Early predictors of 6-month non-recovery in drug-induced liver injury (DILI) remain limited. Genetic variants are stable host characteristics that may complement baseline clinical variables. We aimed to develop and validate an interpretable, clinical-genetic machine learning model for predicting 6-month non-recovery in patients with DILI.

This retrospective, single-center study included 338 patients with DILI, who were classified as recovered (n = 171) or non-recovered (n = 167) at 6 months. Candidate single-nucleotide polymorphisms and baseline clinical variables were collected during initial hospitalization. Features were selected using complementary screening approaches. Multiple machine learning models were developed and compared. Model discrimination, calibration, clinical utility, the incremental value of genetic predictors, and interpretability using SHapley Additive exPlanations (SHAP) were assessed.

Five predictors were consistently retained for model development: rs72631567, rs28521457, alanine aminotransferase, monocyte percentage, and low-density lipoprotein. Among the candidate algorithms, the light gradient boosting machine model showed the best performance, with area under the receiver operating characteristic curve (AUC) values of 0.92 (95% confidence interval [CI] 0.89–0.95) in the training set and 0.81 (95% CI 0.70–0.91) in the validation set. The model showed acceptable calibration and favorable decision-curve performance. In ablation analysis, the clinical-only model showed limited discrimination (AUC 0.57, 95% CI 0.43–0.71). SHAP analysis identified rs72631567 as the most influential predictor.

An interpretable model that integrates host genetic variants with baseline clinical variables demonstrated good internal performance for early prediction of 6-month non-recovery in DILI. These findings support external validation of genotype-informed risk stratification in patients with DILI.

This study aimed to evaluate the predictive value of cholesterol, high-density lipoprotein, and glucose index (CHG index) alone and combined with the monocyte-to-high-density lipoprotein cholesterol ratio (MHR) or triglyceride-to-high-density lipoprotein cholesterol ratio (TG/HDL-C) for NSTE-ACS.

This cross-sectional diagnostic study included 150 patients with NSTE-ACS and 76 healthy controls. Based on the median Gensini score, patients were divided into high-risk (Gensini score ≥51, n = 75) and low-risk groups (Gensini score <51, n = 75). MHR, TG/HDL-C, and CHG index were compared between patients and controls and between high- and low-risk groups. Their correlations with Gensini scores were assessed. Univariate and Multivariate binary logistic regression analysis was performed to identify factors independently associated with high-risk coronary lesions among patients with NSTE-ACS. The predictive performance of individual indicators (MHR, TG/HDL-C, and CHG index) and their combinations was evaluated using receiver operating characteristic curve analysis.

MHR, TG/HDL-C, and CHG index were significantly higher in the patient group than in the control group (all P < 0.001) and the high-risk group than the low-risk group. Those indicators positively correlated with Gensini scores and were independently associated with high-risk coronary lesions among patients with NSTE-ACS (odds ratio (OR) = 16.051, 95% confidence interval (CI): 13.677-99.650 for MHR; OR = 3.562, 95% CI: 1.868-6.793 for TG/HDL-C; and OR = 2.455, 95% CI: 1.040-5.791 for CHG index). The areas under the curve (AUCs) were 0.803 (95% CI: 0.730-0.876) for MHR, 0.746 (95% CI: 0.666-0.826) for TG/HDL-C, and 0.659 (95% CI: 0.573-0.746) for CHG index. The combination of CHG index and TG/HDL-C achieved an AUC of 0.821 (95% CI: 0.755-0.887), while the combination of CHG index and MHR achieved the higher AUC of 0.872 (95% CI: 0.815-0.929).

MHR, TG/HDL-C, and CHG index are independently associated with high-risk coronary lesions among patients with NSTE-ACS. Combining CHG index with MHR or TG/HDL-C shows numerically higher AUCs for identifying high-risk coronary lesions.

The modern intensive care unit (ICU) inundates clinicians with large volumes of data, leading to cognitive overload and a gap between data availability and actionable insight. While artificial intelligence (AI) promises a solution, its clinical adoption is limited by systemic barriers, including algorithmic bias, a lack of trust, and validation failures. This paper argues that a design philosophy that envisions AI as an autonomous decision-maker, rather than an integrated collaborative tool, has hindered its clinical adoption. We propose an alternative: a collaborative framework designed to augment the intensivist’s expertise by offloading specific cognitive burdens. This framework redefines AI’s purpose as managing data-intensive tasks, illustrated through four collaborative example roles: a synthesizer to create coherent clinical narratives, a sentinel for proactive deterioration surveillance, a simulator to forecast patient responses to interventions, and a stratifier to identify meaningful subphenotypes within complex syndromes. By delegating these computational tasks, this collaborative model frees clinicians to focus on complex synthesis, nuanced judgment, and compassionate communication. Realizing this vision requires a deliberate translational pathway focused on robust data infrastructure, human-centered design, and rigorous validation through prospective clinical trials. Ultimately, the successful integration of AI in critical care depends not on replacing clinicians but on empowering them, creating a more functional ICU in which technology supports the delivery of safer, more precise, and more humane care.

Atrial fibrillation and chronic kidney disease (CKD) frequently coexist, increasing thromboembolic and bleeding risks. This is a narrative review of pathophysiology and clinical evidence for anticoagulation strategies in CKD patients. Direct oral anticoagulants are preferred in CKD stages 1–4. Recent data suggest that the efficacy of apixaban and rivaroxaban is comparable to that of warfarin in end-stage renal disease. In advanced CKD, anticoagulation should be tailored with close monitoring.

Dysregulated lipid metabolism contributes to hepatocellular carcinoma (HCC) progression, but the prognostic value and mechanistic roles of lipid metabolism-related long noncoding RNAs (LRLs) remain insufficiently characterized. This study aimed to construct and validate an LRL-based prognostic model and to investigate the biological function and metabolic mechanism of AC026412.3 in HCC.

Transcriptomic and clinical data from the The Cancer Genome Atlas Liver Hepatocellular Carcinoma cohort were analyzed to identify LRLs based on their correlation with curated lipid metabolism genes. Differential expression, univariate Cox, least absolute shrinkage and selection operator (LASSO), and multivariate Cox analyses were performed to construct a prognostic signature, which was evaluated using Kaplan–Meier survival and time-dependent receiver operating characteristic (ROC) analyses. Functional enrichment analyses Gene Ontology [GO], Kyoto Encyclopedia of Genes and Genomes [KEGG] and gene set enrichment analysis [GSEA], mutation profiling, tumor mutational burden, immune infiltration estimation, and consensus clustering were applied to characterize associated features. A key LRL was identified through integrated bioinformatic screening and prioritization. Its biological role was assessed by quantitative reverse transcription polymerase chain reactionq (RT-PCR), western blotting, BODIPY staining, colony formation, Transwell assays, and xenograft models. RNA sequencing followed by pathway enrichment analysis was conducted to explore underlying mechanisms.

A three-LRL signature (AL031985.3, NRAV, and AC026412.3) stratified HCC patients into distinct risk groups with significantly different survival outcomes and demonstrated independent prognostic value. AC026412.3 was markedly upregulated in HCC and associated with poor prognosis. Functional assays demonstrated that AC026412.3 promoted proliferation, invasion, and tumor growth while reducing lipid accumulation. Mechanistically, AC026412.3 upregulated solute carrier family 22 member 5 (SLC22A5), enhanced fatty acid β-oxidation, and increased adenosine triphosphate (ATP) production, thereby driving metabolic reprogramming.

This study establishes a robust LRL-based prognostic model and identifies AC026412.3 as a key regulator of lipid metabolic reprogramming via the SLC22A5–fatty acid β-oxidation axis, highlighting its potential as a biomarker and therapeutic target in HCC.