Background: Colorectal Cancer (CRC) has multiple risk factors and depends highly on diet. Positive associations of red meat and processed meat intake and CRC have been proven, but no research has been conducted on the relation of spice intake and CRC risk. Various in-vitro studies have demonstrated the anticancer activity of chemicals present in spices, which is the main driving force for our statistical analysis.
Methods: We analyzed Global Burden of Disease (GBD) database, Food and Agricultural Organization of United Nations (FAO) database, and Global Dietary Database (GDD) using Pearson correlation statistics to find any significant correlation, mainly between spice intake and CRC risk. Data from 1990 to 2013 of 100 countries was collected for the analysis. Twenty-three-year average values (±SD) were calculated for CRC risk, spice, red meat, processed meat, vegetable, and fruit intake. CRC risk is taken as dependent variable whereas all other were independent variables. All variables were analyzed using Pearson correlation analysis. Results with p<0.05 were further analyzed using regression analysis.
Results: Pearson correlation showed that spice intake had a significant negative correlation (r= -0.301, p=0.002) whereas red meat (r= 0.722, p<0.001) and processed meat (r= 0.339, p<0.001) had a significant positive correlation with CRC risk.
Conclusion: Significant negative correlation between spice intake and CRC risk indicates that higher spice intake can be preventive against cancer and possibly decrease the risk of colorectal cancer in populations with higher CRC risk.
The Cooperative Spectrum sensing model is gaining importance among the cognitive radio network sharing groups. While the crowd-sensing model (technically the cooperative spectrum sensing) model has positive developments, one of the critical challenges plaguing the model is the false or manipulated crowd sensor data, which results in implications for the secondary user’s network. Considering the efficacy of the spectrum sensing by crowd-sensing model, it is vital to address the issues of falsifications and manipulations, by focusing on the conditions of more accurate determination models. Concerning this, a method of avoiding falsified crowd sensors from the process of crowd sensors centric cooperative spectrum sensing has portrayed in this article. The proposal is a protocol that selects affirmed crowd sensor under diversified factors of the decision credibility about spectrum availability. An experimental study is a simulation approach that evincing the competency of the proposal compared to the other contemporary models available in recent literature.
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