Sepsis is a condition resulting from the uncontrolled immune response to infection, leading to widespread inflammatory damage and potentially fatal organ dysfunction. Currently, there is a lack of specific prevention and treatment strategies for sepsis across different age groups. Programmed Cell Death (PCD) can regulate the enrichment of effector immune cells or regulatory immune cells, providing a new perspective for immunotherapy. Within the framework of computational biology and machine learning strategies, and against the backdrop of global multicenter sepsis cohort data, this study aims to deeply mine and screen specific biomarkers related to the immune microenvironment and programmed cell death in populations across different life stages (neonates, children, and adults). This will provide foundational data for precision treatment and drug development in artificial intelligence-assisted sepsis diagnosis and treatment management. Gene expression data from sepsis patients across global multicenter populations, including China, Europe, and the United States, were obtained from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) were identified. A literature review was conducted to obtain 18 PCD-related genes, which were intersected with DEGs to identify DEGs associated with specific types of PCD. Nine machine learning algorithms (Logistic Regression LR, Decision Tree DT, Gradient Boosting Machine GBM, K-Nearest Neighbors KNN, LASSO, Principal Component Analysis PCA, Random Forest RF, Support Vector Machine SVM, and XGBoost) were applied to training and testing datasets with 10-fold cross-validation to select three optimized algorithm models. The SHAP algorithm was further used to quantify the contribution of each gene based on cell death features to the prediction of sepsis. Key PCD patterns were identified based on model evaluation metrics (Accuracy, Precision, Recall, F1 score, and Receiver Operating Characteristic Curve ROC), and their associated DEGs were obtained through intersection, followed by immune-related analysis of DEGs. The study included a total of 1507 sepsis cases and 484 controls globally, with 90 neonatal cases and 95 controls, 527 children cases and 101 controls, and 890 adult cases and 288 controls. The best model for predicting sepsis across different populations was GBM.The key PCD patterns selected by machine learning for different age groups were Pyroptosis (neonates), Ferroptosis (children), and Autophagy (adults). (1) In neonatal sepsis, the models constructed by GBM, XGBoost, and RF algorithms performed the best, and identified 5 key DEGs associated with Pyroptosis (CHMP7, NLRC4, AIM2, GZMB, PRKACA), with NLRC4 showing the best predictive ability (AUC = 0.902, P < 0.05), significantly positively correlated with neutrophils and negatively correlated with CD8 + T cells. (2) In the children sepsis population, models constructed using the Gradient Boosting Machine (GBM), Support Vector Machine (SVM), and Least Absolute Shrinkage and Selection Operator (LASSO) algorithms demonstrated the best performance. Six key DEGs associated with Ferroptosis were identified (AKR1C3, GCLM, PEBP1, CARS, MAP1LC3B, SCL11A2), among which MAP1LC3B, playing a role in mitochondrial reactive oxygen species energy metabolism, showed the strongest predictive ability (AUC = 0.883, P < 0.05). It was significantly positively correlated with M0-type macrophages and significantly negatively correlated with activated CD4 + memory T cells. (3) In the adult sepsis population, models constructed using GBM, SVM, and LASSO algorithms showed the best performance. Three key DEGs associated with Autophagy were identified (TSPO, HTRA2, USP10), with TSPO, which mediates oxidative stress regulation, iron homeostasis, and cholesterol transport, showing the strongest predictive ability (AUC = 0.825, P < 0.05). It was significantly positively correlated with M1-type macrophages and significantly negatively correlated with CD8 + T cells. This study, through the integrated application of computational biology and machine learning algorithms, discovered biomarkers of PCD patterns that affect cytokine storm-mediated inflammation and immunosuppressive effects in sepsis populations across different age groups (neonates, children, and adults). These findings have specific clinical application and drug development value, providing a scientific basis for the global application of artificial intelligence-assisted sepsis diagnosis and treatment management.

Chronic obstructive pulmonary disease (COPD) is a common condition that poses significant health risks to humans. Pulmonary interstitial fibrosis (PIF) often manifests in advanced stages of COPD. Fluorofenidone (AKF) has a wide range of pharmacological effects, including anti-fibrotic, antioxidant, and anti-inflammatory effects. Therefore, this study aimed to assess the role of AKF in lung injury and its underlying mechanisms. The COPD mice model was constructed by cigarette smoke (CS) combined with lipopolysaccharide (LPS) treatment. The effect of AKF on COPD mice was evaluated by lung injury, lipid peroxidation, inflammatory factors, and the expression of ferroptosis markers. Furthermore, the normal human bronchial epithelial cell line, Beas-2B, was used to verify the mechanism underlying the association between ferroptosis and inflammation. AKF attenuated the cigarette smoke (CS)/LPS-induced inflammatory response in the mouse lungs. Additionally, AKF attenuated the CS/LPS-induced fibrosis response in the mouse lungs. AKF inhibits ferroptosis in lung tissues of CS/LPS-exposed mice. Furthermore, AKF suppressed the inflammatory response and ferroptosis in CSE-treated BEAS-2B cells via NF-κB signaling pathway. AKF can function as a novel ferroptosis inhibitor by inhibiting NF-κB to inhibit airway inflammation and fibrosis, providing a scientific basis for the use of AKF to prevent the progression of COPD and pulmonary fibrosis.

Dengue fever poses a significant diagnostic and therapeutic challenge in immunocompromised hosts, particularly in solid organ transplant recipients. We rep

Ovarian cancer (OC) remains the deadliest gynecological malignancy, with aged tumor microenvironments linked to poorer outcomes. Our prior work identified reduced levels of free fatty acids (FFAs) within tumor-surrounding adipose tissue of aged OC xenograft rats compared to younger counterparts. In this study, we investigated the therapeutic potential of one such FFA, punicic acid (PunA). We evaluated PunA’s effects on OC and normal cell viability and compared its activity with that of its structural isomer, α-eleostearic acid (α-ESA). Both compounds decreased OC cell viability; however, α-ESA was cytotoxic to normal cells, whereas PunA selectively impaired OC cell viability while sparing normal cells. Additionally, PunA enhanced cisplatin efficacy, demonstrating its potential for use in combination therapy to reduce cisplatin dosage and toxicity without compromising antitumor activity. Mechanistically, PunA induced ferroptosis in OC cells while sparing normal cells by differently modulating lipid peroxidation, fatty acid oxidation, and mitochondrial function. Transcriptomic profiling further revealed broad gene expression and pathway reprogramming in PunA-treated OC and normal cells. In a preliminary C57BL/6J-ID8 OC mouse model, PunA suppressed tumor growth. Collectively, these findings identify PunA as a promising therapeutic candidate for OC, acting through ferroptosis and mitochondrial dysfunction, and enhancing cisplatin efficacy while sparing normal cells.

Scrub typhus, a re-emerging zoonosis caused by Orientia tsutsugamushi, can present with severe, life-threatening complications, including multi-organ dysfu

Background: The triglyceride-glucose (TyG) index is a recognized surrogate marker of insulin resistance. However, validated reference intervals (RIs) for the TyG index in large, general healthy populations are currently lacking, limiting its standardized application in clinical practice. Methods: This retrospective study established and validated TyG index RIs using data from adults (≥18 years) undergoing routine health examinations. The derivation cohort included individuals without known diabetes, dyslipidemia, or obesity. The TyG index was calculated as ln[fasting triglyceride (mg/dL) × fasting blood glucose (mg/dL)/2]. After outlier exclusion, we analyzed the age-TyG relationship using restricted cubic splines and threshold analysis to determine optimal age stratification. Gender- and age-specific RIs were defined as the 2.5th–97.5th percentiles. An independent cohort of 127,143 healthy individuals was used for validation, with success defined as <10% of values falling outside the proposed RIs. Results: A total of 201,623 individuals were initially screened for the derivation cohort. Analysis revealed a significant nonlinear relationship between TyG and age, with an inflection point at 64.21 years, justifying stratification into 18–64 and ≥64-year groups. The overall RI was 7.47–8.90. Stratified RIs were: 7.47–8.91 for males aged 18–64, 7.46–8.90 for females aged 18–64, 7.44–8.90 for males aged ≥64, and 7.50–8.90 for females aged ≥64. In the independent validation cohort, only 4.76% to 5.37% of values fell outside the corresponding RIs, confirming their robustness. Conclusion: This study establishes and validates age- and gender-stratified reference intervals for the TyG index in a large Chinese healthy population. These intervals, benchmarked against a critical age threshold of 64 years, provide a reliable standard for clinical interpretation and enhance the utility of the TyG index in metabolic risk assessment.