Results 201 to 210 of about 676,265 (337)

Lipid Nanoparticles for the Delivery of CRISPR/Cas9 Machinery to Enable Site‐Specific Integration of CFTR and Mutation‐Agnostic Disease Rescue

Advanced Functional Materials, EarlyView.
Lipid nanoparticles (LNPs) are optimized to co‐deliver Cas9‐encoding messenger RNA (mRNA), a single guide RNA (sgRNA) targeting the endogenous cystic fibrosis transmembrane conductance regulator (CFTR) gene, and homologous linear double‐stranded donor DNA (ldsDNA) templates encoding CFTR.
Ruth A. Foley, Paul G. Ayoub, Vrishti Sinha, Colin Juett, Alicia Sanoyca, Emily C. Duggan, Lindsay E. Lathrop, Priyanka Bhatt, Kevin Coote, Beate Illek, Brigitte N. Gomperts, Donald B. Kohn, Steven J. Jonas   +12 more
wiley   +1 more source

Patterning the Void: Combining L‐Systems with Archimedean Tessellations as a Perspective for Tissue Engineering Scaffolds

Advanced Functional Materials, EarlyView.
This study introduces a novel multi‐scale scaffold design using L‐fractals arranged in Archimedean tessellations for tissue regeneration. Despite similar porosity, tiles display vastly different tensile responses (1–100 MPa) and deformation modes. In vitro experiments with hMSCs show geometry‐dependent growth and activity. Over 55 000 tile combinations
Maria Kalogeropoulou, Kenny A. van Kampen, Pierpaolo Fucile, Carlos Mota, Lorenzo Moroni   +4 more
wiley   +1 more source

Next‐Generation Bio‐Reducible Lipids Enable Enhanced Vaccine Efficacy in Malaria and Primate Models

Advanced Functional Materials, EarlyView.
Structure–activity relationship (SAR) optimization of bio‐reducible ionizable lipids enables the development of highly effective lipid nanoparticle (LNP) mRNA vaccines. Lead LNPs show superior tolerability and antibody responses in rodents and primates, outperforming approved COVID‐19 vaccine lipids.
Ruben De Coen, Sabah Kasmi, Shrinivas Dumbre, Lies Baekens, Elise Seynaeve, Louise Van den Hauwe, Niccolò Cantini, Aello Garantoudi, Filipe Branco Madeira, Alain Marrot, Emma Tuypens, Sofie Seghers, Anna Kuchmiy, Elisabeth Brabants, Bart Vanderborght, Ine Maes, Sarah Vandenberghe, Jessica Filtjens, Alícia Gordún Peiró, Daliya Kancheva, Ayla Debraekeleer, Florence Lambolez, Emily De Lombaerde, Yong Chen, Rafael Miyazawa Martins, Pauline Formaglio, Damya Laoui, Sofie Deschoemaeker, Bruno G. De Geest, Rogerio Amino, Stefaan De Koker   +30 more
wiley   +1 more source

Statistics of Medical Libraries.

, 1898

openalex   +1 more source

Index Catalogue (Volume XI) of the Library of the Surgeon-General's Office — Autopsies in America — Memorial: Henry J. Bigelow, M.D. — Medical Notes

, 1890
C. P. Worcester
openalex   +2 more sources

Introduction to psychological medicine: joint meeting of the NLM Classification User Group UK and the LA Medical, Health and Welfare Libraries Group, 13 May 1985, Wellcome Institute for the History of Medicine [PDF]

, 1985
J.C.L. Wade
openalex   +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

The use of Academic Library and Information Seeking Behavior of Students at Lusaka Apex Medical University

Dalitso Mvula, Matildah Muchinga
openalex   +2 more sources

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