AIMC Topic: Lipids
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Antibacterial effects of thyme oil loaded solid lipid and chitosan nano-carriers against Salmonella Typhimurium and Escherichia coli as food preservatives.
PloS one
Dec 31, 2024
OBJECTIVES: Escherichia coli and Salmonella Typhimurium are frequent causes of foodborne illness affecting many people annually. In order to develop natural antimicrobial agents against these microorganisms, thyme oil (TO) was considered as active an...
Artificial intelligence-driven rational design of ionizable lipids for mRNA delivery.
Nature communications
Dec 30, 2024
Lipid nanoparticles (LNPs) have proven effective in mRNA delivery, as evidenced by COVID-19 vaccines. Its key ingredient, ionizable lipids, is traditionally optimized by inefficient and costly experimental screening. This study leverages artificial i...
LipoCLEAN: A Machine Learning Filter to Improve Untargeted Lipid Identification Confidence.
Analytical chemistry
Dec 23, 2024
In untargeted lipidomics experiments, putative lipid identifications generated by automated analysis software require substantial manual filtering to arrive at usable high-confidence data. However, identification software tools do not make full use o...
Harnessing artificial neural networks to model caffeine degradation by High-Yield biodiesel algae Desmodesmus pannonicus.
Bioresource technology
Dec 10, 2024
In this study, Desmodesmus pannonicus IITISM-DIX2, outperforming Chlorella sorokiniana IITISM-DIX3 in caffeine degradation, was used to develop an artificial neural network (ANN) model for predicting caffeine removal efficiency under varying pH, phot...
Combination of plasma-based lipidomics and machine learning provides a useful diagnostic tool for ovarian cancer.
Journal of pharmaceutical and biomedical analysis
Nov 2, 2024
Ovarian cancer (OC), the second leading cause of death among gynecological cancers, is often diagnosed at an advanced stage due to its asymptomatic nature at early stages. This study aimed to explore the diagnostic potential of plasma-based lipidomic...
Optimizing lipid nanoparticles for fetal gene delivery in vitro, ex vivo, and aided with machine learning.
Journal of controlled release : official journal of the Controlled Release Society
Oct 28, 2024
There is a clinical need to develop lipid nanoparticles (LNPs) to deliver congenital therapies to the fetus during pregnancy. The aim of these therapies is to restore normal fetal development and prevent irreversible conditions after birth. As a firs...
Review of machine learning for lipid nanoparticle formulation and process development.
Journal of pharmaceutical sciences
Sep 27, 2024
Lipid nanoparticles (LNPs) are a subset of pharmaceutical nanoparticulate formulations designed to encapsulate, stabilize, and deliver nucleic acid cargoes in vivo. Applications for LNPs include new interventions for genetic disorders, novel classes ...
Rational Design of Lipid Nanoparticles for Enhanced mRNA Vaccine Delivery via Machine Learning.
Small (Weinheim an der Bergstrasse, Germany)
Sep 12, 2024
Since the coronavirus pandemic, mRNA vaccines have revolutionized the field of vaccinology. Lipid nanoparticles (LNPs) are proposed to enhance mRNA delivery efficiency; however, their design is suboptimal. Here, a rational method for designing LNPs i...
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...
Lipidomics combined with random forest machine learning algorithms to reveal freshness markers for duck eggs during storage in different rearing systems.
Poultry science
Aug 14, 2024
The differences in lipids in duck eggs between the 2 rearing systems during storage have not been fully studied. Herein, we propose untargeted lipidomics combined with a random forest (RF) algorithm to identify potential marker lipids based on ultra-...
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