BackgroundCedrus deodara is one of the traditional Chinese medicinal herbs that exhibits a line of biological activities. The current study extracted the total flavonoids from the pine needles of Cedrus deodara (TFPNCD), and investigated its anti-cancer effects in tumor cell lines.MethodsThe total flavonoids was extracted from pine needles of Cedrus deodara by ethanol hot refluxing and purified by HPD722 macroporous resin. The contents of total flavonoids and the active ingredients of TFPNCD were analyzed through UV and HPLC. MTT assay was used to investigate its inhibitory effect on tumor cell lines. The flow cytometry was employed to determine the apoptosis and cell cycle distribution after treated TFPNCD on HepG2 cells.ResultsThe TFPNCD, in which the contents of total flavonoid reached up to 54.28 %, and the major ingredients of myricetin, quercetin, kaempferol and isorhamnetin in TFPNCD were 1.89, 2.01, 2.94 and 1.22 mg/g, respectively. The MTT assays demonstrated that TFPNCD inhibited the growth of HepG2 cells in a dose-dependent manner, with the IC50 values of 114.12 μg/mL. By comparison, TFPNCD inhibited the proliferation to a less extent in human cervical carcinoma HeLa, gastric cancer MKN28 cells, glioma SHG-44 cells and lung carcinoma A549 than HepG2 cells. We found that even at the lower doses, the total flavonoids effectively inhibited the proliferation of HepG2 cells. Comparison of IC50 values implicated that HepG2 cells might be more sensitive to the treatment with total flavonoids. TFPNCD was able to increase the population of HepG2 cells in G0 /G1 phase. Meanwhile, TFPNCD treatment increased the percentage of apoptotic HepG2 cells.ConclusionThese data suggested that TFPNCD might have therapeutic potential in cancer through the regulation of cell cycle and apoptosis.
Deepfake detection automatically recognizes the manipulated medias through the analysis of the difference between manipulated and non-altered videos. It is natural to ask which are the top performers among the existing deepfake detection approaches to identify promising research directions and provide practical guidance. Unfortunately, it's difficult to conduct a sound benchmarking comparison of existing detection approaches using the results in the literature because evaluation conditions are inconsistent across studies. Our objective is to establish a comprehensive and consistent benchmark, to develop a repeatable evaluation procedure, and to measure the performance of a range of detection approaches so that the results can be compared soundly. A challenging dataset consisting of the manipulated samples generated by more than 13 different methods has been collected, and 11 popular detection approaches (9 algorithms) from the existing literature have been implemented and evaluated with 6 fair-minded and practical evaluation metrics. Finally, 92 models have been trained and 644 experiments have been performed for the evaluation. The results along with the shared data and evaluation methodology constitute a benchmark for comparing deepfake detection approaches and measuring progress.
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