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.

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.

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.

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.

The optimal surgical management for spontaneous supratentorial intracerebral hemorrhage (SSICH) remains controversial because conventional approaches often fail to balance rapid decompression with effective hematoma evacuation. This study aimed to evaluate the efficacy and safety of new combined surgical strategies (“two-in-one” and “three-in-one”) versus conventional methods for SSICH.

This retrospective cohort study included 451 SSICH patients treated between January 2019 and December 2023. Based on clinical severity, patients were stratified into Group I (non-herniation, n = 374) and Group II (herniation, n = 77). Within each subgroup, patients were further categorized by treatment period: a historical control cohort (2019–2020) receiving conventional surgery, and an intervention cohort (2021–2023) receiving combined strategies (“two-in-one” for Group I; “three-in-one” for Group II). Outcomes included decompression time, hematoma evacuation rate, complications, and six-month functional recovery (Glasgow Outcome Scale/modified Rankin Scale), were compared.

In Group I, the “two-in-one” strategy achieved faster decompression (4.65 min) and a high evacuation rate (92.15%), which was comparable to neuroendoscopy alone (90.58%) and significantly higher than stereotactic aspiration alone (44.55%). This was associated with improved six-month outcomes (poor outcome rates were 39.39%, 54.35%, and 42.86% in Groups I-A, I-B, and I-C, respectively, overall P = 0.034). In Group II, the “three-in-one” strategy demonstrated shorter decompression time (4.73 vs. 37.85 min, P < 0.001) and higher evacuation rates (80.51% vs. 63.50%, P < 0.001) than decompressive craniectomy alone. Logistic regression further supported the prognostic advantage of the “two-in-one” strategy in Group I.

These combined strategies may integrate the advantages of multiple techniques to enable rapid decompression and effective hematoma clearance in SSICH. Prospective studies are warranted.

Myelodysplastic syndromes (MDS) are a group of hematopoietic disorders characterized by ineffective hematopoiesis, with manifestations of cytopenias in one, two, or three lineages. CD34+ micromegakaryocytes and giant platelets are very rarely seen in MDS patients but may lead to unnecessary treatments. Therefore, we report and follow up on an MDS case with such an unusual finding.

A 57-year-old male veteran with a history of MDS, alcoholic cirrhosis, and portal hypertension presented to the Emergency Department in 2020 for evaluation after a blackout, at which time peripheral blood samples and bone marrow biopsies were obtained. Flow cytometry analysis of his peripheral blood detected 8% CD34+ cells. This finding raised the possibility of acute leukemic transformation from MDS. Further studies revealed that these CD34+ cells represented dysplastic micromegakaryocytes and giant platelets rather than blasts. During his 4-year follow-up, the patient was alive and complained only of easy fatigability, lasting several weeks. His laboratory results showed pancytopenia and persistent iron-deficiency anemia.

The distinction between micromegakaryocytes and giant platelets versus megakaryoblasts is extremely important in patients with MDS. This distinction may prevent misdiagnosis of acute leukemia and unnecessary treatments such as chemotherapy.

Gastrointestinal (GI) cancers account for approximately one-third of annual cancer-related deaths globally, while outcomes remain poor despite advances in surgery, chemotherapy, radiotherapy, and immunotherapy. Given the challenges of persistent resistance and treatment-related toxicities in current therapies, the pivotal roles of the gut microbiota and fecal microbiota transplantation (FMT) in GI cancer therapy are increasingly recognized. This review aims to explore the potential and mechanisms of FMT as a therapeutic adjuvant in the treatment of GI cancers. FMT may enhance antitumor treatment efficacy and reduce treatment-related toxicity through multiple mechanisms, including enhancing antigen presentation, reshaping the tumor microenvironment, and preserving intestinal barrier function. Preliminary clinical evidence indicates that FMT combined with immune checkpoint inhibitors, chemotherapy, or radiotherapy can improve treatment response rates in some trials and may reverse resistance and alleviate associated intestinal toxicities in selected cases. However, clinical application is hindered by donor microbiota functional heterogeneity, substantial interindividual variability in engraftment, and the absence of validated predictive models. To advance FMT toward precision intervention, we propose a functional screening framework: the Healthy Donor-derived Microbiota Xenograft model as a preclinical functional screening platform and its subsequent clinical application, Xenograft-screened FMT, which links donor-level functional validation with personalized microbiota delivery. By integrating mechanistic insights, emerging preclinical and clinical evidence, and a functional screening framework, this review contributes to advancing FMT from an empirical intervention toward a precision adjuvant strategy and offers insights into future clinical investigation of FMT as a therapeutic approach in GI oncology.