Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.jcin.2018.07.054 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
A prediction model for electrical strength of gaseous medium based on molecular reactivity descriptors and machine learning method.
J Mol Model
January 2025
Hubei Key Laboratory·for High-Efficiency-Utilization of Solar Energy and Operation, Control of Energy-Storage System, Hubei-University of Technology, Wuhan, 430068, China.
Context: Ionization and adsorption in gas discharge are similar to electrophilic and nucleophilic reactions. The molecular descriptors characterizing reactions such as electrostatic potential descriptors are useful in predicting the electrical strength of environmentally friendly gases. In this study, descriptors of 73 molecules are employed for correlation analysis with electrical strength.
View Article and Find Full Text PDFInvestigating the decomposition mechanism of DNAN/DNB cocrystal explosive under high temperature using ReaxFF/lg molecular dynamics simulations.
J Mol Model
January 2025
Shanxi Jiangyang Chemical Limited Company, Taiyuan, 030041, Shanxi, China.
Context: DNAN/DNB cocrystals, as a newly developed type of energetic material, possess superior safety and thermal stability, making them a suitable alternative to traditional melt-cast explosives. Nonetheless, an exploration of the thermal degradation dynamics of the said cocrystal composite has heretofore remained uncharted. Consequently, we engaged the ReaxFF/lg force field modality to delve into the thermal dissociation processes of the DNAN/DNB cocrystal assembly across a spectrum of temperatures, encompassing 2500, 2750, 3000, 3250, and 3500 K.
View Article and Find Full Text PDFThermophysical properties of graphene reinforced with polymethyl methacrylate nanoparticles for technological applications: a molecular model.
J Mol Model
January 2025
Escuela Superior de Física y Matemáticas, IPN S/N, Edificio 9 de la Unidad Profesional "Adolfo López Mateos", Col. Lindavista, Alc. Gustavo A. Madero, 07738, Mexico City, Mexico.
Context: "Nanostructure of graphene-reinforced with polymethyl methacrylate" (PMMA-G), and vice versa, is investigated using its molecular structure, in the present work. The PMMA-G nanostructure was constructed by bonding PMMA with graphene nanosheet in a sense to get three different configurations. Each configuration consisted of polymeric structures with three degrees of polymerization (such as monomers, dimers, and trimers polymers, respectively).
View Article and Find Full Text PDFMachine learning-based assessment of morphometric abnormalities distinguishes bipolar disorder and major depressive disorder.
Neuroradiology
January 2025
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
Introduction: Bipolar disorder (BD) and major depressive disorder (MDD) have overlapping clinical presentations which may make it difficult for clinicians to distinguish them potentially resulting in misdiagnosis. This study combined structural MRI and machine learning techniques to determine whether regional morphological differences could distinguish patients with BD and MDD.
Methods: A total of 123 participants, including BD (n = 31), MDD (n = 48), and healthy controls (HC, n = 44), underwent high-resolution 3D T1-weighted imaging.
Population pharmacokinetic modeling and exposure-response analysis of Anrikefon: insights and implications in clinical analgesia.
Expert Rev Clin Pharmacol
January 2025
Department of Medical Cosmetology, Hunan Provincial Hospital of Maternal and Child Health Care.
Background: Anrikefon (HSK21542), a potent and selective peripheral kappa opioid receptor (KOR) agonist developed by Haisco, effectively blocks pain and itch signals.
Aim: To develop a population pharmacokinetic (PK) model for anrikefon and conduct exposure-response (E-R) analysis for safety and efficacy in postoperative pain patients.
Method: The Population PK analysis uses NONMEM software with data from six trials.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!