This current research was performed to investigate the role of typhae pollen polysaccharides (TPP) in hypoxia-treated PC12 cell which was an in vitro cell model of cerebral ischemia. Hypoxia-treated cells were treated with TPP for 12 h. Cell viability and apoptosis were detected by 3-(4,5-dimethylthiazol-2-yl)-2 5-diphenyl-2H-tetrazolium bromide (MTT) assay and flow cytometry, respectively. Cell apoptotic proteins and PI3K/AKT and Ras/Raf/MEK/ERK signal pathway–associated proteins were also examined by western blot. Furthermore, abnormal expression of miR-34a and silent information regulator 1 (SIRT1) was achieved by transfection. Besides, the expression of miR-34a and SIRT1 was examined by quantitative real-time polymerase chain reaction (qRT-PCR). The expression of SIRT1 was detected by qRT-PCR and western blot. The relationship between miR-34a and SIRT1 was verified by luciferase assay. We found that TPP enhanced cell viability and inhibited apoptosis in hypoxia-treated PC12 cells. Moreover, TPP increased the accumulated levels of Bcl-2 while decreased expression of Bax, cleaved Caspase-3, and cleaved PARP. TPP downregulated miR-34a expression while induced by hypoxia. Further results showed that miR-34a overexpression reversed the results led by TPP in cell viability, apoptosis, and its related proteins. In addition, SIRT1 was upregulated by TPP and was verified to be a target of miR-34a. Silence of SIRT1 led to the opposite results led by TPP. In the end, TPP activated PI3K/AKT and Ras/Raf/MEK/ERK signal pathways. In conclusion, TPP plays important roles in regulating cell viability and apoptosis in hypoxia-treated PC12 cells via modulating miR-34a/SIRT1, as well as activating PI3K/AKT and Ras/Raf/MEK/ERK signal pathways.

The purpose of this study was to investigate the precision of deep learning (DL)-based auto-reconstruction in localizing interstitial needles in post-operative cervical cancer brachytherapy (BT) using three-dimensional (3D) computed tomography (CT) images.Material and methods: A convolutional neural network (CNN) was developed and presented for automatic reconstruction of interstitial needles. Data of 70 post-operative cervical cancer patients who received CT-based BT were used to train and test this DL model. All patients were treated with three metallic needles. Dice similarity coefficient (DSC), 95% Hausdorff distance (95% HD), and Jaccard coefficient (JC) were applied to evaluate the geometric accuracy of auto-reconstruction for each needle. Dose-volume indexes (DVI) between manual and automatic methods were used to analyze the dosimetric difference. Correlation between geometric metrics and dosimetric difference was evaluated using Spearman correlation analysis. Results:The mean DSC values of DL-based model were 0.88, 0.89, and 0.90 for three metallic needles. Wilcoxon signed-rank test indicated no significant dosimetric differences in all BT planning structures between manual and automatic reconstruction methods (p > 0.05). Spearman correlation analysis demonstrated weak link between geometric metrics and dosimetry differences.Conclusions: DL-based reconstruction method can be used to precisely localize the interstitial needles in 3D-CT images. The proposed automatic approach could improve the consistency of treatment planning for post-operative cervical cancer brachytherapy.