Precision Clinical Medicine最新文献

Pharmacogenomics, therapeutic drug monitoring, and the assessments of hepatic and renal function have made significant contributions to the advancement of individualized medicine. However, their lack of direct correlation with protein abundance/non-genetic factors, target drug concentration, and drug metabolism/excretion significantly limits their application in precision drug therapy. The primary task of precision medicine is to accurately determine drug dosage, which depends on a precise assessment of the ability to handle drugs in vivo, and drug metabolizing enzymes and transporters are critical determinants of drug disposition in the body. Therefore, accurately evaluating the functions of these enzymes and transporters is key to assessing the capacity to handle drugs and predicting drug concentrations in target organs. Recent advancements in the evaluation of enzyme and transporter functions using exogenous probes and endogenous biomarkers show promise in advancing personalized medicine. This article aims to provide a comprehensive overview of the latest research on markers used for the functional evaluation of drug-metabolizing enzymes and transporters. It also explores the application of marker omics in systematically assessing their functions, thereby laying a foundation for advancing precision pharmacotherapy.

This study introduces a novel Transformer-based time-series framework designed to revolutionize risk stratification in Intensive Care Units (ICUs) by predicting patient outcomes with high temporal precision. Leveraging sequential data from the eICU database, our two-stage architecture dynamically captures evolving health trajectories throughout a patient's ICU stay, enabling real-time identification of high-risk individuals and actionable insights for personalized interventions. The model demonstrated exceptional predictive power, achieving a progressive AUC increase from 0.87 (±0.021) on admission day to 0.92 (±0.009) by day 5, reflecting its capacity to assimilate longitudinal physiological patterns. Rigorous external validation across geographically diverse cohorts-including an 81.8% accuracy on Chinese sepsis data (AUC=0.73) and 76.56% accuracy on MIMIC-IV-3.1 (AUC=0.84)-confirmed robust generalizability. Crucially, SHAP-derived temporal heatmaps unveiled mortality-associated feature dynamics over time, bridging the gap between model predictions and clinically interpretable biomarkers. These findings establish a new paradigm for ICU prognostics, where data-driven temporal modeling synergizes with clinician expertise to optimize triage, reduce diagnostic latency, and ultimately improve survival outcomes in critical care.

Background: Epstein-Barr virus (EBV) infection is associated with clinical symptoms, treatment response, need for surgical intervention, and an enhanced likelihood of lymphoma among patients with ulcerative colitis (UC). However, existing studies have primarily concentrated on the epidemiological and clinical associations between EBV and UC, leaving the mechanisms by which EBV exacerbates colitis poorly understood.

Methods: Clinical specimens of UC patients with EBV infection and a mouse model of dextran sulfate sodium-induced colitis with concurrent murine γ-herpesvirus 68 (MHV-68) infection were utilized to investigate the relationship between EBV infection and macrophage pyroptosis. In vivo, adoptive transfer of MHV-68-induced macrophages and macrophage depletion were performed to elucidate the underlying mechanisms. In vitro, myeloid leukemia mononuclear cells of human (THP-1) and macrophages derived from mouse bone marrow (BMDMs) were stimulated with EBV and MHV-68, respectively, to assess macrophage pyroptosis and glycolysis.

Results: EBV-induced activation of macrophage pyroptosis was positively correlated with clinical disease activity in UC patients. Furthermore, MHV-68 infection activated pyroptosis by upregulating gasdermin D, NLRP3, interleukin-1β, and interleukin-18 in colonic tissues and peritoneal macrophages of mice with colitis. In vitro, EBV and MHV-68 also mediated activation of pyroptosis in human THP-1 cells and mouse BMDMs, respectively. Additionally, the adoptive transfer of MHV-68-induced BMDMs aggravated murine colitis, whereas macrophage depletion attenuated MHV-68-induced intestinal injury. Mechanistically, MHV-68 promoted macrophage pyroptosis by upregulating glycolysis, while the glycolysis inhibitor, 2-deoxy-D-glucose, blocked this process in vitro.

Conclusion: EBV infection exacerbates UC by driving macrophage pyroptosis through upregulation of glycolysis, indicating a potential therapeutic approach to mitigate EBV-induced intestinal inflammation.

Background: Intratumor heterogeneity is common in cancers, with different cell subtypes supporting each other to become more malignant. Nasopharyngeal carcinoma (NPC), a highly metastatic cancer, shows significant heterogeneity among its cells. This study investigates how NPC cell subtypes with varying metastatic potentials influence each other through exosome-transmitted molecules.

Methods: Exosomes were purified and characterized. MicroRNA expression was analyzed via sequencing and qRT-PCR. The effects of miR-30a-5p on migration, invasion, and metastasis were evaluated in vitro and in vivo. Its impact on desmoglein glycoprotein (DSG2) was assessed using dual-luciferase assays and Western blotting. Immunohistochemistry (IHC) and statistical models linked miR-30a-5p/DSG2 levels to patient prognosis.

Results: Different NPC cell subtypes transmit metastatic potential via exosomes. High-metastatic cells enhance the migration, invasion, and metastasis of low-metastatic cells through exosome-transmitted miR-30a-5p. Plasma levels of exosomal miR-30a-5p are reliable indicators of NPC prognosis. miR-30a-5p may promote metastasis by targeting DSG2 and modulating Wnt signaling. Plasma exosomal miR-30a-5p inversely correlates with DSG2 levels, predicting patient outcomes.

Conclusion: High-metastatic NPC cells can increase the metastatic potential of low-metastatic cells through exosome-transmitted miR-30a-5p, which is a valuable prognostic marker assessable via liquid biopsy.

Inflammatory bowel disease (IBD) is a chronic inflammatory disorder of the gastrointestinal tract, and its pathogenesis is believed to be associated with an imbalance between commensal organisms and the intestinal immune system. This imbalance is significantly influenced by the intestinal microbiota and metabolites and plays a critical role in maintaining intestinal mucosal homeostasis. However, disturbances in the intestinal microbiota cause dysregulated immune responses and consequently induce intestinal inflammation. Recent studies have illustrated the roles of the intestinal microbiota in the pathogenesis of IBD and underscored the potential of precision diagnosis and therapy. This work summarises recent progress in this field and particularly focuses on the application of the intestinal microbiota and metabolites in the precision diagnosis, prognosis assessment, treatment effectiveness evaluation, and therapeutic management of IBD.

Type 2 diabetes mellitus (T2DM) is a metabolic disease that is characterized by chronic hyperglycaemia. MicroRNAs (miRNAs) are single-stranded, small non-coding RNAs that play important roles in post-transcriptional gene regulation. They are negative regulators of their target messenger RNAs (mRNAs), in which they bind either to inhibit mRNA translation, or to induce mRNA decay. Similar to proteins, miRNAs exist in different isoforms (isomiRs). miRNAs and isomiRs are selectively loaded into small extracellular vesicles, such as the exosomes, to protect them from RNase degradation. In T2DM, exosomal miRNAs produced by different cell types are transported among the primary sites of insulin action. These interorgan crosstalk regulate various T2DM-associated pathways such as adipocyte inflammation, insulin signalling, and β cells dysfunction among many others. In this review, we first focus on the mechanism of exosome biogenesis, followed by miRNA biogenesis and isomiR formation. Next, we discuss the roles of exosomal miRNAs and isomiRs in the development of T2DM and provide evidence from clinical studies to support their potential roles as T2DM biomarkers. Lastly, we highlight the use of exosomal miRNAs and isomiRs in personalized medicine, as well as addressing the current challenges and future opportunities in this field. This review summarizes how research on exosomal miRNAs and isomiRs has developed from the very basic to clinical applications, with the goal of advancing towards the era of personalized medicine.

Objective: This study aimed to find out whether phenotypic age could mediate the protective effects of a healthy lifestyle on mortality.

Methods: We included adult participants with available data for individual phenotypic age (PhenoAge) and Life's Essential 8 (LE8) scores from the National Health and Nutrition Examination Survey 2005-2010 (three cycles) and linked mortality records until 31 December 2019. Adjusted hazard ratios (HR) were estimated to evaluate the associations of PhenoAge and LE8 scores with all-cause and cardiovascular mortality risk. Mediation analyses were performed to estimate the proportional contribution of PhenoAge to the effect of LE8 on mortality risks.

Results: A 1-year increment in PhenoAge was associated with a higher risk of all-cause (HR = 1.04 [95% confidence interval, 1.04-1.05]) and cardiovascular (HR = 1.04 [95% confidence interval, 1.04-1.05]) mortality, independent of chronological age, demographic characteristics, and disease history. High level of LE8 (score: 80-100) was associated with a 3.30-year younger PhenoAge. PhenoAge was estimated to mediate 36 and 22% of the effect of LE8 on all-cause and cardiovascular mortality, respectively (all P < 0.001). As for single-metric scores of LE8, PhenoAge mediated 30%, 11%, 9%, and 7% of the effects of the healthy diet, smoking status, blood pressure, and physical activity on all-cause mortality risk, respectively (all P < 0.05).

Conclusion: Adherence to LE8 recommendations slows phenotypic aging. PhenoAge could mediate the effect of LE8 on mortality risk.

Background: The prognosis of breast cancer is often unfavorable, emphasizing the need for early metastasis risk detection and accurate treatment predictions. This study aimed to develop a novel multi-modal deep learning model using preoperative data to predict disease-free survival (DFS).

Methods: We retrospectively collected pathology imaging, molecular and clinical data from The Cancer Genome Atlas and one independent institution in China. We developed a novel Deep Learning Clinical Medicine Based Pathological Gene Multi-modal (DeepClinMed-PGM) model for DFS prediction, integrating clinicopathological data with molecular insights. The patients included the training cohort (n = 741), internal validation cohort (n = 184), and external testing cohort (n = 95).

Result: Integrating multi-modal data into the DeepClinMed-PGM model significantly improved area under the receiver operating characteristic curve (AUC) values. In the training cohort, AUC values for 1-, 3-, and 5-year DFS predictions increased to 0.979, 0.957, and 0.871, while in the external testing cohort, the values reached 0.851, 0.878, and 0.938 for 1-, 2-, and 3-year DFS predictions, respectively. The DeepClinMed-PGM's robust discriminative capabilities were consistently evident across various cohorts, including the training cohort [hazard ratio (HR) 0.027, 95% confidence interval (CI) 0.0016-0.046, P < 0.0001], the internal validation cohort (HR 0.117, 95% CI 0.041-0.334, P < 0.0001), and the external cohort (HR 0.061, 95% CI 0.017-0.218, P < 0.0001). Additionally, the DeepClinMed-PGM model demonstrated C-index values of 0.925, 0.823, and 0.864 within the three cohorts, respectively.

Conclusion: This study introduces an approach to breast cancer prognosis, integrating imaging and molecular and clinical data for enhanced predictive accuracy, offering promise for personalized treatment strategies.