Structure-Guided Antiviral Peptides Identification Targeting the HIV-1 Integrase.

HIV-1 integrase (IN), a major protein in the HIV life cycle responsible for integrating viral cDNA into the host DNA, represents a promising drug target. Small peptides have emerged as antiviral therapeutics for HIV because of their facile synthesis, highly selective nature, and fewer side effects. However, selecting the best candidates from a vast pool of peptides is a daunting task. In this study, multistep virtual screening was employed to identify potential peptides from a list of 280 HIV inhibitory peptides. Initially, 80 peptides were selected based on their minimum inhibitory concentrations (MIC). Then, molecular docking was performed to evaluate their binding scores compared to HIP000 and HIP00N which are experimentally validated HIV-1 integrase binding peptides that were used as a positive and negative control, respectively. The top-scoring docked complexes, namely, IN-HIP1113, IN-HIP1140, IN-HIP1142, IN-HIP678, IN-HIP776, and IN-HIP777, were subjected to initial 500 ns molecular dynamics (MD) simulations. Subsequently, HIP776, HIP777, and HIP1142 were selected for an in-depth mechanistic study of peptide interactions, with multiple simulations conducted for each complex spanning one microsecond. Independent simulations of the peptides, along with comparisons to the bound state, were performed to elucidate the conformational dynamics of the peptides. These peptides exhibit strong interactions with specific residues, as revealed by snapshot interaction analysis. Notably, LYS159, LYS156, VAL150, and GLU69 residues are prominently involved in these interactions. Additionally, residue-based binding free energy (BFE) calculations highlight the significance of HIS67, GLN148, GLN146, and SER147 residues within the binding pocket. Furthermore, the structure-activity relationship (SAR) analysis demonstrated that aromatic amino acids and the overall volume of peptides are the two major contributors to the docking scores. The best peptides will be validated experimentally by incorporating SAR properties, aiming to develop them as therapeutic agents and structural models for future peptide-based HIV-1 drug design, addressing the urgent need for effective HIV treatments.

Integrated Gene Expression Data-Driven Identification of Molecular Signatures, Prognostic Biomarkers, and Drug Targets for Glioblastoma.

Glioblastoma (GBM) is a highly prevalent and deadly brain tumor with high mortality rates, especially among adults. Despite extensive research, the underlying mechanisms driving its progression remain poorly understood. Computational analysis offers a powerful approach to explore potential prognostic biomarkers, drug targets, and therapeutic agents for GBM. In this study, we utilized three gene expression datasets from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) associated with GBM progression. Our goal was to uncover key molecular players implicated in GBM pathogenesis and potential avenues for targeted therapy. Analysis of the gene expression datasets revealed a total of 78 common DEGs that are potentially involved in GBM progression. Through further investigation, we identified nine hub DEGs that are highly interconnected in protein-protein interaction (PPI) networks, indicating their central role in GBM biology. Gene Ontology (GO) and pathway enrichment analyses provided insights into the biological processes and immunological pathways influenced by these DEGs. Among the nine identified DEGs, survival analysis demonstrated that increased expression of GMFG correlated with decreased patient survival rates in GBM, suggesting its potential as a prognostic biomarker and preventive target for GBM. Furthermore, molecular docking and ADMET analysis identified two compounds from the NIH clinical collection that showed promising interactions with the GMFG protein. Besides, a 100 nanosecond molecular dynamics (MD) simulation evaluated the conformational changes and the binding strength. Our study highlights the potential of GMFG as both a prognostic biomarker and a therapeutic target for GBM. The identification of GMFG and its associated pathways provides valuable insights into the molecular mechanisms driving GBM progression. Moreover, the identification of candidate compounds with potential interactions with GMFG offers exciting possibilities for targeted therapy development. However, further laboratory experiments are required to validate the role of GMFG in GBM pathogenesis and to assess the efficacy of potential therapeutic agents targeting this molecule.

Unveiling Neuroprotective Potential of Spice Plant-Derived Compounds against Alzheimer's Disease: Insights from Computational Studies.

Alzheimer's disease (AD) is a serious threat to the global health care system and is brought on by a series of factors that cause neuronal dysfunction and impairment in memory and cognitive decline. This study investigated the therapeutic potential of phytochemicals that belong to the ten regularly used spice plants, based on their binding affinity with AD-associated proteins. Comprehensive docking studies were performed using AutoDock Vina in PyRx followed by molecular dynamic (MD) simulations using AMBER 14. The docking study of the chosen molecules revealed the binding energies of their interactions with the target proteins, while MD simulations were carried out to verify the steadiness of bound complexes. Through the Lipinski filter and admetSAR analysis, the chosen compounds' pharmacokinetic characteristics and drug likeness were also examined. The pharmacophore mapping study was also done and analyzed for best selected molecules. Additionally, principal component analysis (PCA) was used to examine how the general motion of the protein changed. The results showed quercetin and myricetin to be potential inhibitors of AChE and alpha-amyrin and beta-chlorogenin to be potential inhibitors of BuChE, exhibiting best binding energies comparable to those of donepezil, used as a positive control. The multiple descriptors from the simulation study, root mean square deviation (RMSD), root mean square fluctuation (RMSF), hydrogen bond, radius of gyration (Rg), and solvent-accessible surface areas (SASA), confirm the stable nature of the protein-ligand complexes. Molecular mechanic Poisson-Boltzmann surface area (MM-PBSA) binding free energy calculations indicated the energetically favorable binding of the ligands to the protein. Finally, according to pharmacokinetic properties and drug likeness, characteristics showed that quercetin and myricetin for AChE and alpha-amyrin and beta-chlorogenin for BuChE were found to be the most effective agents for treating the AD.

Pseudocapacitive Properties of Isostructural Oxides Sr2 LaBMnO7 (B=Co, Fe).

Pseudocapacitors promise to fill the gap between traditional capacitors and batteries by delivering reasonable energy densities and power densities. In this work, pseudocapacitive charge storage properties are demonstrated for two isostructural oxides, Sr2 LaFeMnO7 and Sr2 LaCoMnO7 . These materials comprise spatially separated bilayer stacks of corner sharing BO6 units (B=Fe, Co or Mn). The spaces between stacks accommodate the lanthanum and strontium ions, and the remaining empty spaces are available for oxide ion intercalation, leading to pseudocapacitive charge storage. Iodometric titrations indicate that these materials do not have oxygen-vacancies. Therefore, the oxide ion intercalation becomes possible due to their structural features and the availability of interstitial sites between the octahedral stacks. Electrochemical studies reveal that both materials show promising energy density and power density values. Further experiments through fabrication of a symmetric two-electrode cell indicate that these materials retain their pseudocapacitive performance over hundreds of galvanostatic charge-discharge cycles, with little degradation even after 1000 cycles.

Directing the size and dispersity of silver nanoparticles with kudzu leaf extracts.

Kudzu is an abundant and invasive species in the Southeastern United States. The prospective use of kudzu as a non-toxic, green and biocompatible reducing and stabilizing agent for one-pot Ag nanoparticle synthesis was investigated. Ag nanoparticles were synthesized using aqueous and ethanolic kudzu leaf and stem extracts. The size and dispersity of the synthesized nanoparticles were found to depend on the extract used. Ultraviolet-visible and Fourier transform infrared spectroscopies were used to characterize the extracts. Surface-enhanced fluorescence and Raman scattering were used to characterize the surface species on synthesized Ag nanoparticles. The primary reducing and stabilizing agents in aqueous kudzu leaf extracts were determined to be reducing sugars and saponins which result in Ag nanoparticles with average diameters of 21.2 ± 4.8 nm. Ethanolic kudzu leaf extract was determined to be composed of chlorophyll, reducing sugars and saponins, producing Ag nanoparticles with average diameters of 9.0 ± 1.6 nm. Control experiments using a chlorophyllin standard as the reducing and stabilizing agent reveal that chlorophyll has a key role in the formation of small and monodisperse Ag nanoparticles. Experiments carried out in the absence of light demonstrate that reducing sugars and saponins also contribute to the formation of Ag nanoparticles in ethanolic kudzu leaf extracts. We propose a mechanism by which reducing sugars donate electrons to reduce Ag+ leading to the formation of Ag nanoparticles, forming carboxylic acid sugars which stabilize and partially stabilize Ag nanoparticles synthesized with aqueous and ethanolic kudzu leaf extracts, respectively. In the ethanolic extract, photoexcited chlorophyll serves as a co-reducing and co-stabilizing agent, leading to small and monodisperse Ag nanoparticles.

Unraveling the impact of ORF3a Q57H mutation on SARS-CoV-2: insights from molecular dynamics.

ORF3a is a conserved accessory protein of SARS-CoV-2, linked to viral infection and pathogenesis, with acquired mutations at various locations. Previous studies have shown that the occurrence of the Q57H mutation is higher in comparison to other positions in ORF3a. This mutation is known to induce conformational changes, yet the extent of structural alteration and its role in the viral adaptation process remain unknown. Here we performed molecular dynamics (MD) simulations of wt-ORF3a, Q57H, and Q57A mutants to analyze structural changes caused by mutations compared to the native protein. The MD analysis revealed that Q57H and Q57A mutants show significant structural changes in the dimer conformation than the wt-ORF3a. This dimer conformer narrows down the ion channel cavity, which reduces Na + or K + permeability leading to decrease the antigenic response that can help the virus to escape the host immune system. Non-bonding interaction analysis shows the Q57H mutant has more interacting residues, resulting in more stability within dimer conformation than the wt-ORF3a and Q57A. Moreover, both mutant dimers (Q57H and Q57A) form a novel salt-bridge interaction at the same position between A:Asp142 and B:Lys61, whereas such an interaction is absent in the wt-ORF3a dimer. We have also noticed that the TM3 domain's flexibility in Q57H is increased because of strong inter-domain interactions of TM1 and TM2 within the dimer conformation. These unusual interactions and flexibility of Q57H mutant can have significant impacts on the SARS-CoV-2 adaptations, virulence, transmission, and immune system evasion. Our findings are consistent with the previous experimental data and provided details information on the structural perturbation in ORF3a caused by mutations, which can help better understand the structural change at the molecular level as well as the reason for the high virulence properties of this variant.Communicated by Ramaswamy H. Sarma.

Exploring Prognostic Biomarkers of Acute Myeloid Leukemia to Determine Its Most Effective Drugs from the FDA-Approved List through Molecular Docking and Dynamic Simulation.

Acute myeloid leukemia (AML) is a blood cancer caused by the abnormal proliferation and differentiation of hematopoietic stem cells in the bone marrow. The actual genetic markers and molecular mechanisms of AML prognosis are unclear till today. This study used bioinformatics approaches for identifying hub genes and pathways associated with AML development to uncover potential molecular mechanisms. The expression profiles of RNA-Seq datasets, GSE68925 and GSE183817, were retrieved from the Gene Expression Omnibus (GEO) database. These two datasets were analyzed by GREIN to obtain differentially expressed genes (DEGs), which were used for performing the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, protein-protein interaction (PPI), and survival analysis. The molecular docking and dynamic simulation were performed to identify the most effective drug/s for AML from the drug list approved by the Food and Drug Administration (FDA). By integrating the two datasets, 238 DEGs were identified as likely to be affected by AML progression. GO enrichment analyses exhibited that the upregulated genes were mainly associated with inflammatory response (BP) and extracellular region (CC). The downregulated DEGs were involved in the T-cell receptor signalling pathway (BP), an integral component of the lumenal side of the endoplasmic reticulum membrane (CC) and peptide antigen binding (MF). The pathway enrichment analysis showed that the upregulated DEGs were mainly associated with the T-cell receptor signalling pathway. Among the top 15 hub genes, the expression levels of ALDH1A1 and CFD were associated with the prognosis of AML. Four FDA-approved drugs were selected, and a top-ranked drug was identified for each biomarker through molecular docking studies. The top-ranked drugs were further confirmed by molecular dynamic simulation that revealed their binding stability and confirmed their stable performance. Therefore, the drug compounds, enasidenib and gilteritinib, can be recommended as the most effective drugs against the ALDH1A1 and CFD proteins, respectively.

Vacancy effect on the electrocatalytic activity of LaMn1/2Co1/2O3-δ for hydrogen and oxygen evolution reactions.

Development of efficient electrocatalysts for water splitting can be a significant step toward green hydrogen generation. In this work, a remarkable enhancement of electrocatalytic properties is achieved through the incorporation of oxygen-vacancies in a perovskite oxide, while maintaining the same structural framework. The oxygen-deficient material La2MnCoO6-δ (LaMn0.5Co0.5O3-δ) is isostructural to the parent stoichiometric material, but shows drastically enhanced electrocatalytic properties for both half-reactions of water-splitting, namely hydrogen-evolution and oxygen-evolution reactions, due to the oxygen-vacancies.

A review on structural, non-structural, and accessory proteins of SARS-CoV-2: Highlighting drug target sites.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative agent of COVID-19, is a highly transmittable and pathogenic human coronavirus that first emerged in China in December 2019. The unprecedented outbreak of SARS-CoV-2 devastated human health within a short time leading to a global public health emergency. A detailed understanding of the viral proteins including their structural characteristics and virulence mechanism on human health is very crucial for developing vaccines and therapeutics. To date, over 1800 structures of non-structural, structural, and accessory proteins of SARS-CoV-2 are determined by cryo-electron microscopy, X-ray crystallography, and NMR spectroscopy. Designing therapeutics to target the viral proteins has several benefits since they could be highly specific against the virus while maintaining minimal detrimental effects on humans. However, for ongoing and future research on SARS-CoV-2, summarizing all the viral proteins and their detailed structural information is crucial. In this review, we compile comprehensive information on viral structural, non-structural, and accessory proteins structures with their binding and catalytic sites, different domain and motifs, and potential drug target sites to assist chemists, biologists, and clinicians finding necessary details for fundamental and therapeutic research.

Antioxidant assessment of agricultural produce using fluorescence techniques: a review.

The study of bioactive compounds like food antioxidants is getting huge attention and curiosity by researchers and other relevant stakeholders (e.g., food and pharmaceutical industries) due to their health benefits. However, the currently available protocols to estimate the antioxidant activity of foods are time-consuming, destructive, require complex procedures for sample preparation, need technical persons, and not possible for real-time application, which are very important for large-scale or industrial applications. On the other hand, fluorescence spectroscopy and imaging techniques are relatively new, fast, mostly nondestructive, and possible to apply real-time to detect the antioxidants of foods. However, there is no review article on fluorescence techniques for estimating antioxidants in agricultural produces. Therefore, the present review comprehensively summarizes the overview of fluorescence phenomena, techniques (i.e., spectroscopy and computer vision), and their potential to monitor antioxidants in fruits and vegetables. Finally, opportunities and challenges of fluorescence techniques are described toward developing next-generation protocols for antioxidants measurement. Fluorescence techniques (both spectroscopy and imaging) are simpler and faster than available traditional methods of antioxidants measurement. Moreover, the fluorescence imaging technique has the potential to apply in real-time antioxidant identification in agricultural produce such as fruits and vegetables. Therefore, this technique might be used as a next-generation protocol for qualitative and quantitative antioxidants measurement after improvements like new material technologies for sensor (detector) and light sources for higher sensitivity and reduce the cost of implementing real-world applications.

Predicting protein function and orientation on a gold nanoparticle surface using a residue-based affinity scale.

The orientation adopted by proteins on nanoparticle surfaces determines the nanoparticle's bioactivity and its interactions with living systems. Here, we present a residue-based affinity scale for predicting protein orientation on citrate-gold nanoparticles (AuNPs). Competitive binding between protein variants accounts for thermodynamic and kinetic aspects of adsorption in this scale. For hydrophobic residues, the steric considerations dominate, whereas electrostatic interactions are critical for hydrophilic residues. The scale rationalizes the well-defined binding orientation of the small GB3 protein, and it subsequently predicts the orientation and active site accessibility of two enzymes on AuNPs. Additionally, our approach accounts for the AuNP-bound activity of five out of six additional enzymes from the literature. The model developed here enables high-throughput predictions of protein behavior on nanoparticles, and it enhances our understanding of protein orientation in the biomolecular corona, which should greatly enhance the performance and safety of nanomedicines used in vivo.

Comprehensive study of semi-supervised learning for DNA methylation-based supervised classification of central nervous system tumors.

BACKGROUND: Precision medicine for cancer treatment relies on an accurate pathological diagnosis. The number of known tumor classes has increased rapidly, and reliance on traditional methods of histopathologic classification alone has become unfeasible. To help reduce variability, validation costs, and standardize the histopathological diagnostic process, supervised machine learning models using DNA-methylation data have been developed for tumor classification. These methods require large labeled training data sets to obtain clinically acceptable classification accuracy. While there is abundant unlabeled epigenetic data across multiple databases, labeling pathology data for machine learning models is time-consuming and resource-intensive, especially for rare tumor types. Semi-supervised learning (SSL) approaches have been used to maximize the utility of labeled and unlabeled data for classification tasks and are effectively applied in genomics. SSL methods have not yet been explored with epigenetic data nor demonstrated beneficial to central nervous system (CNS) tumor classification. RESULTS: This paper explores the application of semi-supervised machine learning on methylation data to improve the accuracy of supervised learning models in classifying CNS tumors. We comprehensively evaluated 11 SSL methods and developed a novel combination approach that included a self-training with editing using support vector machine (SETRED-SVM) model and an L2-penalized, multinomial logistic regression model to obtain high confidence labels from a few labeled instances. Results across eight random forest and neural net models show that the pseudo-labels derived from our SSL method can significantly increase prediction accuracy for 82 CNS tumors and 9 normal controls. CONCLUSIONS: The proposed combination of semi-supervised technique and multinomial logistic regression holds the potential to leverage the abundant publicly available unlabeled methylation data effectively. Such an approach is highly beneficial in providing additional training examples, especially for scarce tumor types, to boost the prediction accuracy of supervised models.

Light chain deposition disease masquerading as smoking-associated nodular glomerulosclerosis with deposits identified by electron microscopy only.

Light chain deposition disease (LCDD) is a form of monoclonal gammopathy of renal significance. The diagnosis is based on the immunofluorescence (IF) findings of linear monoclonal light chain staining of basement membranes throughout the kidney, which appear as non-organized, granular punctate to powdery electron dense deposits by electron microscopy (EM). Although "LCDD by IF only" without EM deposits has been well-described, LCDD identified by EM with negative IF is very rare and hardly mentioned in the literature. Herein we describe a case of lambda-type LCDD that appeared negative by IF and showed light microscopic findings of nodular glomerulosclerosis, which was initially attributed to the patient's history of significant tobacco use and uncontrolled hypertension. However, EM later showed powdery electron dense material in focal glomerular and tubular basement membranes and mesangium. Subsequent bone marrow analysis revealed greater than 60% lambda-restricted plasma cells. We report this case to illustrate that within the differential diagnosis of nodular sclerosis, monoclonal immunoglobulin deposition disease (MIDD) should remain in the differential even if immunofluorescence appears negative as EM can prove to be crucial in identifying cases of MIDD.

Perovskite Oxides as Electrocatalysts for Hydrogen Evolution Reaction.

Hydrogen generation through electrocatalytic splitting of water, i.e., hydrogen evolution reaction (HER), is an attractive method of converting the electricity generated from renewable sources into chemical energy stored in hydrogen molecules. A wide variety of materials have been studied in an effort to develop efficient and cost-effective electrocatalysts that can replace the traditional platinum/carbon catalyst. One family of functional materials that holds promise for this application is perovskite oxides. This mini-review discusses some of the progress made in the development of HER electrocatalysts based on perovskite oxides in the past decade. Given the diverse range of possible compositions of perovskite oxides, various studies have focused on compositional modifications to develop single-phase catalysts, whereas others have investigated heterostructures and composites that take advantage of synergistic interactions of different compounds with perovskite oxides. The recent advances indicate that this family of materials have great potential for utilization in HER electrocatalysis.

Predicting the strength of concrete made with stone dust and nylon fiber using artificial neural network.

Excessive demand of concrete is causing depletion of natural sand resources. Especially, the extraction of river sand negatively affects its surrounding environment. A sustainable solution to this problem can be the proper utilization of waste materials and by-products like stone dust (SD) as fine aggregate replacement in concrete. The recycling of stone dust as a construction material lessens the use of natural resources and helps to solve landfill scarcity as well as environmental problems. Addition of nylon fiber (NF) as fiber reinforcement can also attribute to enhance the properties of concrete. This research aims at utilizing SD as fine aggregate along with NF, and assessing the compressive strength and splitting tensile strength of concrete. Although the individual effects of incorporating stone dust and nylon fiber in concrete have been investigated in previous researches, their combined effects, as well as effects of water cement (WC) ratio on concrete strength, have not been studied yet. In this study, volumetric percentages of stone dust (20%-50%) and nylon fiber (0.25%-0.75%) and three different water cement ratio (0.45, 0.50 and 0.55) have been considered as three independent variables to develop probabilistic models for compressive strength and splitting tensile strength of concrete using artificial neural network (ANN). The values of coefficient of determination (R2) and other statistical parameters of the developed probabilistic models indicate the accuracy of the models to predict the concrete strength. In terms of compressive strength at early age, the optimal percentages of SD and NF have been found as 20% and 0.25%, respectively. However, the strength gradually drops as water cement ratio elevates from 0.45 to 0.55. The reduction of the splitting tensile strength has been observed for increasing SD from 20% to 50%, whereas, strength increases for rising NF and WC up to mid-level.

Fractal, recurrent, and dense U-Net architectures with EfficientNet encoder for medical image segmentation.

Purpose: U-Net is a deep learning technique that has made significant contributions to medical image segmentation. Although the accomplishments of deep learning algorithms in terms of image processing are evident, many challenges still need to be overcome to achieve human-like performance. One of the main challenges in building deeper U-Nets is black-box problems, such as vanishing gradients. Overcoming this problem allows us to develop neural networks with advanced network designs. Approach: We propose three U-Net variants, namely efficient R2U-Net, efficient dense U-Net, and efficient fractal U-Net, that can create highly accurate segmentation maps. The first part of our contribution makes use of EfficientNet to distribute resources in the network efficiently. The second part of our work applies the following layer connections to design the U-Net decoders: residual connections, dense connections, and fractal expansion. We apply EfficientNet as the encoder to our three decoders to design three conceivable models. Results: The aforementioned three proposed deep learning models were tested on four benchmark datasets, including the CHASE DB1 and digital retinal images for vessel extraction (DRIVE) retinal image databases and the ISIC 2018 and HAM10000 dermoscopy image databases. We obtained the highest Dice coefficient of 0.8013, 0.8808, 0.8019, and 0.9295 for CHASE DB1, ISIC 2018, DRIVE, and HAM10000, respectively, and a Jaccard (JAC) score of 0.6686, 0.7870, 0.6694, and 0.8683 for CHASE DB1, ISIC 2018, DRIVE, and HAM10000, respectively. Statistical analysis revealed that the proposed deep learning models achieved better segmentation results compared with the state-of-the-art models. Conclusions: U-Net is quite an adaptable deep learning framework and can be integrated with other deep learning techniques. The use of recurrent feedback connections, dense convolution, residual skip connections, and fractal convolutional expansions allow for the design of improved deeper U-Net models. With the addition of EfficientNet, we can now leverage the performance of an optimally scaled classifier for U-Net encoders.

Quantitative Measurement of Multiprotein Nanoparticle Interactions Using NMR Spectroscopy.

An effective intensity-based reference is a cornerstone for quantitative nuclear magnetic resonance (NMR) studies, as the molecular concentration is encoded in its signal. In theory, NMR is well suited for the measurement of competitive protein adsorption onto nanoparticle (NP) surfaces, but current referencing systems are not optimized for multidimensional experiments. Presented herein is a simple and novel referencing system using 15N tryptophan (Trp) as an external reference for 1H-15N 2D NMR experiments. The referencing system is validated by the determination of the binding capacity of a single protein onto gold NPs. Then, the Trp reference is applied to protein mixtures, and signals from each protein are accurately quantified. All results are consistent with previous studies, but with substantially higher precision, indicating that the Trp reference can accurately calibrate the residue peak intensities and reduce systematic errors. Finally, the proposed Trp reference is used to kinetically monitor in situ and in real time the competitive adsorption of different proteins. As a challenging test case, we successfully apply our approach to a mixture of protein variants differing by only a single residue. Our results show that the binding of one protein will affect the binding of the other, leading to an altered NP corona composition. This work therefore highlights the importance of studying protein-NP interactions in protein mixtures in situ, and the referencing system developed here enables the quantification of binding kinetics and thermodynamics of multiple proteins using various 1H-15N 2D NMR techniques.

Molecular docking and dynamics study of natural compound for potential inhibition of main protease of SARS-CoV-2.

Newly emerged SARS-CoV-2 made recent pandemic situations across the globe is accountable for countless unwanted death and insufferable panic associated with co-morbidities among mass people. The scarcity of appropriate medical treatment and no effective vaccine or medicine against SARS-CoV-2 has turned the situation worst. Therefore, in this study, we made a deep literature review to enlist plant-derived natural compounds and considered their binding mechanism with the main protease of SARS-CoV-2 through combinatorial bioinformatics approaches. Among all, a total of 14 compounds were filtered where Carinol, Albanin, Myricetin were had better binding profile than the rest of the compounds with having binding energy of -8.476, -8.036, -8.439 kcal/mol, respectively. Furthermore, MM-GBSA calculations were also considered in this selection process to support docking studies. Besides, 100 ns molecular dynamics simulation endorsed the rigid nature, less conformational variation and binding stiffness. As this study, represents a perfect model for SARS-CoV-2 main protease inhibition through bioinformatics study, these potential drug candidates may assist the researchers to find a superior and effective solution against COVID-19 after future experiments.Communicated by Ramaswamy Sarma.

Decrease in acute coronary syndrome presentations during the COVID-19 pandemic in upstate New York.

The COVID-19 virus is a devastating pandemic that has impacted the US healthcare system significantly. More than one study reported a significant decrease in acute coronary syndrome admissions during that pandemic which is still due to unknown reasons. METHODS: This is a retrospective non-controlled multi-centered study of 180 patients (117 males and 63 females) with acute coronary syndrome (STEMI and NSTEMI) admitted during March/April of 2019 and March/April 2020 in Upstate New York. RESULTS: A total of 113 patients (61.9% males, 38.1% females) with a mean age of 72.3 ±â€¯14.2 presented during March/April 2019 with ACS (STEMI + NSTEMI) while only 67 (70.1% males, 29.9% females) COVID-19 negative patients with a mean age of 65.1 ±â€¯14.5 presented during the same period (March/April) in 2020. This is a drop by 40.7% (P < .05) of total ACS cases during the COVID-19 pandemic. In NSTEMI patients, 36.4% presented late (>24 hours of symptoms) during the COVID-19 pandemic in comparison with 2019 (27.1%, P = .033). CONCLUSION: The COVID-19 pandemic led to a substantial drop by 40.7% (P < .05) of total ACS admissions in our area. This decrease in hospital admissions and late presentations can be a worrisome sign for an increase in future complications of myocardial infarctions.