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.

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.

Steatotic donor livers are highly susceptible to post-transplant dysfunction; however, the underlying mechanisms remain incompletely understood. This study aimed to investigate the role of galectin-3 (LGALS3)-mediated pyroptosis in steatotic liver graft injury and explore its therapeutic potential.

A mouse model of steatotic liver transplantation was established. Graft tissues were subjected to RNA sequencing to identify key regulators. In vitro, LGALS3 was modulated in steatotic hepatocytes under ischemia/reperfusion stress to assess its impact on the NLRP3 inflammasome and pyroptosis. The regulatory mechanism by which LGALS3 modulates NLRP3 ubiquitination was further examined. Finally, the therapeutic efficacy of LGALS3 inhibition was evaluated in an orthotopic liver transplantation model.

Transcriptomic analysis identified LGALS3 as a key upregulated molecule in steatotic grafts, associated with pyroptosis pathways. In vitro, LGALS3 overexpression enhanced NLRP3 inflammasome activation and pyroptotic cell death, whereas LGALS3 knockdown exerted protective effects. Mechanistically, LGALS3 modulated NLRP3 inflammasome activity by regulating its ubiquitination. In vivo, pharmacological inhibition of LGALS3 significantly improved graft function, reduced histological injury, suppressed pyroptosis, and prolonged recipient survival.

This study demonstrates that LGALS3 drives steatotic graft injury by promoting NLRP3-mediated pyroptosis through the regulation of ubiquitination. These findings identify LGALS3 as a promising therapeutic target for improving the outcomes of liver transplantation using steatotic donor organs.

Liver fibrosis is a central pathological process driving the progression of chronic liver disease, yet effective antifibrotic therapies remain limited. Increasing evidence has identified the mineralocorticoid receptor (MR), a ligand-activated nuclear receptor, as a key regulator of intrahepatic homeostasis and fibrogenesis. MR is expressed across multiple hepatic cell types, including hepatocytes, hepatic stellate cells, macrophages, and liver sinusoidal endothelial cells, where it integrates metabolic, inflammatory, and microvascular signaling. Under pathological conditions, MR activation—mediated by both aldosterone-dependent and ligand-independent mechanisms such as hypoxia and oxidative stress—amplifies core profibrotic pathways, including transforming growth factor-β (TGF-β) signaling, reactive oxygen species (ROS) generation, and nuclear factor-kappa B (NF-κB)–driven inflammation. These molecular mechanisms are executed in a cell-type–specific manner, promoting hepatic stellate cell activation, macrophage-mediated inflammation, hepatocyte metabolic dysfunction, and liver sinusoidal endothelial cell capillarization, thereby forming a self-reinforcing fibrogenic network. Preclinical studies consistently demonstrate that mineralocorticoid receptor antagonists attenuate fibrosis by targeting these interconnected pathways. However, clinical evidence remains limited, with only early-phase trials in metabolic dysfunction-associated steatohepatitis and indirect support from cardiorenal studies. Nonsteroidal mineralocorticoid receptor antagonists, particularly finerenone, exhibit improved receptor selectivity and safety profiles, highlighting their therapeutic potential. Future research should focus on disease-specific patient stratification, validated antifibrotic endpoints, and rigorous safety evaluation to enable effective clinical translation of MR-targeted therapies in liver fibrosis.

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 role of the gut microbiota and the potential role of fecal microbiota transplantation (FMT) in GI cancer therapy are receiving increasing attention. This review aims to explore the potential and mechanisms of FMT as a therapeutic adjuvant in the treatment of GI cancers. Microbiome studies suggest that FMT might influence antitumor treatment efficacy and treatment-related toxicity through three proposed pathways, including enhancing antigen presentation, reshaping the tumor microenvironment, and preserving intestinal barrier function. Preliminary clinical studies of FMT combined with immune checkpoint inhibitors, chemotherapy, or radiotherapy have reported response signals, and selected studies and case reports have described improvements in treatment-related intestinal toxicities. However, these observations do not establish that the outcomes were attributable to FMT. Clinical application is also hindered by donor microbiota functional heterogeneity, substantial interindividual variability in engraftment, and the absence of validated predictive models. We also propose an unvalidated, hypothesis-generating functional screening framework: the Healthy Donor-derived Microbiota Xenograft model as a preclinical functional screening platform and the proposed Xenograft-screened FMT concept for future clinical evaluation, which links donor-level functional evaluation with model-informed candidate donor selection. By integrating mechanistic insights, emerging preclinical and clinical evidence, and a functional screening framework, this review presents a testable concept for future evaluation and offers insights into future clinical investigation of FMT as a therapeutic approach in GI oncology.

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.

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.

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.

The Asia-Pacific region accounts for a larger share of the hepatitis B burden than any other regions of the world, presenting a challenge to meeting the World Health Organization (WHO) 2030 elimination targets. In this study, we aimed to quantify the burden of chronic hepatitis B (CHB) and project its trends through 2030 using the Global Burden of Disease 2023 framework, thereby identifying gaps and priorities for the Asia-Pacific region to achieve WHO 2030 targets.

Using data from the Global Burden of Disease Study 2023, we analyzed CHB prevalence, mortality, and disability-adjusted life years. We evaluated temporal trends (1990–2023) using average annual percent changes and projected the 2024–2030 burden using Bayesian age-period-cohort models.

In 2023, the Asia-Pacific region accounted for 63% of global CHB cases (178.0 million), 66% of deaths (259.1 thousand), and 65% of disability-adjusted life years (8.4 million). Regional prevalence and mortality rates exceeded global averages, although childhood (<5 years) prevalence was comparatively lower (590.3 vs. 1,325.3 per 100,000). East Asia bore the highest absolute burden (99.2 million cases), and South Asia had the largest pediatric caseload. Between 1990 and 2023, Western Asia showed the steepest decline in adult prevalence (−1.99%), whereas Southeast and Central Asia exhibited upward mortality trends. Projections indicate that the Asia-Pacific region is off track to meet the WHO 2030 disease elimination targets, as the prevalence rate in children under five years remains above the 0.1% target threshold and absolute mortality is projected to increase.

The Asia-Pacific region continues to contribute the largest share of the global CHB burden and now faces persistent gaps despite progress. Although substantial progress has been made in reducing prevalence through immunization, the region is currently off track to meet the WHO 2030 targets for both incidence and mortality.

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.