Background/Objectives: Acute heart failure (AHF) is a common critical condition in emergency departments, and traditional diagnostic methods have limitations, including high subjectivity and limited accuracy. This study aimed to develop an integrated machine learning model based on lung ultrasound (LUS) radiomics and clinical data for diagnosing AHF in patients presenting with acute dyspnea. Methods: A total of 301 patients were included and randomly split into training (n = 210) and testing (n = 91) sets. Using PyRadiomics 3.0, 107 radiomics features were extracted from standardized 6-zone LUS images, combined with 52 clinical features. Three random forest models were developed: clinical-only, radiomics-only, and integrated models. Results: The integrated model achieved optimal performance on the testing set with an AUC of 0.976 (95% CI: 0.950-0.994), accuracy of 90.1%, sensitivity of 91.1%, and specificity of 89.1%, significantly outperforming the radiomics model (AUC 0.940, p = 0.046) and clinical model (AUC 0.931, p = 0.111). Feature importance analysis revealed that radiomics features contributed 75.6% of the model's predictive power, with gray level run length matrix (GLRLM) features dominating the top-ranked features. Conclusions: As a proof-of-concept study, this research demonstrates the potential value of multimodal data fusion strategies for AHF diagnosis in the emergency department; however, external validation and prospective studies are required to further confirm its clinical applicability.

BACKGROUND:Sepsis-associated liver injury (SALI) is characterized by dysregulated inflammation and NF-κB pathway activation, The E3 ubiquitin ligase KPC1 is known to process NF-κB1 p105 into p50, yet its specific role in SALI remains unclear. METHODS:Using a murine cecal ligation/puncture (CLP) model and LPS-stimulated human Kupffer cells, we knocked down KPC1 with lentiviral shRNA. Liver injury (ALT/AST), inflammatory cytokines (IL-1β/IL-6/IL-18/TNF-α), LDH release, pyroptosis markers (NLRP3, cleaved caspase-1, GSDMD-N), and survival were assessed. KPC1-p105 interactions, p65 nuclear translocation, promoter occupancy, and ubiquitination were analyzed. Dual knockdown of KPC1 and p105 was performed to confirm pathway specificity. RESULTS:KPC1 was significantly upregulated in both in vivo and cellular SALI models. KPC1 knockdown improved survival, attenuated liver injury, suppressed inflammatory cytokine and LDH release, and reduced pyroptosis. Mechanistically, KPC1 directly interacted with p105, promoted its ubiquitination, and enhanced its processing into the transcriptionally active p50 subunit. The anti‑inflammatory effect of KPC1 silencing was reversed upon concurrent p105 knockdown, confirming the functional dependence on the KPC1-p105 axis. CONCLUSION:KPC1 exacerbates SALI by driving p105/NF-κB‑dependent inflammation and pyroptosis in Kupffer cells, highlighting its potential as a therapeutic target for SALI.

Objective To evaluate the early expression of programmed cell death receptor ligand-1 (PD-L1) on different subtype of monocyte and investigate their relation in term the severity of sepsis in the older adult population. Methods Eighty sepsis patients in older adult and 40 age and sex matched healthy controls were included in this study. The flow cytometry method was used to measure the monocyte subsets and PD-L1 expression on different subsets of monocyte. Results PD-L1 expression on M1 monocyte was lower in septic shock group compared to patients without shock [1.19% (0.69%, 2.42%) vs. 1.96% (0.65%, 4.10%), p = 0.012]. PD-L1 expression on M1 monocyte (OR = 0.542, CI: 0.324, 0.907, p = 0.020) still exhibited significant effect in predicating septic shock in logistic regression analysis. The aera under the curve (AUC) from the receiver operating characteristic curve displayed that the of PD-L1 expression on M1 monocyte for predicting septic shock was 0.689 (p = 0.006). The AUC for the combined analysis of PD-L1 expression on M1 monocytes and the sequential organ failure assessment score was 0.826 (p < 0.001). Conclusion The PD-L1 expression on M1 monocyte could be a new predictor for the severity of sepsis and provided a new therapeutic breakthrough for sepsis in older adult population.

OBJECTIVE:Sepsis is a life-threatening critical illness characterized by dysregulated host responses, in which excessive neutrophil extracellular trap (NET) formation and sepsis-associated coagulopathy play central roles in organ dysfunction and mortality. Targeting a single pathological pathway has shown limited clinical benefit. Cl-amidine, a peptidylarginine deiminase 4 inhibitor, suppresses NET formation, whereas heparin is widely used in critically ill patients to modulate coagulation and neutralize histone-mediated cytotoxicity. Whether simultaneous targeting of NET-driven inflammation and coagulation dysregulation provides enhanced protection in sepsis remains unclear. METHODS:Sepsis was induced in C57BL/6J mice using the cecal ligation and puncture model. Mice received postinsult treatment with Cl-amidine (50 mg/kg, intraperitoneally) and/or heparin (1,000 U/kg, intravenously). Seven-day survival was assessed. Circulating NET-related markers (citrullinated histone H3 and myeloperoxidase), inflammatory cytokines, coagulation parameters, and histopathological injury of the lung, liver, and kidney were evaluated. RESULTS:Combined treatment with Cl-amidine and heparin significantly improved 7-day survival compared with untreated septic mice, whereas Cl-amidine monotherapy did not confer a survival benefit despite significantly reducing NET markers. Cl-amidine significantly reduced NET formation, whereas heparin alone did not show a statistically significant effect on NET markers. Combined treatment further enhanced the reduction of myeloperoxidase levels. Histopathological analysis demonstrated that pulmonary, hepatic, and renal injury were most markedly attenuated in the combination group compared with either monotherapy. Although inflammatory and coagulation parameters were generally improved following treatment, the effects of the combined regimen were comparable to those of the more effective monotherapy for several markers, and no statistically confirmed pharmacological synergy was observed. CONCLUSIONS:Combined administration of Cl-amidine and heparin provides a meaningful survival benefit and attenuates multiorgan injury in experimental sepsis. Although no statistically confirmed pharmacological synergy was observed across all parameters, the combined intervention exerted additive protective effects beyond single-agent treatment. Importantly, the additional histological protection could not be explained by further suppression of systemic cytokines or by changes in global coagulation parameters, and the underlying mechanisms therefore remain incompletely defined. Further preclinical studies addressing bleeding risk, optimal dosing strategies, and mechanistic pathways are warranted before clinical translation.

Background Concern has been growing regarding tuberculosis (TB) morbidities, including neurologic and vascular comorbidities. The impact of dementia on the occurrence and progression of pulmonary tuberculosis (PTB) remains unclear. This study aimed to describe the risk factors associated with in-hospital mortality in patients with PTB and comorbid dementia. Methods This retrospective study consecutively enrolled 152 patients with PTB complicated by dementia admitted to Beijing Chest Hospital, Capital Medical University, from January 2015 to December 2025. In addition, 110 PTB patients without dementia were randomly selected as the control group. General demographic characteristics, clinical symptoms, laboratory test results, and other relevant data were collected. Multivariate logistic regression analysis was performed to identify risk factors associated with dementia in PTB. PTB with dementia were further divided into severe group (n = 66) and mild group (n = 86) based on disease progression during hospitalization. Univariate and multivariate logistic regression analyses were used to identify risk factors for severe disease in PTB with dementia. According to the 60-day outcome after admission, the dementia group was divided into survival group (n = 133) and death group (n = 19). LASSO regression was used for variable selection, and the selected variables were then included in a multivariate Cox regression model to analyze factors associated with mortality. Results Among the 152 patients with tuberculosis complicated by dementia, 133 survived and 19 died, with a case fatality rate of 12.5%. Multivariate logistic regression analysis showed that age, albumin (ALB), D-dimer, and neutrophil-to-lymphocyte ratio (NLR) were independent risk factors for dementia in PTB, with odds ratios (ORs) and 95% confidence intervals (CIs) of 1.098 (1.045–1.153), 0.700 (0.584–0.840), 1.537 (1.119–2.112), and 1.438 (1.121–1.845), respectively. Blood urea nitrogen (BUN) and D-dimer were independent risk factors for progression to severe disease in PTB patients with dementia, with ORs and 95% CIs of 1.198 (1.006–1.425) and 1.274 (1.026–1.583), respectively. LASSO and multivariate Cox regression analyses showed that NLR (hazard ratio [HR] = 1.030, 95% CI: 1.007–1.053) and D-dimer (HR = 2.796, 95% CI: 1.180–6.622) were significantly associated with mortality, increasing the risk of death in these patients. Conclusion Advanced age, low ALB, elevated D-dimer, and elevated NLR were independent risk factors for dementia in PTB patients. BUN and D-dimer were independent risk factors for progression to severe disease in PTB patients with dementia. Both NLR and D-dimer significantly increased the risk of mortality in these patients.

Ubiquitin C-terminal hydrolase 1 (UCHL1), a critical deubiquitinating enzyme (DUB), cleaves ubiquitin/polyubiquitin chains from substrate proteins andhas been shown to promoteoxidative stress and inflammatory response. However, UCHL1's precise role in sepsis-induced liver injury (SILI) pathogenesis remains unknown. Here, we show elevated UCHL1 in cecal ligation puncture (CLP)-induced livers and lipopolysaccharide (LPS)-treated bone marrow-derived macrophages (BMDMs). Deficiency or pharmacological inhibition of UCHL1 with LDN-57444 (LDN) conferred protection against SILI by inhibiting oxidative stress and M1 macrophage polarization through targeting the degradation of glucose-6-phosphate isomerase (Gpi) and suppressing Gpi-mediated glycolysis. In contrast, the overexpression of Gpi reversed the inhibitory effects of UCHL1 deficiency on sepsis. Bone marrow transplantation combined within vitrostudies further showed that myeloid UCHL1 deficiency similarly improved SILI through suppression of Gpi stabilization and glycolysis. Hence, targeting UCHL1 in myeloid cells emerged as a protective strategy against SILI, highlighting its potential as a novel therapeutic avenue for patient care.

To develop a machine learning-based prediction model that integrating T cell subset data with clinical features to predict the 28-day mortality risk in sepsis patients. A retrospective cohort study was conducted using the MIMIC-IV database. Collected data included demographics, T cell subsets, laboratory results, SOFA score, GCS, and 28-day mortality. Feature selection was performed using LASSO regression combined with 10-fold cross-validation. We compared the performance of six machine learning models, namely RF, SVM, XGB, GLM, GBM, and LASSO logistic regression. Model performance was evaluated using the AUROC, calibration curves, and DCA, while the SHAP method was employed to interpret the optimal model. A total of 781 sepsis patients were included in our study, with a 28-day mortality rate of 18.5

​​ Background:​​ Sepsis-associated delirium is a frequent and serious complication in critically ill patients with sepsis. However, no prediction tool has been specifically developed to assess delirium risk during the first 7 days after sepsis onset—a period associated with the highest risk and clinical urgency. Objective: Our objective was to develop and validate an interpretable machine learning model to predict the risk of sepsis-associated delirium within the first week of diagnosis to facilitate early risk stratification and intervention. ​​ Methods:​​ We conducted a retrospective cohort study using the MIMIC-IV (v3.1) database for model development and the eICU-CRD (v2.0) for external validation. Adult ICU patients fulfilling Sepsis-3 criteria were included. The primary outcome was new-onset delirium within 7 days of sepsis diagnosis, identified through CAM-ICU assessments or International Classification of Diseases (ICD)-10 codes. Using clinical data from the first 24 hours of ICU admission, Multiple machine learning algorithms were trained after feature preselection via the Boruta algorithm. Model performance was assessed by discrimination, calibration, and clinical utility. The best-performing model was further simplified and externally validated. Shapley Additive exPlanations (SHAP) were applied for interpretability at both the global and individual levels. ​​ Results: ​​ A total of 30,843 patients were included, with 20,665 in MIMIC-IV for model development and 10,178 in eICU-CRD for validation. The initial model included 51 features. Among the tested algorithms, CatBoost achieved the best performance (AUROC = 0.785) with excellent calibration and the highest net clinical benefit. The model was simplified to 14 routinely available variables while maintaining robust performance (AUROC = 0.751). External validation yielded an AUROC of 0.706 and SHAP analysis revealed that the lowest Glasgow Coma Scale score within 24 hours was the most influential predictor. A publicly accessible web tool was developed for clinical application. ​​ Conclusion:​​ We developed and externally validated an interpretable machine learning model that reliably predicts new-onset sepsis-associated delirium during the first week after sepsis diagnosis. This tool may facilitate early risk stratification, targeted monitoring, and timely intervention to improve clinical outcomes.

OBJECTIVE: To investigate the relationship between frailty assessed by the HFRS and in-hospital mortality in ICU patients with sepsis. METHOD: A retrospective analysis of septic ICU patients from the MIMIC-IV database assessed frailty through the Hospital Frailty Risk Score (HFRS). Patients were categorized into non-frail (HFRS < 5, n = 4,882), pre-frail (5 ≤ HFRS < 15, n = 3,134), and frail (HFRS ≥ 15, n = 2,575) groups. The primary outcome was in-hospital mortality. Logistic regression combined with restricted cubic splines (RCS) was employed to evaluate the association between HFRS (categorical and continuous) and mortality. Inverse probability weighting (IPW) validated the results, and subgroup analyses explored frailty-mortality correlations in different patient groups. RESULTS: A total of 10,591 patients were included, with 4,737 (44.7%) males and median age of 68.9[57.6, 79.6] years. Altogether, 3,024 (28.6%) experienced mortality during hospitalization. Elevated frailty levels were associated with increased in-hospital mortality, consistent across both continuous and categorical HFRS analyses. A linear association between HFRS and mortality risk was indicated by results from RCS. After controlling for potential confounders, both pre-frail and frail statuses were significantly correlated with higher in-hospital mortality risk (pre-frailty, RR = 1.15, 95% CI: [1.06, 1.26], P = 0.002; frailty, RR = 1.29, 95% CI: [1.17, 1.42], P < 0.001). Furthermore, frailty was significantly positively correlated with longer hospital and ICU stays. These findings were confirmed by IPW. CONCLUSION: Elevated frailty assessed via HFRS was associated with an increased risk of in-hospital mortality and prolonged hospital and ICU stays in sepsis.

Dysregulated macrophage M1 polarization is a core pathological feature of inflammatory disorders. However, the crosstalk between metabolic reprogramming and cell-cycle regulator Ccnd2 during macrophage polarization remains largely unclear. This study explored the role of the PI3K-Akt-Ccnd2 axis in LPS-induced macrophage inflammation and verified its therapeutic potential in sepsis-associated acute kidney injury (SA-AKI). In vitro experiments were performed using RAW264.7 macrophages (Sham, LPS, PI3K inhibitor + LPS, M-CSF alone, M-CSF + LPS, Ccnd2 inhibitor, Ccnd2 inhibitor + Akt activator). In vivo studies were conducted using a cecal ligation and puncture (CLP)-induced SA-AKI mouse model with M-CSF intervention. Transcriptomic/metabolomic profiling, Western blotting, qRT-PCR, flow cytometry, and histopathological analysis were applied. Renal function, systemic inflammation, and signaling pathway activation were evaluated. LPS triggered transcriptional reprogramming enriched in PI3K-Akt, cell-cycle, and inflammatory pathways, and downregulated Ccnd2 expression; M-CSF restored Ccnd2 via PI3K-Akt signaling, which was abolished by PI3K inhibition. Metabolomic analysis identified marked alterations in purine, glycerophospholipid, and amino acid metabolism in LPS-stimulated macrophages. LPS enhanced M1 polarization, whereas M-CSF or PI3K inhibition suppressed this effect. In CLP mice, M-CSF significantly reduced serum Cre/BUN levels, alleviated systemic inflammation and renal histopathological damage, and activated the renal PI3K-Akt-Ccnd2 axis; these renoprotective effects were reversed by PI3K inhibition. LPS-induced metabolic reprogramming cooperates with PI3K-Akt signaling to regulate Ccnd2, thereby coupling macrophage cell-cycle progression and M1 polarization. The PI3K-Akt-Ccnd2 axis modulates macrophage inflammation in vitro and ameliorates CLP-induced SA-AKI in vivo, representing a promising therapeutic target for sepsis and related inflammatory organ injury. The PI3K-Akt-Ccnd2 axis may represent a candidate for further investigation for sepsis and other inflammatory disorders.

OBJECTIVE:We tested the a priori hypothesis that a ventilation rate of 20 breaths per minute (bpm) is noninferior to 10 and 5 bpm with respect to resuscitation outcomes and physiology following advanced airway placement in a porcine ventricular fibrillation (VF) cardiac arrest model. METHODS:Twenty-four pigs underwent 4 minutes of untreated VF and 4 minutes of chest compressions without ventilation. Animals were randomized to 5, 10, or 20 bpm (n = 8 per group) for 4 minutes of asynchronous cardiopulmonary resuscitation (CPR), followed by defibrillation. The primary endpoint was achieving return of spontaneous circulation (ROSC); secondary endpoints included 24-hour survival, neurological scores (cerebral performance category), hemodynamic parameters, and respiratory/acid-base physiological parameters. RESULTS:For the primary endpoint, ROSC rates were comparable across groups: 6/8 (75%) in the 5 bpm group, 7/8 (87.5%) in the 10 bpm group, and 6/8 (75%) in the 20 bpm group ( P = 0.837). All animals that achieved ROSC survived for 24 hours with favorable neurological function (CPC 1-2). Notably, this high survival rate reflects the controlled experimental model (short no-flow time and healthy subjects), rather than direct human clinical outcomes. During CPR, intrathoracic pressure and hemodynamics were comparable across groups (all P > 0.05). During the ventilation stage, the 20 bpm group showed significantly better CO 2 clearance, with lower PaCO 2 than the 5 bpm group (mean difference: -12.88 mmHg, 38.50 ± 8.26 vs. 51.38 ± 15.41 mmHg, P = 0.022) and higher arterial pH than the 5 bpm group (mean difference: 0.15, 7.29 ± 0.12 vs. 7.14 ± 0.12, P = 0.001). At 1 hour post-ROSC, the 20 bpm group exhibited significantly higher diastolic blood pressure (mean difference: 26.26 mmHg, 97.83 ± 15.09 vs. 71.57 ± 14.86 mmHg, P = 0.002) and coronary perfusion pressure (CPP, mean difference: 27.96 mmHg, 90.67 ± 13.76 vs. 62.71 ± 18.84 mmHg, P = 0.001), as well as higher arterial pH (mean difference: 0.10, 7.37 ± 0.04 vs. 7.27 ± 0.04, P = 0.029) and smaller base deficit (mean difference: 6.77 mEq/L, -2.83 ± 2.32 vs. -9.60 ± 2.20 mEq/L, P = 0.001) compared with the 10 bpm group. Although peak airway pressure ( Ppeak ) was higher in the 20 bpm group compared with the 5 bpm group (mean difference: 4.75 cmH 2 O, 32.63 ± 3.16 cmH 2 O vs. 27.88 ± 1.81 cmH 2 O, P = 0.010), mean airway pressure ( Pmean ) did not differ significantly among groups ( P = 0.473). CONCLUSION:Ventilation at 20 bpm did not compromise ROSC, hemodynamics, or increases intrathoracic pressure compared with lower rates in the porcine VF model. Furthermore, 20 bpm facilitated superior acid-base balance and postresuscitation stability, providing a physiological basis for further investigation of moderate increases in the ventilation rate in specific, controlled clinical settings to optimize metabolic clearance.

Sepsis, marked by hyperinflammation and subsequent immunosuppression, lacks effective phase-specific therapies. Although anisodamine hydrobromide (Ani HBr) reduced 28-day mortality in our prior trial, its mechanisms remained unclear. Here, we integrated network pharmacology, machine learning, immunological profiling, molecular simulations, and single-cell transcriptomics to elucidate Ani HBr's multi-target actions. Among 30 cross-species targets, ELANE and CCL5 emerged as core regulators via protein interaction networks, survival modeling (AUC: 0.72-0.95), and statistical significance (p < 0.05). Ani HBr inhibited ELANE-driven NET formation (HR = 1.176), associated with immunosuppression and endothelial damage, while enhancing CCL5-related cytotoxic T-cell recruitment (HR = 0.810). Docking and dynamics simulations showed Ani HBr binds ELANE's catalytic cleft, suggesting direct inhibition of its enzymatic activity, and interacts stably with CCL5 at potential receptor-binding interfaces, indicating a modulatory role. Single-cell analysis revealed ELANE upregulation in CCI-phase neutrophils and widespread yet stage-specific CCL5 expression. These findings support Ani HBr as a phase-tailored agent that targets ELANE in early hyperinflammation while preserving CCL5-mediated immunity. The ELANE/CCL5 prognostic model offers a framework for precision immunotherapy in sepsis.

Studies on the prevalence of sarcopenia and its relationship with other geriatric syndromes among older patients with sepsis in the emergency department (ED) are scarce. This study aimed to investigate the prevalence of sarcopenia among older patients with sepsis and explore its association with specific geriatric symptoms: frailty and malnutrition. This cross-sectional study was carried out in China from January to November 2022. Muscle mass, as indicated by the skeletal muscle index (SMI) was measured using abdominal plain computed tomography. Sarcopenia, frailty, and nutritional status were defined according to the Asian Working Group for Sarcopenia 2019 criteria, Clinical Frailty Scale (CFS) score, and Mini Nutritional Assessment-Short Form (MNA-SF) score. The correlations of the SMI with the CFS and MNA-SF scores were analyzed using nonparametric Spearman correlation analysis. Univariate and multivariable logistic regression analyses were performed to determine the associations of sarcopenia with frailty and malnutrition. A total of 602 patients (332 men; median age, 78 years; median body mass index, 23.1 kg/m2) were included. Sarcopenia, frailty, and malnutrition were present in 36.2 www.chictr.org.cn (registration number: ChiCTR2300070377, date: 11–04-2023).

BackgroundAcute pancreatitis (AP) represents a critical medical condition where timely and precise prediction of in-hospital mortality is crucial for guiding optimal clinical management. This study focuses on the development of advanced machine learning (ML) models to accurately predict in-hospital mortality among AP patients admitted to intensive care unit (ICU).MethodOur study utilized data from three distinct sources: the Medical Information Mart for Intensive Care III (MIMIC-III), MIMIC-IV databases, and Beijing Chaoyang Hospital. We systematically developed and evaluated 11 distinct machine learning (ML) models, employing a comprehensive set of evaluation metrics to assess model performance, including the area under the curve (AUC). To enhance interpretability and identify key predictive features, we implemented Shapley Additive Explanations (SHAP) analysis for the top-performing model. Furthermore, we developed a streamlined version of the model through strategic feature reduction, followed by rigorous hyperparameter optimization (HPO) to maximize predictive performance. To facilitate clinical implementation, we designed and deployed an intuitive web-based calculator, enabling convenient access and practical application of our optimized predictive model.ResultThe study analyzed 1802 AP patients, with 266 (14.8%) experiencing in-hospital mortality. A set of 27 features was utilized to construct various models, and among them, CatBoost demonstrated the highest performance in both the validation and test sets. To create a more concise model, we selected the top 13 features. After HPO, the AUC in the test set reached 0.835 (95% CI: 0.793-0.872), the AUC in the external validation from Beijing Chaoyang hospital was 0.782 (95% CI: 0.699-0.860).ConclusionML models have shown promising reliability in predicting in-hospital mortality among patients with AP in the ICU. Among these models, the CatBoost model exhibits superior predictive performance, providing valuable assistance to clinical practitioners in identifying high-risk patients and facilitating early interventions to enhance prognosis. The development of a compact model and a web-based calculator further enhances the convenience of using these models in clinical practice.

Objective: To investigate the relationship between frailty assessed by the HFRS and in-hospital mortality in ICU patients with sepsis. Method: A retrospective analysis of septic ICU patients from the MIMIC-IV database assessed frailty using the Hospital Frailty Risk Score (HFRS). Patients were categorized into non-frail (HFRS < 5, n = 3,744), pre-frail (5 ≤HFRS < 15, n = 2,539), and frail (HFRS ≥15, n = 2,147) groups. The primary outcome was in-hospital mortality. Logistic regression, with restricted cubic splines (RCS), analyzed the relationship between HFRS ,both as a categorical and continuous variable, and mortality. Inverse probability weighting (IPW) validated the results, and subgroup analyses explored frailty-mortality correlations in different patient groups. Results: A total of 8,430 patients were included, with 3,761 (44.6%) males and a mean age of 69.39 [58.37, 79.76] years. Among them, 2,704 (32.1%) died during hospitalization. The analysis showed that in-hospital mortality increased with higher frailty levels, regardless of whether HFRS was treated as a continuous or categorical variable. RCS revealed a nonlinear relationship between HFRS and mortality. After adjusting for confounders, both pre-frail and frail statuses were significantly associated with higher in-hospital mortality risk (OR [95% CI]: pre-frail vs. non-frail: 1.33 [1.15–1.53], p < 0.001; frail vs. non-frail: 1.38 [1.18–1.62], p < 0.001). These findings were confirmed by IPW. Subgroup analyses showed significant interactions between frailty and mortality in patients receiving vasopressors, continuous renal replacement therapy (CRRT), mechanical ventilation, and those with varying heart rates, respiratory rates, and creatinine levels. Conclusion: Elevated HFRS is an independent risk factor for in-hospital mortality in septic ICU patients.

Coagulation factor V (FV) is an essential cofactor in the coagulation cascade. However, the precise function of FV in lower grade glioma (LGG) is little known. We first performed a pan-cancer investigation of FV expression and prognosis using TCGA and GTEx databases. Single-cell RNA sequencing confirmed FV expression in LGG tissues. We then investigated the mRNA expression level, prognostic value, and DNA methylation of FV in LGG using bioinformatics tools. The relationship between FV expression and tumor immune invasion was investigated using TIMER. FV was highly expressed in a variety of tumors, including LGG, and was associated with tumor prognosis. By combining a series of in silico analysis (including expression and survival analysis), we found that the hsa-miR-665 was the most potent upstream miRNA of FV in LGG. Tumors with high FV expression had less infiltration of lymphocytes and myeloid cells, and FV level was negatively correlated with immune checkpoint expression. Our findings suggest that FV was a potential biomarker for evaluating the prognosis and therapeutics in LGG.

Uncontrolled inflammation can result in severe status and even death in influenza patients, and there is a lack of early clinical evaluation models. We recruited patients with influenza pneumonia and healthy controls from the emergency departments of three urban teaching hospitals in Beijing, China, during the winter of 2018–2019. Donated plasma samples were screened using protein and lectin microarrays to assess changes in the glycosylation patterns of immunoglobulins. These changes were used to develop and validate an Immunoglobulin Glycosylation Profile for Severe Status Identification Algorithm (IGPSSIA). A combined model of IGPSSIA score and clinical indicators was constructed to identify severe influenza pneumonia cases. We enrolled 114 patients, including 56 in the mild and 58 in the severe groups, and recruited 27 volunteers as healthy controls. We screened out the differentially expressed glycan moieties between the mild and the severe groups and included them in the LASSO regression analysis. In the training set (70

BackgroundAcute kidney injury (AKI) is a common complication in heart failure (HF) patients. Patients with heart failure who experience renal injury tend to have a poor prognosis. The objective of this study is to examine the correlation between the occurrence of AKI in heart failure patients and different mean arterial pressure (MAP) trajectories, with the goal of improving early identification and intervention for AKI.MethodsA retrospective study was conducted on patients with heart failure using data from the Medical Information Mart for Intensive Care IV (MIMIC-IV). We utilized the group-based trajectory modeling (GBTM) method to classify the 24-hour MAP change trajectories in heart failure patients. The occurrence of AKI within the first 7 days of intensive care unit (ICU) admission was considered the outcome. The impact of MAP trajectories on AKI occurrence in heart failure patients was analyzed using Cox proportional hazards models, competing risk models, and doubly robust estimation methods.ResultsA cohort of 8,502 HF patients was analyzed, with their 24-hour MAP trajectories categorized into five groups: Low MAP group (Class 1), Medium MAP group (Class 2), Low-medium MAP group (Class 3), High-to-low MAP group (Class 4), and High MAP group (Class 5). The results from the doubly robust analysis revealed that Class 4 exhibited a significantly increased AKI risk than Class 3 (HR 1.284, 95% CI 1.085-1.521, p = 0.003; HR 1.271, 95% CI 1.074-1.505, p = 0.005). Conversely, the risks of Class 2 were significantly lower than those of Class 3 (HR 0.846, 95% CI 0.745-0.960, p = 0.009; HR 0.879, 95% CI 0.774-0.998, p = 0.047).ConclusionsThe 24-hour MAP trajectory in HF patients influences the risk of AKI. A rapid decrease in MAP (Class 4) is associated with a higher AKI risk, while maintaining MAP at a moderate level (Class 2) significantly reduces this risk. Therefore, closely monitoring MAP changes is crucial for preventing AKI in HF.

Objective To develop a predictive tool capable of early identification of the risk of acute respiratory failure within 48 hours of hospital admission in patients with community-acquired pneumonia (CAP). Method A retrospective cohort of 257 CAP patients (median age: 76.0 years, IQR: 68.0–84.0; 56.4% male) was analyzed, among whom 148 (57.6%) developed respiratory failure within 48 hours. From 55 clinical variables, key predictors were selected using LASSO regression. Predictive models were then constructed using multivariable logistic regression (MLR) and machine learning algorithms including XGBoost, LightGBM, and Random Forest. To address the probability calibration issue of the XGBoost model, Platt scaling was applied. A final ensemble model was built by weighted averaging of the calibrated XGBoost, LightGBM, and MLR models. Feature importance was analyzed using SHAP (SHapley Additive exPlanations), and clinical utility was evaluated via decision curve analysis (DCA) and calibration plots. Result Respiratory rate, TNF-α, IL-1β, heart rate, pleural effusion, and body temperature were identified as the most important predictors. Other key features included total bilirubin, serum calcium, albumin/globulin ratio, and platelet count. The weighted ensemble model outperformed individual models, achieving an AUC of 0.792 on the test set. Conclusion We developed a predictive tool based on multi-model ensemble learning and interpretable machine learning techniques (SHAP), which provides a basis for early risk stratification and prevention of acute respiratory failure in hospitalized CAP patients. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The author(s) received no specific funding for this work. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The study has been approved by the Ethics Committee of Beijing Chaoyang Hospital, affiliated with Capital Medical University. Based on the committee's review, the research uses only anonymized data and does not involve direct human participation. Therefore, the Ethics Committee has waived the requirement for written informed consent. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The datasets used and analysed during the current study available from the corresponding author on reasonable request.

BACKGROUND:Sepsis-induced acute respiratory distress syndrome (SI-ARDS) is associated with high mortality rates, necessitating early risk stratification. This study aimed to develop and validate a radiomics-based nomogram integrating computed tomography (CT) features and clinical parameters to predict 28-day mortality in older patients with SI-ARDS. METHODS:In this retrospective cohort study, 302 older patients (≥60 years) diagnosed with SI-ARDS between January 2019 and December 2023 were enrolled. Radiomic features were extracted from admission chest CT images. Patients were randomly allocated to training (n = 242) and validation (n = 60) cohorts. Three predictive models-radiomic, clinical, and combined-were constructed using Maximum Relevance Minimum Redundancy (MRMR) algorithm and Least Absolute Shrinkage and Selection Operator (LASSO) regression. Model performance was assessed using the concordance index (C-index), calibration curves, and decision curve analysis. A nomogram was developed based on the optimal model for clinical application. RESULTS:The fusion model achieved superior discrimination compared with the radiomic model, clinical model, and Sequential Organ Failure Assessment score in both cohorts (C-index: training, 0.850 vs. 0.798, 0.781, and 0.654; validation, 0.839 vs. 0.768, 0.779, and 0.696; all p < 0.001). The model demonstrated excellent calibration and provided greater net clinical benefit across threshold probabilities of 10 %-90 %. Risk stratification using the nomogram identified distinct prognostic groups with significantly different 28-day survival (log-rank p < 0.001). CONCLUSION:The nomogram developed from the fusion model demonstrated superior predictive performance for 28-day mortality in older patients with SI-ARDS compared to conventional scoring systems, though multicenter validation is required to confirm clinical utility.