AIMC Topic: Biomarkers
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Predicting doxorubicin-induced cardiotoxicity in breast cancer: leveraging machine learning with synthetic data.
Medical & biological engineering & computing
Jan 20, 2025
Doxorubicin (DOXO) is a primary treatment for breast cancer but can cause cardiotoxicity in over 25% of patients within the first year post-chemotherapy. Recognizing at-risk patients before DOXO initiation offers pathways for alternative treatments o...
Perspective: Multiomics and Artificial Intelligence for Personalized Nutritional Management of Diabetes in Patients Undergoing Peritoneal Dialysis.
Advances in nutrition (Bethesda, Md.)
Jan 20, 2025
Managing diabetes in patients on peritoneal dialysis (PD) is challenging due to the combined effects of dietary glucose, glucose from dialysate, and other medical complications. Advances in technology that enable continuous biological data collection...
Integrative bioinformatics and machine learning approach unveils potential biomarkers linking coronary atherosclerosis and fatty acid metabolism-associated gene.
Journal of cardiothoracic surgery
Jan 17, 2025
BACKGROUND: Atherosclerosis (AS) is increasingly recognized as a chronic inflammatory disease that significantly compromises vascular health and acts as a major contributor to cardiovascular diseases. Advancements in lipidomics and metabolomics have ...
Assessing donor kidney function: the role of CIRBP in predicting delayed graft function post-transplant.
Frontiers in immunology
Jan 17, 2025
INTRODUCTION: Delayed graft function (DGF) shortens the survival time of transplanted kidneys and increases the risk of rejection. Current methods are inadequate in predicting DGF. More precise tools are required to assess kidney suitability for tran...
Role of Artificial Intelligence in Identifying Vital Biomarkers with Greater Precision in Emergency Departments During Emerging Pandemics.
International journal of molecular sciences
Jan 16, 2025
The COVID-19 pandemic has accelerated advances in molecular biology and virology, enabling the identification of key biomarkers to differentiate between severe and mild cases. Furthermore, the use of artificial intelligence (AI) and machine learning ...
Deep learning and generative artificial intelligence in aging research and healthy longevity medicine.
Aging
Jan 16, 2025
With the global population aging at an unprecedented rate, there is a need to extend healthy productive life span. This review examines how Deep Learning (DL) and Generative Artificial Intelligence (GenAI) are used in biomarker discovery, deep aging ...
Development and Validation of a Machine Learning Method Using Vocal Biomarkers for Identifying Frailty in Community-Dwelling Older Adults: Cross-Sectional Study.
JMIR medical informatics
Jan 16, 2025
BACKGROUND: The two most commonly used methods to identify frailty are the frailty phenotype and the frailty index. However, both methods have limitations in clinical application. In addition, methods for measuring frailty have not yet been standardi...
Kernel representation-based End-to-End network-enabled decoding strategy for precise and medical diagnosis.
Journal of hazardous materials
Jan 15, 2025
Artificial intelligence-assisted imaging biosensors have attracted increasing attention due to their flexibility, allowing for the digital image analysis and quantification of biomarkers. While deep learning methods have led to advancements in biomar...
Identifying semaphorin 3C as a biomarker for sarcopenia and coronary artery disease via bioinformatics and machine learning.
Archives of gerontology and geriatrics
Jan 15, 2025
OBJECTIVE: Sarcopenia not only affects patients' quality of life but also may exacerbate the pathological processes of coronary artery disease (CAD). This study aimed to identify potential biomarkers to improve the combined diagnosis and treatment of...
Screening of Aβ and phosphorylated tau status in the cerebrospinal fluid through machine learning analysis of portable electroencephalography data.
Scientific reports
Jan 15, 2025
Diagnosing Alzheimer's disease (AD) through pathological markers is typically costly and invasive. This study aims to find a noninvasive, cost-effective method using portable electroencephalography (EEG) to detect changes in AD-related biomarkers in ...
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