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.

Predicting the malignant transformation of rectal precancerous lesions remains challenging because conventional Whole Slide Images (WSIs) capture morphological information but lack molecular insight. Multiomics data provide complementary biological signals that often precede visible morphological changes. This study aimed to develop an artificial intelligence (AI)-based multimodal framework integrating WSI and multiomics data for accurate early prediction of malignant transformation.

WSI patches (512×512 px at 20× magnification) and matched multiomics profiles were used for 450 rectal tissue samples from the publicly available The Cancer Genome Atlas dataset. A multimodal architecture was designed, employing a Vision Transformer (ViT-B/16) for WSI feature extraction and a Variational Autoencoder for multiomics representation learning. Features were fused via a cross-attention mechanism to capture inter-modality dependencies. Baseline models, including a convolutional neural network-only image model and an omics-only multilayer perceptron, were trained for comparison. Five-fold cross-validation was applied, with binary cross-entropy loss, the AdamW optimizer, early stopping, and hyperparameter tuning to ensure reproducibility.

The multimodal Vision Transformer–Variational Autoencoder fusion model outperformed unimodal baselines, achieving an accuracy of 0.892 ± 0.012 and an area under the receiver operating characteristic curve of 0.927 ± 0.009, corresponding to a 7–10% improvement over WSI-only and omics-only models. Cross-attention–based fusion improved prediction stability and classification performance, while interpretability analyses (Grad-CAM and SHAP) highlighted biologically meaningful histopathological regions and molecular feature contributions.

This study presents a robust and scalable AI-based framework for integrating WSI and multiomics data in rectal precancerous lesions. The model improves predictive precision compared with unimodal baselines and offers preliminary interpretability insights through attention mechanisms. These findings support the potential of multimodal AI for early cancer risk assessment and precision pathology.

Development of mixed histiocytosis (Langerhans cell histiocytosis (LCH))/Erdheim–Chester disease (ECD)) after treatment in patients with an initial skull LCH lesion has not been well recognized. An elderly woman initially developed LCH at the left temporal bone, preceded by polyuria and polydipsia five years earlier; the lesion was surgically removed. Two years thereafter, she experienced her first LCH relapse with a right parietal skull lesion, in which a BRAF V600E mutation was confirmed, and chemotherapy was initiated. After a second LCH relapse involving the left parietal bone, the patient presented with a third relapse at the L2 vertebra. This lesion was pathologically diagnosed as mixed histiocytosis (LCH/ECD), resulting in refractoriness to conventional chemotherapy, and was successfully treated with targeted therapy using BRAF and MEK inhibitors. Spinal mixed histiocytosis (LCH/ECD) may develop following relapses of skull LCH after chemotherapy, for which targeted therapy could be effective.

Immunothrombosis, the interplay between immune activation and coagulation, contributes to disease progression in inflammatory disorders. Its role in hepatitis B virus–related acute-on-chronic liver failure (HBV-ACLF) and the involvement of neutrophil extracellular traps (NETs) remain unclear. This study aimed to elucidate NETs-mediated immunothrombosis in HBV-ACLF.

Liver single-cell RNA sequencing data from HBV-ACLF patients and healthy controls were analyzed to define immune and endothelial transcriptional profiles. A cohort of 46 HBV-ACLF patients, 20 chronic hepatitis B patients, and 20 healthy controls was assessed for circulating NETs, endothelial injury markers, and coagulation parameters. Histopathology and in vitro assays examined NETs distribution and endothelial interactions.

NETs were markedly elevated in HBV-ACLF and correlated with endothelial injury markers (syndecan-1, von Willebrand factor, soluble thrombomodulin), coagulopathy, and prognostic scores. Histology revealed NETs colocalization with endothelial cells and platelets within hepatic microthrombi. NETs from patient neutrophils impaired endothelial integrity and enhanced procoagulant activity in vitro. Mechanistically, toll-like receptor 2 (TLR2) and complement component 5a receptor 1 (C5aR1) signaling were involved in NETs formation, and their pharmacological inhibition reduced NETs generation.

NETs are associated with endothelial injury and immunothrombosis in HBV-ACLF. Mechanistic analyses suggest a role for TLR2 and C5aR1 pathways in NETs formation, indicating potential targets for future therapeutic investigation.

Metabolic dysfunction–associated steatotic liver disease (MASLD), defined by the American Association for the Study of Liver Diseases as hepatic steatosis with at least one cardiometabolic risk factor, affects approximately 30–40% of adults worldwide. This condition may progress to fibrosis, cirrhosis, and hepatocellular carcinoma. The rising prevalence, alongside obesity and type 2 diabetes, underscores the need for early risk stratification and integrated therapeutic strategies. Circadian homeostasis, orchestrated by the suprachiasmatic nucleus and core clock gene feedback loops, synchronizes hepatic metabolic pathways with environmental light–dark cycles. The objective of this review is to evaluate the role of circadian disruption and metabolic dysfunction in the development of hepatic steatosis, as well as to assess current and potential treatment modalities for both disorders. Circadian disruption through shift work, artificial light at night, sleep restriction, and chrono-nutritional misalignment destabilizes hepatic clocks, promoting insulin resistance, dyslipidemia, inflammation, and steatosis. Experimental models demonstrate that clock gene dysfunction alone can induce steatohepatitis, while progressive MASLD further impairs central circadian regulation, establishing a self-reinforcing chrono-metabolic cycle. Pharmacologic therapies, including glucagon-like peptide-1 receptor agonists and thyroid hormone receptor-β agonists, improve histologic endpoints and fibrosis regression, although heterogeneity among clinical trials precludes direct comparison. Recent evidence characterizing MASLD as a predominantly nocturnal metabolic disorder further highlights persistent nighttime insulin dysregulation despite weight loss, emphasizing the potential role of circadian-targeted interventions such as melatonin. In conclusion, the peripheral circadian clock is intricately linked with MASLD pathogenesis, and metabolic dysfunction, in turn, disrupts circadian pathways. Several pharmacologic therapies offer potential for the treatment of MASLD and circadian dysfunction.

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.

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.

Primary liver cancer is one of the most common malignant tumors in China, which seriously threatens people’s lives and health. In recent years, with the advancement of basic and clinical research, the diagnosis and treatment methods for primary liver cancer have been continually enriched. Traditional Chinese medicine (TCM) has played an important role in the diagnosis and treatment of primary liver cancer, but there is no unified standard for differentiation and treatment, and efficacy evaluation. In order to further standardize the TCM diagnosis and treatment of primary liver cancer, according to the requirements of TCM standardization and related technical guidance documents, the drafting team compiled this guideline through literature research, expert interviews, questionnaire surveys, consensus meetings, etc., for reference by clinicians. This guideline is approved and issued by the China Association of Chinese Medicine, standard number: T/CACM 1575-2024.