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Abstract Background Breast cancer is the world’s most prevalent cancer among women. Microorganisms have been the richest source of antibiotics as well as anticancer drugs. Moricin peptides have shown antibacterial properties; however, the anticancer potential and mechanistic insights into moricin peptide-induced cancer cell death have not yet been explored. Methods An investigation through in silico analysis, analytical methods (Reverse Phase-High Performance Liquid Chromatography (RP-HPLC), mass spectroscopy (MS), circular dichroism (CD), and in vitro studies, has been carried out to delineate the mechanism(s) of moricin-induced cancer cell death. An in-silico analysis was performed to predict the anticancer potential of moricin in cancer cells using Anti CP and ACP servers based on a support vector machine (SVM). Molecular docking was performed to predict the binding interaction between moricin and peptide-related cancer signaling pathway(s) through the HawkDOCK web server. Further, in vitro anticancer activity of moricin was performed against MDA-MB-231 cells. Results In silico observation revealed that moricin is a potential anticancer peptide, and protein–protein docking showed a strong binding interaction between moricin and signaling proteins. CD showed a predominant helical structure of moricin, and the MS result determined the observed molecular weight of moricin is 4544 Da. An in vitro study showed that moricin exposure to MDA-MB-231 cells caused dose dependent inhibition of cell viability with a high generation of reactive oxygen species (ROS). Molecular study revealed that moricin exposure caused downregulation in the expression of Notch-1, NF-ƙB and Bcl2 proteins while upregulating p53, Bax, caspase 3, and caspase 9, which results in caspase-dependent cell death in MDA-MB-231 cells. Conclusions In conclusion, this study reveals the anticancer potential and underlying mechanism of moricin peptide-induced cell death in triple negative cancer cells, which could be used in the development of an anticancer drug.

In an interactive online learning system (OLS), it is crucial for the learners to form the questions correctly in order to be provided or recommended appropriate learning materials. The incorrect question formation may lead the OLS to be confused, resulting in providing or recommending inappropriate study materials, which, in turn, affects the learning quality and experience and learner satisfaction. In this paper, we propose a novel method to assess the correctness of the learner's question in terms of syntax and semantics. Assessing the learner’s query precisely will improve the performance of the recommendation. A tri-gram language model is built, and trained and tested on corpora of 2,533 and 634 questions on Java, respectively, collected from books, blogs, websites, and university exam papers. The proposed method has exhibited 92% accuracy in identifying a question as correct or incorrect. Furthermore, in case the learner's input question is not correct, we propose an additional framework to guide the learner leading to a correct question that closely matches her intended question. For recommending correct questions, soft cosine based similarity is used. The proposed framework is tested on a group of learners' real-time questions and observed to accomplish 85% accuracy.

Diabetes is a disease that occurs when blood glucose level is too high. To control blood glucose levels in diabetic patients, modern therapies with a healthy diet and regular physical activity has been a good approach for the management of the diabetes. However, the disease eventually becomes deepen in most of the patients with age, and current approaches are not sufficient, demanding supportive and alternative approaches. The present paper portrays a contextual analysis of the impact of Yagya Therapy on diabetic level (HbA1C) in 2 weeks, using an appropriate herbal formulation on 10 patients, who had been experiencing diabetes since recent years. 10 diabetic patients (5 males and 5 females) on allopathic medicine for past more than 1 year without any change in medication and dose in past 3 months participated in the study. They were given 13 days of Yagya Therapy twice a day and their pre and post blood level of fasting glucose, Post Prandial (PP) glucose, and HbA1C were measured. Among 10 patients only 6 had attended nearly all of the sessions. Among 6 Patient, all of them showed reduction in the HbA1c value. The four of the patients, it was remarkable HbA1c difference i.e. 0.4, 0.4, 0.3, 0.2 respectively indicating impressive results i.e. affecting 3 months glucose sugar average and producing reduction in them with just 26 sessions in 13 days. The present study indicated Yagya therapy as a potential supportive and alternative solution in the management of diabetes. The increase in the time duration of the Yagya Therapy for more than 3-6 months may give the desired results for managing the diabetes.

Background: Skin disease is a major problem among people worldwide. Different machine learning techniques can be applied to identify classes of skin disease. Herein, we have applied machine learning algorithms to categorize classes of skin disease using ensemble techniques, and then a feature selection method is utilized to compare the results obtained. Method: In the proposed study, we present a new method which applies six different data mining classification techniques, and then develop an ensemble approach using Bagging, AdaBoost and Gradient Boosting classifier techniques to predict classes of skin disease. Furthermore, a feature importance method is utilized to select the most salient 15 features which will play a major role in prediction. A subset of the original dataset is obtained after selecting the 15 features, to compare the results of six machine learning techniques, and an ensemble approach is applied to the entire dataset. Results: The ensemble method is compared with the subset obtained from the feature selection method. The outcome shows that the dermatological prediction accuracy of the test dataset is increased as compared to the use of an individual classifier, and improved accuracy is obtained as compared with the feature selection subset method. Conclusion: The ensemble method and feature selection applied to dermatology datasets yields a better performance as compared to individual classifier algorithms. The ensemble method provides a more accurate and effective skin disease prediction. Keywords: Skin disease, Dermatology, Extra tree classifier, Radius neighbors classifier, Passive aggressive classifier

Breast cancer is the second most leading cancer occurring in women compared to all other cancers. Around 1.1 million cases were recorded in 2004. Observed rates of this cancer increase with industrialization and urbanization and also with facilities for early detection. It remains much more common in high-income countries but is now increasing rapidly in middle- and low-income countries including within Africa, much of Asia, and Latin America. Breast cancer is fatal in under half of all cases and is the leading cause of death from cancer in women, accounting for 16% of all cancer deaths worldwide. The objective of this research paper is to present a report on breast cancer where we took advantage of those available technological advancements to develop prediction models for breast cancer survivability. We used three popular data mining algorithms (Naïve Bayes, RBF Network, J48) to develop the prediction models using a large dataset (683 breast cancer cases). We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. The results (based on average accuracy Breast Cancer dataset) indicated that the Naïve Bayes is the best predictor with 97.36% accuracy on the holdout sample (this prediction accuracy is better than any reported in the literature), RBF Network came out to be the second with 96.77% accuracy, J48 came out third with 93.41% accuracy.