Oncology/Hematology

A systems approach identifies MERTK as a therapeutic vulnerability in ZFTA-RELA-driven ependymomas.

Ependymomas (EPN) are rare central nervous system tumors that account for approximately 10% of intracranial tumors in children and 4% in adults. Despite their clinical and molecular heterogeneity, spanning supratentorial, posterior fossa, and spinal subtypes, treatment remains limited to surgery and radiotherapy, with chemotherapy offering minimal benefit. Here, we performed transcriptomic analysi...

Predictive modeling of molecular activity underlying physical cell-cell interactions.

Interactions between cells are central to tissue organization and function in health and disease. Labeling immune partnerships by sortagging intercellular contacts (LIPSTIC) quantitatively measures direct physical cell-cell interactions. Combined with single-cell RNA sequencing (scRNA-seq), it jointly profiles cell interaction intensities and intracellular transcriptomes. Here, we present group la...

MoRE-Net: An Interpretable and Modality-robust Model for Brain Tumor Grading.

PURPOSE: Interpretability and robustness are both critical for developing trustworthy artificial intelligence, especially in high-stakes domains such ...

Development of a Real-time Radiation Detection System that Uses IoT to Provide Real-time Monitoring in Custom-built Website.

Effective radiation monitoring is crucial for ensuring public security and safety, particularly in the event of nuclear (e.g., nuclear accident, fallo...

Toward Integration of Molecular Measures and Artificial Intelligence-Based Assessments With Clinical End Points in Inflammatory Bowel Disease.

Multimodal profiling of inflammatory bowel disease (IBD) patient tissue and blood samples has revealed the disease spectrum in unprecedented detail, a...

Clinical feasibility test of 60 kVp double-low-dose coronary CT angiography with a deep learning reconstruction algorithm.

OBJECTIVES: To test the feasibility of 60 kVp double-low-dose coronary CT angiography (CCTA) with a deep learning reconstruction (DLR) algorithm. MATE...

Development and validation of a prognosis model for low-grade gliomas based on metabolic gene risk scoring and immune microenvironment interaction.

PURPOSE: Low-grade gliomas(LGGs) show significant clinical and molecular heterogeneity, complicating progression prediction with conventional indicato...

Identifying Post-Surgical Recurrence Subtype of T1 Stage Colorectal Cancer by Machine Learning.

INTRODUCTION: Traditional risk stratification heavily relies on expert judgment and manually established thresholds. This study aims to automatically ...

Digitized Canine Olfaction and Multimodal Biosensing for Breath-Based Multi-Cancer Detection: A Hypothesis-Driven Perspective.

Multi-Cancer Early Detection (MCED) is critical for reducing cancer mortality, however current screening technologies have limitations in accessibilit...

Attention-guided enhanced deconvolution enables reference-free cell type estimation in spatial transcriptomics.

Spatial transcriptomics technologies profile gene expression across tissue sections while retaining spatial information, yet most platforms capture si...

Integrative network toxicology and experimental evidence reveal mechanisms underlying diethyl phthalate-induced initiation and progression of endometrial cancer.

Diethyl phthalate (DEP) is a ubiquitous environmental endocrine-disrupting chemical (EDC). Epidemiological studies have suggested a potential associat...

Predicting renal-cell carcinoma recurrence after partial nephrectomy: a CT-radiomics approach.

BACKGROUND: About 30-40% of patients with renal-cell carcinoma experience recurrence within 5 years after partial nephrectomy despite current prognost...

A novel integrative machine learning-based prognostic model reveals lactylation regulation in hepatocellular carcinoma progression.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common malignant liver tumor with poor clinical outcomes. Accumulated evidence has demon...

Health-oriented sleep states: making sleep states reflect health conditions.

STUDY OBJECTIVES: The rich information in sleep offers insights into brain function and overall health. The current guidelines for sleep staging by th...

Mitochondrial DNA methylation predicts immunotherapy response and prognosis in lung adenocarcinoma: evidence from scRNA-Seq and machine learning.

Integrative bioinformatics and experiments identify RIBC2 as a key regulator in the esophageal cancer.

Early detection of esophageal cancer (EC) remains a major challenge due to the limited understanding of its initial molecular alterations. Therefore, ...

Magnetic resonance imaging (MRI) of the myometrium - benign and malignant disease.

Myometrial lesions are a common indication for pelvic imaging, with accurate characterisation crucial for guiding management. While ultrasonography re...

Development of a hybrid deep learning-based framework for liver fibrosis classification using ultrasound images.

BACKGROUND AND AIMS: Liver fibrosis is a progressive accumulation of extracellular matrix proteins with distortion of hepatic architecture and can pro...

Prediction of malignancy and metastasis of thyroid cancer by combined feature sets through advanced machine learning.

Implementing generative artificial intelligence in precision oncology: safety, governance, and significance.

The paramount challenge in precision oncology lies in further improving quality of life and response rates for individual patients. Efforts toward the...