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.

The clinical and genetic characteristics of TMED3, a p24-family protein, across different cancer types remain incompletely understood. This study aimed to evaluate its expression patterns, prognostic relevance, epigenetic regulation, immune associations, genetic alterations, functional networks, and chemical-gene interactions across six cancer types.

Public, de-identified data from UALCAN, GENT2, the Human Protein Atlas (HPA), Kaplan-Meier Plotter, MEXPRESS, cBioPortal, TIMER2.0, the Comparative Toxicogenomics Database (CTD), STRING, and DAVID were analyzed. The clinical and genetic characteristics of TMED3 in bladder cancer (BLCA), head and neck squamous cell carcinoma (HNSC), kidney renal clear cell carcinoma (KIRC), kidney renal papillary cell carcinoma (KIRP), liver hepatocellular carcinoma (LIHC), and lung adenocarcinoma (LUAD) were analyzed. Database-reported nominal P-values were used because unified multiple-testing correction was not feasible.

In UALCAN analysis, TMED3 mRNA was upregulated in BLCA, KIRP, LIHC, KIRC, and LUAD and downregulated in HNSC. HPA data showed higher TMED3 protein expression in BLCA, HNSC, and LUAD. Kaplan-Meier Plotter analysis showed that higher TMED3 expression was associated with shorter overall survival in HNSC, KIRC, KIRP, LIHC, and LUAD, but not in BLCA, and was not significantly associated with recurrence-free survival in any of the six cancers. MEXPRESS analysis suggested an inverse association between promoter methylation and TMED3 expression. TIMER analysis showed negative correlations between TMED3 expression and CD8+ T-cell infiltration in BLCA, HNSC, and LUAD, but a positive correlation in LIHC. cBioPortal showed low TMED3 alteration frequencies across the six cancers, and STRING and DAVID analyses linked TMED3-associated genes mainly to endoplasmic reticulum-Golgi trafficking and vesicle-mediated transport pathways. CTD analysis identified azacitidine, doxorubicin, and MK-2206 as chemicals associated with altered TMED3 expression.

TMED3 is a cancer-type-specific prognostic candidate associated with shorter overall survival in five of the six analyzed cancers. Its transcript-protein discordance, methylation pattern, and immune correlations define testable biological hypotheses, but independent experimental and clinical validation is required before clinical application.

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.

Ferroptosis, an iron-dependent form of regulated cell death, serves as an important mechanism associated with cancer progression. Natural compounds, particularly alkaloids, have emerged as attractive candidates for regulating ferroptotic cell death, thereby providing a promising anticancer strategy. However, the molecular mechanisms underlying their antitumor effects remain poorly understood. In this study, we aimed to investigate the antitumor effects and underlying mechanisms of natural alkaloids against HCC.

An alkaloid library was used to screen alkaloids with anti-hepatocellular carcinoma (HCC) activity. A combination of proteomic profiling, in vitro functional assays, and in vivo animal experiments was performed to explore the biological roles and molecular mechanisms of the lead compound.

Through high-throughput screening of an alkaloid library, we identified palmatine (PAL), an active component isolated from Fibraurea recisa Pierre, as a potential ferroptosis sensitizer in HCC cells. PAL treatment triggered typical ferroptotic features in HCC cells, including elevated ferrous iron, reactive oxygen species, and lipid peroxidation, along with decreased glutathione levels. Importantly, the pro-ferroptotic effect of PAL was significantly abolished by two ferroptosis inhibitors, ferrostatin-1 and deferoxamine. Mechanistically, PAL directly interacted with troponin T1 (TNNT1) to trigger its K48-linked polyubiquitination and subsequent protein degradation. Ectopic TNNT1 overexpression significantly rescued PAL-mediated ferroptosis and abrogated its tumor-suppressive effects. In vivo animal models further confirmed that PAL suppressed tumor growth by downregulating TNNT1, with a favorable safety profile.

Therefore, this study reveals a previously unrecognized role of PAL as a ferroptosis sensitizer and highlights its translational potential for anti-HCC therapy.