Preeclampsia and eclampsia are serious complications of pregnancy, leading to high rates of maternal and neonatal mortality. During pregnancy, there are changes in relevant serum metabolites in women. However, it remains unclear if these serum metabolites contribute to the development of associated disorders during pregnancy. Therefore, we conducted a Mendelian randomization study to explore the causal relationship between serum metabolites and preeclampsia and eclampsia. We utilized the inverse variance weighted model as our primary analysis approach. We complemented this with sensitivity analyses, including the heterogeneity test, horizontal pleiotropy test, and leave-one-out analysis, to ensure the robustness of our findings. Furthermore, we conducted linkage disequilibrium score regression, multivariable Mendelian randomization, and metabolic pathway analysis to further explore the genetic data. The Mendelian randomization analysis has identified γ-glutamylglutamine, inosine, and isoleucine 10 metabolites that are significantly associated with preeclampsia, and γ-glutamylglutamine and phenylacetate 8 metabolites that may potentially contribute to the development of eclampsia. Notably, γ-glutamylglutamine has been found to have a causal relationship with both preeclampsia and eclampsia. In the multivariable Mendelian randomization analysis, our research findings suggest that both isoleucine and X-14304–leucylalanine directly impact preeclampsia within the context of amino acids and peptides. Moreover, our observations reveal that carbohydrates can also have a direct effect on preeclampsia. Importantly, it should be emphasized that only 3-lactate in amino acids has been shown to have a direct influence on eclampsia. This research has the potential to enhance our understanding of the biological variances related to disease status, providing a foundation for future investigations.

Objective: Multidrug-resistant organisms (MDROs) complicate hospital-acquired and ventilator-associated pneumonia (HAP/VAP). We aimed to develop and validate a machine learning (ML) model to predict MDR risk in pneumonia patients and assess its utility for clinical decision support. Patients and Methods: We developed multiple ML models using data from the MIMIC-IV database (n=802). Feature selection was performed using LASSO regression and chi-square tests. Four models—Logistic Regression, Random Forest, XGBoost, and LightGBM—were trained, tuned, and calibrated. Model performance was evaluated using ROC curves and calibration plots. The final model was selected based on discrimination, calibration, and interpretability (assessed via SHAP). External validation on an independent cohort from a Chinese tertiary hospital (n=213) demonstrated the reproducibility and generalizability of its performance. Results: Among the evaluated ML models, Logistic Regression demonstrated the best overall performance. On the MIMIC-IV internal test set, it achieved an area under the curve (AUC) of 0.798 (95% CI: 0.718– 0.872) with an accuracy of 0.807. External validation on an independent Chinese cohort confirmed the model’s robust generalizability, achieving an AUC of 0.845. SHAP analysis identified key predictive features consistently across both cohorts, including the systemic immune-inflammation index (SII), albumin level, C-reactive protein-to-albumin ratio (CAR), number of antibiotic classes, white blood cell count (WBC), and absolute lymphocyte count (LYM abs). All of these features were significantly associated with MDR risk. Conclusion: Multiple ML models effectively predicted MDR infections in pneumonia patients, with Logistic Regression exhibiting particularly strong overall performance. Model reliability was enhanced through feature selection and probability calibration, while interpretability was improved by SHAP analysis. External validation confirmed the generalizability of our approach, supporting its potential application in clinical infection control. Future studies should focus on validation and the integration of more diverse clinical data sources.

Immune checkpoint inhibitors (ICIs) have transformed cancer treatment, yet durable benefit remains limited to selected patients. Established biomarkers, including PD-L1 expression, tumor mutational burden, and microsatellite instability, remain predominantly tumor-centered and incompletely capture inter-individual variability. In this Perspective, we highlight systemic host physiology as an underappreciated determinant of the tumor microenvironment, ICI efficacy, and immune-related toxicity. We discuss how the hypothalamic–pituitary–adrenal axis, thyroid function, insulin signaling, nutritional status, micronutrients such as vitamin D and iron, emerging metabolic modulators, body composition, and the gut microbiome may influence T-cell activation, exhaustion, and myeloid-cell function. We also examine unresolved controversies, including the obesity paradox, the potential risk of GLP-1 receptor agonist-associated sarcopenia, and the lack of standardized biomarker thresholds and sampling protocols. To facilitate clinical translation, we outline a conceptual, hypothesis-generating spatiotemporal management framework integrating pre-treatment host profiling, peri-treatment metabolic and nutritional modulation, and post-treatment dynamic monitoring. Composite indices such as the Prognostic Immune and Nutritional Index, together with precise body-composition assessment, may improve risk stratification. However, host-directed interventions should be interpreted cautiously because current evidence remains heterogeneous and largely observational or preclinical. Prospective, multidisciplinary studies are required to validate these concepts and determine whether host-directed strategies can improve ICI efficacy while limiting immune-related toxicity.

This study aimed to investigate the association between the blood urea nitrogen-to-albumin ratio (BAR) and both short- and long-term all-cause mortality in