Evaluation of the toxic potential of Bisphenol-A glycidylmethacrylate (BisGMA) on the third instar larvae of transgenic Drosophila.

Introduction: In the present study the cytotoxic and genotoxic effects of Bisphenol-A glycidyl methacrylate (BisGMA) was studied on the third instar larvae of transgenic Drosophila melanogaster (hsp70-lacZ)Bg9. Materials and methods: The concentration of BisGMA i.e. 0.005, 0.010, 0.015 and 0.020 M were established in diet and the larvae were allowed to feed on it for 24 h. Results: A dose dependent significant increase in the activity of ß-galactosidase was observed compared to control. A significant dose dependent tissue damage was observed in the larvae exposed to 0.010, 0.015 and 0.020 M of BisGMA compared to control. A dose dependent significant increase in the Oxidative stress markers was observed compared to control. BisGMA also exhibit significant DNA damaged in the third instar larvae of transgenic D. melanogaster (hsp70-lacZ)Bg9 at the doses of 0.010, 0.015 and 0.020 M compared to control. Conclusion: BisGMA at 0.010, 0.015 and 0.020 M was found to be cytotoxic for the third instar larvae of transgenic D. melanogaster (hsp70-lacZ) Bg9.

Using machine learning on clinical data to identify unexpected patterns in groups of COVID-19 patients.

As clinicians are faced with a deluge of clinical data, data science can play an important role in highlighting key features driving patient outcomes, aiding in the development of new clinical hypotheses. Insight derived from machine learning can serve as a clinical support tool by connecting care providers with reliable results from big data analysis that identify previously undetected clinical patterns. In this work, we show an example of collaboration between clinicians and data scientists during the COVID-19 pandemic, identifying sub-groups of COVID-19 patients with unanticipated outcomes or who are high-risk for severe disease or death. We apply a random forest classifier model to predict adverse patient outcomes early in the disease course, and we connect our classification results to unsupervised clustering of patient features that may underpin patient risk. The paradigm for using data science for hypothesis generation and clinical decision support, as well as our triaged classification approach and unsupervised clustering methods to determine patient cohorts, are applicable to driving rapid hypothesis generation and iteration in a variety of clinical challenges, including future public health crises.