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.

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.

Antimitochondrial antibody (AMA)-negative primary biliary cholangitis (PBC) is a rare presentation of PBC that comprises 5%–10% of all PBC patients. The pathogenesis of AMA-negative PBC appears to be similar to that of AMA-positive PBC. AMA-negative PBC presents similarly to AMA-positive PBC, with symptoms of cholestasis, fatigue, and pruritus most commonly reported. Defective bicarbonate production, resulting in acidification of bile and bile acids, has been proposed as the primary mechanism of damage to bile ducts and hepatocytes and is reflected in elevations of alkaline phosphatase and aminotransferases. Chronic damage can lead to the development of cirrhosis. The diagnosis is made by the finding of AMA negativity by ELISA or assays of similar sensitivity and a positive PBC-specific antinuclear antibody (ANA; anti-glycoprotein 210 and anti-speckled 100 kDa protein) test. In cases in which anti-glycoprotein 210 and anti-speckled 100 kDa protein assays are also negative, a liver biopsy is required to make the diagnosis after exclusion of other causes of cholestasis by magnetic resonance cholangiopancreatography or endoscopic retrograde cholangiopancreatography. Treatment for AMA-negative PBC is the same as for AMA-positive cases, with ursodeoxycholic acid as the first-line treatment. Current treatment is most effective in early stages, where it slows but does not eliminate progression. Risk stratification by validated tools such as the GLOBE and UK-PBC scores remains useful in AMA-negative PBC.

Pancreatic cystic neoplasms (PCNs) are increasingly detected in clinical practice, yet substantial variability exists in imaging interpretation and reporting, which may affect clinical decision-making. This guideline was developed to standardize diagnostic imaging evaluation and reporting for PCNs. A multidisciplinary expert panel conducted literature search and critical appraisal of domestic and international evidence, identified key clinical questions, and formulated recommendations using the Grading of Recommendations Assessment, Development and Evaluation framework. A modified Delphi consensus process and external review were performed to ensure the robustness of the recommendations. A total of 21 key questions were addressed, covering essential aspects of imaging evaluation and reporting for PCNs, including the preferred imaging modality for suspected lesions; standardized measurement of cyst size and mural nodules and their clinical significance; definitions of cyst wall and septal thickening; optimal imaging approaches for assessing the relationship between cystic lesions and the main pancreatic duct; measurement and evaluation of main pancreatic duct diameter and dilation; imaging-based classification of intraductal papillary mucinous neoplasms and serous cystic neoplasms; assessment of ductal obstruction, calcification, hemorrhage, and pancreatitis-related changes; criteria for suspicious lymph nodes; differentiation of PCNs from pancreatic pseudocysts or retention cysts; and recommended imaging modalities and follow-up intervals. This guideline provides a structured and evidence-based framework for imaging evaluation and reporting of PCNs, which may improve the consistency and clarity of imaging reports and support clinical decision-making.

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 a 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 (inverse variance weighted odds ratio = 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.

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.

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.