Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source
Development and Pilot Evaluation of an e-Learning Module for Autologous Fat Transfer (AFT) in Total Breast Reconstruction in the Dutch Healthcare System: Insights From 9 Plastic Surgeons. [PDF]
Van der Venne WBW +5 more
europepmc +1 more source
Collaborative partnerships to improve E-Learning design: Enhancing information skills training for the healthcare workforce. [PDF]
Day A +4 more
europepmc +1 more source
Effectiveness of E-Learning in the Field of Biochemistry.
Ambad R, Dhok A, Jha RK.
europepmc +1 more source
Educational technology enhanced interprofessional E-learning for engaging cross-institutional and cross-border healthcare students: A mixed-methods study. [PDF]
Chan SL +14 more
europepmc +1 more source
The role of E-learning in institutions of higher education in achieving the goals of sustainable development in Jordan. [PDF]
Derbas A +9 more
europepmc +1 more source
E-Learning Modules for the Care of Caregivers of Older Filipinos. [PDF]
Dela Vega SF.
europepmc +1 more source

