Autoimmune hepatitis (AIH) frequently coexists with extrahepatic autoimmune diseases (EADs), but their prevalence, characteristics, progression, and treatment effect in the Han Chinese population remain unclear. This study aimed to evaluate the prevalence and spectrum of EADs and to assess their clinical features, disease course, and treatment outcomes in Han Chinese patients with AIH.

Medical records of 371 Han Chinese patients with AIH (diagnosed from March 2016 to October 2023) were retrospectively analyzed.

Among the 371 AIH patients, 304 (81.94%) were female, with a median age of 52.5 years (interquartile range, 46.0–61.0). A total of 23.98% (89/371) had at least one EAD, including 27.06% (82/303) in type 1 AIH, 11.11% (7/63) in antibody-negative AIH, and none in type 2. A single EAD was the most common (20.21%, 75/371). The most frequent EADs were Sjogren’s syndrome (8.63%) and autoimmune thyroid disease (8.36%). Compared with patients without EADs, those with EADs had lower alanine aminotransferase, red blood cell, and hemoglobin levels, but higher aspartate aminotransferase/alanine aminotransferase ratio and antinuclear antibody (ANA) positivity (all P < 0.05). ANA positivity was independently associated with EADs (odds ratio = 2.209, 95% confidence interval = 1.242–3.927, P = 0.007). After three months of treatment, the complete biochemical response rate was lower in the EADs group than in the non-EADs group (40.0% vs. 55.3%, P = 0.024), whereas no significant differences were observed at 6, 12, 24, or 36 months (all P > 0.05).

In the Han Chinese population, 23.98% of AIH patients had EADs, with Sjogren’s syndrome and autoimmune thyroid disease being the most common. ANA positivity was a significant risk factor for EADs. EAD patients had a poorer initial treatment response at three months, but comparable long-term biochemical response from six months.

Mild traumatic brain injury (mTBI) represents the majority of head injury presentations in emergency departments (EDs), yet only a minority of patients have acute intracranial lesions on computed tomography (CT). This leads to widespread use of unnecessary CT scans. Point-of-care (POC) biosensing, defined as analytical testing performed at or near the site of patient care, offers a promising solution to this dilemma by enabling rapid biomarker quantification to inform CT decision-making. This review aims to evaluate POC-compatible biosensing strategies for ultra-early mTBI triage, with emphasis on platforms, matrix effects, and benchmarking aligned with CT-based decision-making. Two key precedents support this approach: (1) the integration of S100B into Scandinavian Neurotrauma Committee guidelines, which has demonstrated the potential for safe reduction of CT scans, and (2) the regulatory clearance of glial fibrillary acidic protein (GFAP) and ubiquitin C-terminal hydrolase-L1 (UCH-L1) testing to rule out the need for head CT in adults with suspected mTBI (Glasgow Coma Scale 13–15) when serum is collected within 12 hours of injury. Accordingly, this review focuses on the most implementable use case for mTBI, namely CT triage/rule-out. It synthesizes the current biomarker landscape (S100B, GFAP, UCH-L1), analyzes POC-suitable sensing modalities, and proposes a practical validation and benchmarking framework aligned with this intended use. A critical component is interference testing and real-world sample robustness, including vulnerabilities such as hemolysis-related elevation of UCH-L1. In conclusion, the most reliable path for biosensor translation in mTBI is to anchor development and validation to the ED CT-triage use case, emphasizing decision-point robustness and resilience to real-world sample variability over pure analytical sensitivity.

Diabetic cardiomyopathy (DCM), a diabetes-specific cardiovascular complication, is pathologically characterized by cardiomyocyte apoptosis, oxidative stress, inflammatory responses, and myocardial fibrosis, distinguishing it from other cardiac disorders, such as hypertension and coronary artery disease. Challenges in early diagnosis, coupled with the limited efficacy and adverse effects of current treatments, have made DCM a significant contributor to heart failure and mortality in patients with diabetes. Natural products, recognized for their diverse sources, structural variety, and multitarget therapeutic potential, have shown promise in preventing and treating DCM. Drawing on advances over the past five years, this review systematically summarizes the pharmacological effects and molecular mechanisms of natural products (e.g., flavonoids, terpenoids, phenylpropanoids, alkaloids, and polysaccharides) in the treatment of DCM, with the aim of providing a theoretical foundation for further research and drug development.

Cannabinoid hyperemesis syndrome is an underrecognized cause of recurrent vomiting, weight loss, and abdominal pain in adolescents, often overlooked due to its nonspecific presentation and overlap with other gastrointestinal conditions. This case report highlights a 13-year-old female who presented with significant weight loss and postprandial bilious vomiting initially attributed to superior mesenteric artery syndrome. Persistent symptoms, despite surgical removal of an incidental ovarian dermoid cyst, prompted reevaluation after nondiagnostic imaging and lack of improvement. Further history obtained on hospital day 12 revealed daily cannabis use since age 10, with reported cessation approximately six weeks prior to admission and probable resumed use approximately two weeks prior to presentation, confirmed by a positive urine toxicology screen for tetrahydrocannabinol consistent with chronic heavy use. Following supportive care and counseling on cannabis cessation, her acute symptoms resolved and she was discharged. She subsequently experienced a symptomatic relapse with resumed cannabis use requiring readmission two months later, but achieved sustained clinical remission at approximately six months following definitive cessation. This case illustrates how incomplete social histories and incidental findings can delay the identification of cannabinoid hyperemesis syndrome and lead to unnecessary procedures, and emphasizes that long-term symptom resolution requires ongoing cannabis abstinence. Early use of validated structured substance use screening tools (CRAFFT, BSTAD, S2BI), with urine toxicology applied as a confirmatory adjunct when the history is unreliable or symptoms remain unexplained, can facilitate timely recognition of cannabinoid hyperemesis syndrome in adolescents with persistent vomiting and abdominal pain, reduce hospital length of stay, and improve outcomes.

The gut microbiota engages in a complex, bidirectional dialogue with the liver via the gut–liver axis, and its dysbiosis plays a central role in the initiation and progression of various liver diseases. This review comprehensively integrates recent advances in the common features and etiology-specific patterns of gut microbial dysbiosis in liver diseases, signal decoding of key microbial metabolite axes, gut–liver immune crosstalk mechanisms, the accelerating role of gut barrier disruption, and recent progress in the use of the microbiome as diagnostic and prognostic biomarkers. We focus on analyzing the common patterns of reduced diversity, depletion of beneficial bacteria, and enrichment of pathogenic bacteria associated with gut flora dysbiosis across different liver diseases, ranging from nonalcoholic fatty liver disease and alcoholic liver disease to cirrhosis and hepatocellular carcinoma, as well as their unique etiology-related characteristics. Core findings reveal that microbial metabolites act as key chemical messengers that precisely drive liver disease progression by modulating host metabolic, immune, and inflammatory pathways. Meanwhile, the translocation of microbes and their products resulting from disruption of gut barrier integrity serves as a key accelerator, exacerbating liver injury and related complications. Based on these mechanisms, this review further explores ecological niche remodeling strategies targeting the gut microbiota, including the existing evidence and limitations of fecal microbiota transplantation and probiotics/prebiotics, as well as the prospects of emerging precision interventions such as phage therapy, microbial enzyme inhibitors, and engineered bacteria. Finally, we emphasize the potential and personalized implementation pathways of synergistically integrating microbiota modulation with existing therapies such as antivirals, antifibrotics, immunotherapy, and metabolic surgery. Future research must focus on promoting the translation of microbiome research from association studies to clinical applications through multi-omics integration and prospective clinical trials, ultimately achieving precise prevention and treatment of liver diseases based on gut–liver axis regulation.

Artificial intelligence (AI) is increasingly reshaping diagnostic pathology, with breast pathology representing one of the most advanced and clinically impactful areas of adoption. Despite rapid progress, many practicing pathologists remain unfamiliar with core AI concepts and their practical implications. This review provides a concise and accessible overview of AI in breast pathology, focusing on foundational principles, current clinical applications, and future directions.

Pertinent literature was reviewed. Personal experiences were also summarized and incorporated.

Key AI concepts, including algorithms, models, architectures, machine learning, deep learning, neural networks, and multimodal and foundational models, are introduced to establish a common framework. Important distinctions among generative, black-box, and explainable AI are highlighted, emphasizing the need for transparency and interpretability in clinical settings. The evolution of AI in breast pathology is reviewed, from early rule-based computer-assisted diagnostic systems to modern deep learning approaches leveraging large-scale whole-slide imaging datasets. Current applications span multiple domains, including detection of lymph node metastases, Nottingham grading, classification of benign and malignant lesions, and automated quantification of critical biomarkers. AI-based approaches to prognosis, risk stratification, prediction of treatment response, and analysis of the tumor microenvironment are also discussed. Finally, the review addresses challenges associated with real-world implementation, including data quality, bias, regulatory considerations, cost, infrastructure, and workflow integration.

As AI continues to evolve toward large-scale, multimodal, and explainable models, it is expected to function as an augmentative tool rather than a replacement for pathologists, supporting diagnostic accuracy, standardization, and personalized management in breast cancer care.

Glioblastoma remains a highly challenging malignancy with a pronounced tendency for recurrence. The hypoxic microenvironment is a key contributor to its therapy resistance. Hyperbaric oxygen therapy (HBOT), which elevates tissue oxygen pressure and reverses hypoxia, exhibits a “dual effect” in glioblastoma management. This review aims to evaluate the therapeutic potential of HBOT in glioblastoma by examining its multifaceted effects on tumor biology and treatment response. On one hand, it enhances radiosensitivity through reactive oxygen species generation, increases chemotherapy efficacy by augmenting cytotoxicity and improving vascular perfusion, and remodels the tumor microenvironment via vessel normalization, edema reduction, and immune cell modulation. Furthermore, HBOT attenuates cancer stem cell properties by downregulating stemness markers and inhibiting self-renewal capacity. On the other hand, HBOT may also promote tumor progression: oxidative stress can induce genomic instability, while concomitant activation of HIF-, NF-κB-, and VEGF-mediated pro-survival pathways may facilitate malignant cell adaptation and proliferation. Given these opposing considerations, the clinical application of HBOT in glioblastoma management remains exploratory. In conclusion, future research should focus on optimizing HBOT protocols. In addition, exploring combination with other therapeutic approaches is equally important. These efforts are essential for the safe and effective integration of HBOT into comprehensive treatment strategies for glioblastoma.

Stroke patients have a high incidence of venous thromboembolism (VTE). Improving the prevention and control rates of VTE in stroke patients can enhance their quality of life. The aim of this study was to analyze the effect of 4R crisis management combined with the health belief model in the prevention and control of VTE in stroke patients.

A randomized controlled trial was conducted on 86 stroke patients in the neurosurgery department of a tertiary hospital in Wuhan. The control group was treated with the routine VTE prevention and control strategy, while the experimental group was treated with 4R crisis management combined with the health belief model. The primary outcome measures were the incidence rates of deep vein thrombosis and pulmonary thromboembolism, while the secondary outcome measures were the Short Form Health Belief Model Scale score, medical quality evaluation, and stroke patients’ health behavior scale score. The statistical analysis methods included t-tests and non-parametric tests.

After the intervention, the incidence rate of deep vein thrombosis in the control group was 14.6% (6/41), while in the experimental group it was 2.4% (1/41). The difference was statistically significant (χ2 = 3.905, P = 0.048). The incidence rates of pulmonary thromboembolism in both groups were 0%. The scores of all dimensions of the Short Form Health Belief Model Scale in the experimental group were higher than those in the control group, and the difference was statistically significant (P < 0.05, P < 0.01). The medical quality for each item showed that the experimental group performed better than the control group, with the difference being statistically significant (P < 0.05, P < 0.01). The scores on the stroke patients’ health behavior scale in the experimental group were higher than in the control group, except for responsibility, tobacco, and alcohol (P < 0.01).

The application of 4R crisis management combined with the health belief model can effectively improve the health beliefs and health behaviors of stroke patients to prevent VTE, thereby reducing the incidence of VTE.