Liver Transplantation

Integrative single-cell sequencing and machine learning analyses identify chronic inflammation and cancer-associated fibroblast subsets as drivers of adverse outcomes in ovarian cancer.

BACKGROUND: Ovarian cancer remains the deadliest gynecological malignancy, with neoadjuvant chemotherapy (NACT) often leaving residual fibroblast-enriched disease. METHODS: Single-cell RNA sequencing data from 64,097 cells were analyzed using Seurat (v4.1.3) with Harmony batch correction. CellChat (v1.1.3) mapped intercellular communication, while Monocle3 constructed pseudotime trajectories. High...

Revisiting clinical scoring tools for the diagnosis of thrombotic thrombocytopenic purpura (TTP).

Thrombotic thrombocytopenic purpura (TTP) is a rare, life-threatening hematologic emergency that leads to increased complexities and mortality due to diagnostic delays. Therapeutic plasma exchange (TPE) along with immunosuppressants is life-saving in cases with severe ADAMTS13 deficiency. Routine ADAMTS13 activity testing is unavailable in many diagnostic centers. Clinical heterogeneity also compl...

Machine learning of sunitinib resistance genes reveals that SP5 regulates angiogenesis and sunitinib resistance in RCC via the VEGFA/AKT axis.

BACKGROUND: Sunitinib resistance remains a major clinical challenge in renal cell carcinoma, and the underlying molecular mechanisms are incompletely ...

Unclassifiable Invasive Pulmonary Aspergillosis in the Intensive Care Unit: Insights From a Multicenter, Retrospective Cohort Study in France (2022-2024).

BACKGROUND: Suspected invasive pulmonary aspergillosis (IPA) is increasingly treated in intensive care, including in patients without classical immuno...

Large-scale AI-guided liver malignancy diagnosis: multicenter study and a single-arm trial.

Liver malignancies are frequently evaluated on contrast-enhanced computed tomography (CE-CT), but missed or delayed diagnoses remain a clinically impo...

Nanotechnology-based immunotherapy: integrating Artificial Intelligence (AI) with current strategies in combating brain cancer disease.

Brain cancer is one of the most challenging malignancies and a major contributor to worldwide morbidity and mortality. Glioblastoma, the most aggressi...

Radiotherapy and immunotherapy in cancer treatment: mechanisms of clinical synergy.

Synergizing radiotherapy (RT) with immune checkpoint inhibitors has emerged as a promising strategy for solid tumors. RT acts as a potent immunomodula...

Multi-Omics Integration Identifies a CDH3-Associated Malignant Epithelial State and Immunosuppressive Niche to Predict Prognosis in Thymic Epithelial Tumors.

Thymic epithelial tumors (TETs) are rare and heterogeneous malignancies whose aggressive epithelial states and microenvironmental organization remain ...

A novel adrenomedullin receptor signaling-based prognostic model predicts immunosuppressive microenvironment and informs therapeutic stratification in hepatocellular carcinoma: A retrospective bioinformatics study.

The adrenomedullin receptor signaling pathway plays a crucial role in tumor progression, yet its comprehensive implication in hepatocellular carcinoma...

Machine-learning-based Phenomapping of Patients with Keratinocyte Carcinoma: Data-driven Subgrouping by Disease Burden, Comorbidities and Socioeconomic Status.

Keratinocyte carcinoma (KC) places a considerable and growing burden on healthcare systems. Given the KC population's heterogeneity, tailored clinical...

Cancer immunotherapy under immune pressure: Mechanisms of resistance and the shift toward precision immune engineering.

Cancer immunotherapy has transformed the treatment landscape across multiple malignancies; however, durable responses remain limited to a subset of pa...

Stephania cepharantha Hayata (Baiyaozi) attenuates cirrhosis-associated intestinal barrier injury by restoring mitochondrial function and suppressing PI3K/AKT signaling.

ETHNOPHARMACOLOGICAL RELEVANCE: Baiyaozi, the dried tuberous root of Stephania cepharantha Hayata, has long been used in traditional Chinese medicine ...

Single-Atom Nanozymes for High-Efficiency Cancer Immunotherapy by Targeting Tumor Microenvironment.

Tumor immunotherapy has emerged as a transformative strategy for cancer treatment. However, its clinical efficacy remains limited by the immunosuppres...

MAFLD/MASLD: Past, Present, and Future.

Metabolic dysfunction-associated steatotic liver disease (MASLD), also known as nonalcoholic fatty liver disease and metabolic dysfunction-associated ...

The molecular landscape of chordoma: Current frontiers from multi-omics to artificial intelligence.

Chordoma is a rare and aggressive malignant bone tumor of the axial skeleton that has historically challenged clinicians due to its complex anatomical...

Transforming Cancer Treatment: Integrative Strategies Targeting the Tumor Microenvironment through Biological Innovation and Artificial Intelligence.

Cancer is no longer viewed solely as a consequence of tumor-intrinsic genetic alterations but rather as a disease sustained by a complex and evolving ...

Machine learning-derived clinical phenotypes of ascites in cirrhosis: A multi-center latent class analysis with external validation.

BACKGROUND: Ascites development in cirrhosis reduces five-year survival from 80% to 30%, yet substantial heterogeneity exists among patients with comp...

Clinical Research on Microecological Landscape for Infection Risk Stratification in Newly Diagnosed Patients with Hematological Conditions.

INTRODUCTION: Infection is a common and potentially fatal complication during the treatment of hematological diseases, particularly in the context of ...

C1R suppresses hepatocellular carcinoma cell aggressiveness through promoting HIF-1α degradation and modulating cuproptosis-related protein expression.

BACKGROUND: Hepatocellular carcinoma (HCC) frequently develops in the setting of chronic hepatitis B virus (HBV) infection, yet the molecular events s...

Predicting Intratumoral Heterogeneity and Stratifying Prognostic Risk in Gastric Cancer Using a Histology-Based Pathomics-Deep Learning Fusion Model.

Intratumoral heterogeneity (ITH) is a fundamental driver of clonal evolution and therapeutic resistance in gastric cancer (GC). However, the clinical ...