AIMC Topic: Particle Size
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Quantification of Size-Binned Particulate Matter in Electronic Cigarette Aerosols Using Multi-Spectral Optical Sensing and Machine Learning.
Sensors (Basel, Switzerland)
Nov 3, 2024
To monitor health risks associated with vaping, we introduce a multi-spectral optical sensor powered by machine learning for real-time characterization of electronic cigarette aerosols. The sensor can accurately measure the mass of particulate matter...
Optimising the production of PLGA nanoparticles by combining design of experiment and machine learning.
International journal of pharmaceutics
Nov 2, 2024
Poly(lactic-co-glycolic acid) (PLGA) is a widely used biodegradable polymer in drug delivery and nanoparticle (NP) formulation due to its controlled drug release properties and safety profiles. Among the methods available for NP production, nanopreci...
A Machine Learning-Optimized System for Pulsatile, Photo- and Chemotherapeutic Treatment Using Near-Infrared Responsive MoS-Based Microparticles in a Breast Cancer Model.
ACS nano
Oct 27, 2024
Multimodal cancer therapies are often required for progressive cancers due to the high persistence and mortality of the disease and the negative systemic side effects of traditional therapeutic methods. Thus, the development of less invasive modaliti...
Calibration of discrete meta-parameters of bamboo flour based on magnitude analysis and BP neural network.
PloS one
Oct 22, 2024
In the research and development of technology and equipment for bamboo products deep processing, such as filling, drying, and medicinal use of bamboo flour (BF), the poor compaction and fluidity of BF materials entails the need for accurate discrete ...
Rheological modelling of apple puree based on machine learning combined Monte Carlo simulation: Insight into the fundamental light- particle structure interaction processes.
Food chemistry
Oct 10, 2024
In this work, apple purees from different particle concentration and verifying in size were reconstituted to investigate their impacts on rheological behaviors, optical properties and light interaction at 900-1650 nm. The optical scattering of differ...
Particle formation in response to different protein formulations and containers: Insights from machine learning analysis of particle images.
Journal of pharmaceutical sciences
Oct 9, 2024
Subvisible particle count is a biotherapeutics stability indicator widely used by pharmaceutical industries. A variety of stresses that biotherapeutics are exposed to during development can impact particle morphology. By classifying particle morpholo...
Constructing a visual detection model for floc settling velocity using machine learning.
Journal of environmental management
Oct 3, 2024
Optimizing the dosage of coagulant is a time-consuming process, and real-time evaluation of floc settling velocity can quickly predict the coagulation effect and optimize the dosage. This study used a convolutional neural network (CNN) model to analy...
Transitioning the production of lipidic mesophase-based delivery systems from lab-scale to robust industrial manufacturing following a risk-based quality by design approach augmented by artificial intelligence.
Journal of colloid and interface science
Sep 12, 2024
Lipidic mesophase drug carriers have demonstrated the capacity to host and effectively deliver a wide range of active pharmaceutical ingredients, yet they have not been as extensively commercialized as other lipid-based products, such as liposomal de...
Development of fucoidan/polyethyleneimine based sorafenib-loaded self-assembled nanoparticles with machine learning and DoE-ANN implementation: Optimization, characterization, and in-vitro assessment for the anticancer drug delivery.
International journal of biological macromolecules
Aug 27, 2024
This study aims to develop sorafenib-loaded self-assembled nanoparticles (SFB-SANPs) using the combined approach of artificial neural network and design of experiments (ANN-DoE) and to compare it with other machine learning (ML) models. The central c...
Machine learning-driven QSAR models for predicting the cytotoxicity of five common microplastics.
Toxicology
Aug 11, 2024
In the field of microplastics (MPs) toxicity prediction, machine learning (ML) computer simulation techniques are showing great potential. In this study, six ML algorithms were utilized to predict the toxicity of MPs on BEAS-2B cells based on quantit...
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