Natural products with diverse functional groups and stereogenic centers have inspired therapeutics and underpin the modern drug discovery process. Their three-dimensional molecular structures need to be unambiguously determined in order to be realized as clinical candidates or to achieve further activity-guided structural optimization. Although recent advances in spectroscopic methods have made it possible for researchers to determine the structures of microgram samples of complex natural products, there is no universally accepted method for determining the relative and absolute configuration of a naturally occurring compound. We report the determination of the full stereostructure of valactamide A, an eight-stereogenic-center-containing fungal metabolite by the synergy of prediction rule-guided analysis and chemical synthesis. The expedient total synthesis resulted in unambiguous verification of the predicted stereochemistry for valactamide A.
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http://dx.doi.org/10.1021/prechem.3c00109 | DOI Listing |
J Biomed Mater Res A
January 2025
PRISM Research Institute, Technological University of the Shannon: Midlands Midwest, Athlone, Ireland.
This study provides a comprehensive investigation of antimicrobial additives (ZnO/AgNPs and SiO/AgNPs) on the properties of biodegradable ternary blends composed of poly(hydroxybutyrate) (PHB), poly(lactic acid) (PLA), and polycaprolactone (PCL) by examining the morphology, thermal stability, crystallinity index, and cell viability of these blends. Overall, transmission electron microscopy (TEM) analysis revealed that AgNPs and SiO exhibited comparable sizes, whereas ZnO was significantly larger, which influences their release profiles and interactions with the blends. The addition of antimicrobials influences the rheology of the blends, acting as compatibilizers by reducing the intermolecular forces between biopolymers.
View Article and Find Full Text PDFSci Prog
January 2025
Yazd Cardiovascular Research Center, Non-communicable Diseases Research Institute, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
Objective: Coronary artery disease (CAD) remains a significant global health burden, characterized by the narrowing or blockage of coronary arteries. Treatment decisions are often guided by angiography-based scoring systems, such as the Synergy Between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery (SYNTAX) and Gensini scores, although these require invasive procedures. This study explores the potential of electrocardiography (ECG) as a noninvasive diagnostic tool for predicting CAD severity, alongside traditional risk factors.
View Article and Find Full Text PDFSensors (Basel)
December 2024
AI and Big Data Department, Endicott College, Woosong University, Daejeon 34606, Republic of Korea.
Sensor networks generate vast amounts of data in real-time, which challenges existing predictive maintenance frameworks due to high latency, energy consumption, and bandwidth requirements. This research addresses these limitations by proposing an edge-cloud hybrid framework, leveraging edge devices for immediate anomaly detection and cloud servers for in-depth failure prediction. A K-Nearest Neighbors (KNNs) model is deployed on edge devices to detect anomalies in real-time, reducing the need for continuous data transfer to the cloud.
View Article and Find Full Text PDFFront Bioeng Biotechnol
December 2024
Department of Rehabilitation Medicine, University of Hong Kong-Shenzhen Hospital, Shenzhen, China.
Introduction: Parkinson's disease (PD) is characterized by muscle stiffness, bradykinesia, and balance disorders, significantly impairing the quality of life for affected patients. While motion pose estimation and gait analysis can aid in early diagnosis and timely intervention, clinical practice currently lacks objective and accurate tools for gait analysis.
Methods: This study proposes a multi-level 3D pose estimation framework for PD patients, integrating monocular video with Transformer and Graph Convolutional Network (GCN) techniques.
J Health Organ Manag
January 2025
University of Malta, Msida, Malta.
Purpose: This study explores how corporate social responsibility (CSR) and artificial intelligence (AI) can be combined in the healthcare industry during the post-COVID-19 recovery phase. The aim is to showcase how this fusion can help tackle healthcare inequalities, enhance accessibility and support long-term sustainability.
Design/methodology/approach: Adopting a viewpoint approach, the study leverages existing literature and case studies to analyze the intersection of CSR and AI.
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