AIMC Topic: Hypoglycemic Agents

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Properties of AgNPs stabilized with polyvinylpyrrolidone relevant to antidiabetic agents.

Nanoscale
Type 2 diabetes mellitus (DM2) is a chronic metabolic disease. Silver nanoparticles (AgNPs) show promise in their treatment. This study assessed the potential of AgNPs as DM2 treatment agent using in vitro, in vivo, and machine learning approaches. M...

Machine learning driven diabetes care using predictive-prescriptive analytics for personalized medication prescription.

Scientific reports
The increasing prevalence of type 2 diabetes (T2D) is a significant health concern worldwide. Effective and personalized treatment strategies are essential for improving patient outcomes and reducing healthcare costs. Machine learning (ML) has the po...

BertADP: a fine-tuned protein language model for anti-diabetic peptide prediction.

BMC biology
BACKGROUND: Diabetes is a global metabolic disease that urgently calls for the development of new and effective therapeutic agents. Anti-diabetic peptides (ADPs) have emerged as a research hotspot due to their therapeutic potential and natural safety...

Research Gaps, Challenges, and Opportunities in Automated Insulin Delivery Systems.

Journal of diabetes science and technology
BACKGROUND: Since the discovery of the life-saving hormone insulin in 1921 by Dr Frederick Banting in 1921, there have been many critical discoveries and technical breakthroughs that have enabled people living with type 1 diabetes (T1D) to live longe...

In-silico guided identification and studies of potential FFAR4 agonists for type 2 diabetes mellitus therapy.

Expert opinion on drug discovery
BACKGROUND: The activation of free fatty acid receptor 4 (FFAR4) enhances insulin sensitivity and glucose uptake while mitigating inflammation. It is a promising therapeutic approach for managing type 2 diabetes mellitus (T2DM).

Chemical Properties-Based Deep Learning Models for Recommending Rational Daily Diet Combinations to Diabetics Through Large-Scale Virtual Screening of α-Glucosidase Dietary-Derived Inhibitors and Verified In Vitro.

Journal of agricultural and food chemistry
The lack of suitable chemical research methodologies has hindered the discovery of rational daily diet combinations from large-scale dietary-derived compounds. Three deep learning models based on chemical properties for α-glucosidase inhibitors (AGIs...

Enhancing Antidiabetic Drug Selection Using Transformers: Machine-Learning Model Development.

JMIR medical informatics
BACKGROUND: Diabetes affects millions worldwide. Primary care physicians provide a significant portion of care, and they often struggle with selecting appropriate medications.

Machine learning applications to classify and monitor medication adherence in patients with type 2 diabetes in Ethiopia.

Frontiers in endocrinology
BACKGROUND: Medication adherence plays a crucial role in determining the health outcomes of patients, particularly those with chronic conditions like type 2 diabetes. Despite its significance, there is limited evidence regarding the use of machine le...

SERPINA3: A Novel Therapeutic Target for Diabetes-Related Cognitive Impairment Identified Through Integrated Machine Learning and Molecular Docking Analysis.

International journal of molecular sciences
Diabetes-related cognitive impairment (DCI) is a severe complication of type 2 diabetes mellitus (T2DM), with limited understanding of its molecular mechanisms hindering effective therapeutic development. This study identified SERPINA3 as a potential...