Results 241 to 250 of about 682,341 (324)
Gallstones and gallbladder wall thickening in patients with cirrhosis: Prevalence and clinical impact. [PDF]
Tsankof A +9 more
europepmc +1 more source
Reevaluating the Activity of ZIF‐8 Based FeNCs for Electrochemical Ammonia Production
Though receiving much attention, the field of electrochemical nitrogen reduction reaction (eNRR) to ammonia is marked by doubts about whether this reaction is possible in aqueous media. This work sheds light on this question for iron single‐atom on N‐doped carbon (FeNC) catalysts—a class of well‐known catalysts that is also worth testing for the sister
Caroline Schneider +6 more
wiley +1 more source
Magnetic resonance vessel wall imaging is superior to MRA in assessing the extent of vascular involvement in patients with moyamoya disease. [PDF]
Soni S +9 more
europepmc +1 more source
A programmable interpenetrating double‐network architecture, created via 3D‐TIPS printing and resin infusion, synergistically combines thermoplastic and thermosetting elastomers to balance structural rigidity and surface softness—crucial for paediatric laryngeal stents.
Elizabeth F. Maughan +14 more
wiley +1 more source
Intriguing choroidal lesions in Birdshot chorioretinopathy: a diagnostic and clinical dilemma. [PDF]
Bertolani Y +3 more
europepmc +1 more source
MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance
Metal‐organic frameworks (MOFs) and covalent organic frameworks (COFs) hold promise for advanced electronics. However, discrepancies in reported electrical conductivities highlight the importance of measurement methodologies. This review explores intrinsic charge transport mechanisms and extrinsic factors influencing performance, and critically ...
Jonas F. Pöhls, R. Thomas Weitz
wiley +1 more source
Impairment of local metabolic coronary control involves perfusion-contraction matching not supply-demand imbalance. [PDF]
Essajee SI +7 more
europepmc +1 more source
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

