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.

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.

Acute leukemias with chimeric fusion genes involving FET (FUS, EWSR1, and TAF15) family proteins and ETS (E26 transformation-specific)-like transcription factors often present with unique clinical and pathological characteristics. This mini-review aims to summarize the clinical and pathological features of acute leukemia cases harboring rearrangements involving the fused in sarcoma (FUS) or Ewing sarcoma breakpoint region 1 (EWSR1) genes.

An extensive literature review was performed on reported acute leukemia cases with fusions involving FUS or EWSR1. The details of the reported cases, as well as summarized information, are presented.

Rare cases of acute leukemia have been found to harbor either FUS or EWSR1 gene rearrangements with ETS or non-ETS proteins as partners and demonstrate heterogeneous clinical and pathological features. Acute leukemias carrying FUS gene rearrangements present with diverse immunophenotypes and are predominantly, but not exclusively, acute myeloid leukemia (AML), with ERG as the most frequent fusion partner. In contrast, acute leukemias with EWSR1 gene rearrangements more commonly present as B-cell acute lymphoblastic leukemia (ALL) and mixed phenotypic acute leukemia (MPAL), with ZNF384 as the predominant partner. At present, FUS::ERG-positive AML is the only specific entity with a FET::ETS fusion that is formally recognized in the World Health Organization 5th edition hematolymphoid tumor classification (WHO-HEM5) and the International Consensus Classification (ICC) systems. Cytogenetic karyotyping and fluorescence in situ hybridization remain crucial tools for detecting chromosomal translocations in over half of acute leukemias harboring FUS or EWSR1 gene rearrangements. However, a subset of patients may exhibit a normal karyotype and require advanced molecular diagnostic methods. EWSR1-rearranged leukemias can be difficult to distinguish from Ewing sarcoma and therefore require particular attention.

As more cases and additional data become available, it may be justified to expand this category of acute leukemias to include other specific acute leukemia entities with fusions involving FET::ETS, such as FUS::FLI1 and FUS::FEV, in addition to FUS::ERG-positive AML. However, additional data are required to support such subclassification. In contrast, AML cases with EWSR1 rearrangements are exceedingly rare and display considerable variability. Cases of B-ALL or B/myeloid MPAL with the EWSR1::ZNF384 fusion may be more appropriately classified together with other ZNF384-rearranged leukemia subtypes. Advanced molecular diagnostic methods, especially RNA-based next-generation sequencing, are suggested to improve the accurate diagnosis of acute leukemias with FUS or EWSR1 fusions. Additional pathologic workup, particularly immunohistochemical staining with hematopoietic markers, is highly recommended to differentiate EWSR1-rearranged leukemia from Ewing sarcoma.