Environmental geochemistry and health
May 31, 2025
It is necessary to predict hair mercury (Hg) levels and specify the related effective factors to develop preventive strategies to reduce Hg exposure in different regions. This study is the first effort to investigate the effectiveness of eight machin...
Congenital heart disease affects approximately 1% of children worldwide, with a number of cases in resource-limited settings remaining undiagnosed through school age. While cardiac auscultation is a key screening method, its effectiveness varies wide...
Colon adenocarcinoma (COAD) is one of the most prevalent malignancies worldwide and its prognosis is extremely poor. Angiogenesis has been linked to clinical outcomes, tumor progression, and treatment sensitivity. However, the role of angiogenesis in...
OBJECTIVES: To develop and validate an automated diabetic macular oedema (DME) classification system based on the images from different three-dimensional optical coherence tomography (3-D OCT) devices.
Colorectal cancer (CRC) represents a major global disease burden with nearly 1 million cancer-related deaths annually. TNM staging has served as the foundation for predicting patient prognosis, despite variation across staging groups. The consensus m...
FASEB journal : official publication of the Federation of American Societies for Experimental Biology
May 31, 2025
Anoikis resistance plays a crucial role in the proliferation, metastasis, and invasion of hepatocellular carcinoma (HCC). However, the key genes involved remain to be identified. This study aimed to investigate the prognostic value and impact of anoi...
PURPOSE: To compare the image quality and pulmonary nodule detectability and measurement accuracy between deep learning reconstruction (DLR) and hybrid iterative reconstruction (HIR) of chest ultra-low-dose CT (ULDCT).
BMC medical informatics and decision making
May 30, 2025
PURPOSE: This study aims to develop a deep learning framework that leverages modality imputation and subregion segmentation to improve grading accuracy in high-grade gliomas.
OBJECTIVES: This study aims to develop an imaging-based machine learning model for evaluating the severity of ischemic stroke in the middle cerebral artery (MCA) territory.
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