Inhibitory Effects on Staphylococcus aureus Sortase A by Aesculus sp. Extracts and Their Toxicity Evaluation.

A promising strategy for combating bacterial infections involves the development of agents that disarm the virulence factors of pathogenic bacteria, thereby reducing their pathogenicity without inducing direct lethality. Sortase A, a crucial enzyme responsible for anchoring virulence factors to the cell surface of several pathogenic bacteria, has emerged as a possible target for antivirulence strategies. A series of hippocastanum species (Aesculus pavia, A. parviflora, Aesculus x carnea, and A. hippocastanum) were used to prepare ethanol- and water-based extracts for assessing their effect on Staphylococcus aureus sortase A. The extracts were characterized through HPLC analysis, and their polyphenols content was determined using the Folin-Ciocalteu method. The specific toxicity profile was evaluated in Daphnia magna using the median lethal concentration (LC50) and against the fibroblast MRHF cell line. The half maximal inhibitory concentration (IC50) values on sortase A, determined after 30 min of incubation, ranged from 82.70 to 304.31 µg/mL, with the A. pavia water extract exhibiting the highest inhibitory effect. The assessment of the A. pavia water extract on human fibroblasts revealed no significant signs of toxicity, even at a concentration of 500 µg/mL. This reduced toxicity was further validated through the Daphnia assay. These findings highlight the low toxicity and the potential of this extract as a promising source of future development of bacteria antivirulence solutions.

Human health risk assessment of potentially toxic elements and microplastics accumulation in products from the Danube River Basin fish market.

The present study aimed to quantify the concentration levels of potentially toxic elements (PTEs) such as aluminum, arsenic, cadmium, chromium, copper, nickel, lead, zinc, and mercury, as well as microplastics occurrence in various tissues of fish and seafood species, commercialized in the Lower Danube River Basin. A health risk assessment analysis was performed based on the PTEs concentration levels in the muscle tissue. Estimated daily intake (EDI), target hazard quotient (THQ), hazard index (HI), and target cancer risk (TR) of PTEs were calculated. It was observed that the species within the seafood category registered the highest levels of PTEs. For instance, in the muscle tissue of bivalve Mytilus galloprovincialis (from the Black Sea), the highest value was observed in the case of Zn (37.693 mg/kg), and the presence of polystyrene polymer was identified. The values associated with EDI, THQ, HI, and TR of PTE exposure were significantly lower than 1.

Effects of cooking methods and co-ingested foods on mercury bioaccessibility in pontic shad (Alosa immaculata).

Human mercury (Hg) exposure is mostly caused by eating fish. However, there are major differences between the measured and predicted mercury concentration on Hg bioavailability. This study investigated the effects of cooking (steaming, baking, frying, marinating, and smoking) and selected components' co-ingestion on Hg bioaccessibility. Baking and frying reduced Hg bioaccessibility compared to the raw sample. The bioaccessible Hg fraction in fish was assessed through in vitro digestion method. Hg bioaccessibility varied from 4.31 to nearly 24.95% and the Hg recovery rate varied from 63.44 to 78.74%. Co-ingested garlic and broccoli with pontic shad had a positive effect on decreasing fish Hg bioaccessibility. The antioxidant activity of co-ingested food items was also calculated and correlated with mercury bioaccessibility. These results highlighted a possible positive role of plant-based foods and other food processing techniques in the bioaccessibility reduction of other chemical contaminants found in food sources.

Assessing and forecasting water quality in the Danube River by using neural network approaches.

The health and quality of the Danube River ecosystems is strongly affected by the nutrients loads (N and P), degree of contamination with hazardous substances or with oxygen depleting substances, microbiological contamination and changes in river flow patterns and sediment transport regimes. Water quality index (WQI) is an important dynamic attribute in the characterization of the Danube River ecosystems health and quality. The WQ index scores do not reflect the actual condition of water quality. We proposed a new forecast scheme for water quality based on the following qualitative classes very good (0-25), good (26-50), poor (51-75), very poor (76-100) and extremely polluted/non-potable (>100). Water quality forecasting by using Artificial Intelligence (AI) is a meaningful method of protecting public health because of its possibility to provide early warning regarding harmful water pollutants. The main objective of the present study is to forecast the WQI time series data based on water physical, chemical and flow status parameters and associated WQ index scores. The Cascade-forward network (CFN) models, along with the Radial Basis Function Network (RBF) as a benchmark model, were developed using data from 2011 to 2017 and WQI forecasts were produced for the period 2018-2019 at all sites. The nineteen input water quality features represent the initial dataset. Moreover, the Random Forest (RF) algorithm refines the initial dataset by selecting eight features considered the most relevant. Both datasets are employed for constructing the predictive models. According to the results of appraisal, the CFN models produced better outcomes (MSE = 0.083/0,319 and R-value 0.940/0.911 in quarter I/quarter IV) than the RBF models. In addition, results show that both the CFN and RBF models could be effective for predicting time series data for water quality when the eight most relevant features are used as input variables. Also, the CFNs provide the most accurate short-term forecasting curves which reproduce the WQI for the first and fourth quarters (the cold season). The second and third quarters presented a slightly lower accuracy. The reported results clearly demonstrate that CFNs successfully forecast the short-term WQI as they may learn historic patterns and determine the nonlinear relationships between the input and output variables.

Role of Meteorological Parameters in the Diurnal and Seasonal Variation of NO2 in a Romanian Urban Environment.

The main purpose of this study was to investigate whether meteorological parameters (temperature, relative humidity, direct radiation) play an important role in modifying the NO2 concentration in an urban environment. The diurnal and seasonal variation recorded at a NO2 traffic station was analyzed, based on data collected in situ in a Romanian city, Braila (45.26° N, 27.95° E), during 2009-2014. The NO2 atmospheric content close to the ground had, in general, a summer minimum and a late autumn/winter maximum for most years. Two diurnal peaks were observed, regardless of the season, which were more evident during cold months. Traffic is an important contributor to the NO2 atmospheric pollution during daytime hours. The variability of in situ measurements of NO2 concentration compared relatively well with space-based observations of the NO2 vertical column by the Ozone Monitoring Instrument (OMI) satellite for most of the period under scrutiny. Data for daytime and nighttime (when the traffic is reduced) were analyzed separately, in the attempt to isolate meteorological effects. Meteorological parameters are not fully independent and we used partial correlation analysis to check whether the relationships with one parameter may be induced by another. The correlation between NO2 and temperature was not coherent. Relative humidity and solar radiation seemed to play a role in shaping the NO2 concentration, regardless of the time of day, and these relationships were only partially interconnected.