Analysis of the first genetic engineering attribution challenge.

The ability to identify the designer of engineered biological sequences-termed genetic engineering attribution (GEA)-would help ensure due credit for biotechnological innovation, while holding designers accountable to the communities they affect. Here, we present the results of the first Genetic Engineering Attribution Challenge, a public data-science competition to advance GEA techniques. Top-scoring teams dramatically outperformed previous models at identifying the true lab-of-origin of engineered plasmid sequences, including an increase in top-1 and top-10 accuracy of 10 percentage points. A simple ensemble of prizewinning models further increased performance. New metrics, designed to assess a model's ability to confidently exclude candidate labs, also showed major improvements, especially for the ensemble. Most winning teams adopted CNN-based machine-learning approaches; however, one team achieved very high accuracy with an extremely fast neural-network-free approach. Future work, including future competitions, should further explore a wide diversity of approaches for bringing GEA technology into practical use.

Evaluating Use Cases for Human Challenge Trials in Accelerating SARS-CoV-2 Vaccine Development.

Human challenge trials (HCTs) have been proposed as a means to accelerate SARS-CoV-2 vaccine development. We identify and discuss 3 potential use cases of HCTs in the current pandemic: evaluating efficacy, converging on correlates of protection, and improving understanding of pathogenesis and the human immune response. We outline the limitations of HCTs and find that HCTs are likely to be most useful for vaccine candidates currently in preclinical stages of development. We conclude that, while currently limited in their application, there are scenarios in which HCTs would be extremely beneficial. Therefore, the option of conducting HCTs to accelerate SARS-CoV-2 vaccine development should be preserved. As HCTs require many months of preparation, we recommend an immediate effort to (1) establish guidelines for HCTs for COVID-19; (2) take the first steps toward HCTs, including preparing challenge virus and making preliminary logistical arrangements; and (3) commit to periodically re-evaluating the utility of HCTs.

Development and Prospective Validation of a Machine Learning-Based Risk of Readmission Model in a Large Military Hospital.

BACKGROUND: Thirty-day hospital readmissions are a quality metric for health care systems. Predictive models aim to identify patients likely to readmit to more effectively target preventive strategies. Many risk of readmission models have been developed on retrospective data, but prospective validation of readmission models is rare. To the best of our knowledge, none of these developed models have been evaluated or prospectively validated in a military hospital. OBJECTIVES: The objectives of this study are to demonstrate the development and prospective validation of machine learning (ML) risk of readmission models to be utilized by clinical staff at a military medical facility and demonstrate the collaboration between the U.S. Department of Defense's integrated health care system and a private company. METHODS: We evaluated multiple ML algorithms to develop a predictive model for 30-day readmissions using data from a retrospective cohort of all-cause inpatient readmissions at Madigan Army Medical Center (MAMC). This predictive model was then validated on prospective MAMC patient data. Precision, recall, accuracy, and the area under the receiver operating characteristic curve (AUC) were used to evaluate model performance. The model was revised, retrained, and rescored on additional retrospective MAMC data after the prospective model's initial performance was evaluated. RESULTS: Within the initial retrospective cohort, which included 32,659 patient encounters, the model achieved an AUC of 0.68. During prospective scoring, 1,574 patients were scored, of whom 152 were readmitted within 30 days of discharge, with an all-cause readmission rate of 9.7%. The AUC of the prospective predictive model was 0.64. The model achieved an AUC of 0.76 after revision and addition of further retrospective data. CONCLUSION: This work reflects significant collaborative efforts required to operationalize ML models in a complex clinical environment such as that seen in an integrated health care system and the importance of prospective model validation.

The design of a medical school social justice curriculum.

The acquisition of skills to recognize and redress adverse social determinants of disease is an important component of undergraduate medical education. In this article, the authors justify and define "social justice curriculum" and then describe the medical school social justice curriculum designed by the multidisciplinary Social Justice Vertical Integration Group (SJVIG) at the Geisel School of Medicine at Dartmouth. The SJVIG addressed five goals: (1) to define core competencies in social justice education, (2) to identify key topics that a social justice curriculum should cover, (3) to assess social justice curricula at other institutions, (4) to catalog institutionally affiliated community outreach sites at which teaching could be paired with hands-on service work, and (5) to provide examples of the integration of social justice teaching into the core (i.e., basic science) curriculum. The SJVIG felt a social justice curriculum should cover the scope of health disparities, reasons to address health disparities, and means of addressing these disparities. The group recommended competency-based student evaluations and advocated assessing the impact of medical students' social justice work on communities. The group identified the use of class discussion of physicians' obligation to participate in social justice work as an educational tool, and they emphasized the importance of a mandatory, longitudinal, immersive, mentored community outreach practicum. Faculty and administrators are implementing these changes as part of an overall curriculum redesign (2012-2015). A well-designed medical school social justice curriculum should improve student recognition and rectification of adverse social determinants of disease.