Oncology/Hematology

Integrated single-cell and spatial mapping coupled with machine learning unveils core stemness landscapes and regulatory drivers in triple-negative breast cancer.

OBJECTIVE: Triple-negative breast cancer (TNBC) exhibits pronounced intratumoral heterogeneity, and cancer stem cells (CSCs) are thought to play a pivotal role in this process. However, the molecular regulatory mechanisms linking CSC-associated stemness features to tumor progression remain insufficiently elucidated. METHODS: We integrated single-cell RNA sequencing (scRNA-seq), spatial transcripto...

Early prediction of adult lymphoma-associated haemophagocytic lymphohistiocytosis using an interpretable machine learning model.

Lymphoma-associated haemophagocytic lymphohistiocytosis (LA-HLH) is associated with a high mortality rate, making early diagnosis and appropriate treatment critical for improving patient outcomes. In this study, we enrolled 126 patients diagnosed with LA-HLH and 254 with non-LA-HLH. A machine learning-based predictive model was developed and validated to enable timely differentiation of LA-HLH fro...

Stage-dependent gut microbiome and functional signatures across the liver disease spectrum: an integrative multicohort study.

BACKGROUND: The gut-liver axis plays a critical role in liver disease progression; however, how gut microbial ecology and function vary across disease...

Digital twin technologies for supporting self-care in adults with diet-related chronic conditions: a systematic review.

BACKGROUND: Diet-related chronic conditions are major contributors to global morbidity and mortality. Effective management of these conditions require...

Integrative machine learning analysis suggests novel molecular targets for liver cancer diagnosis and therapy.

BACKGROUND: Hepatocellular carcinoma (HCC) is a critical condition characterized by unchecked cellular growth in the liver, often leading to systemic ...

Probing uric acid-related prognostic genes and their molecular mechanisms in prostate cancer based on transcriptomic data.

Recent studies highlight the role of uric acid in tumor development, but its impact on prostate cancer (PCa) remains underexplored. This study aimed t...

LLM-powered prostate cancer staging from PSMA-PET/CT reports using PROMISE v2.

Accurate staging of prostate cancer is essential for guiding therapy and predicting outcomes. Prostate-specific membrane antigen (PSMA) PET/CT has bec...

Co-localization analysis of spatial transcriptomics in ligand-receptor pairs from tumor microenvironment.

Ligand-receptor pair analysis, a key aspect of cell-to-cell interactions, is vital for understanding physiological and pathological processes in organ...

Precision oncology meets nanotechnology: AI-driven liposomal nanocarriers for enhanced cancer immunotherapy.

The integration of artificial intelligence (AI) with nanotechnology is redefining therapeutic strategies in cancer immunotherapy and chemotherapy. The...

Preoperative CT-based topologically distinct intratumoral heterogeneity scores for predicting intratumoral tertiary lymphoid structures and outcomes in hepatocellular carcinoma: A multicenter study.

OBJECTIVES: Intratumoral tertiary lymphoid structures (iTLSs) are prognostic biomarkers for hepatocellular carcinoma (HCC). This study aimed to develo...

Listening in: Orthopaedic Oncology Physicians' Perspectives on Implementation of Audio Recording/Artificial Intelligence Assist in Office Visits.

INTRODUCTION: With rapid expansion of artificial intelligence (AI) in clinical documentation, responsible implementation of this tool is imperative in...

Unveiling the prognostic and therapeutic landscape of the zinc transporter protein SLC39A family in colorectal cancer through multi-omics and machine learning approaches.

Colorectal cancer (CRC) remains a significant global health challenge due to its high prevalence and mortality rates. Zinc transporter proteins of the...

Public Transcriptomic Data Mining for SCLC: From Candidate Ma rkers to Therapeutic Exploration.

BACKGROUND: Lung cancer, especially small-cell lung cancer (SCLC), is a widespread and deadly disease often detected at advanced stages, resulting in ...

An interpretable machine learning model based on habitat radiomics combined with deep learning for predicting the WHO/ISUP grade of patients with clear cell renal cell carcinoma.

Development and validation of an interpretable prediction model for the risk of unplanned reoperation in patients underwent intracranial tumor surgery.

Brain and central nervous system (CNS) malignancies represent a substantial burden on healthcare systems worldwide, and unplanned reoperations followi...

Recent Advances in the Non-viral Delivery of Genes to Central Nervous System Disorders.

Disorders of the central nervous system (CNS), neurological disorders, neurodegenerative disorders, genetic disorders) constitute a significant burden...

Application of Artificial Intelligence in MedDRA Coding: A Practical Exploration from Clinical Data Management Perspective.

BACKGROUND: Traditional manual MedDRA coding in clinical data management (CDM) faces persistent challenges, including suboptimal site data quality, te...

Integrated single-cell and bulk transcriptomics reveals STAB1 as a novel therapeutic target for ovarian cancer.

The immunosuppressive tumor microenvironment (TME) and intrinsic heterogeneity of ovarian cancer (OC) are primary drivers of therapeutic resistance an...

Deep learning-based computer-aided diagnosis for parotid gland tumors on MRI.

OBJECTIVE: Accurate imaging-based differentiation of benign and malignant parotid tumors is essential for determining appropriate therapy. We investig...

The Wnt receptor Frizzled3 (FZD3) drives aggressive phenotypes in small cell lung cancer.

BACKGROUND: Small cell lung cancer (SCLC) is an aggressive neuroendocrine malignancy characterized by rapid progression and poor prognosis. This study...