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According to WHO, obesity, a global pandemic, is an important cause of cancer. Obesity-induced adipose tissue expansion is associated with infiltration of immune cells which causessecretion of adipokines, chemokines, cytokines like IL-1,2,6,8, TNF-α, etc. The purpose of thisstudy is to provide a comprehensive review of the effects of obesity-induced inflammatorycytokines on breast cancer while discussing available therapeutics & chemotherapy in thetreatment of breast cancer. We discussed the identification of inflammatory biomarkers releasedby adipose tissue, and alterations in their pathway in the pathogenesis of breast cancer. Our studyhelps in improving diagnostic accuracy, identifying targets of therapy, and suggesting usefullifestyle behaviors for this aspect. We conducted our research in PubMed to identify relevantjournal articles published within the last 10 years. We extracted pertinent data and discussedmajor signaling pathways regulated by inflammatory cytokines and adipokines in mediatingbreast cancer. Obesity involves different changes that may contribute to the development ofbreast cancer, such as excess inflammatory cytokines and chronic inflammation,hyperinsulinemia, insulin resistance, and raised leptin and estrogens. It leads to a low-gradechronic inflammatory state in the body, which leads to increased estrogen and aromatase,increased growth proliferation, and angiogenesis. All of these contribute to the development ofbreast cancer, mostly ductal breast cancer and ER-positive breast cancer. Leptin antagonist,Adiponectin agonists, aromatase inhibitors, and lipid-lowering drugs have shown favorable resultsin the treatment of breast cancer. Metformin inhibits the IL-6 mRNA expression and activatesthe IL-1R expression which acts as an anti-inflammatory cytokine. In breast cancer cells,Metformin interferes with mTOR pathway signaling and decreases the expression of HER-2protein. Weight loss with exercise and reduced calorie intake reduces the risk of breast cancer inboth premenopausal and postmenopausal women. Early detection using “biomarkers” shows apromising improvement in the survival rate.

Stock price prediction is a popular research domain for its complex data structure and confounding factors. The use of Data science tools enormously increased along with the advancement of data mining and artificial intelligence tools. Classification is a famous machine learning tool with vast potential use in the stock market. However, predicting stock price through a perfect classification model is vital as different stock market data have individual patterns and dependencies as precise information about increasing or decreasing the market can significantly influence selling or buying the shares. The linear model of classification including logistic regression classification (LR), linear discriminant analysis (LDA), partial last-square discriminant analysis (PLS-DA), penalized discriminant analysis (PDA), and nearest Shrunken discriminant analysis are considered in this study to compare according to predict the stock market price of top six banks stock prices of Bangladesh. The existing literature recommends that PLS-DA fit well if data contain a high correlation among the predictors. On the contrary, PDA performs better if there is any multicollinearity problem or chance of overfitting; LDA gave better approximation when data got multivariate normality, and the nearest shrunken method fit well if there is any existence of high dimensionality. Interestingly, this study's data contain all the mentioned characteristics; still, LR gives less misclassification rate or apparent error rate. Thus, this study recommends that one may choose LR among the linear classification model if there is a high correlation, multicollinearity, multivariate normality, and high-dimensionality among predictors.

Despite the advances in diagnosis and management of breast cancer, metastasis has been responsible for the staggering percentage of breast cancer-related death. Mortality threat can be explained mostly by the lack of proper understanding of the diversity of pathological features and underlying mechanism of breast cancer metastasis and effective targeted therapy. Breast cancer stem cells (BCSCs) are the potential source of tumor cells spread to distant organs. BCSCs targeted therapy can suppress the breast cancer progression to metastasis. Spreading of tumor cells to the bone, lung, liver, and brain occurs through a distinct non-random process; called metastasis organotropism. Recently, brain metastasis in breast cancer patients has been detected more frequently, causing a significant clinical burden. BRCA1 and BRCA2 associated breast cancers carry a remarkably higher propensity of CNS metastasis. BRCA1 and BRCA2 associated breast cancers commonly have the propensity to be the triple-negative (TN) and hormone receptors (HR)-positive/human epidermal growth factor receptor 2 (HER2)-negative molecular subtypes, respectively. Regardless of molecular subtypes, metastasis is most commonly evident at the bone. Heterogeneity is a critical pathological feature, leads to therapeutic resistance. BCSCs, biomarkers expression patterns, and mutations contribute to heterogeneity. In this paper, we discuss crucial pathological features of breast cancer metastasis, emphasizing metastasis organotropism and heterogeneity; and mechanisms of breast cancer metastasis, highlighting the pathways of metastasis to the brain. We consider that this paper reinforces future research areas and benefits the general readers, physicians, and researchers to identify potential areas to develop targeted therapies.

Unauthorized access or intrusion is a massive threatening issue in the modern era. This study focuses on designing a model for an ideal intrusion detection system capable of defending a network by alerting the admins upon detecting any sorts of malicious activities. The study proposes a two layered anomaly-based detection model that uses filter co-relation method for dimensionality reduction along with Random forest and Support Vector Machine as its classifiers. It achieved a very good detection rate against all sorts of attacks including a low rate of false alarms as well. The contribution of this study is that it could be of a major help to the computer scientists designing good intrusion detection systems to keep an industry or organization safe from the cyber threats as it has achieved the desired qualities of a functional IDS model.

Background: The novel coronavirus (COVID-19) is now in a horrific situation around the world. Prediction about the number of infected and death cases may help to take immediate action to prevent the epidemic as well as control the situation of a country. The ongoing debate about the climate factors may need more validation with more studies. The climate factors of the top-five affected countries and three south Asian countries have considered in this study to have a real-time forecast and robust validation about the impact of climate variables. Methods: The ARIMA model have included to model the univariate cumulative confirmed and death cases separately. The MLP, ELM and likelihood-based GLM count time series also considered as they consider the external variables as exogenous regressors. As the death count includes zero itself, zero-inflated count time series model has included instead of likelihood-based GLM. The better fitting of the ARIMA model will validate the underwhelm of meteorological factors was the initial hypothesis. The best model has identified through the application and comparison with the real data points. Results: The results depict that there is an influence of meteorological variables like temperature and humidity mostly for all the selected countries cumulative confirm cases excluding Italy and Sri-Lanka. However, the best models for deaths count of each country also identify the impact of meteorological variables for each country. Conclusion: The authors make the sixty days ahead forecast for each country which will be beneficial for the policymakers.