Results 141 to 150 of about 664,070 (248)

Development of an Explainable Machine Learning Computational Model for the Prediction of Severe Complications After Orchiectomy in Stallions. [PDF]

Animals (Basel)
Tyrnenopoulou P, Kalatzis D, Kiouvrekis Y, Flouraki E, Folias L, Loukopoulos E, Starras A, Chalvatzis P, Tsioli V, Mavrogianni VS, Fthenakis GC.   +10 more
europepmc   +1 more source

Selective Benzene Capture by Metal‐Organic Frameworks

Advanced Functional Materials, EarlyView.
Metal‐organic frameworks (MOFs) hold significant potential for capturing benzene from air emissions and hydrocarbon mixtures in liquid phases. This capability stems from their precisely engineered structures, versatile chemistries, and diverse binding interactions.
Zongsu Han, Ankit Mondal, Jiatong Huo, Yihao Yang, Hong‐Cai Zhou   +4 more
wiley   +1 more source

Dopamine, Distraction, and Disruption: Perspectives on How Technology and Generation Z Are Reshaping Medical Education.

JMIR Med Educ
Andrade BB, Araújo Pereira M, Avena K.
europepmc   +1 more source

DEVELOPMENT OF USABILITY CRITERIA FOR E-LEARNING CONTENT DEVELOPMENT SOFTWARE

, 2015
Revolutionary advancements have been observed in e-learning technologies though an amalgamated evaluation methodology for new generation e-learning content development tools is not available. The evaluation of educational software for online use must consider its usability and as well as its pedagogic effectiveness.
openaire   +3 more sources

Host‐Directed Biomaterials for Combatting Bloodstream Infections: From Macrocyclic Peptides to Immune‐Activating Cell Backpacks

Advanced Functional Materials, EarlyView.
Bloodstream infections (BSI) are one of the leading causes of mortality and morbidity in both civilian and military populations. This paper summarizes recent progress in novel treatment strategies to manage BSI arising from both bacterial and fungal pathogens using molecules, particles, and materials to elicit host‐directed immunity.
Thomas Thomou, Jagadish Krishnappa, Justin B. Schaal, Prasad Tongaonkar, Dat Q. Tran, Wenchao Gu, Yung‐Fu Chang, Michael E. Selsted, Shaoyi Jiang, Samir Mitragotri, Michael Super, Christopher J. Bettinger   +11 more
wiley   +1 more source

Preparing the AI-Ready Dentist: A Call for a Competency Framework in Dental Education. [PDF]

Int Dent J
Osathanon T, Vacharaksa A, Schwendicke F, Samaranayake L.   +3 more
europepmc   +1 more source

Electroactive Metal–Organic Frameworks for Electrocatalysis

Advanced Functional Materials, EarlyView.
Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska, Yutong Luo, Ruyan Zhao, Jianjun Zhang, Mingchao Wang, Xinliang Feng, Eike Brunner, Stefan Kaskel   +7 more
wiley   +1 more source

Deep learning inversion of water content and relaxation time in water-bearing fracture zones based on surface NMR data. [PDF]

Sci Rep
Li K, Li H, Qiu H, Li L, Lu K, Kong M, Li Z.   +6 more
europepmc   +1 more source

Smart, Bio‐Inspired Polymers and Bio‐Based Molecules Modified by Zwitterionic Motifs to Design Next‐Generation Materials for Medical Applications

Advanced Functional Materials, EarlyView.
Bio‐based and (semi‐)synthetic zwitterion‐modified novel materials and fully synthetic next‐generation alternatives show the importance of material design for different biomedical applications. The zwitterionic character affects the physiochemical behavior of the material and deepens the understanding of chemical interaction mechanisms within the ...
Theresa M. Lutz, Jonas De Breuck, Sahar Salehi, Meike N. Leiske   +3 more
wiley   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
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, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source