The rise of India’s global health diplomacy amid COVID-19 pandemic

The COVID-19 pandemic has highlighted the importance of global health diplomacy (GHD), with India emerging as a key player. India’s commitment to GHD is demonstrated by its active participation in regional and multilateral projects, pharmaceutical expertise, and large-scale manufacturing capabilities, which include the production and distribution of COVID-19 vaccines and essential medicines. India has supported nations in need through bilateral and multilateral platforms, providing vaccines to countries experiencing shortages and offering technical assistance and capacity-building programs to improve healthcare infrastructure and response capabilities. India’s unique approach to GHD, rooted in humanitarian diplomacy, emphasized collaboration and empathy and stressed the well-being of humanity by embracing the philosophy of "Vasudhaiva Kutumbakam," which translates to "the world is one family." Against this background, this paper’s main focus is to analyze the rise of India’s GHD amidst the COVID-19 pandemic and its leadership in addressing various global challenges. India has demonstrated its commitment to global solidarity by offering medical supplies, equipment, and expertise to more than 100 countries. India’s rising global leadership can be attributed to its proactive approach, humanitarian diplomacy, and significant contributions to global health initiatives

Comparison of Predicted Probabilities of Proportional Hazards Regression and Linear Discriminant Analysis Methods Using a Colorectal Cancer Molecular Biomarker Database

Background: Although a majority of studies in cancer biomarker discovery claim to use proportional hazards regression (PHREG) to the study the ability of a biomarker to predict survival, few studies use the predicted probabilities obtained from the model to test the quality of the model. In this paper, we compared the quality of predictions by a PHREG model to that of a linear discriminant analysis (LDA) in both training and test set settings. Methods: The PHREG and LDA models were built on a 491 colorectal cancer (CRC) patient dataset comprised of demographic and clinicopathologic variables, and phenotypic expression of p53 and Bcl-2. Two variable selection methods, stepwise discriminant analysis and the backward selection, were used to identify the final models. The endpoint of prediction in these models was five-year post-surgery survival. We also used linear regression model to examine the effect of bin size in the training set on the accuracy of prediction in the test set.Results: The two variable selection techniques resulted in different models when stage was included in the list of variables available for selection. However, the proportion of survivors and non-survivors correctly identified was identical in both of these models. When stage was excluded from the variable list, the error rate for the LDA model was 42% as compared to an error rate of 34% for the PHREG model.Conclusions: This study suggests that a PHREG model can perform as well or better than a traditional classifier such as LDA to classify patients into prognostic classes. Also, this study suggests that in the absence of the tumor stage as a variable, Bcl-2 expression is a strong prognostic molecular marker of CRC