Publications by authors named "M D Doerr"

Plastic-degrading enzymes facilitate the biocatalytic recycling of poly(ethylene terephthalate) (PET), a significant synthetic polymer, and substantial progress has been made in utilizing PET hydrolases for industrial applications. To fully exploit the potential of these enzymes, a deeper mechanistic understanding followed by targeted protein engineering is essential. Through advanced molecular dynamics simulations and free energy analysis methods, we elucidated the complete pathway from the initial binding of two PET hydrolases-the thermophilic leaf-branch compost cutinase (LCC) and polyester hydrolase 1 (PES-H1)-to an amorphous PET substrate, ultimately leading to a PET chain entering the active site in a hydrolyzable conformation.

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Researchers and practitioners are increasingly using machine-generated synthetic data as a tool for advancing health science and practice, by expanding access to health data while-potentially-mitigating privacy and related ethical concerns around data sharing. While using synthetic data in this way holds promise, we argue that it also raises significant ethical, legal, and policy concerns, including persistent privacy and security problems, accuracy and reliability issues, worries about fairness and bias, and new regulatory challenges. The virtue of synthetic data is often understood to be its detachment from the data subjects whose measurement data is used to generate it.

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Article Synopsis
  • Cardiac symptoms can affect individuals who had mild COVID-19 and no prior heart issues, and cardiac MRI can detect these hidden issues linked to long-term heart problems.
  • This study tests a new treatment combining low-dose prednisolone and losartan against a placebo in people with post-COVID heart inflammation, with assessments like cardiac MRI and exercise testing.
  • The main goal is to see if the treatment improves left ventricular ejection fraction after 16 weeks, while also monitoring changes in symptoms and safety over a year for 280 participants.
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The rapid evolution of artificial intelligence (AI) is structuralizing social, political, and economic determinants of health into the invisible algorithms that shape all facets of modern life. Nevertheless, AI holds immense potential as a public health tool, enabling beneficial objectives such as precision public health and medicine. Developing an AI governance framework that can maximize the benefits and minimize the risks of AI is a significant challenge.

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