Results 151 to 160 of about 775,984 (386)

Smart, Bio‐Inspired Polymers and Bio‐Based Molecules Modified by Zwitterionic Motifs to Design Next‐Generation Materials for Medical Applications

open access: yesAdvanced 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   +3 more
wiley   +1 more source

Randomized Phase II Study of PET Response-Adapted Combined Modality Therapy for Esophageal Cancer: Mature Results of the CALGB 80803 (Alliance) Trial. [PDF]

open access: yesJ Clin Oncol, 2021
Goodman KA   +17 more
europepmc   +1 more source

Emerging 2D Materials and Their Hybrid Nanostructures for Label‐Free Optical Biosensing: Recent Progress and Outlook

open access: yesAdvanced Functional Materials, EarlyView.
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

SUCCESSFUL ASPIRATION OF THROMBOTIC MASSES OUT OF THE LEFT INTERNAL CAROTID ARTERY LUMEN USING A PROXIMAL EMBOLIC PROTECTION DEVICE IN THE CONDITIONS OF THROMBOLYTIC THERAPY PERFORMANCE

open access: yesИнновационная медицина Кубани, 2019
Combined techniques of treatment for ischemic stroke are even more often included into routine work of specialized teams. One of them based on a combination of systemic thrombolytic therapy and endovascular methods is characterized by a high rate of ...
A. S. Nekrasov   +3 more
doaj   +2 more sources

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

open access: yesAdvanced 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   +7 more
wiley   +1 more source

Home - About - Disclaimer - Privacy