Dubin-Johnson syndrome (DJS) and Rotor syndrome (RS) are rare, autosomal recessive disorders that result in chronic, predominantly conjugated hyperbilirubinemia without cholestasis or hepatocellular injury. Although both conditions are benign and non-progressive, they reflect distinct molecular defects in hepatocellular transport pathways. DJS arises from mutations in the ABCC2 gene encoding the canalicular transporter multidrug resistance–associated protein 2, leading to impaired biliary excretion of conjugated bilirubin and organic anions. In contrast, RS results from combined deficiencies of the sinusoidal transporters OATP1B1 and OATP1B3, encoded by SLCO1B1 and SLCO1B3 genes, respectively, which mediate hepatic reuptake of conjugated bilirubin from the sinusoidal blood. These defects explain the characteristic biochemical and clinical distinctions between the syndromes, including the black hepatic pigmentation and markedly elevated urinary coproporphyrin I fraction in DJS, and the absence of pigmentation with moderate coproporphyrin I predominance in RS. Recent studies have expanded the understanding of how these transporters influence not only bilirubin handling but also the hepatic disposition of various drugs and endogenous metabolites. Recognition of DJS and RS is essential to prevent misdiagnosis of cholestatic or hepatocellular disease, avoid unnecessary investigations, and anticipate altered pharmacokinetics in affected individuals. This review synthesizes current evidence from molecular, biochemical, and clinical studies to highlight how these syndromes illuminate broader principles of hepatic transporter physiology and its relevance to inherited and acquired disorders of bilirubin metabolism.

Hepatocellular carcinoma (HCC) is one of the most prevalent and aggressive malignant tumors globally, with a notably low five-year survival rate. Its high mortality is largely attributed to challenges in early detection. Extracellular vesicles (EVs) are naturally occurring nanoparticles secreted by nearly all cell types and carry a diverse array of bioactive molecules, including proteins, nucleic acids (particularly non-coding RNAs), and lipids. EVs play pivotal roles in remodeling the tumor microenvironment and driving cancer progression through intercellular communication. Accumulating evidence has established that EVs are critically involved in the pathogenesis of HCC and are emerging as promising biomarkers for its early detection. With advances in EV isolation technologies, these vesicles have garnered considerable attention in the field of liquid biopsy for HCC. This review provides a comprehensive overview of the diagnostic potential of EV-derived biomarkers in HCC, including DNA, RNA, proteins, and lipids. Additionally, it discusses the advantages of integrating multi-omics approaches for HCC diagnosis. Furthermore, the review highlights the technical challenges in EV isolation and characterization, as well as the crucial role of reference genes in the standardization of EV data. These insights underscore the potential of EVs as novel, minimally invasive liquid biopsy biomarkers for the early diagnosis of HCC.

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, 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.

Hashimoto’s thyroiditis (HT), an autoimmune disease with a prevalence 2–7 times higher in women than in men, is associated with daytime sleepiness. The present study aimed to test the hypothesis that thyroid function is associated with chronotype and daytime sleepiness in women with HT.

This retrospective cross-sectional study included women with confirmed HT. Demographic, clinical and laboratory data were collected. The reduced Morningness-Eveningness Questionnaire (rMEQ) and the Epworth Sleepiness Scale (ESS) were used to assess chronotype and daytime sleepiness, respectively. Based on rMEQ, women were categorized as having a morning (≥18), intermediate (12–17) or evening (≤11) chronotype. Based on ESS, women were categorized as having normal or increased daytime sleepiness.

Overall, 106 women, aged 43 ± 12 years, were included. Most had normal daytime sleepiness (68.9%), and the majority had an intermediate chronotype (61.3%), while only one had a morning chronotype (0.9%). Age was significantly associated with chronotype (P = 0.026). There was a significant association between chronotype and thyroglobulin antibodies (TgAb, P = 0.012). Free triiodothyronine (fT3) levels were significantly higher in women with an evening chronotype than in those with an intermediate chronotype (P = 0.045; OR = 0.500; 95% CI 0.25–0.98). Daytime sleepiness was significantly associated with TgAb (P = 0.016) and thyroid-stimulating hormone (TSH, P = 0.040). TgAb levels were significantly higher in women with increased daytime sleepiness (P = 0.049, OR = 1.003, 95% CI 1.00–1.01) than in those with normal daytime sleepiness.

Approximately one-third of women have an evening chronotype, and approximately one-third had increased daytime sleepiness. TgAb, fT3, and TSH are associated with daytime sleepiness or chronotype in women with HT. Further investigation is required for the underlying mechanisms.

Nonalcoholic fatty liver disease (NAFLD) is a prevalent metabolic disorder with a complex pathogenesis. Although epitranscriptomic modifications such as N6-methyladenosine (m6A) have been implicated in NAFLD, the role of N1-methyladenosine (m1A) and its regulators is largely unexplored. Recently, YTHDF1, a well-characterized m6A reader, was also shown to recognize m1A; however, the functional consequences of this dual specificity are unknown. This study aimed to investigate the role of YTHDF1 in NAFLD pathogenesis and to explore whether its function is mediated through recognition of RNA methylation modification on specific target mRNAs.

Expression of YTHDF1 in NAFLD was analyzed in the GEO database. Loss-of-function studies for YTHDF1 were conducted in vivo (high-fat diet-fed mice) and in vitro (free fatty acid-treated HepG2 cells) in models of NAFLD. We employed RNA-seq and m1A-MeRIP-seq to identify key targets, followed by mechanistic validation of the YTHDF1–m1A–NUPR1 axis using biochemical, histological, and mRNA stability assays.

We identified a critical role for YTHDF1 in promoting hepatic steatosis. NUPR1, a stress-induced transcriptional regulator, undergoes m1A modification. YTHDF1 directly binds to m1A-modified NUPR1 mRNA, enhancing its stability, thereby leading to elevated NUPR1 protein levels. Functionally, upregulated NUPR1 acts as a core driver of NAFLD pathogenesis by activating lipogenic and suppressing fatty acid β-oxidation genes, thereby exacerbating hepatic lipid accumulation.

Our study unveils a novel epitranscriptomic mechanism in which YTHDF1, functioning as a dual-specificity reader, governs NAFLD progression through the m1A-NUPR1 axis. This not only expands the understanding of RNA modification recognition but also establishes the YTHDF1–m1A–NUPR1 pathway as a promising therapeutic target for metabolic liver disease.

Metabolic dysfunction-associated fatty liver disease (MAFLD) has become one of the leading causes of chronic liver diseases in China, imposing a substantial and growing burden on the healthcare system. Considering the large number of individuals affected by MAFLD and the gap in disease management capacity at the primary care level, standardized guidance tailored to primary healthcare settings is urgently needed. In response, the Chronic Disease Management Branch of the China Medical Biotechnology Association convened a multidisciplinary working group incorporating hepatologists, general practitioners, and other specialists to initiate the first China national Guidelines for Diagnosis and Management of Metabolic Dysfunction-associated Fatty Liver Disease in Primary Care (2025). These guidelines provide recommendations and suggestions covering screening, risk assessment, diagnosis, treatment, referral pathways, and follow-up tailored for primary care institutions, thereby improving the long-term outcomes for the population with MAFLD and comprehensively strengthening the role of primary healthcare in chronic liver disease management.

The optimal management strategy for adults with immune-tolerant (IT) chronic hepatitis B infection remains undefined. This study aimed to investigate the efficacy and predictive factors of a pegylated interferon (Peg-IFN)-based treatment strategy in IT patients with chronic HBV infection.

In this pilot, open-label, prospective study, 286 patients aged 18 to 60 years with IT characteristics were enrolled and allocated to one of three groups. The combination group received Peg-IFN for 48–96 weeks, with tenofovir disoproxil fumarate (TDF) initiated at week 12 and continued through week 96 (n = 103). The monotherapy group received TDF monotherapy alone (n = 125), and the control group was monitored without therapeutic intervention (n = 58).

No patients in the control group met any predefined efficacy endpoints. Intention-to-treat analysis showed that patients in the combination group achieved significantly higher virological response rates (71.8% vs. 53.6%, p = 0.005), hepatitis B e antigen seroconversion rates (15.5% vs. 1.6%, p < 0.001), and hepatitis B surface antigen (HBsAg) loss rates (10.7% vs. 0%, p < 0.001) compared with those in the monotherapy group at week 96. In the combination group, the cumulative rate of HBsAg loss was 5.4% at week 48 and increased to 11.8% by week 96. Independent predictors of achieving either hepatitis B e antigen seroconversion or HBsAg loss were baseline age under 30 years (odds ratio = 0.217, 95% confidence interval: 0.048–0.976, p = 0.046) and a decline in HBsAg level greater than 1 log10 IU/mL by week 24 (odds ratio = 13.976, 95% confidence interval: 2.506–77.932, p = 0.003).

A Peg-IFN-based treatment strategy significantly increases response rates compared with TDF monotherapy or observation in patients with IT characteristics.

Vulvovaginal candidiasis, an infection caused by an abnormal proliferation of Candida species in the vagina and vulva, is particularly relevant, affecting up to 75% of women of reproductive age. Because of antifungal drug resistance, a significant number of plants are used to treat vaginal candidoses in Cameroon. Thus, the scientific validation of the use of these plants in treating candidiasis is valuable. This study sought to identify medicinal plants used to treat vaginal infections in the Dschang district and evaluate the antifungal activity of the most promising plants on five Candida species.

The ethnobotanical survey was conducted in Dschang (Menoua Division, West Cameroon) through individual interviews using a semi-structured questionnaire. Extracts from seventeen plants were obtained by maceration using water or a water–ethanol solution (3:7; v/v). Antifungal activity was evaluated using the microdilution method.

Forty-eight plants belonging to 33 families were identified as treating vaginal infections. Decoction and formulation of ovules were the prevalent modes of plant preparation, with leaves and bark being the predominant plant organs used. Out of thirty-four extracts tested, two (CSEHAlc and MIEHAlc) showed antifungal activity, with minimum inhibitory concentrations ranging from 0.315 to 2.5 mg/mL. The determination of the minimum fungicidal concentrations revealed the fungicidal orientation of these bioactive extracts.

This study identifies medicinal plants used to treat vaginal infections in Dschang and their modes of preparation. The in vitro antifungal screening of selected plants indicated Mangifera indica and Canarium schweinfurthii as the anti-Candida plants that can be further exploited for antifungal drug discovery.

Despite rapid advances in computational biology and regulatory reforms encouraging the reduction of animal use, a clear synthesis of how artificial intelligence (AI)-driven polypharmacology can function as a scientific and ethical bridge between traditional in vivo pharmacology and human-relevant drug development remains lacking. The shift from cage-based experimentation to code-based predictive modeling presents both opportunities and unresolved challenges in biological interpretation, regulatory acceptance, and pharmacology education. Therefore, this review aims to critically examine the transition toward AI-enabled, human-centric drug discovery within the framework of the 3R principles (Replacement, Reduction, and Refinement). Specifically, it explores (i) the global regulatory and ethical drivers accelerating non-animal methodologies, (ii) the scientific and educational gaps emerging from reduced dependence on animal models, and (iii) the role of AI and deep learning in reconstructing biological complexity through multi-omics integration and predictive toxicity modeling. By analyzing emerging AI platforms and computational strategies, this review highlights how AI-driven polypharmacology may offer a scalable, ethical, and precision-oriented framework for future pharmacological research.