Effect of the drug cyclophosphamide on the activity of porcine kidney betaine aldehyde dehydrogenase

Pyrroloquinoline quinone ameliorates liver injury in mice induced by cyclophosphamide

The current study aimed to investigate the potential ameliorative effects of pyrroloquinoline quinone (PQQ) on cyclophosphamide (CTX)-induced liver injury in mice. The liver injury model was established by injecting mice with CTX (80 mg/kg/day). Liver function indices, antioxidant enzyme activities, and inflammatory cytokines were evaluated. In addition, protein expression levels of the nuclear factor E2-related factor 2 (Nrf2) and nuclear factor kappa-B (NF-κB) pathways in the liver tissues were determined using western blot. The results indicated that PQQ decreased the serum alanine aminotransferase (ALT), aspartate aminotransferase (AST), and the malondialdehyde (MDA), interleukin (IL)-1β, IL-6, tumor necrosis factor-α (TNF-α) levels in the liver tissues. Moreover, PQQ enhanced the activities of oxidative stress markers to alleviate CTX induced oxidative stress. Furthermore, the expression levels of heme oxygenase-1 (HO-1), glutamate-cysteine ligase modifier subunit (GCLM), and NAD(P)H quinone oxidoreductase 1 (NQO1) were significantly increased, and the expression levels of NF-κB p50, NF-κB p65, and inhibitor of NF-κB kinase alpha (IKKα) were significantly decreased after PQQ administration, suggesting that PQQ alleviated CTX-induced liver injury via activating the Nrf2-mediated antioxidant response pathway, and inhibiting the NF-κB-mediated inflammation pathway. Therefore, PQQ can be potentially used as a dietary supplement or functional foods for alleviating the CTX-induced liver injury.

Accelerating the optimization of enzyme-catalyzed synthesis conditions via machine learning and reactivity descriptors

Enzyme-catalyzed synthesis reactions are of crucial importance for a wide range of applications. An accurate and rapid selection of optimal synthesis conditions is crucial and challenging for both human knowledge and computer predictions. In this work, a new scenario, which combines a data-driven machine learning (ML) model with reactivity descriptors, is developed to predict the optimal enzyme-catalyzed synthesis conditions and the reaction yield. Fourteen reactivity descriptors in total are constructed to describe 125 reactions (classified into five categories) included in different reaction mechanisms. Nineteen ML models are developed to train the dataset and the Quadratic support vector machine (SVM) model is found to exhibit the best performance. The Quadratic SVM model is then used to predict the optimal reaction conditions, which are subsequently used to obtain the highest yield among 109 200 reaction conditions with different molar ratios of substrates, solvents, water contents, enzyme concentrations and temperatures for each reaction. The proposed protocol should be generally applicable to a diverse range of chemical reactions and provides a black-box evaluation for optimizing the reaction conditions of organic synthesis reactions.