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is a critical-class pathogen that shows great resistance to most conventional antibiotics. Hence, it is of utmost importance to search for novel drugs to fight infections caused by this bacterium. This study aimed to evaluate the antibacterial activity of and extracts alone and in combination with antibiotics against multidrug-resistant (MDR) .

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Dye-laden wastewater poses a significant environmental and health threat. This study investigated the potential of green-synthesized zinc oxide nanoparticles (ZnO NPs), derived from Padina pavonica brown algae extract, for the removal of methylene blue (MB) dye. The hypothesis was that utilizing algal extract for ZnO NP synthesis would enhance adsorption capacity and photocatalytic activity for dye removal.

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Machine learning enhances genotoxicity assessment using MultiFlow® DNA damage assay.

Environ Mol Mutagen

December 2024

Research and Development, Preclinical Safety, Sanofi, Industriepark Hoechst, Frankfurt am Main, Germany.

Genotoxicity is a critical determinant for assessing the safety of pharmaceutical drugs, their metabolites, and impurities. Among genotoxicity tests, mechanistic assays such as the MultiFlow® DNA damage assay (MFA) allows the investigations on mode of action (MoA) of DNA damage through four mechanistic markers recorded at two time points. Previous studies have shown that machine learning (ML) can enhance precision on classifying the MoA of genotoxicants.

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Two-spotted spider mite (TSSM), Tetranychus urticae Koch is a devastating polyphagous mite causing considerable economic loss. Acaricides are showered in crops to manage this pest. The pest is known for developing resistance to several classical acaricides.

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The increasing development of technology has led to the increase of digital data in various fields, such as medication-related texts. Sentiment Analysis (SA) in medication is essential to give clinicians insights into patients' feedback about the treatment procedure. Therefore, this study intends to develop Artificial Intelligence (AI) models to predict patients' sentiments.

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