A study of pulmonary function in end-stage renal disease patients on hemodialysis: a cross-sectional study

<div><p>ABSTRACT BACKGROUND: The aim here was to study acute effects of hemodialysis among end-stage renal disease (ESRD) patients. DESIGN AND SETTING: Prospective study in tertiary-level care center. METHODS: Fifty ESRD patients undergoing hemodialysis were studied. Spirometric pulmonary function tests were performed before and after four-hour hemodialysis sessions. RESULTS: The patients’ average age was 45.8 ± 10.0 years; 64% were males and 64% had normal body mass index. Anemia (94%) and hypoalbuminemia (72%) were common. Diabetes mellitus (68%), hypertension (34%) and coronary artery disease (18%) were major comorbidities. Forty-five patients (90%) had been on hemodialysis for six months to three years. The patients’ pre-dialysis mean forced vital capacity (FVC) and forced expiratory volume in 1 second (FEV1) were below normal: 45.8 ± 24.9% and 43.5 ± 25.9% of predicted, respectively. After hemodialysis, these increased significantly, to 51.1 ± 23.4% and 49.3 ± 25.5% of predicted, respectively (P < 0.01). The increase in mean FEV1/FVC, from 97.8 ± 20.8% to 99.3 ± 20.1% of predicted, was not significant (P > 0.05). The pre-dialysis mean forced expiratory flow 25-75% was 50.1 ± 31% and increased significantly, to 56.3 ± 31.6% of predicted (P < 0.05). The mean peak expiratory flow was below normal (43.8 ± 30.7%) and increased significantly, to 49.1 ± 29.9% of predicted (P < 0.05). Males and females showed similar directions of change after hemodialysis. CONCLUSIONS: Pulmonary function abnormalities are common among ESRD patients. Comparison of pre and post-hemodialysis parameters showed significant improvements, but normal predicted values were still not achieved.</p></div

Molecular docking based virtual screening of natural compounds as potential BACE1 inhibitors: 3D QSAR pharmacophore mapping and molecular dynamics analysis

<div><p>Beta-site APP cleaving enzyme1 (BACE1) catalyzes the rate determining step in the generation of Aβ peptide and is widely considered as a potential therapeutic drug target for Alzheimer’s disease (AD). Active site of BACE1 contains catalytic aspartic (Asp) dyad and flap. Asp dyad cleaves the substrate amyloid precursor protein with the help of flap. Currently, there are no marketed drugs available against BACE1 and existing inhibitors are mostly pseudopeptide or synthetic derivatives. There is a need to search for a potent inhibitor with natural scaffold interacting with flap and Asp dyad. This study screens the natural database InterBioScreen, followed by three-dimensional (3D) QSAR pharmacophore modeling, mapping, <i>in silico</i> ADME/T predictions to find the potential BACE1 inhibitors. Further, molecular dynamics of selected inhibitors were performed to observe the dynamic structure of protein after ligand binding. All conformations and the residues of binding region were stable but the flap adopted a closed conformation after binding with the ligand. Bond oligosaccharide interacted with the flap as well as catalytic dyad via hydrogen bond throughout the simulation. This led to stabilize the flap in closed conformation and restricted the entry of substrate. Carbohydrates have been earlier used in the treatment of AD because of their low toxicity, high efficiency, good biocompatibility, and easy permeability through the blood–brain barrier. Our finding will be helpful in identify the potential leads to design novel BACE1 inhibitors for AD therapy.</p></div

MOESM5 of Prediction of anti-inflammatory proteins/peptides: an insilico approach

Additional file 5: Figure S4. Optimization of mtry and ntree for random forest model using amino acid composition as feature input

Additional file 11: of Prediction of peptidoglycan hydrolases- a new class of antibacterial proteins

List of predicted peptidoglycan hydrolases from common pathogenic bacterial species. (PDF 734 kb

MOESM4 of Prediction of anti-inflammatory proteins/peptides: an insilico approach

Additional file 4: Figure S3 Optimization of mtry for random forest model using amino acid composition as feature input

ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use-1

the comparisons. 4 cluster data 20 cluster data. White bars = Cluster; black bars = PKM.<p><b>Copyright information:</b></p><p>Taken from "ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use"</p><p>http://www.biomedcentral.com/1471-2105/9/200</p><p>BMC Bioinformatics 2008;9():200-200.</p><p>Published online 16 Apr 2008</p><p>PMCID:PMC2375128.</p><p></p

ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use-0

He number of compute nodes used in the analysis. The bar graphs at the bottom of each plot illustrate the number of compute nodes where one finds statistically significant increases in speed. The p values presented are for tests of differences between the number of compute nodes for a given number of genes or clusters. The effect of the interaction between the number of genes and number of compute nodes on the speed of execution. The effect of the interaction between the number of clusters and number of compute nodes on the speed of execution.<p><b>Copyright information:</b></p><p>Taken from "ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use"</p><p>http://www.biomedcentral.com/1471-2105/9/200</p><p>BMC Bioinformatics 2008;9():200-200.</p><p>Published online 16 Apr 2008</p><p>PMCID:PMC2375128.</p><p></p

ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use-3

He number of compute nodes used in the analysis. The bar graphs at the bottom of each plot illustrate the number of compute nodes where one finds statistically significant increases in speed. The p values presented are for tests of differences between the number of compute nodes for a given number of genes or clusters. The effect of the interaction between the number of genes and number of compute nodes on the speed of execution. The effect of the interaction between the number of clusters and number of compute nodes on the speed of execution.<p><b>Copyright information:</b></p><p>Taken from "ParaKMeans: Implementation of a parallelized K-means algorithm suitable for general laboratory use"</p><p>http://www.biomedcentral.com/1471-2105/9/200</p><p>BMC Bioinformatics 2008;9():200-200.</p><p>Published online 16 Apr 2008</p><p>PMCID:PMC2375128.</p><p></p

Designing of inhibitors against CTX-M-15 type β-lactamase: potential drug candidate against β-lactamases-producing multi-drug-resistant bacteria

<p>CTX-M-15 are the most prevalent types of β-lactamases that hydrolyze almost all antibiotics of β-lactam group lead to multiple-antibiotic resistance in bacteria. Three β-lactam inhibitors are available for use in combination with different antibiotics of cephalosporine group against the CTX-M-15-producing strains. Therefore, strategies to identify novel anti β-lactamase agents with specific mechanisms of action are the need of an hour. In this study, we screened three novel non-β-lactam inhibitors against CTX-M-15 by multi-step virtual screening approach. The potential for virtually screened drugs was estimated through <i>in vitro</i> cell assays. Hence, we proposed a study to understand the binding mode of CTX-M-15 with inhibitors using bioinformatics and experimental approach. We calculated the dissociation constants (<i>K</i>_<i>d</i>), association constant (<i>K</i>_<i>a</i>), stoichiometry (<i>n</i>) and binding energies (Δ<i>G</i>) of compounds with the respective targets. Molecular dynamic simulation carried out for 25 ns, revealed that these complexes were found stable throughout the simulation with relative RMSD in acceptable range. Moreover, microbiological and kinetic studies further confirmed high efficacies of these inhibitors by reducing the minimum inhibitory concentration (MIC) and catalysis of antibiotics by β-lactamases in the presence of inhibitors. Therefore, we conclude that these potential inhibitors may be used as a lead molecule for future drug candidates against β-lactamases-producing bacteria.</p

Molecular insight into amyloid oligomer destabilizing mechanism of flavonoid derivative 2-(4′ benzyloxyphenyl)-3-hydroxy-chromen-4-one through docking and molecular dynamics simulations

<p>Aggregation of amyloid peptide (Aβ) has been shown to be directly related to progression of Alzheimer’s disease (AD). Aβ is neurotoxic and its deposition and aggregation ultimately lead to cell death. In our previous work, we reported flavonoid derivative (compound 1) showing promising result in transgenic AD model of Drosophila. Compound 1 showed prevention of Aβ-induced neurotoxicity and neuroprotective efficacy in Drosophila system. However, mechanism of action of compound 1 and its effect on the amyloid is not known. We therefore performed molecular docking and atomistic, explicit-solvent molecular dynamics simulations to investigate the process of Aβ interaction, inhibition, and destabilizing mechanism. Results showed different preferred binding sites of compound 1 and good affinity toward the target. Through the course of 35 ns molecular dynamics simulation, conformations_5 of compound 1 intercalates into the hydrophobic core near the salt bridge and showed major structural changes as compared to other conformations. Compound 1 showed interference with the salt bridge and thus reducing the inter strand hydrogen bound network. This minimizes the side chain interaction between the chains A–B leading to disorder in oligomer. Contact map analysis of amino acid residues between chains A and B also showed lesser interaction with adjacent amino acids in the presence of compound 1 (conformations_5). The study provides an insight into how compound 1 interferes and disorders the Aβ peptide. These findings will further help to design better inhibitors for aggregation of the amyloid oligomer.</p