AIMC Topic: Particle Size
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Machine learning predictions of drug release from isocyanate-derived aerogels.
Journal of materials chemistry. B
May 28, 2025
This work utilized machine learning (ML) algorithms to predict and validate the drug release kinetics of a short worm-like nanostructured isocyanate-derived aerogel: the first time ML has been employed to study the drug delivery properties of this i...
Machine-Learning Framework to Predict the Performance of Lipid Nanoparticles for Nucleic Acid Delivery.
ACS applied bio materials
May 19, 2025
Lipid nanoparticles (LNPs) are highly effective carriers for gene therapies, including mRNA and siRNA delivery, due to their ability to transport nucleic acids across biological membranes, low cytotoxicity, improved pharmacokinetics, and scalability....
Machine learning-assisted prediction and identification of key factors affecting nitrogen metabolism for aerobic granular sludge.
Environmental research
May 15, 2025
To achieve higher denitrification efficiency with reduced energy consumption in aerobic granular sludge (AGS) system, a systematic evaluation of the carbon and nitrogen metabolism process for AGS under different stage is essential. Herein, this study...
Analysis of TEM micrographs with deep learning reveals APOE genotype-specific associations between HDL particle diameter and Alzheimer's dementia.
Cell reports methods
Jan 27, 2025
High-density lipoprotein (HDL) particle diameter distribution is informative in the diagnosis of many conditions, including Alzheimer's disease (AD). However, obtaining an accurate HDL size measurement is challenging. We demonstrated the utility of m...
Understanding the Manufacturing Process of Lipid Nanoparticles for mRNA Delivery Using Machine Learning.
Chemical & pharmaceutical bulletin
Jan 1, 2024
Lipid nanoparticles (LNPs), used for mRNA vaccines against severe acute respiratory syndrome coronavirus 2, protect mRNA and deliver it into cells, making them an essential delivery technology for RNA medicine. The LNPs manufacturing process consists...
Single-particle diffusional fingerprinting: A machine-learning framework for quantitative analysis of heterogeneous diffusion.
Proceedings of the National Academy of Sciences of the United States of America
Aug 3, 2021
Single-particle tracking (SPT) is a key tool for quantitative analysis of dynamic biological processes and has provided unprecedented insights into a wide range of systems such as receptor localization, enzyme propulsion, bacteria motility, and drug ...
Flexible and stretchable dual mode nanogenerator for rehabilitation monitoring and information interaction.
Journal of materials chemistry. B
Apr 29, 2020
Motion recognition and information interaction sensors with flexibility and stretchability are key functional modules as interactive media between the mechanical motions and electric signals in an intelligent robotic and rehabilitation training syste...
Supervised Machine Learning Algorithms for Evaluation of Solid Lipid Nanoparticles and Particle Size.
Combinatorial chemistry & high throughput screening
Jan 1, 2018
AIMS AND OBJECTIVES: Solid Lipid Nanoparticles (SLNs) are pharmaceutical delivery systems that have advantages such as controlled drug release, long-term stability etc. Particle Size (PS) is one of the important criteria of SLNs. These factors affect...
Machine Learning Enables Live Label-Free Phenotypic Screening in Three Dimensions.
Assay and drug development technologies
Jan 1, 2018
There is a large amount of information in brightfield images that was previously inaccessible by using traditional microscopy techniques. This information can now be exploited by using machine-learning approaches for both image segmentation and the c...
[Toxicity of vehicle exhaust on BEAS-2B cells in vitro by air-liquid interface].
Wei sheng yan jiu = Journal of hygiene research
Sep 1, 2017
OBJECTIVE: To evaluate the toxic effect of vehicle exhaust( VE) on lung epithelial cells by air-liquid interface( ALI) method in vitro, and analyze the different toxicity of VE after being treated with 0. 2 μm filter.
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