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.

Cerebrospinal fluid leakage and postoperative tissue adhesion are serious complications following dural injury. Current dural substitutes often lack the functional asymmetry of the native dura mater. This study aimed to develop a hydrophilic/hydrophobic Janus polyvinyl alcohol (PVA) hydrogel membrane with a directional structure and dual functionality for effective dural defect repair.

A PVA hydrogel with an aligned porous architecture was fabricated via directional freezing combined with salt leaching, and thermal annealing was applied to enhance mechanical strength and structural stability. The hydrogel was asymmetrically modified to obtain a Janus membrane. Morphology, mechanical properties, degradation, swelling, wettability, in vitro biocompatibility, and cell migration were evaluated by the NIH-3T3 mouse fibroblast cell line. In vivo biocompatibility was assessed using a rat subcutaneous implantation model, including blank control, Durepair®, frozen-salted PVA, and Janus-PVA groups, with 5 rats in each group. Dural repair efficacy was evaluated in a rat cranial dural defect model, including untreated defect control, frozen-salted-annealed PVA, and Janus-PVA groups, with 15 rats in each group.

The Janus membrane exhibited high tensile strength (8.93 ± 1.46 MPa), slow degradation (1.42% mass loss at 28 days), and low swelling (58.13% water content at 28 days). It displayed distinct bilateral wettability, and effectively blocked fibroblast migration on both sides, acting as a physical barrier against fibroblast-driven adhesion. In the rat dural defect model, the Janus membrane reduced cerebrospinal fluid leakage and brain–dura adhesion compared with the untreated defect and frozen-salted-annealed PVA control groups.

The engineered hydrophilic/hydrophobic Janus PVA hydrogel membrane mimics the functional asymmetry of the native dura mater and may serve as a promising candidate for further evaluation as a dural repair material.

Cervical cancer is a major malignancy that threatens women’s health, and early screening is a core strategy for reducing its incidence and mortality. Multimodal fusion artificial intelligence (AI) pathological diagnosis models integrate multidimensional data—including cytological images, colposcopic images, whole-slide histopathological images, clinical data, and molecular testing results—and may enhance the detection sensitivity, grading accuracy, and screening efficiency for early cervical cancer and precancerous lesions. However, traditional cervical cancer screening methods face limitations such as high subjectivity, reliance on single-source information, relatively low efficiency, and insufficient primary care resources. Furthermore, existing reviews mostly focus on single-modal AI models or specific technical aspects, lacking a comprehensive analysis of the full technical framework and clinical translation pathways of multimodal fusion models. This review aims to comprehensively present the development and application of multimodal fusion AI models in pathological diagnosis for early cervical cancer screening. Specifically, it comprehensively details the technical architecture, data modalities, and fusion strategies—including deep learning, attention mechanisms, and cross-modal alignment techniques—that enable the complementary representation of morphological, clinical, and molecular information. Additionally, the review integrates recent advances in clinical applications and evaluates current translational challenges, providing insights into clinical validation pathways to bridge technological innovation and practical healthcare delivery. In conclusion, with further technological refinement and clinical validation, multimodal fusion AI may become a useful tool for improving the precision and efficiency of cervical cancer screening and prevention, and may inform the standardized application and translational research of AI technology in this field.

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.

Pulmonary arterial hypertension (PAH) is a progressive cardiovascular disease with an increasing global burden. Although hemolytic disorders are established causes of PAH, their contribution to the global PAH burden remains unclear. This study aimed to evaluate the association between hemolysis-associated disorders and PAH incidence and to identify the relative contribution of hemolytic disorder subtypes compared with socio-demographic factors.

Using Global Burden of Disease 2021 data, temporal trends in the age-standardized incidence rate (ASIR) of PAH were analyzed using Joinpoint regression. Pearson correlation analysis assessed associations between PAH ASIR and the age-standardized prevalence rates of hemolytic disorder subtypes, hemolysis-related infections, malnutrition, and the Socio-demographic Index (SDI). Random forest regression was used to quantify the contributions of hemolytic disorders to PAH ASIR. Geographic distributions of PAH incidence and hemolytic disorder prevalence were compared, and Bayesian age-period-cohort modeling was used to project their burdens through 2050.

Global PAH ASIR increased from 0.50 to 0.52 per 100,000 from 1990 to 2021. The prevalence of hemoglobinopathies and hemolytic anemias correlated positively with PAH ASIR (R = 0.61, P = 7.70 × 10-22). The random forest model explained 73% of the variance in PAH ASIR (R² = 0.73, P = 0.01), with G6PD trait (percentage increase in mean squared error [%IncMSE]: 18.43), other hemoglobinopathies/hemolytic anemias of unknown etiology (%IncMSE: 18.38), and vitamin A deficiency (%IncMSE: 17.27) identified as the top predictors, surpassing SDI (%IncMSE: 13.25) and sex (%IncMSE: 1.25). Temporal changes in hemolytic disorder prevalence strongly mirrored changes in PAH incidence (R = 0.76, P = 6.34 × 10-39). Exploratory analyses suggested that natural product exposures may contribute to the unexplained hemolytic burden that drives PAH. Projections indicated a continued rise through 2050 in both PAH burden (ASIR increasing from 0.52 in 2022 to 0.57 per 100,000) and hemolytic disease burden (prevalence rising from 27,760.54 to 31,863.72 per 100,000).

Hemolysis-associated disorders, particularly G6PD trait, other hemoglobinopathies/hemolytic anemias, and vitamin A deficiency, are the predominant contributors to the global PAH burden. The projected continued rise in hemolytic disorder prevalence through 2050 signals a persistent exacerbation of the global PAH burden, underscoring the urgent need for targeted prevention strategies.

Observational studies have shown that educational attainment is associated with the risk of myopia, but the causality of this relationship is unclear. The aim of the present study was to investigate the causal association between educational attainment and myopia.

Using publicly available data from genome-wide association studies, single nucleotide polymorphisms associated with educational attainment (college/university completion and years of education) were selected as instrumental variables. Causal associations with myopia risk were examined using two-sample Mendelian randomization (MR) analyses. Sensitivity analyses were conducted to assess the robustness of the results in terms of violations of MR assumptions.

The inverse variance–weighted analysis revealed potential causal associations of college/university completion (odds ratio (OR) = 1.102; 95% confidence interval (CI): 1.085–1.119; P < 0.001) and years of education (OR = 1.009; 95% CI: 1.007–1.010; P < 0.001) with myopia risk. MR-Egger and weighted median methods yielded similar results for both educational attainment measures.

MR evidence supports a potential causal association between educational attainment and myopia. This evidence highlights the need for careful management of myopia risk in individuals with higher educational attainment.

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.

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.

Cardiorenal syndrome is associated with high morbidity and mortality and is characterized by bidirectional interactions between cardiac and renal dysfunction. The advent of sodium-glucose cotransporter 2 inhibitors (SGLT2i) and the nonsteroidal mineralocorticoid receptor antagonist finerenone has substantially changed the therapeutic landscape. Combination therapy with SGLT2i and finerenone may provide additional benefits through complementary mechanisms, representing a potential paradigm shift in the management of cardiorenal syndrome. In this review, we examine the pathophysiological pathways that characterize cardiorenal syndrome, clinical data from major randomized controlled trials, and the rationale for the concomitant use of these two drug classes. SGLT2 inhibitors significantly reduce hospitalization for heart failure, slow renal function decline, and provide benefits in both heart failure with reduced ejection fraction and heart failure with preserved ejection fraction, irrespective of diabetes status. Finerenone has been shown to reduce the risk of cardiovascular events and chronic kidney disease progression in patients with type 2 diabetes and chronic kidney disease, with a more favorable safety profile than steroidal mineralocorticoid receptor antagonists. Emerging evidence suggests that combination therapy may reduce hospitalizations for heart failure and slow renal disease progression beyond the effects of either monotherapy. However, implementation of these therapeutic options requires careful patient selection, ongoing monitoring of renal function and electrolytes, and close collaboration between cardiologists and nephrologists.