Early detection of gastric cancer and timely identification and management of precancerous lesions are critical for reducing gastric cancer mortality and may contribute to incidence reduction and improved survival outcomes. Although gastroscopy remains the gold standard for gastric cancer screening and diagnosis, its invasiveness, discomfort during the procedure, and limited acceptability restrict population participation and screening coverage. Recently, rapid advances in liquid biopsy technologies have led to the discovery of numerous multi-omics biomarkers spanning genomics, transcriptomics, proteomics, and metabolomics, with promising diagnostic performance. However, their translational value for population-based gastric cancer screening and control remains insufficiently characterized. This review aims to provide a comprehensive overview of multi-omics biomarkers for gastric cancer screening and to evaluate their potential role in advancing population-level gastric cancer control. First, we synthesize multi-omics biomarkers with diagnostic and screening relevance across the continuum of gastric carcinogenesis, from chronic inflammation and atrophy to intestinal metaplasia, dysplasia, and early gastric cancer. Furthermore, we highlight the integrative value of multi-omics biomarkers, current limitations, translational challenges, and future opportunities for moving biomarkers from discovery to implementation in organized screening programs. In conclusion, multi-omics biomarkers have the potential to complement existing screening strategies by providing scalable, non-invasive, and risk-adapted approaches for early gastric cancer detection. Bridging the gap between biomarker discovery and real-world implementation will be essential for realizing their value in future gastric cancer screening programs.

Critical care medicine requires rapid, high-stakes decisions informed by dynamic and complex streams of patient data. Traditional predictive models have shown value in forecasting deterioration and identifying subphenotypes. However, this leaves a critical gap between anticipating adverse outcomes and guiding therapeutic interventions. Achieving true personalization demands moving beyond generalized protocols toward individualized strategies that account for patient heterogeneity and consequences of alternative clinical actions. Emerging methods in prescriptive artificial intelligence, particularly causal machine learning (causal ML) and reinforcement learning (RL), are beginning to bridge this gap. Causal ML enables estimation of individualized treatment effects by addressing confounding and enabling counterfactual reasoning, allowing clinicians to ask whether a specific intervention is likely to help or harm a given patient. RL can generate adaptive treatment policies that evolve with patient state. The objective of this review is to examine how critical care can progress from generalized prediction to true personalization through the development of prescriptive artificial intelligence. The review contributes by (1) surveying the achievements and limitations of current predictive models, (2) detailing how causal ML and RL can generate individualized treatment effects and sequential decision strategies, (3) identifying the major translational, technical, clinical, ethical, and regulatory barriers to implementation, and (4) outlining future pathways such as digital twins and clinician in the loop systems that may enable safe and actionable personalized decision support at the bedside.

Development of mixed histiocytosis (Langerhans cell histiocytosis (LCH))/Erdheim–Chester disease (ECD)) after treatment in patients with an initial skull LCH lesion has not been well recognized. An elderly woman initially developed LCH at the left temporal bone, preceded by polyuria and polydipsia five years earlier; the lesion was surgically removed. Two years thereafter, she experienced her first LCH relapse with a right parietal skull lesion, in which a BRAF V600E mutation was confirmed, and chemotherapy was initiated. After a second LCH relapse involving the left parietal bone, the patient presented with a third relapse at the L2 vertebra. This lesion was pathologically diagnosed as mixed histiocytosis (LCH/ECD), resulting in refractoriness to conventional chemotherapy, and was successfully treated with targeted therapy using BRAF and MEK inhibitors. Spinal mixed histiocytosis (LCH/ECD) may develop following relapses of skull LCH after chemotherapy, for which targeted therapy could be effective.

Predicting the malignant transformation of rectal precancerous lesions remains challenging because conventional Whole Slide Images (WSIs) capture morphological information but lack molecular insight. Multiomics data provide complementary biological signals that often precede visible morphological changes. This study aimed to develop an artificial intelligence (AI)-based multimodal framework integrating WSI and multiomics data for accurate early prediction of malignant transformation.

WSI patches (512×512 px at 20× magnification) and matched multiomics profiles were used for 450 rectal tissue samples from the publicly available The Cancer Genome Atlas dataset. A multimodal architecture was designed, employing a Vision Transformer (ViT-B/16) for WSI feature extraction and a Variational Autoencoder for multiomics representation learning. Features were fused via a cross-attention mechanism to capture inter-modality dependencies. Baseline models, including a convolutional neural network-only image model and an omics-only multilayer perceptron, were trained for comparison. Five-fold cross-validation was applied, with binary cross-entropy loss, the AdamW optimizer, early stopping, and hyperparameter tuning to ensure reproducibility.

The multimodal Vision Transformer–Variational Autoencoder fusion model outperformed unimodal baselines, achieving an accuracy of 0.892 ± 0.012 and an area under the receiver operating characteristic curve of 0.927 ± 0.009, corresponding to a 7–10% improvement over WSI-only and omics-only models. Cross-attention–based fusion improved prediction stability and classification performance, while interpretability analyses (Grad-CAM and SHAP) highlighted biologically meaningful histopathological regions and molecular feature contributions.

This study presents a robust and scalable AI-based framework for integrating WSI and multiomics data in rectal precancerous lesions. The model improves predictive precision compared with unimodal baselines and offers preliminary interpretability insights through attention mechanisms. These findings support the potential of multimodal AI for early cancer risk assessment and precision pathology.

Brain tumors represent a common class of life-threatening neoplastic conditions. The core objective of neurosurgery is to achieve maximal safe resection of tumors while preserving the patient’s neurological function. Intraoperative ultrasound (IOUS) assists surgeons in achieving complete lesion removal, helping to avoid insufficient resection or excessive excision of normal tissue, thereby reducing surgical morbidity. Contrast-enhanced ultrasound (CEUS), through harmonic imaging, enables more precise localization of lesions and intracranial structures. This review focuses on the synergistic value of IOUS and CEUS in brain tumor surgery. It traces the technological evolution from two-dimensional ultrasound to elastography, color Doppler flow imaging, microvascular flow imaging, artificial intelligence, and beyond, with an emphasis on CEUS for cranial tumors. It also examines the clinical applications of IOUS and CEUS in precise resection, residual tumor identification, vascular protection, boundary differentiation from peritumoral edema, and prognostic assessment. The review concludes by summarizing diagnostic performance, current limitations, and future directions, offering neurosurgeons a theoretical and practical framework for optimizing intraoperative guidance.

Hepatitis B virus (HBV) infection and hepatitis C virus (HCV) infection are among the leading causes of chronic liver diseases worldwide. Through the same transmission routes, HBV/HCV coinfection is widespread and aggravates liver damage. In this study, we aimed to assess the safety and efficacy of sofosbuvir/velpatasvir (SOF/VEL) and the pre-treatment of tenofovir alafenamide fumarate (TAF) on HBV reactivation in HBV/HCV coinfected patients.

A multicenter, prospective, single-arm, open-label 12-week trial, followed by a 12/48-week observational clinical trial, was conducted. Ninety-six adults with chronic HBV/HCV coinfection were enrolled from May 2021 to December 2024 in thirteen centers in China. Seventy-seven non-cirrhotic patients were included in Group 1 and nineteen compensated cirrhotic patients in Group 2. All subjects were enrolled to receive SOF/VEL once daily for 12 weeks. Non-cirrhotic subjects received TAF once daily for 28 weeks, and compensated cirrhotic subjects received TAF once daily for 64 weeks simultaneously. Statistical significance was set at P < 0.05.

At the end of SOF/VEL treatment, the overall sustained virologic response was 97.9%, of which 100% was achieved in Group 2. HCV RNA, HBV DNA, and HBV RNA levels were substantially decreased in all patients. Alanine aminotransferase (ALT) (61.5 vs. 21.9, P < 0.001) and aspartate aminotransferase (AST) (50.8 vs. 25.7, P < 0.001) levels decreased, and albumin (ALB) (42.4 vs. 45.1, P < 0.001) level increased compared to pre-treatment in Group 1 at 12 weeks post-treatment. ALT (64.1 vs. 25.2, P < 0.001), AST (65.7 vs. 29.7, P < 0.001), alkaline phosphatase (ALP) (111.6 vs. 88.2, P < 0.05), and alpha-fetoprotein (AFP) (17.9 vs. 4.7, P < 0.05) levels decreased, and ALB (41.3 vs. 42.5, P = 0.051) and platelet count (PLT) (114.0 vs. 127.2, P = 0.052) levels showed a trend toward increase compared to pre-treatment in Group 2 at 48 weeks post-treatment. Liver stiffness measurement (LSM) (22.6 vs. 12.7, P < 0.01), aspartate aminotransferase to platelet ratio index (APRI) (1.6 vs. 0.6, P < 0.001), and fibrosis-4 index (FIB-4) (4.7 vs. 2.6, P < 0.05) significantly decreased after treatment in Group 2. Two patients in Group 1 with genotype 3 showed HBV reactivation and HCV relapse, respectively. No drug-related adverse events were observed in the study.

SOF/VEL effectively achieves a sustained virologic response and improves liver function, with an acceptable safety profile in chronic HBV/HCV coinfected patients, including those with compensated cirrhosis, who achieved modest improvement in non-invasive fibrosis indices. Pre-administration of TAF may mitigates the risk of HBV reactivation in this population.

Sepsis is a life-threatening syndrome associated with high morbidity and mortality, underscoring the urgent need for early diagnostic biomarkers and therapeutic targets. However, current diagnostic strategies remain insufficiently precise because of the complex immune dysregulation and immune microenvironment heterogeneity that characterize sepsis. This study aimed to identify reliable diagnostic biomarkers for sepsis and explore their immune regulatory mechanisms together with potential therapeutic relevance using multidimensional bioinformatic analyses.

Single-cell transcriptomic and bulk RNA sequencing datasets were integrated to screen candidate diagnostic genes for sepsis. Immune infiltration, co-expression network and pathway enrichment analyses were performed to explore immune regulatory mechanisms. Machine-learning approaches were used to validate the diagnostic signature, and molecular docking was conducted to predict candidate targeted compounds.

A total of 346 differentially expressed genes were identified and were mainly enriched in immune, coagulation, and metabolic pathways. CIBERSORT and single-cell analyses revealed increased neutrophils, monocytes, and γδ T cells and reduced CD8+ T cells and resting natural killer cells. Four diagnostic genes (S100A12, CD22, CSTA, and UPP1) were prioritized. The four-gene model showed robust external performance (area under the receiver operating characteristic curve = 0.860; sensitivity = 0.781; specificity = 0.780), and interpretability analysis highlighted UPP1 and S100A12 as dominant predictors. Molecular docking suggested potential interactions between these targets and anti-inflammatory compounds.

This integrative framework identifies four immune-related diagnostic genes for sepsis and links them to immune-cell remodeling and candidate therapeutic interactions, providing a basis for future mechanistic and clinical validation.

Atherosclerosis is a chronic inflammatory vascular disease in which macrophages play central roles in lipid uptake, foam cell formation, plaque progression, plaque instability and, under certain conditions, plaque regression. This narrative review summarizes current knowledge on macrophage biology in atherosclerosis, with emphasis on macrophage phenotypic diversity, monocyte-endothelial interactions, foam cell formation, extracellular matrix remodeling, immune-cell interactions, cytokine signaling, mitochondrial dysfunction and cellular senescence. The review also discusses emerging macrophage-targeted strategies, including modulation of inflammatory activity, macrophage polarization, cholesterol efflux and lipid homeostasis. Although these approaches provide promising mechanistic and therapeutic insights, many remain at the preclinical stage. Further studies are needed to validate macrophage subtype-specific biomarkers, clarify the interaction between mitochondrial dysfunction and senescence, and evaluate safe and effective combination strategies for clinical translation.

Generative artificial intelligence (AI), particularly large language models (LLMs) and multimodal systems, is emerging as a potentially important innovation in intensive care medicine. The intensive care unit (ICU) is a data-dense, high-acuity setting where rapid and accurate decisions are critical. These models can translate complex multimodal data into interpretable and clinically actionable insights across diagnostic, prognostic, and documentation workflows. This review outlines six key domains in which generative AI is currently being explored for its potential to reshape critical care: clinical decision support; clinical documentation automation (AI scribe, voice-to-note); predictive analytics, including sepsis and acute respiratory distress syndrome prediction, acute kidney injury management, ventilator liberation readiness, delirium monitoring, and continuous renal replacement therapy optimization; ICU data summarization and multimodal monitoring; synthetic data generation; and legal and ethical governance. In clinical decision support, hybrid models that integrate time-series monitoring data with LLMs can contextualize alerts, generate diagnostic suggestions, and offer treatment plans with explainable reasoning. Documentation tools that leverage ambient listening and voice-to-note AI can streamline progress notes and discharge summaries, thereby reducing clinician workload. In predictive analytics, LLMs enhance model performance by augmenting sparse electronic health record data and translating outputs into interpretable narratives. Synthetic data generation enables algorithm development and training, particularly for rare events, while protecting patient privacy. However, the realism and ethical deployment of such data require rigorous validation. Widespread implementation of generative AI will require careful attention to challenges related to trust, validation, bias, liability, and regulatory compliance. The use of these tools must remain under clinician supervision to ensure transparency and accountability. With responsible deployment, generative AI may augment ICU workflows, improve outcomes, and reduce clinician burden, potentially becoming an indispensable component of critical care delivery.

Disorders of gut–brain interaction (DGBIs) encompass some of the most common gastrointestinal disorders and affect up to 40% of the general population. Despite their inherent heterogeneity and diverse clinical manifestations, many of the underlying pathophysiological mechanisms overlap among different DGBIs. Activation of the gastrointestinal mucosal immune system at a low level (“low-grade inflammation”) and impairments in gut epithelial barrier structure and function have been reported to play a key role in the pathophysiology of multiple DGBIs, but these alterations cannot be detected using routine clinical testing. Confocal laser endomicroscopy (CLE) is an established, readily available technology that can be added to standard gastrointestinal endoscopy, enabling “real-time” microscopic evaluation of the gastrointestinal surface epithelium. CLE has been found to be capable of identifying gastrointestinal mucosal abnormalities that are reflective of epithelial barrier impairment and/or low-grade immune activation. Over the past several years, multiple intriguing studies have utilized CLE as a clinically applicable tool to evaluate the intestinal mucosa in patients with various DGBIs. The aim of this narrative review is to summarize the available literature on the role of CLE in patients with DGBIs and to provide a perspective on the use of this technology in DGBIs.