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A Multiform Heterogeneity Framework for Alzheimer's Disease Based on Multimodal Neuroimaging.
Biol Psychiatry
December 2024
School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China; Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China. Electronic address:
Understanding the heterogeneity of Alzheimer's disease (AD) is crucial for advancing precision medicine specifically tailored to this disorder. Recent research has deepened our understanding of AD heterogeneity, yet translating these insights from bench to bedside via neuroimaging heterogeneity frameworks presents significant challenges. In this review, we systematically revisit prior studies and summarize the existing methodology of data-driven neuroimaging studies for AD heterogeneity.
View Article and Find Full Text PDFEpisode-Based Bundled Payments in Hand Surgery: An Affordable Solution to Overwhelming Health Care Costs.
Hand (N Y)
December 2024
Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ, USA.
Article Synopsis
- The review focuses on episode-based bundled payment models for hand surgery as a means to reduce healthcare costs and improve patient care.
- Traditional fee-for-service models are ineffective in promoting collaboration among healthcare providers, while bundled payments encourage team-based approaches and resource optimization.
- Current literature suggests that implementing these models could lead to cost savings and better patient outcomes, warranting further trials in hand surgery.
Identification of key factors related to digital health observational study adherence and retention by data-driven approaches: an exploratory secondary analysis of two prospective longitudinal studies.
Lancet Digit Health
January 2025
Biomedical Engineering Department, Duke University, Durham, NC, USA; Biostatistics and Bioinformatics Department, Duke University, Durham, NC, USA. Electronic address:
Background: Longitudinal digital health studies combine passively collected information from digital devices, such as commercial wearable devices, and actively contributed data, such as surveys, from participants. Although the use of smartphones and access to the internet supports the development of these studies, challenges exist in collecting representative data due to low adherence and retention. We aimed to identify key factors related to adherence and retention in digital health studies and develop a methodology to identify factors that are associated with and might affect study participant engagement.
View Article and Find Full Text PDFEnhancing the reverse transcriptase function in Taq polymerase via AI-driven multiparametric rational design.
Front Bioeng Biotechnol
December 2024
AO Vector-Best, Novosibirsk, Russia.
Introduction: Modification of natural enzymes to introduce new properties and enhance existing ones is a central challenge in bioengineering. This study is focused on the development of Taq polymerase mutants that show enhanced reverse transcriptase (RTase) activity while retaining other desirable properties such as fidelity, 5'- 3' exonuclease activity, effective deoxyuracyl incorporation, and tolerance to locked nucleic acid (LNA)-containing substrates. Our objective was to use AI-driven rational design combined with multiparametric wet-lab analysis to identify and validate Taq polymerase mutants with an optimal combination of these properties.
View Article and Find Full Text PDFData-Driven Machine Learning Strategy for Designing Metal-Ion-Doped γ-BiMoO Photocatalysts to Enhance Degradation Performance.
J Phys Chem B
December 2024
Science and Technology on Aerospace Chemical Power Laboratory, Laboratory of Emergency Safety and Rescue Technology, Hubei Institute of Aerospace Chemotechnology, Xiangyang 441003, China.
Doped semiconductors are often used to improve photocatalytic efficiency and address the challenges of easy recombination of electron-hole pairs and poor photoluminescence. However, the reproducibility and complexity of experimental studies result in time-consuming and less cost-effective studies, and it is difficult to gain insights into the intrinsic properties of doped photocatalysts to control their performance. Introducing a machine learning approach, we constructed a photocatalytic model of transition-metal- and rare earth metal-ion-doped γ-BiMoO.
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