Deltagen Research Labs, Redwood City, CA 94063, USA.
Published: November 2003
AI Article Synopsis
Article Abstract
In using computational tools for library design it is necessary to understand the performance and limitations of available methods. This letter reports systematic comparisons of applying ligand-based and structure-based tools across therapeutic project-derived data sets. Included are assessments of performance in real-world iterative design applications and the utility of target structural information. The results suggest that combining screening and target structure information is robust; further, a well-designed screening library can compensate for lacking structural information.
Department of Mathematical Sciences, KAIST, Daejeon, 34141, Republic of Korea; Biomedical Mathematics Group, Pioneer Research Center for Mathematical and Computational Sciences, Institute for Basic Science, Daejeon, 34126, Republic of Korea; Department of Medicine, College of Medicine, Korea University, Seoul, 02841, Republic of Korea. Electronic address:
Self-report questionnaires play a crucial role in healthcare for assessing disease risks, yet their extensive length can be burdensome for respondents, potentially compromising data quality. To address this, machine learning-based shortened questionnaires have been developed. While these questionnaires possess high levels of accuracy, their practical use in clinical settings is hindered by a lack of transparency and the need for specialized machine learning expertise.
Background/aims: Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that severely affects cognitive functions and memory. Early detection is crucial for timely intervention and improved patient outcomes. However, traditional diagnostic tools, such as MRI and PET scans, are costly and less accessible.
Enzyme stability can be measured in a number of ways, including melting temperature, activity retention, and size analysis. However, these measurements are often conducted in an idealized storage buffer and not in the relevant enzymatic reaction media. Particularly for reactions that occur in alkaline, volatile, and high ionic strength media, typical analyses using differential scanning calorimetry, light scattering, and sodium dodecyl-sulfate polyacrylamide gel electrophoresis are not satisfactory to track the stability of these enzymes.
Objective: Individuals with Limited English Proficiency (LEP), including Spanish-preferred patients, face healthcare challenges due to language barriers. Despite the potential of digital health technologies to improve access and outcomes, there is a "digital divide" with underutilization among vulnerable populations, including Spanish-speaking LEP individuals, highlighting a need for increased understanding and equitable digital health solutions.
Materials And Methods: A multi-mode, multi-language cross-sectional survey was built based on the Technology Acceptance Model and deployed from a multi-state healthcare practice.
Rapid advancements in medical AI necessitate targeted educational initiatives for clinicians to ensure AI tools are safe and used effectively to improve patient outcomes. To support decision-making among stakeholders in medical education, we propose three tiers of medical AI expertise and outline the challenges for medical education at different educational stages. Additionally, we offer recommendations and examples, encouraging stakeholders to adapt and shape curricula for their specific healthcare setting using this framework.
