 1.88–2.55), respectively. The overall differences were significant for gestational age and birth weight, with p values of 0.010 and 0.007. Hierarchical clustering identified three aPL phenotypes with significant differences in gestational age and birth weight among live births, with p values of 0.011 and 0.018, respectively. In the criteria-aPL-negative and chromosomally normal subgroup, non-criteria aPL positivity was mainly driven by IgM aPE, IgM aPS/PT, and aANXA5. Treatment status was strongly associated with pregnancy outcome.ConclusionComprehensive profiling of non-criteria aPLs may provide additional risk-stratification information in pregnancies complicated by FGR, particularly among patients negative for conventional criteria aPLs. These findings require validation in larger prospective cohorts with standardized antibody testing and treatment protocols.

ObjectivePatients with systemic lupus erythematosus (SLE) exhibit heterogeneous renal courses after acute kidney injury (AKI). This study applied latent class mixed modeling to identify AKI trajectory phenotypes in SLE, developed a risk-stratification model using early post-AKI data, and explored whether hydroxychloroquine (HCQ) use is associated with AKI trajectory class membership.MethodsThis multicenter retrospective study used MIMIC-IV for model development and internal assessment and eICU, NWICU, and local cohorts for exploratory external assessment. Adult SLE patients with AKI were included. The primary model was a race-free, no-imbalance-correction LASSO logistic regression model. Performance was assessed with AUC, Brier score, calibration intercept, and calibration slope. Bootstrap optimism correction was applied. A non-overlap sensitivity analysis examined the influence of structural predictor-outcome overlap. HCQ was evaluated using multivariable logistic regression and propensity score matching.ResultsAmong 279 MIMIC patients, three AKI trajectories were identified (progressive 9.3%, recovery 19.4%, stable 71.3%). The primary race-free LASSO model achieved an apparent AUC of 0.761 (optimism-corrected: 0.706) and a Brier score of 0.086 (corrected: 0.092). When assessed against a non-overlapping post-48-hour renal endpoint, discrimination diminished substantially (AUC 0.593). Exploratory external assessments were event-limited: the main SLE external discrimination subsets contained 6 events in eICU, 3 events in NWICU, and 1 event in the local cohort; therefore, these estimates were considered descriptive rather than definitive validation. HCQ showed an exploratory association not interpretable as causal.ConclusionThis study provides an exploratory risk-stratification framework for early AKI trajectory patterns in SLE. The apparent discrimination was substantially attenuated after removing predictor-outcome overlap and adjusting for optimism. External assessments were event-limited and should be interpreted as exploratory transportability assessments rather than definitive evidence of generalizability.

BACKGROUND:Heart failure with preserved ejection fraction (HFpEF) has emerged as one of the most challenging public health issues worldwide, with few effective preventive and therapeutic options available. S100A8/A9 is an inflammatory mediator that contributes to the development of several cardiovascular diseases via the TLR4/NF-κB pathway. This study aimed to investigate the role of S100A8/A9 in HFpEF and unravel potential mechanisms. METHODS:The HFpEF model was established in C57BL6/J mice by administering a high-fat diet combined with NG-nitroarginine methyl ester hydrochloride (L-NAME). From the eighth week, mice were treated with the S100A8/A9 inhibitor paquinimod (ABR 215757) via oral gavage for 8 weeks. Body weight, tibial length, glucose tolerance, and blood lipids were measured following the intervention. Subsequently, cardiac function, myocardial hypertrophy, cardiac fibrosis, inflammatory markers, and oxidative stress were evaluated. AAV9-TLR4-shRNA and the TLR4 inhibitor TAK-242 were employed in vivo and in vitro, respectively, to explore the probable underlying mechanism. RESULTS:The results showed that paquinimod improved diastolic dysfunction in HFpEF mice without affecting systolic function. It also mitigated cardiomyocyte hypertrophy, suppressed cardiac fibrosis, decreased myocardial inflammation, and attenuated oxidative stress. Mechanistically, paquinimod inhibited the TLR4/NF-κB signalling pathway, and TLR4 inhibition reversed the effects of S100A8/A9 in vitro and partly alleviated the heart failure phenotype in mice with HFpEF. CONCLUSIONS:Inhibition of S100A8/A9-mediated inflammation improved diastolic function and reversed the pathological changes of the heart in mice with HFpEF. The observed effects were potentially mediated via inhibition of the TLR4/NF-κB pathway. The present study identified S100A8/A9 as a possible therapeutic target in HFpEF.

BackgroundMultidrug-resistant Pseudomonas aeruginosa (MDR-PA) infections present a critical healthcare challenge, often progressing to sepsis with high mortality. Current prediction tools lack specificity for drug-resistant organisms, hindering the early identification of high-risk patients. This study aimed to develop and validate an interpretable machine learning (ML) model to predict sepsis development in patients with MDR-PA infections.MethodsWe conducted a multicenter retrospective study analyzing 2,001 patients with laboratory-confirmed MDR-PA infections from two major medical centers between January 2019 and May 2025. The derivation cohort included 1,182 patients, while 819 patients from an independent center served as the external validation cohort. Feature selection was performed using a hybrid approach combining LASSO regression and support vector machine-recursive feature elimination (SVM-RFE). Seven ML algorithms were evaluated, with model interpretability enhanced via SHapley Additive exPlanations (SHAP). A web-based calculator was subsequently developed to facilitate clinical implementation.ResultsThe sepsis incidence was approximately 7% across cohorts. Feature selection identified six key predictors: calcium level, chronic obstructive pulmonary disease (COPD), red blood cell distribution width-standard deviation (RDW-SD), intra-abdominal infection, invasive catheters, and prior antibiotic exposure. The Random Forest model demonstrated superior performance, achieving an AUC of 1.000 in the SMOTE-balanced training set, 0.837 in internal validation, and 0.816 in external validation. SHAP analysis highlighted COPD and calcium levels as the most significant contributors to sepsis risk.ConclusionsThis study presents the first interpretable ML model specifically tailored for predicting sepsis onset in patients with MDR-PA infections. By addressing the limitations of general sepsis scores, our validated model and accompanying web-based tool provide clinicians with a precise, visualizable decision-support system to optimize early intervention strategies.

B cell-targeted therapies represent a transformative frontier for systemic lupus erythematosus (SLE) intervention, yet clinical recommendation of specific treatment is hampered by the challenge to perform head-to-head comparisons of efficacy-toxicity trade-offs and tissue-specific impacts in patients. To address this gap, an advanced Toll-like receptor 7 (TLR7) agonist-induced SLE model is developed in a NCG-M (NOD-Prkdcem26Cd52Il2rgem26Cd22Rosa26em1Cin(hCSF2&IL3&KITLG)/Gpt) human immune system (HIS) mice. This model enables parallel evaluation of multiple B cell-directed therapies within a controlled cohort study. It recapitulates core SLE pathologies, including immune effector cell augmentation, autoantibody production and glomerulonephritis, within 2 months, demonstrating greater severity and clinical fidelity than conventional pristane-induced systems. Validated through replication of clinical responses to rituximab and belimumab, the platform directly compares two emerging modalities: universal chimeric antigen receptor-T cells (UCAR-T) elicits delayed but profound B-cell reset correlating with superior reduction in renal pathology and autoantibodies, while T cell engagers (TCEs) mediate rapid yet partial B-cell depletion and transient efficacy. UCAR-T concurrently induces greater acute inflammatory responses, aligning with its distinct toxicity profile. By resolving therapy-specific effects on human immune dynamics and symptom control, this study empowers context-specific therapeutic selection for SLE, advancing tailored management strategies. ### Competing Interest Statement Y.L. is currently consulting for GemPharmatech Co. The rest of authors declare no conflict of interest. Key Program of the National Natural Science Foundation of China, NO.82330055 National Key Research and Development Program of China, NO.2020YFA0710800 Joint Fund of the National Natural Science Foundation of China, U24A20380 Key Projects of the Jiangsu Basic Research Program, BK20243061 Scientific and Technological Innovation 2030, NO. 2023ZD0500404 National Natural Science Foundation of China, NO.32122035, 32471000, 82001717 Science and Technology Innovation Key R&D Program of Chongqing, CSTB2024TIAD-STX0001

Given the high prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD) and its severe adverse hepatic outcomes, risk stratification for patients with MASLD is crucial. Notably, cardiovascular disease (CVD) represents the leading cause of mortality in this population. High-sensitivity troponin (hs-troponin) is a well-established biomarker of subclinical myocardial injury and predicts cardiovascular outcomes. However, its specific relationship with advanced liver fibrosis (ALF) in MASLD remains unexplored. Therefore, we investigated this association in individuals with MASLD without CVD. We analyzed data from 4684 adults aged ≥18 years in the 1999 to 2004 National Health and Nutrition Examination Survey. Multivariable logistic regression examined associations between hs-troponin and ALF. Diagnostic utility was assessed using receiver operating characteristic curve analysis. Moreover, we conducted a mediation analysis to explore the role of inflammation, as represented by the monocyte-to-lymphocyte ratio, in the aforementioned association. Among 4684 patients without CVD, 169 had ALF. After controlling for confounding factors, hs-troponin T showed a significant positive association with ALF (adjusted odds ratio: 1.02, 95% confidence interval: 1.01-1.03, P = .020). Additionally, receiver operating characteristic analysis indicated that at a cutoff of 7.28 ng/L, hs-troponin T predicted ALF with an area under the curve of 0.85 (95% confidence interval: 0.82-0.88). Further mediation analysis revealed that inflammation partially mediated the association between hs-troponin T and ALF. Our findings indicate that elevated hs-troponin T is independently associated with a higher risk of ALF, which can assist clinicians in risk stratification for this population. These findings suggest that hs-troponin T, a marker of subclinical cardiovascular stress, may also serve as a practical tool for hepatic risk stratification.

The immune microenvironment contributes substantially to the biological and clinical heterogeneity of multiple myeloma (MM), yet immune-related molecular biomarkers with reproducible prognostic value remain limited. Here, we developed a 12-gene immune-related gene signature (IRGS) using an integrative machine-learning framework and evaluated its prognostic performance across multiple MM cohorts. The IRGS consistently stratified overall survival and remained independently associated with outcome after adjustment for established clinical covariates. Its prognostic discrimination was comparable to that of IFM15 and generally exceeded that of MRCIX6 and a mitophagy-related signature across the evaluated validation datasets. Single-cell RNA sequencing further revealed marked cell type-dependent variation in the activity of the 12-gene module, with comparatively low activity in plasma cells and higher activity in several non-plasma compartments, indicating that the bulk-derived IRGS reflects a multicellular bone marrow transcriptional context rather than an exclusively malignant plasma cell intrinsic program. Somatic mutation analysis identified distinct mutational patterns between IRGS-defined groups, including relative enrichment of DIS3 mutations in the low-IRGS group and MUC16 mutations in the high-IRGS group, together with a modestly higher tumor mutational burden in low-IRGS patients. Transcriptome-based drug-response prediction further suggested differential therapeutic vulnerabilities, with high- and low-IRGS groups showing distinct predicted sensitivity patterns across apoptosis-, DNA damage-, BET-, checkpoint-, and replication-stress-related agents. Collectively, these findings define the IRGS as a complementary immune-associated molecular biomarker for prognostic stratification in MM and provide a framework linking prognosis with multicellular transcriptional context, somatic mutational characteristics, and candidate therapeutic vulnerabilities.

Obstructive sleep apnea (OSA), characterized by chronic intermittent hypoxia (CIH) is associated with atrial fibrillation. We explored the effects of OSA on atrial remodeling, fibrosis and inflammation, using both clinical and experimental studies. A total of 105 patients were grouped into control (n = 52), mild to moderate OSA (m-OSA, n = 27), and severe OSA (s-OSA, n = 26) groups. The participants underwent echocardiography and blood tests for interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), collagen type 1 (col-1), and collagen type 3 (col-3). In order to eliminate the interference of obesity, an experimental study was performed using lean mice exposed to CIH. In clinical patients, severe OSA featured higher body mass index (BMI) than control (28.12 ± 3.43 versus 24.40 ± 3.56 kg/m2, p < 0.001). Left atrial diameter was significantly higher in s-OSA group than in control group (38.35 ± 4.89 versus 35.39 ± 5.29 mm, P = 0.012). Patients with OSA had higher TNF-α levels than those in the control (P = 0.007). Consistently, plasma IL-1β levels were significantly elevated in the s-OSA and m-OSA groups compared with those in the control group. However, we did not observe significant changes in plasma col-1 and col-3 in patients with OSA. Subgroup analysis of patients with a BMI less than 25 kg/m2 showed that the inflammatory cytokine TNFα is correlated with OSA, rather than with BMI. In animal experiments, using lean mice exposed to IH for 3 weeks, WGA and Masson's staining showed that IH enlarged the atrial myocardium and promoted atrial fibrosis. Atrial mRNA expression of TGFβ、col-1, col-3, IL-6, and TNF-α was significantly elevated after IH exposure. OSA, characterized by CIH, was associated with the increased atrial structural remodeling, fibrosis and inflammation.

Current studies have demonstrated an association between osteoporosis (OP) and lipidome traits, yet the causal relationship between OP and lipidome remains controversial. Therefore, our aim is to explore the relationship between the lipidome and OP by calculation of genetic susceptibility. This study utilized a two-sample Mendelian randomization (MR) approach which the instrument variables were derived from a large genome-wide database: lipidome (7174 Finnish individuals), osteoporosis (56,637 samples), fractures (426,795 samples), heel bone density (426,824 samples), whole-body bone density (56,284 samples), femoral neck bone density (32,735 samples), lumbar spine bone density (28,498 samples), and forearm bone density (8143 samples). The primary results were based on inverse variance weighting (IVW) with random effects. Based on the IVW results, we identified 138 significant associations between lipidome traits and osteoporosis and its traits (P < 0.05). Among these, 8, 24, 27, 37, 17, 8, and 17 lipid species were significantly associated with femoral neck bone density, forearm bone density, fractures, heel bone density, lumbar spine bone density, osteoporosis, and whole-body bone density, respectively. Triglycerides were found to be significant protective factors for osteoporosis, forearm BMD, and lumbar BMD, whereas phosphatidylcholine was considered a prominent risk factor for osteoporosis, fractures, heel BMD, and lumbar BMD. The results of the heterogeneity test indicated that our IVW analysis was largely free of heterogeneity (P > 0.05). However, the pleiotropy test revealed significant pleiotropy between heel bone density and the measurement of triacylglycerol (56:6) (P < 0.05). By applying MR methods, this study overcomes the biases inherent in traditional observational studies and provides novel mechanistic insights into lipid metabolism in op.

Background:Ischemic heart disease (IHD) among young adults represents an emerging global health concern, yet comprehensive epidemiological assessments remain limited. This study aimed to quantify the global burden of early-onset IHD and project future trends through 2046. Methods:We analyzed data from the Global Burden of Disease (GBD) 2021 study, examining IHD burden among adults aged 15-44 years across 204 countries and territories from 1990 to 2021. Age-standardized rates were calculated using WHO standard population weights. Temporal trends were assessed using estimated annual percentage change (EAPC) methods. Age-Period-Cohort models were employed to project burden through 2046. DALYs were disaggregated into Years Lived with Disability (YLDs) and Years of Life Lost (YLLs). Results:Globally, age-standardized incidence rates increased modestly from 36.54 (95% UI: 21.69-54.31) per 100,000 in 1990 to 39.18 (95% UI: 23.32-58.20) per 100,000 in 2021, representing a 7.2% increase. Despite modest rate changes, absolute incident cases increased substantially from 1.26 millions to 2.17 millions. Age-standardized mortality rates declined significantly by 15.2%, from 12.21 (95% UI: 11.57-12.88) to 10.35 (95% UI: 9.68-11.04) per 100,000. Disaggregated DALYs analysis revealed divergent trends: YLDs rates increased (EAPC: 0.39%) while YLLs rates declined (EAPC: -0.66%), reflecting improved acute survival but growing chronic disease burden. Men consistently demonstrated higher burden across all measures, with male-to-female ratios ranging from 1.7:1 for incidence to 2.7:1 for mortality in the 40-44 years age group. Substantial regional heterogeneity was observed, with East Asia showing the steepest incidence increases (EAPC: 0.63%) while High-income North America demonstrated declining trends (EAPC: -2.4%). Central Europe achieved the most substantial improvements in both mortality decline (EAPC: -2.16%) and overall disease burden reduction (EAPC: -4.46%). Projections indicate continued increases in incidence and prevalence through 2046, with incident cases reaching 2.58 millions and prevalent cases reaching 12.7 millions globally. Conclusions:Early-onset IHD represents a growing global health challenge characterized by increasing incidence and prevalence but improving survival outcomes. The substantial sex and regional disparities, coupled with projected increases in absolute burden, underscore the urgent need for balanced strategies addressing both acute care improvements and long-term disability prevention.

OBJECTIVE:The relationship among body mass index (BMI), Charlson comorbidity index (CCI), and depression forms a complex interplay that affects both physical and mental health. However, whether CCI mediates the association between BMI and depression remains unclear. In this study, we aimed to elucidate the mediating role of CCI in the relationship between BMI and depression. METHODS:This study used data from the National Health and Nutrition Examination Survey, a program of the National Center for Health Statistics in the United States, including 23,639 participants from 2007 to 2020. Wilcoxon rank-sum and Rao-Scott adjusted chi-square tests were employed to compare characteristics between adults with and without depression. Weighted logistic regression and restricted cubic spline models were applied to investigate the pairwise associations among BMI, CCI, and depression. Mediation analysis was performed to assess whether CCI mediated the relationship between BMI and depression. RESULTS:Of the 23,639 participants, 2128 (9.0 %) had depression. Significant associations were observed between BMI and CCI; CCI and depression; and BMI and depression (P < 0.001). A U-shaped relationship between BMI and depression odds was identified, with the lowest odds at a BMI of 23 kg/m2. Mediation analysis revealed that CCI partially mediated the BMI-depression relationship, accounting for 19.5 % of the total effect. CONCLUSIONS:The results suggest that CCI plays a mediating role in the association between BMI and depression, and that improved chronic disease management may be associated with lower odds of depression in high BMI populations.

Genome-wide association studies have identified thousands of genetic variants associated with non-small cell lung cancer (NSCLC), however, it is still challenging to determine the causal variants and to improve disease risk prediction. Here, we applied massively parallel reporter assays to perform NSCLC variant-to-function mapping at scale. A total of 1249 candidate variants were evaluated, and 30 potential causal variants within 12 loci were identified. Accordingly, we proposed three genetic architectures underlying NSCLC susceptibility: multiple causal variants in a single haplotype block (e.g. 4q22.1), multiple causal variants in multiple haplotype blocks (e.g. 5p15.33), and a single causal variant (e.g. 20q11.23). We developed a modified polygenic risk score using the potential causal variants from Chinese populations, improving the performance of risk prediction in 450,821 Europeans from the UK Biobank. Our findings not only augment the understanding of the genetic architecture underlying NSCLC susceptibility but also provide strategy to advance NSCLC risk stratification. Determining the causal variants at GWAS loci is crucial for understanding genetic disease mechanisms. Here, the authors apply MPRA to perform non-small cell lung cancer (NSCLC) variant-to-function mapping at scale and propose distinct genetic architectures underlying NSCLC susceptibility.

BACKGROUND AND OBJECTIVE:Accurate prediction of perioperative major adverse cardiovascular events (MACEs) is crucial, as it not only aids clinicians in comprehensively assessing patients' surgical risks and tailoring personalized surgical and perioperative management plans, but also for information-based shared decision-making with patients and efficient allocation of medical resources. This study developed and validated a machine learning (ML) model using accessible preoperative clinical data to predict perioperative MACEs in stable coronary artery disease (SCAD) patients undergoing noncardiac surgery (NCS). METHODS:We collected data from 9171 adult SCAD patients who underwent NCS and extracted 64 preoperative variables. First, the optimal data imputation, resampling, and feature selection methods were compared and selected to deal with missing data values and imbalances. Then, nine independent machine learning models (logistic regression (LR), support vector machine, Gaussian Naive Bayes (GNB), random forest, gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), light gradient boosting machine, categorical boosting (CatBoost), and deep neural network) and a stacking ensemble model were constructed and compared with the validated Revised Cardiac Risk Index's (RCRI) model for predictive performance, which was evaluated using the area under the receiver operating characteristic curve (AUROC), the area under the precision-recall curve (AUPRC), calibration curve, and decision curve analysis (DCA). To reduce overfitting and enhance robustness, we performed hyperparameter tuning and 5-fold cross-validation. Finally, the Shapley additive interpretation (SHAP) method and a partial dependence plot (PDP) were used to determine the optimal ML model. RESULTS:Of the 9,171 patients, 514 (5.6 %) developed MACEs. 24 significant preoperative features were selected for model development and evaluation. All ML models performed well, with AUROC above 0.88 and AUPRC above 0.39, outperforming the AUROC (0.716) and AUPRC (0.185) of RCRI (P < 0.001). The best independent model was XGBoost (AUROC = 0.898, AUPRC = 0.479). The calibration curve accurately predicted the risk of MACEs (Brier score = 0.040), and the DCA results showed that XGBoost had a high net benefit for predicting MACEs. The top-ranked stacking ensemble model, consisting of CatBoost, GBDT, GNB, and LR, proved to be the best (AUROC 0.894, AUPRC 0.485). We identified the top 20 most important features using the mean absolute SHAP values and depicted their effects on model predictions using PDP. CONCLUSIONS:This study combined missing-value imputation, feature screening, unbalanced data processing, and advanced machine learning methods to successfully develop and verify the first ML-based perioperative MACEs prediction model for patients with SCAD, which is more accurate than RCRI and enables effective identification of high-risk patients and implementation of targeted interventions to reduce the incidence of MACEs.

New studies have revealed an association between chronic heart failure, the severity of septic shock, and the blood urea nitrogen to albumin ratio (BAR). Nevertheless, its role in congestive heart failure patients admitted to the intensive care unit remains unclear. This study aimed to investigate the association between BAR and mortality among these patients. The present study analyzed data from the MIMIC-IV (version 2.2) database, targeting patients with congestive heart failure. The study outcome was all-cause mortality within the first year after discharge. Patients were categorized into three groups-T1, T2, and T3-based on tertiles of BAR levels. To explore the relationship between BAR and mortality, Kaplan-Meier survival curves and multivariate Cox proportional hazards models, adjusted for potential confounders, were employed. Additionally, a dose-response relationship between BAR and mortality risk was evaluated using a restricted cubic spline model and threshold effect analysis. Subgroup analyses were conducted across diverse populations to assess the prognostic value of BAR. Furthermore, the predictive capabilities of blood urea nitrogen, albumin, blood urea nitrogen combined with albumin, and BAR were assessed through receiver operating characteristic analysis. The cohort comprised 4506 patients diagnosed with congestive heart failure. Kaplan-Meier curves revealed that all-cause mortality was significantly elevated in patients within the higher BAR tertiles (p < 0.001). Multivariate Cox regression analysis indicated that patients in the T2 (hazard ratio (HR): 1.20, 95% confidence interval (CI): 1.06 ~ 1.36) and T3 groups (HR: 1.37, 95% CI: 1.18 ~ 1.57) had a significant increase in mortality risk relative to the T1 group (p for linear trend < 0.001). Most subgroups showed this association, with the exception of variations of levels in creatinine, blood urea nitrogen, alkaline phosphatase, and alanine aminotransferase. Notably, the BAR demonstrated superior predictive accuracy for mortality compared to blood urea nitrogen or serum albumin alone, while exhibiting comparable performance to their combined measure. Among ICU patients with congestive heart failure, an elevated BAR was associated with an increased risk of all-cause 1-year mortality, particularly in those with less impaired liver and kidney function. Therefore, BAR may be measured to comprehensively evaluate the patients' prognosis.

This study aimed to evaluate the effectiveness of hydroxychloroquine (HCQ) in reducing disease activity and explore its optimal dosing and combination therapies in patients with primary Sjögren’s syndrome (pSS). We conducted a retrospective study of 1089 hospitalized pSS patients. Multivariable logistic regression was used to estimate adjusted odds ratios (ORs) with 95

BackgroundMultidrug-resistant Klebsiella pneumoniae (MDR-KP) infections pose a significant global healthcare challenge, particularly due to the high mortality risk associated with septic shock. This study aimed to develop and validate a machine learning-based model to predict the risk of MDR-KP-associated septic shock, enabling early risk stratification and targeted interventions.MethodsA retrospective analysis was conducted on 1,385 patients with MDR-KP infections admitted between January 2019 and June 2024. The cohort was randomly divided into a training set (n = 969) and a validation set (n = 416). Feature selection was performed using LASSO regression and the Boruta algorithm. Seven machine learning algorithms were evaluated, with logistic regression chosen for its optimal balance between performance and robustness against overfitting.ResultsThe overall incidence of MDR-KP-associated septic shock was 16.32% (226/1,385). The predictive model identified seven key risk factors: procalcitonin (PCT), sepsis, acute kidney injury, intra-abdominal infection, use of vasoactive medications, ventilator weaning failure, and mechanical ventilation. The logistic regression model demonstrated excellent predictive performance, with an area under the receiver operating characteristic curve (AUC) of 0.906 in the training set and 0.865 in the validation set. Calibration was robust, with Hosmer-Lemeshow test results of P = 0.065 (training) and P = 0.069 (validation). Decision curve analysis indicated substantial clinical net benefit.ConclusionThis study presents a validated, high-performing predictive model for MDR-KP-associated septic shock, offering a valuable tool for early clinical decision-making. Prospective, multi-center studies are recommended to further evaluate its clinical applicability and effectiveness in diverse settings.

Purpose: Sepsis-associated liver injury (SALI) leads to increased mortality in sepsis patients, yet no specialized tools exist for early risk assessment. This study aimed to develop and validate a risk prediction model for early identification of SALI before patients meet full diagnostic criteria. Patients and Methods: This retrospective study analyzed 415 sepsis patients admitted to ICU from January 2019 to January 2022. Patients with pre-existing liver conditions were excluded. Using LASSO regression and multivariate logistic analysis, we developed a predictive nomogram incorporating clinical variables. Model performance was evaluated through internal validation using bootstrapping method. Results: Among the cohort, 97 patients (23.4%) developed SALI. The final model identified five key predictors: total bilirubin, ALT, gamma-GGT, mechanical ventilation, and kidney failure. The model demonstrated good discrimination (AUC=0.841, 95% CI: 0.795-0.887) and calibration. Decision curve analysis showed clinical utility across a threshold probability range of 4-87%. The model outperformed traditional scoring systems (SOFA and SAPS II) in predicting SALI risk. Conclusion: This novel nomogram effectively predicts SALI risk in sepsis patients by integrating readily available clinical parameters. While external validation is needed, the model shows promise as a practical tool for early risk stratification, potentially enabling timely interventions in high-risk patients.

Objective:This study aims to analyze global trends in smoking-attributable peptic ulcer disease (PUD) disability-adjusted life years (DALYs) from 1990 to 2021 and project future trends to 2046. Methods:Data were obtained from the Global Burden of Disease Study 2021. We calculated age-standardized DALYs rates (ASDR) and estimated annual percentage changes (EAPC) for smoking-attributable PUD DALYs. Bayesian Age-Period-Cohort models were used to project future trends. Results:From 1990 to 2021, global smoking-attributable PUD DALYs decreased significantly, with the age-standardized rate declining from 35.4 to 9.4 per 100,000 (EAPC: -4.45%). High-income regions showed faster declines, while some low- and middle-income countries experienced slower progress or even increases. Gender disparities were observed, with males consistently showing higher ASDR. Projections suggest a continued global decline in smoking-attributable PUD DALYs to 2046, with persistent regional disparities. By 2046, the global ASDR is expected to decrease to approximately 3.2 per 100,000, with higher rates persisting in certain regions such as Kiribati (44.6 per 100,000) and Cambodia (45.1 per 100,000). Conclusion:While global smoking-attributable PUD DALYs have significantly decreased and are projected to continue declining, substantial regional and gender disparities persist. These findings underscore the need for targeted tobacco control interventions, particularly in high-risk regions and among vulnerable populations, to further reduce the global burden of smoking-attributable PUD.

Background: Heart failure with improved ejection fraction (HFimpEF) is a distinct subtype of heart failure. Growth differentiation factor-15 (GDF-15), a biomarker linked to adverse cardiovascular outcomes, could help predict left ventricular ejection fraction (LVEF) improvement in patients with heart failure with reduced ejection fraction (HFrEF). Methods: A prospective cohort study was conducted with 162 HFrEF patients admitted to the First Affiliated Hospital of Nanjing Medical University between October 2017 and December 2021, with follow-up until December 2022. Patients were divided into three groups based on serum GDF-15 tertiles: high (≥2474 ng/L), medium (1439–2474 ng/L), and low (<1439 ng/L). Logistic and Cox regression analyses assessed predictors of HFimpEF at one year, heart failure readmission, and cardiovascular mortality. Results: The median age of patients was 57 years, with 39 females. The median GDF-15 level was 1915 ng/L. High GDF-15 levels were associated with a 73% reduced likelihood of HFimpEF (OR=0.27, P=0.003), and medium levels with a 60% reduction (OR=0.40, P=0.037). Over a median follow-up of 41 months, 72 heart failure readmissions and 40 cardiovascular deaths occurred. High GDF-15 levels were independently associated with increased risk of readmission (HR=2.70, P=0.004) and mortality (HR=3.85, P=0.017). HFimpEF was linked to significantly lower risks of readmission (HR=0.33, P<0.001) and mortality (HR=0.25, P<0.001). Conclusion: Serum GDF-15 levels could positively predict LVEF improvement in HFrEF patients and are associated with higher risks of heart failure readmission and cardiovascular mortality. Achieving HFimpEF provides significant protection against these outcomes.

BACKGROUND:Vitamin D is a fat-soluble secosteroid that plays essential roles in calcium homeostasis, bone metabolism, and numerous other physiological processes. Vitamin D deficiency has been associated with increased risk of various diseases and mortality. However, population-based studies examining the relationship between vitamin D and mortality across different age groups remain limited. METHODS:To investigate the correlation between 25-hydroxyvitamin D [25(OH)D] levels, vitamin D status, and mortality in a cohort of 47,478 individuals aged 18-85 years. RESULTS:Higher 25(OH)D levels were associated with lower mortality risk. Compared to the vitamin D deficiency group, the Hazard ratios (HR) for all-cause mortality were 0.71 (95%CI: 0.66-0.76) in the insufficiency group and 0.64 (95%CI: 0.58-0.70) in the sufficiency group. The association varied by age: strongest in adults aged 40-59 years (HR: 0.74, 95% CI: 0.65-0.85), significant in those ≥60 years (HR: 0.86, 95% CI: 0.82-0.90), but non-significant in those aged 18-39 years. The RCS analysis revealed a non-linear relationship between 25(OH)D and mortality, with significant risk reduction observed between 59.25-261.45 nmol/L for the overall population. The optimal 25(OH)D levels (lowest HR) varied by subgroups: 96.81 nmol/L for the overall population, 102.9 nmol/L for females, 67 nmol/L for ages 40-59, and 104.23 nmol/L for ages ≥60 years, while no significant association was found in ages 18-39 years. CONCLUSION:Our findings suggest that Vitamin D are associated with mortality among the whole population. Individuals aged 40-59 may derive potential benefits from vitamin D supplementation.