Results 211 to 220 of about 1,043,746 (297)

PRELIVE: A Framework for Predicting Lipid Nanoparticles In Vivo Efficacy and Reducing Reliance on Animal Testing

Advanced Functional Materials, EarlyView.
PREdicting LNP In Vivo Efficacy (PRELIVE) framework enables the prediction of lipid nanoparticle (LNPs) organ‐specific delivery through dual modeling approaches. Composition‐based models using formulation parameters and protein corona‐based models using biological fingerprints both achieve high predictive accuracy across multiple organs.
Belal I. Hanafy, Chuan‐En Lu, Kai Liu, Audrey Gallud   +3 more
wiley   +1 more source

Universal Electronic‐Structure Relationship Governing Intrinsic Magnetic Properties in Permanent Magnets

Advanced Functional Materials, EarlyView.
Permanent magnets derive their extraordinary strength from deep, universal electronic‐structure principles that control magnetization, anisotropy, and intrinsic performance. This work uncovers those governing rules, examines modern modeling and AI‐driven discovery methods, identifies critical bottlenecks, and reveals electronic fingerprints shared ...
Prashant Singh
wiley   +1 more source

A Machine Learning Approach to Predicting Mortality Risk in Chemotherapy-Treated Lung Cancer: Machine Learning Model Development and Validation.

JMIR Med Inform
Zou J, Huang J, Lu K, Lin A, Xie C, Zhang J, Rao B, Li Z, Xie D, Lu L, Luo F, Chen J, Yang L, Qiu F, Zhang X, Deng Y, Lu J.   +16 more
europepmc   +1 more source

Automated spectrometer alignment via machine learning. Corrigendum. [PDF]

J Synchrotron Radiat
Feuer-Forson P, Hartmann G, Mitzner R, Baumgärtel P, Weniger C, Agåker M, Meier D, Wernet P, Viefhaus J.   +8 more
europepmc   +1 more source

Deciphering the role of lipid metabolism-related genes in Alzheimer’s disease: a machine learning approach integrating Traditional Chinese Medicine [PDF]

Kaikai Wu, Qingsong Liu, Keyu Long, XueQing Duan, X. R. Chen, Jing Zhang, Li Li, Bin Li   +7 more
openalex   +1 more source

Conductance‐Dependent Photoresponse in a Dynamic SrTiO3 Memristor for Biorealistic Computing

Advanced Functional Materials, EarlyView.
A nanoscale SrTiO3 memristor is shown to exhibit dynamic synaptic behavior through the interaction of local electrical and global optical signals. Its photoresponse depends quantitatively on the conductance state, which evolves and decays over tunable timescales, enabling ultralow‐power, biorealistic learning mechanisms for advanced in‐memory and ...
Christoph Weilenmann, Hanglin He, Marko Mladenović, Till Zellweger, Kevin Portner, Klemens Bauer, Guillaume Bellec, Mathieu Luisier, Alexandros Emboras   +8 more
wiley   +1 more source

Bio‐Inspired Molecular Events in Poly(Ionic Liquids)

Advanced Functional Materials, EarlyView.
Originating from dipolar and polar inter‐ and intra‐chain interactions of the building blocks, the topologies and morphologies of poly(ionic liquids) (PIL) govern their nano‐ and micro‐processibility. Modulating the interactions of cation‐anion pairs with aliphatic dipolar components enables the tunability of properties, facilitated by “bottom‐up ...
Jiahui Liu, Marek W. Urban
wiley   +1 more source

Machine learning models for the prediction of levodopa response to tremor in Parkinson's disease. [PDF]

Front Aging Neurosci
Li F, Lin S, Yan R, Cui Y, Ren K, Chen Z, Ma L, Feng T.   +7 more
europepmc   +1 more source

Machine Learning-Based Cardiac Output Estimation Using Photoplethysmography in Off-Pump Coronary Artery Bypass Surgery [PDF]

Cecilia A. Callejas Pastor, Chahyun Oh, Boohwi Hong, Yunseo Ku   +3 more
openalex   +1 more source