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Metals

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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
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...

[Migration of Metals Contained in Laminated Films for Food Packaging].

Shokuhin eiseigaku zasshi. Journal of the Food Hygienic Society of Japan
Multilayer laminated films are widely used as food packaging materials. The substances contained in these films have the potential to migrate into food in contact, but the actual situation is unknown. In this study, we first determined the contents o...

[Deep Learning Reconstruction Algorithm Combined With Smart Metal Artifact Reduction Technique Improves Image Quality of Upper Abdominal CT in Critically Ill Patients].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
OBJECTIVE: To evaluate the effect of deep learning reconstruction algorithm combined with smart metal artifact reduction (DLMAR) on the quality of abdominal CT images in critically ill patients who are unable to raise their arms and require electroca...

DisDock: A Deep Learning Method for Metal Ion-Protein Redocking.

Proteins
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...

High-Throughput Prediction of Metal-Embedded Complex Properties with a New GNN-Based Metal Attention Framework.

Journal of chemical information and modeling
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 ...

Recent advances and future challenges in predictive modeling of metalloproteins by artificial intelligence.

Molecules and cells
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...

Does Deep Learning Reconstruction Improve Ureteral Stone Detection and Subjective Image Quality in the CT Images of Patients with Metal Hardware?

Journal of endourology
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...

Assessing potential toxic metal threats in tea growing soils of India with soil health indices and machine learning technologies.

Environmental monitoring and assessment
This study explores the impact of potentially toxic metals (PTMs) contamination in Indian tea-growing soils on ecosystems, soil quality, and human health using machine learning and statistical analysis. A total of 148 surface soil samples were collec...

Self-supervised U-transformer network with mask reconstruction for metal artifact reduction.

Physics in medicine and biology
. Metal artifacts severely damaged human tissue information from the computed tomography (CT) image, posing significant challenges to disease diagnosis. Deep learning has been widely explored for the metal artifact reduction (MAR) task. Nevertheless,...

Predicting the amount of toxic metals and metalloids in silt loading using neural networks.

Environmental monitoring and assessment
Material deposited on road surfaces, called road dust, are known to contain different toxic elements. According to particle size, there are different fractions. Particles with an aerodynamic size less than or equal to 75 µm are called silt loading. A...