The research foundation for COVID-19 vaccine development.

The development of effective vaccines in <1 year to combat the spread of coronavirus disease 19 (COVID-19) is an example of particularly rapid progress in biomedicine. However, this was only made possible by decades of investment in scientific research. Many important research commentaries and reviews have been provided to describe the various contributions and scientific breakthroughs that led to the development of COVID-19 vaccines. In this work, we sought to complement those efforts by adding a systematic and quantitative study of the research foundations that led to these vaccines. Here, we analyzed citations from COVID-19 vaccine research articles to determine which scientific areas of study contributed the most to this research. Our findings revealed that coronavirus research was cited most often, and by a large margin. However, significant contributions were also seen from a diverse set of fields such as cancer, diabetes, and HIV/AIDS. In addition, we examined the publication history of the most prolific authors of COVID-19 vaccine research to determine their research expertise prior to the pandemic. Interestingly, although COVID-19 vaccine research relied most heavily on previous coronavirus work, we find that the most prolific authors on these publications most often had expertise in other areas including influenza, cancer, and HIV/AIDS. Finally, we used machine learning to identify and group together publications based on their major topic areas. This allowed us to elucidate the differences in citations between research areas. These findings highlight and quantify the relevance of prior research from a variety of scientific fields to the rapid development of a COVID-19 vaccine. This study also illustrates the importance of funding and sustaining a diverse research enterprise to facilitate a rapid response to future pandemics.

Assessment of Smartphone-Based Spiral Tracing in Multiple Sclerosis Reveals Intra-Individual Reproducibility as a Major Determinant of the Clinical Utility of the Digital Test

Technological advances, lack of medical professionals, high cost of face-to-face encounters, and disasters such as the COVID-19 pandemic fuel the telemedicine revolution. Numerous smartphone apps have been developed to measure neurological functions. However, their psychometric properties are seldom determined. It is unclear which designs underlie the eventual clinical utility of the smartphone tests. We have developed the smartphone Neurological Function Tests Suite (NeuFun-TS) and are systematically evaluating their psychometric properties against the gold standard of complete neurological examination digitalized into the NeurExTM app. This article examines the fifth and the most complex NeuFun-TS test, the “Spiral tracing.” We generated 40 features in the training cohort (22 healthy donors [HD] and 89 patients with multiple sclerosis [MS]) and compared their intraclass correlation coefficient, fold change between HD and MS, and correlations with relevant clinical and imaging outcomes. We assembled the best features into machine-learning models and examined their performance in the independent validation cohort (45 patients with MS). We show that by involving multiple neurological functions, complex tests such as spiral tracing are susceptible to intra-individual variations, decreasing their reproducibility and clinical utility. Simple tests, reproducibly measuring single function(s) that can be aggregated to increase sensitivity, are preferable in app design.