Pancreatic fibrosis, a major pathological feature of chronic pancreatitis, is primarily driven by the abnormal activation of pancreatic stellate cells (PSCs) and excessive deposition of extracellular matrix. Traditional Chinese medicine (TCM) offers a holistic and synergistic approach to preventing and treating pancreatic fibrosis through multi-target regulation of PSC activation. This review systematically elucidates the mechanisms by which TCM—encompassing both bioactive monomers and compound formulations—modulates key signaling pathways involved in PSC activation, including the mitogen-activated protein kinase, transforming growth factor-β/Smad, platelet-derived growth factor, nuclear factor kappa B, and Wingless/β-catenin pathways. By simultaneously targeting these interconnected signaling networks, TCM strategies effectively inhibit PSC activation, attenuate inflammatory responses, and reduce extracellular matrix deposition. In contrast to single-target pharmacological inhibitors, TCM embodies a “multi-component, multi-pathway” therapeutic paradigm that aligns with the complex pathophysiology of pancreatic fibrosis. This review also draws comparative insights from liver fibrosis, highlighting conserved pathways and organ-specific regulatory contexts. Ultimately, TCM represents a promising integrative avenue for the prevention and treatment of pancreatic fibrosis, supported by growing preclinical evidence and aligned with the principles of holistic intervention.

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.

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.

The immunosuppressive tumor microenvironment (TME) limits immunotherapy efficacy in intrahepatic cholangiocarcinoma (ICC). Understanding the molecular drivers of this TME is essential for developing new therapies. This study aimed to identify novel oncogenes that modulate the immune landscape of ICC using a multi-omics approach.

We integrated transcriptomic and proteomic data from our ICC cohorts with public datasets (TCGA-CHOL, GSE107943, OEP002768) to identify genes co-upregulated with PD-L1 (CD274). Single-cell RNA sequencing (scRNA-seq) was used to analyze cell-type-specific expression and intercellular communication. Clinical significance was validated through tissue microarrays and multiplex immunofluorescence in an independent ICC cohort.

Multi-omics screening identified TACC3 as a key candidate in ICC. Elevated TACC3 expression in ICC tissues correlated with poor prognosis and promoted tumor cell proliferation and migration. TACC3 activated the STAT3 pathway, increasing PD-L1 transcription. scRNA-seq showed TACC3/PD-L1 interaction in malignant epithelial cells, with PD-L1 co-expressed with FOXP3 in regulatory T cells (Tregs). Cell–cell communication analysis predicted strong interactions between malignant cells and Tregs. TACC3 knockdown reduced PD-L1 expression and inhibited STAT3 and AKT phosphorylation. Clinical validation confirmed co-expression of TACC3, PD-L1, and FOXP3, with high TACC3 levels linked to worse clinicopathological features and shorter progression-free survival.

Our study defines a TACC3-STAT3-PD-L1 axis driving immunosuppression in ICC. TACC3 fosters an immunosuppressive TME by upregulating PD-L1 and is associated with a Treg-rich contexture, suggesting that TACC3 may serve as a potential therapeutic target to overcome ICC immunosuppression.

Unlike the traditional staging treatment of tumors, the core of “Green Tumor Treatment” is to divide the treatment of tumors into three stages: Hegemony (directly targeting the cancer focus), Kingship (supporting the body’s vital energy and eliminating pathogenic factors), and Imperialism (improving the internal environment), based on the urgency of the tumor and the patient’s physical condition. This approach guides the clinical treatment of tumors. Its treatment system incorporates all minimally invasive and low-damage treatment methods, combining internal and external treatments, traditional Chinese medicine and Western medicine, as well as local and systemic treatments. It aims to maximize treatment outcomes while ensuring the patient’s quality of life, which is highly consistent with the treatment goals for primary liver cancer. This review aims to explore the integrated Traditional Chinese and Western medicine treatment model for primary liver cancer under the guidance of the Green Tumor Treatment concept.

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.

Primary sclerosing cholangitis (PSC) is an immune-mediated cholestatic liver disease. Its molecular etiology remains poorly defined, hindering the development of mechanism-based diagnostics and therapies. Therefore, this study aimed to identify key molecular drivers and causal biomarkers of PSC by integrating transcriptomics, machine learning, and genetic causal inference.

We deployed an integrated computational framework combining transcriptomics, network biology, machine learning, and genetic causal inference. Peripheral blood transcriptomes from PSC patients and controls were analyzed to identify disease-associated modules. Candidate genes were refined via protein-protein interaction networks and a multi-algorithm machine learning screen. Causal inference was performed using two-sample Mendelian randomization, integrating plasma protein quantitative trait loci with PSC genome-wide association study summary statistics.

Transcriptomic analysis revealed a PSC-associated module enriched in ribosome biogenesis and protein homeostasis pathways. A machine learning-optimized nine-gene signature (including PTMA, SUMO1, Shwachman-Bodian-Diamond syndrome (SBDS), RPL7, EIF1AX, ANP32A, PCNA, FAM98A, and MPHOSPH6) achieved high diagnostic accuracy (mean AUC = 0.908) and was consistently downregulated in PSC. This signature was linked to a remodeled immune microenvironment characterized by myeloid skewing and specific transcriptional-immune covariation patterns. Mendelian randomization identified SBDS as a putatively causal protective factor, where genetically instrumented higher plasma SBDS protein levels were robustly associated with a lower PSC risk (IVW OR = 0.525, 95% CI: 0.356–0.773, P = 0.001). Sensitivity analyses supported the validity of the Mendelian randomization assumptions.

Our study establishes disrupted ribosome homeostasis as a causal pathway in PSC and nominates plasma SBDS as a high-confidence diagnostic biomarker and therapeutic target. The integrative framework provides a generalizable strategy for discovering causal biomarkers in complex diseases.

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.