AIMC Topic: Nanoparticles
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Harnessing adaptive novelty for automated generation of cancer treatments.
Bio Systems
Nov 17, 2020
Nanoparticles have the potential to modulate both the pharmacokinetic and pharmacodynamic profiles of drugs, thereby enhancing their therapeutic effect. The versatility of nanoparticles allows for a wide range of customization possibilities. However,...
Self-assembled Janus plasmene nanosheets as flexible 2D photocatalysts.
Materials horizons
Nov 16, 2020
A leaf is a free-standing photocatalytic system that can effectively harvest solar energy and convert CO and HO into carbohydrates in a continuous manner without the need for regeneration or tedious product extraction steps. Despite encouraging advan...
Efficient Machine-Learning-Aided Screening of Hydrogen Adsorption on Bimetallic Nanoclusters.
ACS combinatorial science
Nov 4, 2020
Nanoclusters add an additional dimension in which to look for promising catalyst candidates, since catalytic activity of materials often changes at the nanoscale. However, the large search space of relevant atomic sites exacerbates the challenge for ...
A Machine Learning-Assisted Nanoparticle-Printed Biochip for Real-Time Single Cancer Cell Analysis.
Advanced biosystems
Oct 7, 2020
Cancers are a complex conglomerate of heterogeneous cell populations with varying genotypes and phenotypes. The intercellular heterogeneity within the same tumor and intratumor heterogeneity within various tumors are the leading causes of resistance ...
Evaluation of magnesium oxide and zinc oxide nanoparticles against multi-drug-resistance Mycobacterium tuberculosis.
The Indian journal of tuberculosis
Aug 4, 2020
OBJECTIVE: The current study has evaluated the MICs and MBCs of ZnONPs, MgONPs, and MgONPs-ZnONPs against HRv Mtb and MDR-Mtb.
Predicting In Vitro Neurotoxicity Induced by Nanoparticles Using Machine Learning.
International journal of molecular sciences
Jul 25, 2020
The practice of non-testing approaches in nanoparticles hazard assessment is necessary to identify and classify potential risks in a cost effective and timely manner. Machine learning techniques have been applied in the field of nanotoxicology with e...
Classification of grazing-incidence small-angle X-ray scattering patterns by convolutional neural network.
Journal of synchrotron radiation
May 20, 2020
Grazing-incidence small-angle X-ray scattering (GISAXS) patterns have multiple superimposed contributions from the shape of the nanoscale structure, the coupling between the particles, the partial pair correlation, and the layer geometry. Therefore, ...
Nanotoxicology data for tools: a literature review.
Nanotoxicology
Feb 26, 2020
The exercise of non-testing approaches in nanoparticles (NPs) hazard assessment is necessary for the risk assessment, considering cost and time efficiency, to identify, assess, and classify potential risks. One strategy for investigating the toxicolo...
Label-Free Tomographic Imaging of Lipid Droplets in Foam Cells for Machine-Learning-Assisted Therapeutic Evaluation of Targeted Nanodrugs.
ACS nano
Jan 10, 2020
Lipid droplet (LD) accumulation, a key feature of foam cells, constitutes an attractive target for therapeutic intervention in atherosclerosis. However, despite advances in cellular imaging techniques, current noninvasive and quantitative methods hav...
Application of automated electron microscopy imaging and machine learning to characterise and quantify nanoparticle dispersion in aqueous media.
Journal of microscopy
Dec 18, 2019
For many nanoparticle applications it is important to understand dispersion in liquids. For nanomedicinal and nanotoxicological research this is complicated by the often complex nature of the biological dispersant and ultimately this leads to severe ...
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