Celastrol modulates inflammation through inhibition of the catalytic activity of mediators of arachidonic acid pathway: Secretory phospholipase A 2 group IIA, 5-lipoxygenase and cyclooxygenase-2

Celastrol inhibits hepatitis C virus replication by upregulating heme oxygenase-1 via the JNK MAPK/Nrf2 pathway in human hepatoma cells

“…Future studies should examine the effect of celastrol on the kinases involved in JNK activation comprehensively describe the relationship between celastrol and HO-1 induction. Several studies have indicated that celastrol exhibits anti-inflammatory activity by inhibiting of nuclear factor-kB (NF-kB) and downstream cycloxygenase-2 (COX-2) (Ding et al, 2013;Joshi et al, 2016). On the basis of earlier findings on a promising tactic against HCV infection via down-regulation of NF-kB-mediated COX-2 expression Lee et al, 2011b), we propose that the inhibitory effect of celastrol on NF-kB-mediated COX-2 expression may, at least in part contribute to its anti-HCV activity.…”

SAR study on inhibitors of GIIA secreted phospholipase A2 using machine learning methods

“…(Wagener et al., ) In order to investigate the 30 CORINA descriptors fully and find the best descriptors sets for modeling, the 30 descriptors were ranked with two methods: recursive feature elimination (RFE) (Isabelle Guyon et al., ; Lei, Chen et al., ; Lei, Sun et al., ) and information gain (IG; Sokolova & Szpakowicz, ; Xia & Yan, ) Based on either list of ranked descriptors, we built 30 models based on training set 1 when the descriptors were added one by one from 1 to 30. Following the process above, we applied three machine learning algorithms including support vector machine (SVM), (Cortes & Vapnik, ; Guyon et al., ; Sun et al., ; Wang et al., ) decision tree (DT), (Joshi et al., ) and random forest (RF), (Breiman, ; Lei et al., ) respectively. Then, we got six groups of models as shown in Figure , where the MCC values in the prediction of training set 1 and test set 1 are represented.…”

“…Three algorithms including support vector machine (SVM), (Cortes & Vapnik, ; Guyon et al., ; Sun et al., ; Wang et al., ) decision tree (DT), (Joshi et al., ) and random forest (RF; Breiman, ; Lei et al., ) were applied to build classification models. These algorithms were implemented by scikit‐learn Python toolkit…”

“…DT is an algorithm learning simple decision rules inferred from the data features and construct a decision process like a tree. (Joshi et al., ) The decision tree consists of internal nodes and leaf nodes. One internal node is corresponding to a feature test, while one leaf node is corresponding to a decision result.…”

“…Then, we calculated several kinds of molecular descriptors of the compounds and divided them into a training set and a test set. After processing the descriptors of the dataset, we built models with training set by machine learning algorithms including support vector machine (SVM), (Cortes & Vapnik, ; I. Guyon, Guyon, Boser, & Vapnik, ; Sun et al., ; Wang et al., ) decision tree (DT), (Joshi et al., ) and random forest (RF). (Breiman, ; Lei et al., ) The test set was used for the validation of the models.…”