Results 271 to 280 of about 2,183,210 (336)
Burden of Treatment in Children and Adolescents With Type 1 Diabetes Evaluated by Focus Groups. [PDF]
Le Fur S+14 more
europepmc +1 more source
Progress in Surface Plasmon and Other Resonance Biosensors for Biomedical Applications
This is the shortened version: Recent advancements in surface plasmon resonance and other optical resonance biosensors for biomedical applications are presented. Advanced sensing strategies are examined for the detection of diverse analytes, integration of nanomaterials and machine learning, and emerging nonplasmonic modes like guided mode resonance ...
Faten Bashar Kamal Eddin+8 more
wiley +1 more source
Protocol for the development of a core outcome set for type 1 diabetes risk screening. [PDF]
Chen C+10 more
europepmc +1 more source
Abstract Additive carbonylations typically necessitate strong nucleophiles, such as alcohols or amines. In this study, we carbonylated a poorly nucleophilic urea, under oxidant‐free conditions. Our straightforward carbonylative strategy enables access to versatile α,β‐unsaturated γ‐lactams featuring an aminocarbonyl fragment, utilizing readily ...
Debora Schiroli+11 more
wiley +1 more source
Multi-omics investigation of prospective therapeutic targets for type 1 diabetes. [PDF]
Zhang YY, Qiao QT, Chen BX, Wan Q.
europepmc +1 more source
Intrinsically Soft Implantable Electronics for Long‐term Biosensing Applications
Intrinsically soft implantable biosensors address the mechanical mismatch of conventional rigid implants, improving biocompatibility and stability. This review explores soft encapsulation matrices, stretchable conductors, implantation strategies, and chronic fixation techniques.
Su Hyeon Lee+5 more
wiley +1 more source
Low-Carb and Ketogenic Diets in Type 1 Diabetes: Efficacy and Safety Concerns. [PDF]
Korakas E+3 more
europepmc +1 more source
S100A1 and S100B Expression and Target Proteins in Type I Diabetes1
Danna B. Zimmer+3 more
openalex +1 more source
This review examines recent advancements in multimodal bioelectronics, emphasizing machine learning integration to enhance functionality. The application of machine learning methodologies improving biosignal processing, device adaptability, and diagnostic accuracy is discussed to introduce Machine Learning enhanced bioelectronics as a pathway toward ...
Myoungjae Oh+11 more
wiley +1 more source