Sequestration of aromatic volatile organic compounds (VOCs) via metal-organic frameworks (MOFs) as sorbents is a viable means of environmental preservation. In this investigation, we shed light on the key features associated with MOFs that govern the selective uptake of a subclass of VOCs containing benzene, toluene, ethylbenzene, and xylenes (BTEX). We investigate, through a multistep computational framework including electronic structure and classical molecular dynamics simulations, the energetic and dynamical properties associated with BTEX capture in three MOFs: HKUST-1, ZIF-8, and MIL-53. Our work demonstrates the importance of considering both static and dynamical properties upon introduction of guest molecules in such computational investigations. We elucidate the key geometric factors associated with efficient capture of BTEX compounds and highlight possible postsynthetic modifications that can be used to produce next generation sorbents for BTEX capture.
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http://dx.doi.org/10.1021/acs.jpclett.2c02131 | DOI Listing |
ACS Chem Neurosci
January 2025
Department of Bioengineering and Biotechnology, Birla Institute of Technology Mesra, Ranchi, Jharkhand 835215, India.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, extracellular amyloid-β (Aβ) plaque accumulation, and intracellular neurofibrillary tangles. Recent efforts to find effective therapies have increased interest in natural compounds with multifaceted effects on AD pathology. This study explores natural compounds for their potential to mitigate AD pathology using molecular docking, ADME screening, and assays, with ruscogenin─a steroidal sapogenin from emerging as a promising candidate.
View Article and Find Full Text PDFPLoS Comput Biol
January 2025
IRSD-Institut de Recherche en Santé Digestive, Université de Toulouse, INSERM, INRAE, ENVT, UPS, Toulouse, France.
Understanding the interplay between biology and mechanics in tissue architecture is challenging, particularly in terms of 3D tissue organization. Addressing this challenge requires a biological model enabling observations at multiple levels from cell to tissue, as well as theoretical and computational approaches enabling the generation of a synthetic model that is relevant to the biological model and allowing for investigation of the mechanical stresses experienced by the tissue. Using a monolayer human colon epithelium organoid as a biological model, freely available tools (Fiji, Cellpose, Napari, Morphonet, or Tyssue library), and the commercially available Abaqus FEM solver, we combined vertex and FEM approaches to generate a comprehensive viscoelastic finite element model of the human colon organoid and demonstrated its flexibility.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Structural Heart Disease, Cardiovascular Institute and Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background: Assessing the endothelialization of occlusive devices noninvasively remains a challenge. Cardiac computed tomography angiography (CTA) can be employed to evaluate device endothelialization based on contrast uptake within the occluder.
Objective: This study examined device endothelialization using cardiac CTA and investigated the pathological associations.
PLoS One
January 2025
Department of Cardiovascular Medicine, Chiba University Graduate School of Medicine, Chiba, Japan.
Background: Training opportunities, work satisfaction, and the factors that influence them according to gender and subspecialties are understudied among Japanese cardiologists.
Methods: We investigated the career development of Japanese cardiologists with an e-mail questionnaire. Feelings of inequality in training opportunities, work dissatisfaction, and reasons were assessed by examining the cardiologists' gender and invasiveness of subspecialties.
PLoS One
January 2025
Department of Information Technology, Politeknik Negeri Padang, Padang, Sumatera Barat, Indonesia.
Texture is a significant component used for several applications in content-based image retrieval. Any texture classification method aims to map an anonymously textured input image to one of the existing texture classes. Extensive ranges of methods for labeling image texture were proposed earlier.
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