Heming Zhang

The substantial heterogeneity among septic patients with new‑onset atrial fibrillation (NOAF) complicates intensive care unit (ICU) management, and the absence of a parsimonious subphenotype classifier has impeded the implementation of personalized therapeutic strategies. In this study, we aim to identify the subphenotype of septic patients with NOAF, develop a concise classifier model, and reveal the differential efficacy of heart-rate control therapies across these subphenotypes. This retrospective study utilized data from the Medical Information Mart in Intensive Care (MIMIC)-IV and MIMIC-III database. Consensus clustering based on hierarchical clustering was employed for subphenotype derivation. Nine supervised classifiers were employed to construct the subphenotype classifier, including random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), partial least squares (PLS), neural network (NN), naïve Bayes (NB), linear discriminant analysis (LDA), least absolute shrinkage and selection operator (LASSO), and adaptive boosting (AdaBoost.M1). Subphenotype-specific propensity score-derived stabilized inverse probability of treatment weights was used to weight comparisons of intensive care unit and hospital lengths of stay and discharge dispositions across subphenotypes. Among 1535 septic patients with NOAF, three distinct subphenotypes emerged: Phenotype A was the mildest subgroup; Phenotype B exhibited pronounced metabolic acidosis, highest anion gap, severe renal impairment, and elevated severity scores; Phenotype C showed intermediate laboratory values and score profiles with notable hyperchloremia and inflammatory markers. A parsimonious model established by SVM showed the best effectiveness. HR-control therapy was associated with subphenotype-specific reductions in hospital and ICU lengths of stay and more favorable discharge dispositions in Phenotype A, while more favorable survival outcomes were observed across all three subphenotypes in IPTW-weighted analyses. Cluster analysis revealed three clinically relevant subphenotypes among septic patients with NOAF, each demonstrating distinct clinical outcomes and heterogeneous responses to heart rate control therapy.

(1) Background: Sleep deprivation (SD) triggers a range of neuroinflammatory responses. Dexmedetomidine can improve sleep deprivation-induced anxiety by reducing neuroinflammatory response but the mechanism is unclear; (2) Methods: The sleep deprivation model was established by using an interference rod device. An open field test and an elevated plus maze test were used to detect the emotional behavior of mice. Mouse cortical tissues were subjected to RNA sequence (RNA-seq) analysis. Western blotting and immunofluorescence were used to detect the expression of p38/p-p38, MSK1/p-MSK1, and NFκBp65/p- NFκBp65. Inflammatory cytokines were detected using enzyme-linked immunosorbent assay (ELISA); (3) Results: SD triggered anxiety-like behaviors in mice and was closely associated with inflammatory responses and the MAPK pathway (as demonstrated by transcriptome analysis). SD led to increased expression levels of p-p38, p-MSK1, and p-NFκB. P38 inhibitor SB203580 was used to confirm the important role of the p38/MSK1/NFκB pathway in SD-induced neuroinflammation. Dexmedetomidine (Dex) effectively improves emotional behavior in sleep-deprived mice by attenuating SD-induced inflammatory responses and oxidative stress in the cerebral cortex, mainly by inhibiting the activation of the p38/MSK1/NFκB pathway; (4) Conclusions: Dex inhibits the activation of the p38/MSK1/NFκB pathway, thus attenuating SD-induced inflammatory responses and oxidative stress in the cerebral cortex of mice.