Support vector regression-guided unravelling: antioxidant capacity and quantitative structure-activity relationship predict reduction and promotion effects of flavonoids on acrylamide formation

Support vector regression-based QSAR models for prediction of antioxidant activity of phenolic compounds

The Support vector regression (SVR) was used to investigate quantitative structure-activity relationships (QSAR) of 75 phenolic compounds with Trolox-equivalent antioxidant capacity (TEAC). Geometric structures were optimized at the EF level of the MOPAC software program. Using Pearson correlation coefficient analysis, four molecular descriptors [n(OH), Cosmo Area (CA), Core-Core Repulsion (CCR) and Final Heat of Formation (FHF)] were selected as independent variables. The QSAR model was developed from the training set consisting of 57 compounds and then used the leave-one-out cross-validation (LOOCV) correlation coefficient to evaluate the prediction ability of the QSAR model. Used Artificial neural network (ANN) and multiple linear regression (MLR) for comparing. The RMSE (root mean square error) values of LOOCV in SVR, ANN and MLR models were 0.44, 0.46 and 0.54. The RMSE values of prediction of external 18 compounds were 0.41, 0.39 and 0.54 for SVR, ANN and MLR models, respectively. The obtained result indicated that the SVR models exhibited excellent predicting performance and competent for predicting the TEAC of phenolic compounds.

Identification of host-guest systems in green TADF-based OLEDs with energy level matching based on a machine-learning study

Booming progress has been made in both the molecular design concept and the fundamental electroluminescence (EL) mechanism of thermally activated delayed fluorescence (TADF)-based organic light-emitting diodes (OLEDs) in recent years. One of the requirements for TADF-based OLEDs having high external quantum efficiency (EQE) is the favorable energy level alignment between the host and the guest to promote the energy transfer and improve the carrier balance. However, strategies to optimize the TADF-based OLED performance by selecting suitable host-guest systems in the light-emitting layer are far from enough. In this work, we investigated guest-host systems through the use of two machine-learning approaches (feature-based and similarity-based algorithms) from our recent effort for the optimization of TADF-based OLEDs. The Random Forest (RF) algorithm based on the features of electronic and photo-physical properties can accurately predict the EQE of green TADF-based OLEDs with average correlation coefficients of R2 = 0.85 for the training set and R2 = 0.74 for the testing set. Also, the Support Vector Regression (SVR) algorithm based on similarity metrics between pairs of materials (e.g., host and guest) in terms of electronic parameters can provide reasonable device performance prediction (R2 = 0.72) through the optimization procedure of the parameters. These results show that the predictive capability and model applicability of both machine-learning models can be used to identify suitable host-guest systems and explore complex relationships in green TADF-based OLEDs.

Gestational exposure to acrylamide inhibits mouse placental development in vivo

Acrylamide, a carcinogen and neurotoxic substance, recently has been discovered in various heat-treated carbohydrate-rich foods. The aim of this study was to investigate the effects of acrylamide exposure on placental development. Pregnant mice received acrylamide by gavage at dosages of 0, 10, and 50 mg/kg/day from gestational days (GD) 3 until GD 8 or GD 13. The results showed that acrylamide feeding significantly decreased the numbers of viable embryos and increased the numbers of resorbed embryos on GD 13. Acrylamide exposure reduced the absolute and relative weight of placentas and embryos, and inhibited the development of ectoplacental cone (EPC) and placenta, as shown by the atrophy of EPC and reduced placental area. Acrylamide markedly reduced the numbers of labyrinth vessels. Expression levels of most placental key genes such as Esx1, Hand1, and Hand2 mRNA dramatically decreased in acrylamide-treated placentas. Furthermore, acrylamide treatment inhibited proliferation and induced apoptosis of placentas, as shown by decreased Ki67-positive cells and Bcl-2 protein, and increased the expression of Bax, cleaved-caspase-3, and cleaved-caspase-8 proteins. In conclusion, our results indicated that gestational exposure to acrylamide inhibits placental development through dysregulation of placental key gene expression and labyrinth vessels, suppression of proliferation, and apoptosis induction in mice.