ABSTRACT
Biomedical research in the US has long been conducted in a public-private (PP) "ecosystem." Today, especially with gene therapies and genome editing-based medicine, publicly funded researchers frequently hand off their research to the private sector for clinical development, often to small, venture capital-funded startups in which they have a financial interest. This trend raises ethical questions about conflicts of interest, effectiveness of regulatory oversight, and justice in therapy access, that we are addressing in a multi-year, multidisciplinary study of the evolving governance of genome editing. This paper draws on interviews with scientists working across the PP divide and their private sector business and financial partners. We find little concern about potential ethical dilemmas, with two exceptions expressed by public sector scientists: concerns about inequitable access to treatments due to disparities in wealth, ethnicity, and health insurance benefits; and about whether their private collaborators' profit motive may affect their research objectives.
ABSTRACT
ABSTRACT: Facial grimacing is used to quantify spontaneous pain in mice and other mammals, but scoring relies on humans with different levels of proficiency. Here, we developed a cloud-based software platform called PainFace (http://painface.net) that uses machine learning to detect 4 facial action units of the mouse grimace scale (orbitals, nose, ears, whiskers) and score facial grimaces of black-coated C57BL/6 male and female mice on a 0 to 8 scale. Platform accuracy was validated in 2 different laboratories, with 3 conditions that evoke grimacing-laparotomy surgery, bilateral hindpaw injection of carrageenan, and intraplantar injection of formalin. PainFace can generate up to 1 grimace score per second from a standard 30 frames/s video, making it possible to quantify facial grimacing over time, and operates at a speed that scales with computing power. By analyzing the frequency distribution of grimace scores, we found that mice spent 7x more time in a "high grimace" state following laparotomy surgery relative to sham surgery controls. Our study shows that PainFace reproducibly quantifies facial grimaces indicative of nonevoked spontaneous pain and enables laboratories to standardize and scale-up facial grimace analyses.
