AIMC Topic: Humans

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Automated quantification of T1 and T2 relaxation times in liver mpMRI using deep learning: a sequence-adaptive approach.

European radiology experimental
OBJECTIVES: To evaluate a deep learning sequence-adaptive liver multiparametric MRI (mpMRI) assessment with validation in different populations using total and segmental T1 and T2 relaxation time maps.

Comprehensive statistical and machine learning framework for identification of metabolomic biomarkers in breast cancer.

Metabolomics : Official journal of the Metabolomic Society
INTRODUCTION: Breast cancer is the most common cancer among women, with its burden increasing over the past decades. Early diagnosis significantly improves survival rates and reduces lethality. Innovative technologies are being developed for early de...

'Intelligent' proteins.

Cellular and molecular life sciences : CMLS
We present an idea of protein molecules that challenges the traditional view of proteins as simple molecular machines and suggests instead that they exhibit a basic form of "intelligence". The idea stems from suggestions coming from Integrated Inform...

Bio-Inspired spiking tactile sensing system for robust texture recognition across varying scanning speeds in passive touch.

Biological cybernetics
Tactile sensing plays a crucial role in texture recognition, but variations in scanning speed pose a significant challenge for accurate discrimination. Previous studies have demonstrated that scanning speed alters the frequency of texture-induced vib...

Discovery of Novel Anti-Acetylcholinesterase Peptides Using a Machine Learning and Molecular Docking Approach.

Drug design, development and therapy
OBJECTIVE: Alzheimer's disease poses a significant threat to human health. Currenttherapeutic medicines, while alleviate symptoms, fail to reverse the disease progression or reduce its harmful effects, and exhibit toxicity and side effects such as ga...

Predicting drug-drug interactions: A deep learning approach with GCN-based collaborative filtering.

Artificial intelligence in medicine
The use of combination drugs among patients is increasing due to effectiveness compared to monotherapies. However, healthcare providers should continue to be concerned about the potential risks associated with patient safety arising from drug-drug in...

Redefining parameter-efficiency in ADHD diagnosis: A lightweight attention-driven kolmogorov-arnold network with reduced parameter complexity and a novel activation function.

Psychiatry research. Neuroimaging
As deep learning continues to advance in medical analysis, the increasing complexity of models, particularly Convolutional Neural Networks (CNNs), presents significant challenges related to interpretability, computational costs, and real-world applic...

KEM-IoMT: Knowledge graph embedding-enhanced accurate medical service recommendation against diabetes.

Computers in biology and medicine
The Internet of Medical Things (IoMT)-enhanced Recommender System (RS) acquired swift advancement in configuring diverse medical data into intelligent systems to generate personalized medical services. However, due to the heterogeneous and complex na...

Substrate Specificity of the Organic Cation Transporters MATE1 and MATE2K and Functional Overlap with OCT1 and OCT2.

Journal of medicinal chemistry
The multidrug and toxin extrusion proteins MATE1 and MATE2K may determine the pharmacokinetics and drug-drug interactions of many drugs. However, their substrate spectrum and synergy with organic cation transporters OCT1 and OCT2 remain incompletely ...

Enzymatic carbon-fluorine bond cleavage by human gut microbes.

Proceedings of the National Academy of Sciences of the United States of America
Fluorinated compounds are used for agrochemical, pharmaceutical, and numerous industrial applications, resulting in global contamination. In many molecules, fluorine is incorporated to enhance the half-life and improve bioavailability. Fluorinated co...