Drug-target binding affinity prediction based on power graph and word2vec.

Background: Drug and protein targets affect the physiological functions and metabolic effects of the body through bonding reactions, and accurate prediction of drug-protein target interactions is crucial for drug development. In order to shorten the drug development cycle and reduce costs, machine learning methods are gradually playing an important role in the field of drug-target interactions.

Results: Compared with other methods, regression-based drug target affinity is more representative of the binding ability.

Predictive equation derived from 6,497 doubly labelled water measurements enables the detection of erroneous self-reported energy intake.

Nutritional epidemiology aims to link dietary exposures to chronic disease, but the instruments for evaluating dietary intake are inaccurate. One way to identify unreliable data and the sources of errors is to compare estimated intakes with the total energy expenditure (TEE). In this study, we used the International Atomic Energy Agency Doubly Labeled Water Database to derive a predictive equation for TEE using 6,497 measures of TEE in individuals aged 4 to 96 years.

Clinical characteristics and management of reoperation for high jejunal atresia: a retrospective study.

Objective: To analyze the clinical characteristics and available treatment strategies for reoperation of neonatal high jejunal atresia, and recommend preventive measures to reduce the reoperation rate of high jejunal atresia.

Methods: The clinical data of 16 children with high jejunal atresia who underwent reoperation in the Neonatal Surgery Department at Children's Hospital of Zhejiang University School of Medicine from January 2018 to January 2023 were retrospectively analyzed.

Results: Among the 16 unplanned reoperations, 7 (43.

Copper-Catalysed Electrochemical CO2 Methanation via the Alloying of Single Cobalt Atoms.

The electrochemical reduction of carbon dioxide (CO2) to methane (CH4) presents a promising solution for mitigating CO2 emissions while producing valuable chemical feedstocks. Although single-atom catalysts have shown potential in selectively converting CO2 to CH4, their limited active sites often hinder the realization of high current densities, posing a selectivity-activity dilemma. In this study, we developed a single-atom cobalt (Co) doped copper catalyst (Co1Cu) that achieved a CH4 Faradaic efficiency exceeding 60% with a partial current density of -482.

Numerical simulation of fluid-structure interaction analysis for the performance of leaflet reimplantation with different types of artificial graft.

Background And Objective: In clinical practice, valve-sparing aortic root replacement surgery primarily addresses left ventricular dysfunction in patients due to severe aortic regurgitation, but there is controversy regarding the choice of surgical technique. In order to investigate which type of valve-sparing aortic root replacement surgeries can achieve better blood flow conditions, this study examines the impact of changes in the geometric morphology of the aortic root on the hemodynamic environment through numerical simulation.

Methods: An idealized model of the aortic root was established based on data obtained from clinical measurements, including using the model of the aortic root without significant lesions as the control group (Model C), while using surgical models of leaflet reimplantation with tubular graft (Model T), leaflet reimplantation with Valsalva graft (Model V), and the Florida sleeve procedure (Model F) as the experimental groups.