Chikungunya fever, caused by the Chikungunya virus (CHIKV), has re-emerged as a significant global health concern in recent decades. A notable event was the largest-ever local outbreak in China in 2025, marking a critical juncture in its epidemiology. Although conventional treatment remains predominantly supportive, the integration of traditional Chinese medicine (TCM) offers promising complementary strategies for alleviating both acute symptoms and chronic polyarthralgia. This narrative review aims to consolidate current knowledge on the etiology, pathogenesis, clinical manifestations, and management of Chikungunya fever, with a particular focus on the evidence-based application of TCM. By integrating molecular virology with clinical and epidemiological insights, this review offers a comprehensive perspective on the challenges posed by CHIKV and underscores the strategic imperatives essential for its future management. In conclusion, addressing the expanding threat of CHIKV necessitates a multi-pronged public health strategy that integrates standard clinical and preventive measures with evidence-based TCM therapies, highlighting the urgent need for rigorous clinical trials to globally validate these integrative treatments.

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.

Metabolic dysfunction-associated fatty liver disease (MAFLD) represents a predominant cause of chronic liver disease, underscoring the demand for accessible, non-invasive diagnostic tools. Tongue diagnosis in Traditional Chinese Medicine provides a distinctive perspective on systemic health, though it remains largely subjective. This study aimed to develop an interpretable multimodal deep learning model for MAFLD screening by integrating quantitative tongue image features with routine clinical data.

From 904 screened candidates, 477 subjects (157 healthy, 320 MAFLD) were included and randomly allocated to training, validation, and test sets in an 8:1:1 ratio. All participants underwent standardized tongue imaging (International Commission on Illumination L*a*b color features) and comprehensive clinical evaluation. We constructed a dual-stream deep learning model, combining a ConvNeXt-Tiny network for tongue images and a multilayer perceptron for clinical variables. Feature fusion was achieved via a Dynamic Affine Feature Transformation module, and the model was trained using weighted cross-entropy loss.

MAFLD patients showed significant metabolic abnormalities compared to healthy controls. A progressive decrease in tongue yellowness (b* value) was observed with advancing fibrosis. On an independent test set (n = 48), the multimodal model achieved 97.92% accuracy, Quadratic Weighted Kappa of 0.9538, and 96.88% sensitivity, and 100% specificity, outperforming single-modality and serological models. Interpretability analyses confirmed the model’s focus on clinically relevant tongue regions and key metabolic drivers.

We developed an accurate and interpretable multimodal model that synergizes tongue image features with metabolic indicators for MAFLD screening. This approach presents a promising, low-cost tool potentially well-suited for resource-limited settings.

Early risk stratification of severe acute liver injury (SLI) that may progress to acute liver failure (ALF), is vital for timely intervention, but no universal prognostic assessment tool covers both conditions. This study aimed to develop a simplified prognostic model for early risk assessment in SLI/ALF patients.

A retrospective cohort study consecutively enrolled SLI patients (including those progressing to ALF) from July 1, 2020 to May 31, 2025. Baseline clinical and laboratory data on admission were collected, with 90-day transplant-free survival as the primary outcome. Independent prognostic factors were screened via Cox regression to build a simplified scoring model, whose performance was compared with the Model for End-Stage Liver Disease (MELD), King’s College Criteria (KCC), and the Acute Liver Failure Study Group Prognostic Index (ALFSG-PI).

Of 302 patients, 190 (62.9%) achieved 90-day transplant-free survival. Multivariate Cox regression identified international normalized ratio (hazard ratio [HR]: 1.118, 95% confidence interval [CI]: 1.050–1.191), platelet count (HR: 0.995, 95% CI: 0.993–0.998), and hepatic encephalopathy grade ≥ 2 (HR: 5.187, 95% CI: 3.403–7.907) as independent predictors, forming the HIP (derived from the above-mentioned three predictors) model. It showed good discrimination (area under the receiver operating characteristic curve [AUC]: 0.82), outperforming MELD (AUC: 0.76, P = 0.019) and KCC (AUC: 0.72, P = 0.002), and performing comparably to ALFSG-PI (AUC: 0.80, P = 0.429). The model also performed robustly in ALF subgroups defined by the American College of Gastroenterology and the 2024 Chinese Medical Association guidelines (AUCs: 0.80 and 0.76, respectively) and achieved an AUC of 0.85 in the validation set.

The HIP model is a simple and effective tool for prognostic risk stratification in SLI/ALF patients, suitable for emergency and primary care to facilitate timely intervention.

The intestinal barrier, a critical interface between the body and the external environment, is essential for maintaining internal homeostasis. Comprising mechanical, chemical, immune, and biological components, its dysfunction underpins multiple gastrointestinal pathologies. Circular RNAs (circRNAs), covalently closed non-coding RNAs, have emerged as central regulators of gut barrier homeostasis. This review synthesizes advances in circRNA roles in intestinal stem cell renewal, apoptosis-proliferation balance, microbiome interactions, and immune regulation. Key findings highlight circRNA networks operating via competitive endogenous RNA mechanisms, protein interactions, and translational potential to influence barrier function. We further discuss circRNAs as diagnostic biomarkers in inflammatory bowel disease and their therapeutic potential in barrier-related pathologies. Advances in RNA nanotechnology (e.g., lipid nanoparticles) and synthetic biology position engineered circRNAs as next-generation therapies for precision intervention in gastrointestinal disorders. Importantly, this review also critically examines the current limitations of these translational approaches, including delivery challenges, safety considerations, and the preliminary nature of many preclinical findings, providing a balanced perspective on the path from bench to 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.

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.

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.