Post-stroke dysphagia management research has primarily focused on screening, assessment, and intervention strategies, with limited objective indicators for evaluating nursing care quality. This study aimed to develop a dysphagia nursing quality evaluation index system for neurosurgical inpatients with stroke.

Using the “structure-process-outcome” three-dimensional quality model as the theoretical framework, a preliminary quality evaluation index system was constructed through literature analysis and group discussion. A two-round Delphi expert consultation was conducted among 25 purposively selected clinical experts from tertiary Class A hospitals, with inclusion criteria requiring a bachelor’s degree or higher, an intermediate professional title or above, and at least 10 years of clinical experience in stroke nursing or related fields. The analytic hierarchy process was used to determine indicator weights. Outcome measures included expert authority coefficients (Cr), Kendall’s W concordance coefficient, internal consistency reliability (Cronbach’s α), and the final indicator structure.

The Cr values were 0.87 and 0.88 across the two rounds. Kendall’s W concordance coefficient increased from 0.207 to 0.235 (P < 0.001), indicating statistically significant expert agreement. The final index system comprised 3 first-level indicators, 11 second-level indicators, and 44 third-level indicators, with all indicator definitions and weights determined. The overall Cronbach’s α was 0.86, indicating preliminary internal consistency.

This study developed a dysphagia nursing quality evaluation index system for neurosurgical inpatients with stroke using the three-dimensional quality model and the Delphi method. The system showed acceptable expert authority, statistically significant expert agreement, and preliminary internal consistency, suggesting potential applicability for nursing quality monitoring in neurosurgical wards and Neurosurgery Intensive Care Units. Further clinical validation is needed before routine implementation.

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.

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.