We report the crystal structures of three matrine derivatives, namely, the salts (1R,2R,9S,17S)-6-oxo-7,13-diazatetracyclo[7.7.1.02,7.013,17]heptadecan-13-ium (2E)-3-(3,4-dihydroxyphenyl)prop-2-enoate (matrine caffeinate) sesquihydrate, C15H25N2O+·C9H7O4-·1.5H2O (Matrine-CA), and the 2-hydroxybenzoate (salicylate) monohydrate, C15H25N2O+·C7H5O3-·H2O (Matrine-SA), as well as the 1.75-hydrate form, (1R,2R,9S,17S)-7,13-diazatetracyclo[7.7.1.02,7.013,17]heptadecan-6-one 1.75-hydrate, C15H24N2O·1.75H2O (Matrine-H). Each derivative exhibited a consistent molecular conformation for the matrine core, which is notably distinct from that of the anhydrous form. Notably, both salts crystallized in the orthorhombic space group P212121, with an asymmetric unit featuring one cation and one anion. Within the two salt structures, intermolecular proton transfer between matrine and the acid is observed, culminating in the formation of a matrine cation protonated at the tertiary amine N site. The Matrine-CA crystal packing is manifested as a three-dimensional (3D) network arising from one-dimensional (1D) supramolecular helical chains, stabilized by N-H...O and O-H...O hydrogen bonds. In the case of Matrine-SA, the matrine cation is interconnected via hydrogen bonds with salicylate anions and water molecules, also forming a 1D helical motif. The structure of the hydrate form, Matrine-H, is reported again with the disordered solvent molecules accurately located. To further elucidate the structural attributes, Hirshfeld surface analysis and fingerprint plots are employed, offering a nuanced perspective on the intermolecular contacts and interactions within these crystalline forms.

Planewave-corrected methods have proven effective for accurately modeling nuclear magnetic resonance (NMR) parameters in crystalline systems. Recent work extended the application of planewave-corrected calculations beyond the second row, predicting EFG tensor parameters for 35Cl using a simple molecular correction to projector augmented-wave (PAW) density functional theory (DFT). Here we extend this work using fragment and cluster-based calculations coupled with polarizable continuum (PCM) methods to improve further the accuracy of planewave-corrected 35Cl EFG tensor calculations. Benchmark data from a test set comprised of 105 individual 35Cl EFG tensor principal components for chlorine-containing molecular crystals and crystalline chloride salts shows fragment-corrected planewave calculations using the PBE0 hybrid density functional improve the accuracy of predicted EFG tensor components by 30 % relative to traditional planewave calculations. We compare the influence of different geometry optimization methods and density functionals on the accuracy of predicted 35Cl EFG tensor parameters. Four cases of spectral assignment are presented to demonstrate the utility of improving the accuracy of predicted 35Cl EFG tensor parameters.

This study is focused on the utilization of naturally occurring salicylic acid and nicotinamide (vitamin B3) in the development of novel sustainable Active Pharmaceutical Ingredients (APIs) with significant potential for treating acne vulgaris. The study highlights how the chemical structure of the cation significantly influences surface activity, lipophilicity, and solubility in aqueous media. Furthermore, the new ionic forms of APIs, the synthesis of which was assessed with Green Chemistry metrics, exhibited very good antibacterial properties against common pathogens that contribute to the development of acne, resulting in remarkable enhancement of biological activity ranging from 200 to as much as 2000 times when compared to salicylic acid alone. The molecular docking studies also revealed the excellent anti-inflammatory activity of N-alkylnicotinamide salicylates comparable to commonly used drugs (indomethacin, ibuprofen, and acetylsalicylic acid) and were even characterized by better IC50 values than common anti-inflammatory drugs in some cases. The derivative, featuring a decyl substituent in the pyridinium ring of nicotinamide, exhibited efficacy against Cutibacterium acnes while displaying favorable water solubility and improved wettability on hydrophobic surfaces, marking it as particularly promising. To investigate the impact of the APIs on the biosphere, the EC50 parameter was determined against a model representative of crustaceans─Artemia franciscana. The majority of compounds (with the exception of the salt containing the dodecyl substituent) could be classified as \"Relatively Harmless\" or \"Practically Nontoxic\", indicating their potential low environmental impact, which is essential in the context of modern drug development.

Specific stereoisomer is paramount as it is vital for optimizing drug efficacy and safety. The quest for the isolation of desired stereoisomer of active pharmaceutical ingredients or key intermediates drives innovation in drug synthetic and biocatalytic methods. Chiral phosphoramidate is an important building block for the synthesis of antiviral drugs such as remdesivir and sofosbuvir. Given the clinical potency of the (Sp)-diastereomer of the drugs, an enzyme capable of completely hydrolyzing the (Rp)-diastereomer is needed to achieve the purified diastereomers via biocatalytic reaction. In this study, protein engineering of phosphotriesterase (PTE) was aimed to improve the specificity. Employing rational design and site-directed mutagenesis, we generated a small library comprising 24 variants for activity screening. Notably, W131M and I106A/W131M variants demonstrated successful preparation of pure (Sp)-diastereomer of remdesivir and sofosbuvir precursors within a remarkably short hydrolysis time (<20 min). Our work unveils a promising methodology for producing pure stereoisomeric compounds, utilizing novel biocatalysts to enable the chemoenzymatic synthesis of phosphoramidate nucleoside prodrugs.

This article examines the development and implementation of a customized Python script utilizing the Elsevier Scopus and Clarivate Web of Science Journal Citation Reports Application Programming Interfaces (APIs). The aim was to streamline and expedite the labor-intensive process of collecting research metrics, which were traditionally compiled manually by librarians at the University of Miami Miller School of Medicine Louis Calder Memorial Library. The script significantly reduces the time and effort required to generate comprehensive reports on research productivity, thereby enabling more efficient resource allocation and aiding in faculty evaluations.

The presence of harmful substances in the atmosphere poses significant risks to the environment and public health. These pollutants can come from natural sources like dust and wildfires, or from human activities such as industrial, transportation, and agricultural practices. The objective of this study was to assess air quality on the East Coast of Peninsular Malaysia by analyzing historical data from the Department of Environment, Malaysia. Daily measurements of PM10, O3, SO2, NO2, and CO were collected from eight monitoring stations over 11 years (2011-2021) and analyzed using environmetric techniques. Hierarchical agglomerative cluster analysis (HACA) classified two stations as belonging to the high pollution cluster (HPC), three stations as part of the moderate pollution cluster (MPC), and three stations as the low pollution cluster (LPC). Discriminant analysis revealed a correct assignment rate of 90.50%, indicating that all five parameters were able to differentiate pollution levels with high significance (p < 0.0001). Principal component analysis (PCA) was conducted to validate the pattern of air quality variables in relation to the identified clusters (HPC, MPC, and LPC). The results showed that two verifactors (VFs) were extracted in HPC and LPC, while three VFs were identified in MPC. The cumulative variance explained by the PCA for HPC, MPC, and LPC was 69.43%, 82.32%, and 62.16%, respectively. Finally, an artificial neural network (ANN) was used to forecast the air pollutant index (API) levels, using the R2 and RMSE performance metrics. The PCA-MLP Model A yielded an R2 value of 0.8470 and an RMSE of 6.6470, while PCA-MLP Model B achieved an R2 value of 0.8591 and an RMSE of 6.3000, both indicating a significant and strong correlation.

Pharmaceuticals are among the most challenging products to assess by life cycle assessment (LCA). The main drawback highlighted by LCA practitioners is the lack of inventory data, both regarding the synthesis of active pharmaceutical ingredient (API) precursors (upstream) and the details concerning the downstream phases (use and end of life). A short critical review of pharma-LCAs found in the literature is here proposed, with discussion of several tools and models used to predict the environmental impacts derived from the life cycle of pharmaceuticals, emphasizing current strengths and weaknesses, and exploring the possibilities for improvements. The case of antibiotics is selected as a representative class of pharmaceuticals, due to their massive use worldwide and the growing related issue of antimicrobial resistance enrichment, which is generally not included in most of LCAs. Also, we comment on drafting product category rules (PCRs) in the relevant field to develop standard methodologies and enhance the comparability of the studies, ultimately advocating collaboration with companies and improving inventory data quality and availability for the whole value chain of products.

Apigenin (API) is a natural flavonoid compound with antioxidant, anti fibrotic, anti-inflammatory and other effects, but there is limited research on the effect of API on liver fibrosis. This study aims to explore the effect and potential mechanism of API on liver fibrosis induced by CCl4 in mice. The results indicate that API reduces oxidative stress levels, inhibits hepatic stellate cell (HSC) activation, and exerts anti liver fibrosis effects by regulating the PKM2-HIF-1α pathway. We observed that API alleviated liver tissue pathological damage and collagen deposition in CCl4 induced mouse liver fibrosis model, promoting the recovery of liver function in mice with liver fibrosis. In addition, the API inhibits the transition of Pyruvate kinase isozyme type M2 (PKM2) from dimer to tetramer formation by regulating the EGFR-MEK1/2-ERK1/2 pathway, thereby preventing dimer from entering the nucleus and blocking PKM2-HIF-1α access. This change leads to a decrease in malondialdehyde (MDA) and Catalase (CAT) levels and an increase in glutathione (GSH), superoxide dismutase (SOD), glutathione peroxidase (GSH-PX) levels, as well as total antioxidant capacity (T-AOC) in the liver of liver fibrosis mice. At the same time, API downregulated the expression of α-smooth muscle actin (α-SMA), Vimentin and Desmin in the liver tissue of mice with liver fibrosis, inhibited the activation of HSC, and reduced collagen deposition. These results indicate that API can inhibit HSC activation and alleviate CCl4 induced liver fibrosis by inhibiting the PKM2-HIF-1α pathway and reducing oxidative stress, laying an important foundation for the development and clinical application of API as a novel drug for treating liver fibrosis.

In this challenging world, social media plays a vital role as it is at the pinnacle of data sharing. The advancement in technology has made a huge amount of information available for data analysis and it is on the hotlist nowadays. Opinions of the people are expressed and shared across various social media platforms like Twitter, Facebook, and Instagram. Twitter is a prodigious platform containing an ample amount of data and analyzing the data is of topmost priority. One of the most widely utilized approaches for classifying an individual\'s emotions displayed in subjective data is sentiment analysis. Sentiment analysis is done using various algorithms of machine learning like Support Vector Machine, Naive Bayes, Long Short-Term Memory, Decision Tree Classifier, and many more, but this paper aims at the generalized way of performing Twitter sentiment analysis using flask environment. Flask environment provides various inbuilt functionalities to analyze the sentiments of text into three different categories: positive, negative, and neutral. Also, it makes API calls to the Twitter Developer account to fetch the Twitter data. After fetching and analyzing the data, the results get displayed on a webpage containing the percentage of positive, negative, and neutral tweets for a phrase in a pie chart. It displays the language analysis for the same phrase. Furthermore, the webpage calls attention to the tweets done on that phrase and reveals the details of the tweets. Considering the major industry runners of three different sectors namely Enterprises, Sports Apparel Industry, and Multimedia Industry, we have analyzed and compared sentiments of two different Multinational companies from each sector.

BACKGROUND: The purpose of this research is to develop an analytical method and validate it according to ICH guidelines for the estimation of Toremifene by RP-HPLC-PDA with molecular docking and ADMET analysis. From molecular docking, it came to know the receptor affinity specifically to estrogen receptors (ERα and ERβ), which are responsible for cancer therapy. ADMET analyses secure its therapeutic potential as well safety of the drug.
METHODS: An isocratic method has developed by RP-HPLC-PDA (AGILENT 1100) with symmetry of 100 mm x 4.6 mm x 5 μm particle size C18 column and optimise mobile phase is methanol: 0.1% OPA (orthophosphoric acid) water ratio of 43:57% v/v. Under different conditions like acidic, alkaline, oxidative, and neutral environments, toremifene was tested for degradation.
RESULTS: The developed method is validated in accordance with ICH guidelines. A calibration curve with an r2 value of 0.9987 has been prepared across the range of 10 to 50 μg/ml with five standard dilutions. The retention time of the drug is 5.575 minutes. The validation results are system suitability (%RSD-0.76), inter-day precision (%RSD 0.14-0.29), intraday precision (%RSD 0.08-0.34), accuracy (%RSD 0.16-0.96), and robustness (%RSD 0.16-0.35). In different intended conditions, four peaks are in 1 N HCl, two peaks in 1 N NaOH, three peaks in 10% H2O2 (1hr), and one peak in neutral.
CONCLUSIONS: Toremifene, a Selective Estrogen Receptor Modulator (SERM), Drug pharmacokinetic properties and receptor binding affinity results are helpful in designing the analytical method. Developing the RP-HPLC-PDA method is found to be novel, simple and precise. It could be used for testing toremifene in bulk and pharmaceutical tablet dosage forms in quality control, as well as stability tests.