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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.

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.