Changes in industrial air pollution and the onset of childhood asthma in Quebec, Canada.

Ambient air pollution has been associated with asthma onset and exacerbation in children. Whether improvement in air quality due to reduced industrial emissions has resulted in improved health outcomes such as asthma in some localities has usually been assessed indirectly with studies on between-subject comparisons of air pollution from all sources and health outcomes. In this study we directly assessed, within small areas in the province of Quebec (Canada), the influence of changes in local industrial fine particulate matter (PM2.5), nitrogen dioxide (NO2), and sulfur dioxide (SO2) concentrations, on changes in annual asthma onset rates in children (≤12 years old) with a longitudinal ecological design. We identified the yearly number of new cases of childhood asthma in 1282 small areas (census tracts or local community service centers) for the years 2002, 2004, 2005, 2006, and 2015. Annual average concentrations of industrial air pollutants for each of the geographic areas, and three sectors (i.e., pulp and paper mills, petroleum refineries, and metal smelters) were estimated by the Polair3D chemical transport model. Fixed-effects negative binomial models adjusted for household income were used to assess associations; additional adjustments for environmental tobacco smoke, background pollutant concentrations, vegetation coverage, and sociodemographic characteristics were conducted in sensitivity analyses. The incidence rate ratios (IRR) for childhood asthma onset for the interquartile increase in total industrial PM2.5, NO2, and SO2 were 1.016 (95% confidence interval, CI: 1.006-1.026), 1.063 (1.045-1.090), and 1.048 (1.031-1.080), respectively. Positive associations were also found with pollutant concentrations from most individual sectors. Results suggest that changes in industrial pollutant concentrations influence childhood asthma onset rates in small localities.

Revealing the interaction modes of 5-HT2A receptor antagonists and the Structure-Based virtual screening from FDA and TCMNP database.

5-hydroxytryptamine 2A (5-HT2A) receptor is emerging as an important target for numerous psychoactive drugs due to its imperative roles in psychological diseases. In fact, multiple 5-HT2A receptor antagonists were developed to treat numerous psychiatric disorders, however, their clinical outcome was far from ideal probably due to a blurry information of the exact interaction modes between the receptor and its antagonists. Impressively, with a recent release of its crystal structure, we carefully analyzed the receptor-ligand interactions with Protein Contacts Atlas, structure-based pharmacophore models, and molecular dynamics (MD) simulations to sum up the chemical features for antagonists interacting with 5-HT2A receptor. Moreover, the molecular docking-based virtual screening was applied to discover potential 5-HT2A receptor antagonists from FDA and TCMNP databases. Intriguingly, after a systematic assessment of the docking scores, binding modes and free energies, as well as their MD simulations performances, three compounds in TCMNP database were highlighted to be potential 5-HT2A receptor antagonists. Fascinatedly, these three hits also exhibited highly binding affinities with dopamine D2 receptor (D2R) due to the similarity of the ligand binding pockets of the receptors, indicating them to be promising dual target molecules that are of great benefit for anti-psychotic-drug research and development. In addition, ADME/Tox predictions were conducted for a primary evaluation of their developing potential. Together, this study not only revealed the exact interaction modes between 5-HT2A receptor and its antagonists, which shed a light on a better access for developing its novel antagonists, but also provided promising dual D2 and 5-HT2A receptor antagonists.Communicated by Ramaswamy H. Sarma.

Structure-based virtual screening of natural products as potential stearoyl-coenzyme a desaturase 1 (SCD1) inhibitors.

Stearyl coenzyme A desaturase enzyme 1 (SCD1) is a key enzyme that catalyzes the conversion of saturated fatty acids (SFA) into monounsaturated fatty acids (MUFA) and plays a vital role in lipid metabolism of tumor cells. SCD1 is overexpressed in a variety of malignant tumors, and its related inhibitors showed significant anti-tumor activity in vitro and in vivo experiments, which is a new target for tumor therapy. The focus of this study is to identify novel SCD1 inhibitors from natural products through computer simulations. First, 176,602 compounds from natural product databases were virtually screened. By molecular dynamics (MD) simulations, the ligand-protein interactions of 5 compounds with high docking manifestation were analyzed accurately. Then, MM-GBSA and MM-PBMA methods were used to verify the results. Finally, ADMET prediction was performed for the 5 compounds. As a result, two natural products with potential inhibition towards SCD1 were identified, which had the excellent docking manifestation, binding mode within SCD1 pocket and stability during molecular dynamics simulation. This study provides a meaningful model for the development and optimization of new inhibitors and anti-tumor drugs targeting SCD1.

Refined pharmacophore features for virtual screening of human thromboxane A2 receptor antagonists.

For a long time, the structural basis of TXA2 receptor is limited due to the lack of crystal structure information, till the release of the crystal structure of TXA2 receptor, which deepens our understanding about ligand recognition and selectivity mechanisms of this physiologically important receptor. In this research, we report the successful implementation in the discovery of an optimal pharmacophore model of human TXA2 receptor antagonists through virtual screening. Structure-based pharmacophore models were generated based on two crystal structures of human TXA2 receptor (PDB entry 6IIU and 6IIV). Docking simulation revealed interaction modes of the virtual screening hits against TXA2 receptor, which was validated through molecular dynamics simulation and binding free energy calculation. ADMET properties were also analyzed to evaluate the toxicity and physio-chemical characteristics of the hits. The research would provide valuable insight into the binding mechanisms of TXA2 receptor antagonists and thus be helpful for designing novel antagonists.

Computational insights into the binding pattern of mitochondrial calcium uniporter inhibitor through homology modeling, molecular dynamics simulation, binding free energy prediction and density functional theory calculation.

The mitochondrial calcium uniporter (MCU) is the critical protein of the inner mitochondrial membrane that is the primary mediator for calcium uptake into the mitochondrial matrix. Herein we built the optimal homology model of human MCU which was refined through all-atom molecular dynamics simulation. Then, the binding mode of known inhibitor was predicted through molecular docking method, along with molecular dynamics simulation and binding free energy calculation to verify the docking result and stability of the protein-inhibitor complex. Finally, density functional theory (DFT) calculation enhanced our understanding of the molecular interaction of MCU inhibitor. Our research would provide a deeper insight into the interactions between human MCU and its inhibitor, which boosts to develop novel therapy against MCU related disease.Communicated by Ramaswamy H. Sarma.

Review of the effect of air pollution exposure from industrial point sources on asthma-related effects in childhood.

We reviewed epidemiologic studies of the association between exposure to air pollution from industries and asthma-related outcomes in childhood. We searched bibliographic databases and reference lists of relevant articles to identify studies examining the association between children's exposure to air pollution from industrial point-sources and asthma-related outcomes, including asthma, asthma-like symptoms, wheezing, and bronchiolitis. We extracted key characteristics of each study and when appropriate we performed a random-effects meta-analysis of results and quantified heterogeneity (I 2). Thirty-six studies were included in this review. Meta-analysis was generally not possible and limited to a few studies because of substantial variation across design characteristics and methodologies. In case-crossover studies using administrative health data, pooled odds ratio (OR) of hospitalization for asthma and bronchiolitis in children <5 years were 1.02 [95% confidence intervals (CI): 0.96, 1.08; I 2 = 56%] and 1.01 (95% CI: 0.97, 1.05; I 2 = 64%) per 10 ppb increase in the daily mean and hourly maximum concentration of sulfur dioxide (SO2), respectively. For PM2.5, pooled ORs were 1.02 (95% CI: 0.93, 1.10; I 2 = 56%) and 1.01 (95% CI: 0.98, 1.03 I 2 = 33%) per 10 µg/m3 increment in the daily mean and hourly maximum concentration. In cross-sectional studies using questionnaires, pooled ORs for the prevalence of asthma and wheezing in relation to residential proximity to industry were 1.98 (95% CI: 0.87, 3.09; I 2 =71%) and 1.33 (95% CI: 0.86, 1.79; I 2= 65%), respectively. In conclusion, this review showed substantial heterogeneity across study designs and methods. Meta-analysis results suggested no evidence of an association for short-term asthma-related effects and an indication for long-term effects, but heterogeneity between results and limitations in terms of design and exposure assessment preclude drawing definite conclusions. Further well-conducted studies making use of a longitudinal design and of refined exposure assessment methods are needed to improve risk estimates.

Sensitive Impedimetric Immunoassay of Japanese Encephalitis Virus Based on Enzyme Biocatalyzed Precipitation on a Gold Nanoparticle-modified Screen-printed Carbon Electrode.

A sensitive and disposable electrochemical impedance biosensor to detect Japanese encephalitis virus (JEV) was developed based on a gold nanoparticle (AuNP)-modified screen-printed carbon electrode (SPCE). A biosensor was fabricated through covalent grafting of a mixed self-assembled monolayer on AuNPs with a specific antibody. To detect JEV and achieve signal amplification, the horseradish peroxidase (HRP)-labeled second antibody was linked to the biosensor through a sandwich immunity reaction. HRP was used to catalyze 4-chloro-1-naphthol oxidation to produce an insoluble precipitate, which introduced a barrier to electron transfer on the electrode. Electrochemical impedance spectroscopy (EIS) was used to monitor the precipitation on the electrode. The electron-transfer resistance (Ret) of the biosensor was directly correlated with the concentration of JEV in the solution. Under optimal conditions, the method generated a linear response range between 500 and 5 × 105 pfu mL-1, and the detection limit was 167 pfu mL-1. The biosensor exhibited good selectivity against other viruses.

Signal-off impedimetric immunosensor for the detection of Escherichia coli O157:H7.

A signal-off impedimetric immune-biosensor based on gold nanoparticle (AuNP)-mediated electron transfer (ET) across a self-assembled monolayer (SAM) was the developed for highly sensitive detection of Escherichia coli O157:H7 bacteria. The biosensor was fabricated by covalently grafting an anti-Escherichia coli O157:H7 antibody onto SAM-modified gold electrodes. Following bacterial capture, the sensor was further modified by the gold nanoparticles (AuNPs). Due to the strong interaction between AuNPs and Escherichia coli O157:H7, AuNPs attached to the surface of the bacteria and acted as ET pathways across the insulating SAMs on the electrode surface, resulting in a significant reduction of the electron transfer resistance (Ret) between the [Fe(CN)6](3-/4-) redox probe in the solution and the substrate gold surface. Therefore, the attachment AuNPs to captured bacteria significantly enhanced the sensitivity for Escherichia coli O157:H7 bacteria detection.