Protective effect of Syzygium cumini against pesticide-induced cardiotoxicity

Pesticide-induced toxicity is a serious issue which has resulted in plethora of diseases all over the world. The organophosphate pesticide malathion has caused many incidents of poisoning such as cardiac manifestations. The present study was designed to evaluate the effect of Syzygium cumini on malathion-induced cardiotoxicity. Dose optimization of malathion and polyphenols such as curcumin, (?)-epicatechin, gallic acid, butylated hydroxyl toluene, etc. was done by MTT cell proliferation assay. Nuclear deformities, ROS production, and integrity of extra cellular matrix components were analyzed by different techniques. S. cumini methanolic pulp extract (MPE), a naturally derived gallic acid-enriched antioxidant was taken to study its effect on malathion-induced toxicity. Nuclear deformities, ROS production, and integrity of extra cellular matrix components were also analyzed. Twenty micrograms per milliliter LD50 dose of malathion was found to cause stress-mediated responses in H9C2 cell line. Among all the polyphenols, gallic acid showed the most significant protection against stress. Gallic acid-enriched methanolic S. cumini pulp extract (MPE) showed 59.76 %?±?0.05, 81.61 %?±?1.37, 73.33 %?±?1.33, 77.19 %?±?2.38 and 64.19 %?±?1.43 maximum inhibition for DPPH, ABTS, NO, H₂O₂ and superoxide ion, respectively, as compared to ethanolic pulp extract and aqueous pulp extract. Our study suggests that S. cumini MPE has the ability to protect against the malathion-mediated oxidative stress in cardiac myocytes.     

Space-time analysis of the Air Quality Model Evaluation International Initiative (AQMEII) Phase 1 air quality simulations

This study presents an evaluation of summertime ozone concentrations over North America (NA) and Europe (EU) using the database generated from Phase 1 of the Air Quality Model Evaluation International Initiative (AQMEII). The analysis focuses on identifying temporal and spatial features that can be used to stratify operational model evaluation metrics and to test the extent to which the various modeling systems can replicate the features seen in the observations. Using a synoptic map typing approach, it is demonstrated that model performance varies with meteorological conditions associated with specific synoptic-scale flow patterns over both eastern NA and EU. For example, the root mean square error of simulated daily maximum 8-hr ozone was twice as high when cloud fractions were high compared with when cloud fractions were low over eastern NA. Furthermore, results show that over both NA and EU the regional models participating in AQMEII were able to better reproduce the observed variance in ambient ozone levels than the global model used to specify chemical boundary conditions, although the variance simulated by almost all regional models is still less that the observed variance on all spatiotemporal scales. In addition, all modeling systems showed poor correlations with observed fluctuations on the intraday time scale over both NA and EU. Furthermore, a methodology is introduced to distinguish between locally influenced and regionally representative sites for the purpose of model evaluation. Results reveal that all models have worse model performance at locally influenced sites. Overall, the analyses presented in this paper show how observed temporal and spatial information can be used to stratify operational model performance statistics and to test the modeling systems’ ability to replicate observed temporal and spatial features, especially at scales the modeling systems are designed to capture.

Implications: The analyses presented in this paper demonstrate how observed temporal and spatial information can be used to stratify operational model performance and to test the modeling systems’ ability to replicate observed temporal and spatial features. Decisions for the improvement of regional air quality models should be based on the information derived from only regionally representative sites.     

A performance evaluation of the National Air Quality Forecast Capability for the summer of 2007

This paper provides a performance evaluation of the real-time, CONUS-scale National Air Quality Forecast Capability (NAQFC) that supported, in part, its transition into operational status. This evaluation focuses primarily on discrete forecasts for the maximum 8-h O₃ concentrations covering the 4-month period, June through September, 2007, using measurements obtained from EPA's AIRNow network. Results indicate that the 2007 NAQFC performed as well or better than previous configurations, despite the expansion of the forecast domain into the western half of the nation that is dominated by complex terrain. The mean, domain-wide, season-long correlation was 0.70. When examined over time, the domain-wide correlations exhibit a fairly consistent nature, with values exceeding 0.60 (0.70) over 90% (55%) of the days. The NAQFC systematically over-predicted the 8-h O₃ concentrations, continuing a trend established by earlier NAQFC configurations, though to a lesser degree. The summer-long mean forecast value of 53.2 ppb was 4.2 ppb higher than the observed value, resulting in a domain-wide Normalized Mean Bias (NMB) of 8.7%. Most of the over-prediction is associated with observed concentrations less than 50 ppb. In fact the model tends to under-predict when concentrations exceed 70 ppb. As with the bias, the error associated with the latest configuration was also lower. The summer-long Root Mean Square Error of 13.0 ppb (Normalized Mean Error (NME) = 20.4%) represented marked improvements over earlier forecasts. Examination of the spatial distribution of both the NMB and NME reveals that the NAQFC was generally within 25% for the NME and 25% for the NMB over a majority of the domain. Several areas of poorer performance, where the NMB and NME often exceed 25% and in some cases 50%, were noted. These areas include southern California, where the NAQFC tended to under-predict concentrations (especially on weekends) and the southeast Atlantic and Gulf coasts regions, where the model over-predicted. Subsequent analysis revealed that the incorrect temporal allocation of precursor emissions was likely the source of the under-prediction in southern California, while inaccurate simulation of PBL heights likely contributed to the over-prediction in the coastal regions.     

Evaluation of effects of exposure time on aquatic toxicity with zooplanktons using a reduced life expectancy model

Traditionally in toxicological studies time is not studied as quantifiable variable but as a fixed endpoint. The Reduced Life Expectancy (RLE) model which relates exposure time and exposure concentration with lethal toxic effects was tested previously using fish data. In this current paper the effects of exposure time on aquatic toxicity with zooplanktons and various toxicants were evaluated using the RLE model based on ambient exposure concentration. The model was evaluated by plotting ln LT₅₀ against LC₅₀ using toxicity data with zooplanktons from the literature for metal, metalloid and organic compounds. Most of the experimental data sets can be satisfactorily correlated by use of the RLE model, but deviations occurred for some data sets. Those data sets were satisfactorily fitted by a two stage RLE model. This model was based on two phases: one in the peripheral system and other in the central system. Both the single and two stage RLE model support the hypothesis that toxicity is time dependent and decreases in a systematic way with increasing exposure time. A calculated normal life expectancy (NLT) can be obtained from the single stage model and is in accord with reported NLT but those obtained from the two stage RLE model are in excellent agreement.     

Health care industries: potential generators of genotoxic waste

Health care waste includes all the waste generated by health care establishments, research facilities, and laboratories. This constitutes a variety of chemical substances, such as pharmaceuticals, radionuclides, solvents, and disinfectants. Recently, scientists and environmentalists have discovered that wastewater produced by hospitals possesses toxic properties due to various toxic chemicals and pharmaceuticals capable of causing environmental impacts and even lethal effects to organisms in aquatic ecosystems. Many of these compounds resist normal wastewater treatment and end up in surface waters. Besides aquatic organisms, humans can be exposed through drinking water produced from contaminated surface water. Indeed, some of the substances found in wastewaters are genotoxic and are suspected to be potential contributors to certain cancers. The aim of this study was to evaluate the genotoxic and cytotoxic potential of wastewaters from two hospitals and three clinical diagnostic centers located in Jaipur (Rajasthan State), India using the prokaryotic Salmonella mutagenicity assay (Ames assay) and the eukaryotic Saccharomyces cerevisiae respiration inhibition assay. In the Ames assay, untreated wastewaters from both of the health care sectors resulted in significantly increased numbers of revertant colonies up to 1,000–4,050 as measured by the Salmonella typhimurium TA98 and TA100 strains (with and without metabolic activation) after exposure to undiluted samples, which indicated the highly genotoxic nature of these wastewaters. Furthermore, both hospital and diagnostic samples were found to be highly cytotoxic. Effective concentrations at which 20 % (EC₂₀) and 50 % (EC₅₀) inhibition of the respiration rate of the cells occurred ranged between ～0.00 and 0.52 % and between 0.005 and 41.30 % (calculated with the help of the MS excel software XLSTAT 2012.1.01; Addinsoft), respectively, as determined by the S. cerevisiae assay. The results indicated that hospital wastewaters contain genotoxic and cytotoxic components. In addition, diagnostic centers also represent small but significant sources of genotoxic and cytotoxic wastes.