Discovering Influenza Virus Neuraminidase Inhibitors via Computational and Experimental Studies.

Influenza A and B viruses spread out worldwide, causing several global concerns. Discovering neuraminidase inhibitors to prevent influenza A and B viruses is thus of great interest. In this work, a machine learning model was trained and tested to evaluate the ligand-binding affinity to neuraminidase.

Dynamic of composite nanobeams resting on an elastic substrate with variable stiffness.

This study introduces a novel approach by combining finite simulation and an enhanced shear deformation theory to analyze the dynamic behavior of multi-layer composite nanobeams supported by an elastic foundation. The calculation formulae are derived from nonlocal theory in order to account for the impact of size effect. An intriguing aspect of this research is the presence of intricate curved profiles in the two material layers of the beam.

Arsenic and Heavy Metals in Soils and Plants near Sulfide Mines: Implications for Phytoremediation and Phytomanagement.

The accumulation of heavy metals (i.e., As, Cu, Ni, Pb, and Zn) in soils and native plant species near copper, nickel, and pyrite mines in Vietnam was assessed.

Pt Single Atom-Doped Triphasic VP-NiP-MoP Heterostructure: Unveiling a Breakthrough Electrocatalyst for Efficient Water Splitting.

Enhancement of an alkaline water splitting reaction in Pt-based single-atom catalysts (SACs) relies on effective metal-support interactions. A Pt single atom (Pt)-immobilized three-phased Pt@VP-NiP-MoP heterostructure on nickel foam is presented, demonstrating high catalytic performance. The existence of Pt on triphasic metal phosphides gives an outstanding performance toward overall water splitting.

Searching for potential acetylcholinesterase inhibitors: a combined approach of multi-step similarity search, machine learning and molecular dynamics simulations.

Targeting acetylcholinesterase is one of the most important strategies for developing therapeutics against Alzheimer's disease. In this work, we have employed a new approach that combines machine learning models, a multi-step similarity search of the PubChem library and molecular dynamics simulations to investigate potential inhibitors for acetylcholinesterase. Our search strategy has been shown to significantly enrich the set of compounds with strong predicted binding affinity to acetylcholinesterase.

MedChemExpress compounds prevent neuraminidase N1 physics- and knowledge-based methods.

Influenza A viruses spread out worldwide, causing several global concerns. Hence, discovering neuraminidase inhibitors to prevent the influenza A virus is of great interest. In this work, a machine learning model was employed to evaluate the ligand-binding affinity of 10 000 compounds from the MedChemExpress (MCE) database for inhibiting neuraminidase.

Realizing the Tailored Catalytic Performances on Atomic Pt-Promoted Transition Metal Moieties Implanted Layered Double Hydroxides for Water Electrolysis.

High-performance production of green hydrogen gas is necessary to develop renewable energy generation technology and to safeguard the living environment. This study reports a controllable engineering approach to tailor the structure of nickel-layered double hydroxides via doped and absorbed platinum single atoms (Pt) promoted by low electronegative transition metal (Mn, Fe) moieties (Pt-Mn,Fe-Ni LDHs). We explore that the electron donation from neighboring transition metal moieties results in the well-adjusted -band center with the low valence states of Pt and Pt, thus optimizing adsorption energy to effectively accelerate the H release.

Correction: Association of Strongyloides stercoralis infection and type 2 diabetes mellitus in northeastern Thailand: Impact on diabetic complication-related renal biochemical parameters.

[This corrects the article DOI: 10.1371/journal.pone.

Alchemical approach performance in calculating the ligand-binding free energy.

Alchemical binding free energy calculations are one of the most accurate methods for estimating ligand-binding affinity. Assessing the accuracy of the approach over protein targets is one of the most interesting issues. The free energy difference of binding between a protein and a ligand was calculated the alchemical approach.

Triphasic NiP-NiP-Ru with Amorphous Interface Engineering Promoted by Co Nano-Surface for Efficient Water Splitting.

This research designs a triphasic NiP-NiP-Ru heterostructure with amorphous interface engineering strongly coupled by a cobalt nano-surface (Co@NiP-Ru) to form a hierarchical 3D interconnected architecture. The Co@NiP-Ru material promotes unique reactivities toward hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) in alkaline media. The material delivers an overpotential of 30 mV for HER at 10 mA cm and 320 mV for OER at 50 mA cm in freshwater.

A Fluorescent Chemosensor for Detection pH and Cu Ion Base on 7-((2-Aminoethyl)amino)-5-Bromo-6-Hydroxy-1-Methylquinolin-1-ium-3-Sulfonate: Experimental and DFT Calculation.

A quinoline derivative 7-((2-aminoethyl)amino)-5-bromo-6-hydroxy-1-methylquinolin-1-ium-3-sulfonate (QEt) containing quinoline ring, - sulfonate, -OH phenol, and amine groups was synthesized and studied luminescence properties. The aqueous solutions QEt 10µM change luminescence color from green (λ = 490 nm) to yellow (λ = 563 nm) as increasing pH and the intensity at a peak of 563 nm is linearly proportional with pH value in the range of pH = 3,0-4,0. The QEt solution can be used as a chemosensor for Cu with an LOD value at 0.

Morphology of a Transmembrane Aβ Tetramer via REMD Simulations.

The folding/misfolding of membrane-permiable Amyloid beta (Aβ) peptides is likely associated with the advancing stage of Alzheimer's disease (AD) by disrupting Ca homeostasis. In this context, the aggregation of four transmembrane Aβ peptides was investigated using temperature replica-exchange molecular dynamics (REMD) simulations. The obtained results indicated that the secondary structure of transmembrane Aβ peptides tends to have different propensities compared to those in solution.

Upgrading nirmatrelvir to inhibit SARS-CoV-2 Mpro via DeepFrag and free energy calculations.

The first oral drug for the treatment of COVID-19, Paxlovid, has been authorized; however, nirmatrelvir, a major component of the drug, is reported to be associated with some side effects. Moreover, the appearance of many novel variants raises concerns about drug resistance, and designing new potent inhibitors to prevent viral replication is thus urgent. In this context, using a hybrid approach combining machine learning (ML) and free energy simulations, 6 compounds obtained by modifying nirmatrelvir were proposed to bind strongly to SARS-CoV-2 Mpro.

Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds.

To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of SARS-CoV-2. In this study, we combined machine-learning (ML) model with atomistic simulations to computationally search for highly promising SARS-CoV-2 Mpro inhibitors from the representative natural compounds of the National Cancer Institute (NCI) Database.

Correction: Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro physics- and knowledge-based approaches.

Correction for 'Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro physics- and knowledge-based approaches' by Son Tung Ngo , , 2022, https://doi.org/10.1039/d2cp04476e.

Characterizing the ligand-binding affinity toward SARS-CoV-2 Mpro physics- and knowledge-based approaches.

Computational approaches, including physics- and knowledge-based methods, have commonly been used to determine the ligand-binding affinity toward SARS-CoV-2 main protease (Mpro or 3CLpro). Strong binding ligands can thus be suggested as potential inhibitors for blocking the biological activity of the protease. In this context, this paper aims to provide a short review of computational approaches that have recently been applied in the search for inhibitor candidates of Mpro.

Investigating Effects of COVID-19 Pandemic on the Mental Health of Nursing Students During Remote Learning: An Experience from Dong A University, Vietnam.

Introduction: There was a radically changed in nursing education during the nationwide lockdown due to the COVID-19 outbreaks. The transition to remote learning stressed nursing students in many countries, particularly in Vietnam. However, there is still lacking a novel study to describe the mental characteristics of nursing students in detail.

Searching for potential inhibitors of SARS-COV-2 main protease using supervised learning and perturbation calculations.

Inhibiting the biological activity of SARS-CoV-2 Mpro can prevent viral replication. In this context, a hybrid approach using knowledge- and physics-based methods was proposed to characterize potential inhibitors for SARS-CoV-2 Mpro. Initially, supervised machine learning (ML) models were trained to predict a ligand-binding affinity of ca.

Bayesian regression and model selection for isothermal titration calorimetry with enantiomeric mixtures.

Bayesian regression is performed to infer parameters of thermodynamic binding models from isothermal titration calorimetry measurements in which the titrant is an enantiomeric mixture. For some measurements the posterior density is multimodal, indicating that additional data with a different protocol are required to uniquely determine the parameters. Models of increasing complexity-two-component binding, racemic mixture, and enantiomeric mixture-are compared using model selection criteria.

Frizzled-6 promotes hematopoietic stem/progenitor cell mobilization and survival during LPS-induced emergency myelopoiesis.

Emergency hematopoiesis involves the activation of bone marrow hematopoietic stem/progenitor cells (HSPCs) in response to systemic inflammation by a combination of cell-autonomous and stroma-dependent signals and leads to their release from bone marrow and migration to periphery. We have previously shown that FZD6 plays a pivotal role in regulating HSPC expansion and long-term maintenance. Now we sought to better understand the underlying mechanisms.

Strongyloides stercoralis infection induces gut dysbiosis in chronic kidney disease patients.

Background: Strongyloides stercoralis infection typically causes severe symptoms in immunocompromised patients. This infection can also alter the gut microbiota and is often found in areas where chronic kidney disease (CKD) is common. However, the relationship between S.

Identifying Possible AChE Inhibitors from Drug-like Molecules via Machine Learning and Experimental Studies.

Acetylcholinesterase (AChE) is one of the most important drug targets for Alzheimer's disease (AD) treatment. In this work, a machine learning model was trained to rapidly and accurately screen large chemical databases for the potential inhibitors of AChE. The obtained results were then validated via in vitro enzyme assay.

Searching for AChE inhibitors from natural compounds by using machine learning and atomistic simulations.

Acetylcholinesterase (AChE) is one of the most important drug targets for Alzheimer's disease treatment. In this work, a combined approach involving machine-learning (ML) model and atomistic simulations was established to predict the ligand-binding affinity to AChE of the natural compounds from VIETHERB database. The trained ML model was first utilized to rapidly and accurately screen the natural compound database for potential AChE inhibitors.