AIMC Topic: Metals
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High-Throughput Prediction of Metal-Embedded Complex Properties with a New GNN-Based Metal Attention Framework.
Journal of chemical information and modeling
Feb 14, 2025
Metal-embedded complexes (MECs), including transition metal complexes (TMCs) and metal-organic frameworks (MOFs), are important in catalysis, materials science, and molecular devices due to their unique metal atom centrality and complex coordination ...
Does Deep Learning Reconstruction Improve Ureteral Stone Detection and Subjective Image Quality in the CT Images of Patients with Metal Hardware?
Journal of endourology
Feb 11, 2025
Diagnosing ureteral stones with low-dose CT in patients with metal hardware can be challenging because of image noise. The purpose of this study was to compare ureteral stone detection and image quality of low-dose and conventional CT scans with and...
Recent advances and future challenges in predictive modeling of metalloproteins by artificial intelligence.
Molecules and cells
Feb 10, 2025
Metal coordination is essential for structural/catalytic functions of metalloproteins that mediate a wide range of biological processes in living organisms. Advances in bioinformatics have significantly enhanced our understanding of metal-binding sit...
Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning.
Frontiers in immunology
Jan 24, 2025
INTRODUCTION: Type 2 diabetes (T2D) is a complex metabolic disorder with significant global health implications. Understanding the molecular mechanisms underlying T2D is crucial for developing effective therapeutic strategies. This study employs sing...
DisDock: A Deep Learning Method for Metal Ion-Protein Redocking.
Proteins
Jan 22, 2025
The structures of metalloproteins are essential for comprehending their functions and interactions. The breakthrough of AlphaFold has made it possible to predict protein structures with experimental accuracy. However, the type of metal ion that a met...
Machine Learning-Assisted Prediction of Photothermal Metal-Phenolic Networks.
Angewandte Chemie (International ed. in English)
Jan 20, 2025
Photothermal therapy (PTT) demonstrates significant potential in cancer treatment, wound healing, and antibacterial therapy, with its efficacy largely depending on the performance of photothermal agents (PTAs). Metal-phenolic network (MPN) materials ...
Blood metal levels predict digestive tract cancer risk using machine learning in a U.S. cohort.
Scientific reports
Jan 8, 2025
BACKGROUND: Environmental metal exposure has been implicated in the development of digestive tract cancers, although the specific associations remain poorly defined. This study aimed to investigate the relationship between blood metal levels and the ...
Liquid-Metal-Based Multichannel Strain Sensor for Sign Language Gesture Classification Using Machine Learning.
ACS applied materials & interfaces
Jan 7, 2025
Liquid metals are highly conductive like metallic materials and have excellent deformability due to their liquid state, making them rather promising for flexible and stretchable wearable sensors. However, patterning liquid metals on soft substrates h...
Advanced Low-Cost Technology for Assessing Metal Accumulation in the Body of a Metropolitan Resident Based on a Neural Network Model.
Sensors (Basel, Switzerland)
Nov 7, 2024
This study is devoted to creating a neural network technology for assessing metal accumulation in the body of a metropolis resident with short-term and long-term intake from anthropogenic sources. Direct assessment of metal retention in the human bod...
CT metal artefact reduction for hip and shoulder implants using novel algorithms and machine learning: A systematic review with pairwise and network meta-analyses.
Radiography (London, England : 1995)
Nov 6, 2024
INTRODUCTION: Many tools have been developed to reduce metal artefacts in computed tomography (CT) images resulting from metallic prosthesis; however, their relative effectiveness in preserving image quality is poorly understood. This paper reviews t...
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