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Background: After breast cancer, cervix neoplasm is the most common disease among young women. Nowadays, natural substances are used in the treatment of diseases, because of the known side effects of the chemical drugs. Dunaliella is a green alga that lives in the saltwater lakes of Iran and is abundant in antioxidant substances. This paper aimed to study the effect of Dunaliella extract on the expression of the anti-apoptotic BCL-2 gene in Hela cell line. Expression of this gene is increased during cancer. It is expected that gene expression will be reduced if the alga extract is effective. Methods: Hela was prepared from the Center for Genetic and Biological Reserves of Iran and cultured. After culture, cells were divided into two treatment and control groups. Different concentrations of algae extract were applied to the treatment group for 48 hours. Then its toxicity was measured using MTT assay and IC50 was determined. RNA was extracted from cells of two groups, to determine the relative amount of gene expression at a concentration of IC50. Then Real-time PCR was used. Results: The result of Real-time PCR showed that the relative expression of BCL-2, in treatment group cells that were affected by algae extract, was four times lower than the control group. Since the P-value is less than 0.05 (Pvalue = 0), this decrease is significant. Conclusion: After 48 hours, at a concentration of IC50 of algae extract, the relative expression of BCL-2 was four times lower than control group. [ABSTRACT FROM AUTHOR]

Background. Cervical cancer begins in superficial cells and over time can invade deeper tissues and surrounding tissues. This paper presents a creative idea of using an ensemble classification algorithm that improves the predictive performance of an artificial intelligence system based on cervical cancer screening. This study aimed to classify Pap-smear images by different machine learning methods to achieve high accuracy detection. Methods. This study was performed on 917 Pap-smear images from the Herlev public database. In the feature extraction stage, 20 geometric features and 76 texture features were extracted. After that, using ensemble classification method, the images were classified into two categories (i.e., normal and abnormal) and then into seven categories (i.e., superficial epithelial, intermediate epithelial, columnar epithelial, mild dysplasia, moderate dysplasia, severe dysplasia and carcinoma) and the accuracy of the proposed method was evaluated. Results. The algorithm in the ensemble classification was able to achieve accuracy of 99.9% with a processing time of 0.028 second in the two-class classification and accuracy of 76.5% with a processing time of 0.033 second in the seven-class classification. Conclusion. Based on the results, the designed algorithm can be used as a computer aided diagnostic tool to increase the accuracy and speed of predicting the risk of cervical cancer. Practical Implications. Cervical cancer is one of the most common cancers among women. Early diagnosis of the disease can save various costs and prevent the patients’ frequent visits to medical centers. This research proposed an artificial intelligence method for automatic classification of cervical cells and improving the accuracy of diagnosis. [ABSTRACT FROM AUTHOR]