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Alzheimer's disease: an integrative bioinformatics and machine learning analysis reveals glutamine metabolism-associated gene biomarkers.
BMC Pharmacol Toxicol
January 2025
Yantai Affiliated Hospital of Binzhou Medical University, Yantai, Shandong, 264100, PR China.
Background: Alzheimer's disease (AD), a hallmark of age-related cognitive decline, is defined by its unique neuropathology. Metabolic dysregulation, particularly involving glutamine (Gln) metabolism, has emerged as a critical but underexplored aspect of AD pathophysiology, representing a significant gap in our current understanding of the disease.
Methods: To investigate the involvement of GlnMgs in AD, we conducted a comprehensive bioinformatic analysis.
hERGAT: predicting hERG blockers using graph attention mechanism through atom- and molecule-level interaction analyses.
J Cheminform
January 2025
Department of Intelligent Electronics and Computer Engineering, Chonnam National University, Gwangju, Republic of Korea.
The human ether-a-go-go-related gene (hERG) channel plays a critical role in the electrical activity of the heart, and its blockers can cause serious cardiotoxic effects. Thus, screening for hERG channel blockers is a crucial step in the drug development process. Many in silico models have been developed to predict hERG blockers, which can efficiently save time and resources.
View Article and Find Full Text PDFEstablishing a preoperative predictive model for gallbladder adenoma and cholesterol polyps based on machine learning: a multicentre retrospective study.
World J Surg Oncol
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Department of Hepatobiliary and Pancreas, Affiliated Hospital of Qingdao University, NO.1677 Wutaishan Road, Qingdao, Shandong Province, 266555, China.
Background: With the rising diagnostic rate of gallbladder polypoid lesions (GPLs), differentiating benign cholesterol polyps from gallbladder adenomas with a higher preoperative malignancy risk is crucial. This study aimed to establish a preoperative prediction model capable of accurately distinguishing between gallbladder adenomas and cholesterol polyps using machine learning algorithms.
Materials And Methods: We retrospectively analysed the patients' clinical baseline data, serological indicators, and ultrasound imaging data.
Impacted lower third molar classification and difficulty index assessment: comparisons among dental students, general practitioners and deep learning model assistance.
BMC Oral Health
January 2025
Bangkok Hospital Dental Center Holistic Care and Dental Implant, Bangkok Hospital, Bangkok, 10310, Thailand.
Background: Assessing the difficulty of impacted lower third molar (ILTM) surgical extraction is crucial for predicting postoperative complications and estimating procedure duration. The aim of this study was to evaluate the effectiveness of a convolutional neural network (CNN) in determining the angulation, position, classification and difficulty index (DI) of ILTM. Additionally, we compared these parameters and the time required for interpretation among deep learning (DL) models, sixth-year dental students (DSs), and general dental practitioners (GPs) with and without CNN assistance.
View Article and Find Full Text PDFThe interaction network and potential clinical effectiveness of dimensional psychopathology phenotyping based on EMR: a Bayesian network approach.
BMC Psychiatry
January 2025
West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
The current DSM-oriented diagnostic paradigm has introduced the issue of heterogeneity, as it fails to account for the identification of the neurological processes underlying mental illnesses, which affects the precision of treatment. The Research Domain Criteria (RDoC) framework serves as a recognized approach to addressing this heterogeneity, and several assessment and translation techniques have been proposed. Among these methods, transforming RDoC scores from electronic medical records (EMR) using Natural Language Processing (NLP) has emerged as a suitable technique, demonstrating clinical effectiveness.
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