Lymphoma

Advanced neuroimaging for central nervous system infections: when, where, how, and why?

PURPOSE OF REVIEW: Central nervous system (CNS) infections remain a major cause of morbidity and mortality worldwide, particularly in children, older adults, and immunocompromised patients. Timely recognition and classification of CNS infections are critical, yet early clinical and cerebrospinal fluid (CSF) findings are often non-specific. This review discusses when advanced neuroimaging adds valu...

PSMEKL: Positional and structural multiple empirical kernel learning for node embedding.

Graph Neural Networks (GNNs) are powerful for processing graph-structured data, where nodes are linked through edges. Generally, incorporating positional and structural information into node embeddings can capture distribution characteristics of nodes. However, traditional GNNs struggle to perfectly capture both types of information simultaneously. Moreover, existing node embedding methods struggl...

Quantitative regression of qFibrosis with resmetirom: Exploratory histologic endpoints from the MAESTRO-NASH phase III clinical trial.

BACKGROUND & AIMS: Resmetirom, a thyroid hormone beta (THR-β) agonist, has been shown to improve metabolic dysfunction-associated steatohepatitis (MAS...

Associations of epigenetic age acceleration with motor impairment: Evidence from the Parkinson's Progression Markers Initiative cohort.

Motor decline is a hallmark of Parkinson's disease (PD) and biological aging, yet the specific relationship between systemic biological aging and neur...

Convolutional neural networks detected changes in facial expressions of mice that had conditioned fear stress.

Animal models are essential for studying aversive states such as fear and pain. Facial expressions may provide non-invasive readouts of aversive state...

Identification of Novel Non-coding Genetic Variants of Serum Urate Using Whole Genome Sequencing in 7,339 Chinese.

OBJECTIVE: To investigate the genetic architecture of low-frequency and rare variants of serum urate (SU) in East Asian populations, aiming to clarify...

PepLM-GNN: A graph neural network framework leveraging pre-trained language models for peptide-protein binding prediction.

MOTIVATION: The precise prediction of peptide-protein interaction (PepPI) is a core support for promoting breakthroughs in peptide drug research, as w...

A pipeline for meso- and microplastic identification in aquatic systems using machine learning and hyperspectral imaging.

Microplastic pollution presents major environmental and health challenges, requiring accurate identification and quantification to assess its distribu...

Determining the percentage of recycled plastic content in a plastic product.

As global plastic waste continues to rise, accurately identifying and quantifying recycled content in plastic products is critical for developing a ci...

Mathematical Prediction Models for Sentinel Node Status in Early-Stage Breast Cancer: Protocol for a Systematic Review.

BACKGROUND: The status of the axilla remains a significant prognostic factor and influences adjuvant systemic and locoregional treatment choices in ea...

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 trea...

Development and validation of the ultrasound-based radiomics and deep learning prognostic models for diffuse large B-cell lymphoma.

A dorsal versus ventral network for understanding others in the developing brain.

Young children strongly depend on others, and learning to understand their mental states (referred to as Theory of Mind, ToM) is a key challenge of ea...

Non-invasive assessment of PD-L1 status and histology in non-small cell lung cancer using 18F-FDG PET/CT radiomics.

Assessing Disorders of Consciousness Using Temporal Sleep Dynamics Extracted From Whole-Night PSG.

Accurate assessment of patients with disorders of consciousness (DoC) remains a major clinical challenge due to the limitations of behavior-based eval...

Application value of gastric contrast ultrasonography in gastric tumors.

In recent years, gastric oral contrast ultrasonography (OCUS) and double contrast-enhanced ultrasonography (DCEUS) have emerged as promising imaging t...

Value of Endobronchial Ultrasound-Derived Radiomics in Differentiating Pulmonary Sarcoidosis from Mediastinal Lymph Node Tuberculosis.

INTRODUCTION: To explore the feasibility of an ultrasound radiomics machine learning model based on endobronchial ultrasound (EBUS) for differentiatin...

Development and validation of machine learning prognostic models for overall survival in non-surgical prostate cancer patients with bone metastases.

OBJECTIVE: To construct and interpret a machine learning model for predicting overall survival in nonsurgical prostate cancer with bone metastases (PC...

Are large language models consistent with the ASPS and AAPS guidelines? A comparison of AI chatbot recommendations and plastic surgery clinical guidance.

INTRODUCTION: Assessing the ability of AI chatbots to provide information consistent with clinical guidelines is essential for evaluating the accuracy...

Deep learning-based non-invasive prediction of axillary lymph node metastasis in breast cancer: performance of the YOLO-v11 object detection algorithm.