Results 241 to 250 of about 22,871,906 (293)

Exposure to Suaahara II Interventions and Knowledge of Maternal and Child Nutrition and Health Among Mothers, Grandmothers, and Male Household Heads: An Association Study in Nepal. [PDF]

Matern Child Nutr
Suresh S, Frongillo EA, Thapa DK, Adhikari RP, Kole SK, Sapkota F, Shrestha ML, Garn K, Pandey PR, Kshetri I, Sharma D, Cunningham K.   +11 more
europepmc   +1 more source

Microporous Microgel Assemblies Facilitating the Recruitment and Osteogenic Differentiation of Progenitor Cells for Bone Regeneration

Advanced Functional Materials, EarlyView.
There is a significant need for biomaterials with well‐defined stability and bioactivity to support tissue regeneration. In this study, we developed a tunable microgel platform that enables the decoupling of stiffness from porosity, thereby promoting bone regeneration.
Silvia Pravato, Haishuo Guan, Bianca M. Carrara, Mira Jacobs, Matteo Monticelli, Lukas Moser, Thomas Moragues, Andrew J. deMello, Tomaso Zambelli, Martin Ehrbar   +9 more
wiley   +1 more source

Spatial distribution and determinants of fertility preferences among female adolescents and young adults in Ethiopia. [PDF]

PLoS One
Alemu MB, Lassi ZS, Alemu SB, Ebrahim AB, Belay DG, Tessema GA.   +5 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

Cardiovascular health metrics among an Iranian population: insights from life's essential 8. [PDF]

BMC Cardiovasc Disord
Kazemi T, Jafarizadeh R, Bizhaem SK, Bijari B, Karbasi S.   +4 more
europepmc   +1 more source

A Bespoke Programmable Interpenetrating Elastomer Network Composite Laryngeal Stent for Expedited Paediatric Laryngotracheal Reconstruction

Advanced Functional Materials, EarlyView.
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, Lei Wu, Dong Zhang, Linxiao Wu, Graca Ferreira Vaz Pinto Mendes, Wanqiao J. Tu, Heping Ma, Toby J. Proctor, Colin R. Butler, Thomas Kjærgaard, Mark W. Lowdell, Martin A. Birchall, Robert E. Hynds, Richard Hewitt, Wenhui Song   +14 more
wiley   +1 more source

Critical points and syzygies for Feynman integrals. [PDF]

J High Energy Phys
Page B, Song Q.
europepmc   +1 more source

MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance

Advanced Functional Materials, EarlyView.
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

Ideal efficacy photoswitching for chromocontrol of TRPC4/5 channel functions in live tissues. [PDF]

Nat Chem Biol
Müller M, Niemeyer K, Ojha NK, Porav SA, Vinayagam D, Urban N, Büchau F, Oleinikov K, Makke M, Bauer CC, Johnson AV, Muench SP, Zufall F, Bruns D, Schwarz Y, Raunser S, Leinders-Zufall T, Bon RS, Schaefer M, Thorn-Seshold O.   +19 more
europepmc   +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