Lymphoma

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 well as understanding the regulatory mechanisms of biomolecules. Researchers have developed several computational methods to predict PepPI. However, existing computational methods also have significant limitations. At the level of data feature charact...

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 distribution and impact. Conventional methods such as chromatography and spectrometry provide precise results but are destructive, time-consuming, and resource intensive. Hyperspectral Imaging in the Near-Infrared range (HSI-NIR) offers a non-destructive alt...

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...

dMMR prediction from colorectal cancer histopathology: Leveraging non-tumor and low-magnification regions.

BACKGROUND AND OBJECTIVE: Colorectal cancer is the second leading cause of cancer-related mortality worldwide, posing a substantial burden on healthca...

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.

Combining computed tomography radiomics and clinical features to predict lymph node metastasis in patients with lung cancer.

BACKGROUND: Accurate preoperative assessment of lymph node metastasis (LNM) is crucial for treatment planning and prognostic stratification in patient...

Enhancing heat and mass transfer in hybrid nanofluid with gyrotactic microbes and local thermal non equlibrium effects using artificial neural network.

This study analyzes the impact of local thermal non-equilibrium on the bioconvection flow of hybrid nanofluid across a slender extending sheet contain...

Development and temporal validation of an interpretable point score for in-hospital mortality in CLL/SLL using the U.S. National Inpatient Sample, 2016-2022.

In-hospital mortality among patients with chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) is ∼6%, yet no validated, transparent beds...

AI in the Prediction of Hepatic Fibrosis Progression Using Non-Coding RNAs.

Hepatic fibrosis is a dynamic and progressive condition that can lead to cirrhosis and hepatocellular carcinoma (HCC) if left untreated. Appropriate a...

Advanced Prompting Techniques Informed by Clinical Expertise Improve the Accuracy of LLM Data Extraction but Increase Non-Determinism.

Prompt engineering techniques which aid in the use of generative artificial intelligence to address classification tasks have expanded considerably in...