Estimating PM2.5 utilizing multiple linear regression and ANN techniques.

The accurate prediction of air pollutants, particularly Particulate Matter (PM), is critical to support effective and persuasive air quality management. Numerous variables influence the prediction of PM, and it's crucial to combine the most relevant input variables to ensure the most dependable predictions. This study aims to address this issue by utilizing correlation coefficients to select the most pertinent input and output variables for an air pollution model. In this work, PM2.5 concentration is estimated by employing concentrations of sulfur dioxide, nitrogen dioxide, and PM10 found in the air through the application of Artificial Neural Networks (ANNs). The proposed approach involves the comparison of three ANN models: one trained with the Levenberg-Marquardt algorithm (LM-ANN), another with the Bayesian Regularization algorithm (BR-ANN), and a third with the Scaled Conjugate Gradient algorithm (SCG-ANN). The findings revealed that the LM-ANN model outperforms the other two models and even surpasses the Multiple Linear Regression method. The LM-ANN model yields a higher R2 value of 0.8164 and a lower RMSE value of 9.5223.

Production and structural characterization of Lactobacillus helveticus derived biosurfactant.

A probiotic strain of lactobacilli was isolated from traditional soft Churpi cheese of Yak milk and found positive for biosurfactant production. Lactobacilli reduced the surface tension of phosphate buffer saline (PBS) from 72.0 to 39.5 mNm(-1) pH 7.2 and its critical micelle concentration (CMC) was found to be 2.5 mg mL(-1). Low cost production of Lactobacilli derived biosurfactant was carried out at lab scale fermenter which yields 0.8 mg mL(-1) biosurfactant. The biosurfactant was found least phytotoxic and cytotoxic as compared to the rhamnolipid and sodium dodecyl sulphate (SDS) at different concentration. Structural attributes of biosurfactant were determined by FTIR, NMR ((1)H and (13)C), UPLC-MS, and fatty acid analysis by GCMS which confirmed the presence of glycolipid type of biosurfactant closely similar to xylolipids. Biosurfactant is mainly constituted by lipid and sugar fractions. The present study outcomes provide valuable information on structural characterization of the biosurfactant produced by L. helveticus MRTL91. These findings are encouraging for the application of Lactobacilli derived biosurfactant as nontoxic surface active agents in the emerging field of biomedical applications.

Synthesis, biological evaluation and molecular modeling study of 5-trifluoromethyl-Δ²-pyrazoline and isomeric 5/3-trifluoromethylpyrazole derivatives as anti-inflammatory agents.

Searching for new anti-inflammatory agents, we have prepared a series of potential COX-2 inhibitors, 1-(4,6-dimethylpyrimidin-2-yl)-5-hydroxy-5-trifluoromethyl-Δ(2)-pyrazolines (3) and 1-(4,6-dimethylpyrimidin-2-yl)-3-trifluoromethylpyrazoles (4), by refluxing 2-hydrazino-4,6-dimethylpyrimidine (1) with a number of trifluoromethyl-ß-diketones (2) in ethanol. Further dehydration of compounds (3) to the corresponding 1-(4,6-dimethylpyrimidin-2-yl)-5-trifluoromethylpyrazoles (5) was also achieved. Fifteen of these compounds were screened for their anti-inflammatory activity using the carrageenan-induced rat paw edema assay. While all the compounds exhibited significant anti-inflammatory activity (47-76%) as compared to indomethacin (78%), 3-trifluoromethylpyrazoles (4) were found to be the most effective agents (62-76%). To rationalize this anti-inflammatory activity, docking experiments molecular dynamics simulations were performed to study the ability of these compounds to bind into the active site of the COX-2 enzyme.