The recent discovery of compounds that target the translation termination factor eRF1 for proteasomal degradation offers a new avenue for the development of drugs to treat genetic diseases caused by nonsense mutations.
Mycobacterium abscessus infections present a major challenge to antibiotic therapy. Bacteriophages have a potential role to play in treatment of these infections, but neutralizing antibody reactions can limit the efficacy of the phages when administered intravenously.
How can vector-borne diseases be impacted by non-linear climate instabilities, such as a rapid melting of the ice sheet, that can have a strong impact on climate? We try to answer this question by studying the impact of an accelerated melting of the Greenland ice sheet on climate and malaria in Africa.
Our study illustrates the potential of ML models for accurately distinguishing the hemodynamic presentations of the two viral infections. This may have utility as a diagnostic tool to aid healthcare workers in triaging patients as the viral infections start co-circulating in the communities.
Despite substantial efforts to develop digital biomarkers, benchmarking and validating these algorithms remains difficult. Open, crowd-sourced efforts through DREAM Challenges is one approach to speed the process.
During the height of COVID-19 pandemic, clinicians were forced to make difficult treatment decisions, given the large number of patients and limited resources. To help guide treatment strategies, we evaluated the performance of 18 machine learning models in predicting COVID-19 patient outcomes.
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