No abstract

Although many groups have been trying to prepare perovskite solar cells (PSCs) in ambient air, the power conversion efficiency (PCE) is still low. Besides, the effect of moisture on the formation of perovskite films is still controversial. In this paper, we studied the effect of moisture on the formation of perovskite films in detail, and found that moisture can speed up the crystallizing process of PbI2 films to form poor-quality films with large grain size and surface roughness, while, for the conversion of PbI2 to perovskite films, a small amount of moisture is not adverse, and even beneficial. On this basis, we report the successful fabrication of efficient mesoporous PSCs with PCE of 16.00% under ambient air conditions at 25% relative humidity by adding a small amount of n-butyl amine into the solution of PbI2 to enhance the quality of PbI2 films and thus to achieve high-quality perovskite films with smooth surface, large crystal grains, and high crystal quality.

This study aimed to investigate the feasibility of predicting oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation in diffuse gliomas by developing a deep learning approach using MRI radiomics. A total of 111 patients with diffuse gliomas participated in the retrospective study (56 patients with MGMT promoter methylation and 55 patients with MGMT promoter unmethylation). The radiomics features of the two regions of interest (ROI) (the whole tumor area and the tumor core area) for four sequences, including T1 weighted image (T1WI), T2 weighted image (T2WI), apparent diffusion coefficient (ADC) maps, and T1 contrast-enhanced (T1CE) MR images were extracted and jointly fed into the residual network. Then the deep learning method was developed and evaluated with a five-fold cross-validation, where in each fold, the dataset was randomly divided into training (80%) and validation (20%) cohorts. We compared the performance of all models using area under the curve (AUC) and average accuracy of validation cohorts and calculated the 10 most important features of the best model via a class activation map. Based on the ROI of the whole tumor, the predictive capacity of the T1CE and ADC model achieved the highest AUC value of 0.85. Based on the ROI of the tumor core, the T1CE and ADC model achieved the highest AUC value of 0.90. After comparison, the T1CE combined with the ADC model based on the ROI of the tumor core exhibited the best performance, with the highest average accuracy (0.91) and AUC (0.90) among all models. The deep learning method using MRI radiomics has excellent diagnostic performance with a high accuracy in predicting MGMT promoter methylation in diffuse gliomas.

Alkaloids from lotus plumule regulated BDNF-mediated ER stress and autophagy, subsequently attenuating neuroinflammation in LPS-stimulated BV2 microglia and LPS-induced depressive C56BL/6N mice.