Results 181 to 190 of about 362,095 (261)
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 +3 more
wiley +1 more source
Conceptual model of pharmaceutical care for patients with coronary heart disease and comorbid conditions in Ukraine. [PDF]
Bilousova N.
europepmc +1 more source
The CMO System Model: A Dynamic Framework to Operationalize Adaptive Pharmaceutical Care and Drive Health-System Sustainability [PDF]
Ramón A Morillo-Verdugo +3 more
openalex +1 more source
This review highlights recent advances in label‐free optical biosensors based on 2D materials and rationally designed mixed‐dimensional nanohybrids, emphasizing their synergistic effects and novel functionalities. It also discusses multifunctional sensing platforms and the integration of machine learning for intelligent data analysis.
Xinyi Li, Yonghao Fu, Yuehe Lin, Dan Du
wiley +1 more source
Impact of collaborative pharmaceutical care on older inpatients' medication safety: multicentre stepped-wedge cluster randomised trial (MEDREV Study). [PDF]
Leguelinel-Blache G +14 more
europepmc +1 more source
Non‐covalent protein–protein interactions mediated by SH3, PDZ, or GBD domains enable the self‐assembly of stable and biocatalytically active hydrogel materials. These soft materials can be processed into monodisperse foams that, once dried, exhibit enhanced mechanical stability and activity and are easily integrated into microstructured flow ...
Julian S. Hertel +5 more
wiley +1 more source
The impact of early pregnancy disease control in systemic lupus erythematosus patients receiving comprehensive pharmaceutical care on pregnancy outcomes and offspring long-term health. [PDF]
Zheng L +12 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

