ArticleScientific reports2020
Artificial intelligence application for rapid fabrication of size-tunable PLGA microparticles in microfluidics.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
27 citing papers in PubMed.
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- Organ-on-chip (OoC) and nano-biomaterials: next generation of precision oral and dental healthcare research.Journal of nanobiotechnology · 2026Review
- Article
- Applications of Artificial Intelligence in Biotech Drug Discovery and Product Development.MedComm · 2025Review
- Machine learning integrated with in vitro experiments for study of drug release from PLGA nanoparticles.Scientific reports · 2025Article
- Machine learning-driven optimization of mRNA-lipid nanoparticle vaccine quality with XGBoost/Bayesian method and ensemble model approaches.Journal of pharmaceutical analysis · 2024Article
- Machine learning assisted exploration of the influential parameters on the PLGA nanoparticles.Scientific reports · 2024Article
- Simple microfluidic devices forFrontiers in bioengineering and biotechnology · 2024Review
- Research Progress on Immunomodulatory Effects of Poly (Lactic-co- Glycolic Acid) Nanoparticles Loaded with Traditional Chinese Medicine Monomers.Current drug delivery · 2024Review
- Rationalized design to explore the full potential of PLGA microspheres as drug delivery systems.Drug delivery · 2023Article
- Microfluidic Platform for Microparticle Fabrication and Release of a Cathepsin Inhibitor.Tissue engineering. Part C, Methods · 2023Article
- Generation of Photopolymerized Microparticles Based on PEGDA Hydrogel Using T-Junction Microfluidic Devices: Effect of the Flow Rates.Micromachines · 2023Article
- Microsystem Advances through Integration with Artificial Intelligence.Micromachines · 2023Review
- Biomimetic natural biomaterials for tissue engineering and regenerative medicine: new biosynthesis methods, recent advances, and emerging applications.Military Medical Research · 2023Review
- In situ Formation of Polymer Microparticles in Bacterial Nanocellulose Using Alternative and Sustainable Solvents to Incorporate Lipophilic Drugs.Pharmaceutics · 2023Article
- Chemo-radiotherapy withFrontiers in medicine · 2023Article
- Role of Polymers in Microfluidic Devices.Polymers · 2022Review
- Machine learning for microfluidic design and control.Lab on a chip · 2022Review
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
- Controlled release of carnosine from poly(lactic-Nanoscale advances · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, synthetic polymeric particles were effectively fabricated by combining modern technologies of artificial intelligence (AI) and microfluidics. Because size uniformity is a key factor that significantly influences the stability of polymeric particles, therefore, this work aimed to establish a new AI application using machine learning technology for prediction of the size of poly(D,L-lactide-co-glycolide) (PLGA) microparticles produced by diverse microfluidic systems either in the form of single or multiple particles. Experimentally, the most effective factors for tuning droplet/particle sizes are PLGA concentrations and the flow rates of dispersed and aqueous phases in microfluidics. These factors were utilized to develop five different and simple in structure artificial neural network (ANN) models that are capable of predicting PLGA particle sizes produced by different microfluidic systems either individually or jointly merged. The systematic development of ANN models allowed ultimate construction of a single in silico model which consists of data for three different microfluidic systems. This ANN model eventually allowed rapid prediction of particle sizes produced using various microfluidic systems. This AI application offers a new platform for further rapid and economical exploration of polymer particles production in defined sizes for various applications including biomimetic studies, biomedicine, and pharmaceutics.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.