Community Engagement in Academic Health Centers: A Model for Capturing and Advancing Our Successes

Academic health centers (AHCs) are under increased pressure to demonstrate the effectiveness of their community-engaged activities, but there are no common metrics for evaluating community engagement in AHCs. Eight AHCs piloted the Institutional Community Engagement Self-Assessment (ICESA), a two-phase project to assess community-engagement efforts. The first phase uses a framework developed by the University of Rochester Medical Center, which utilizes structure, process, and outcome criteria to map CE activities. The second phase uses the Community-Campus Partnerships for Health (CCPH) Self-Assessment to identify institutional resources for community engagement, and potential gaps, to inform community engagement goal-setting. The authors conducted a structured, directed content analysis to determine the effectiveness of using the two-phase process at the participating AHCs. The findings suggest that the ICESA project assisted AHCs in three key areas, and may provide a strategy for assessing community engagement in AHCs

Assessment of Local Public Health Workers' Willingness to Respond to Pandemic Influenza through Application of the Extended Parallel Process Model

Local public health agencies play a central role in response to an influenza pandemic, and understanding the willingness of their employees to report to work is therefore a critically relevant concern for pandemic influenza planning efforts. Witte's Extended Parallel Process Model (EPPM) has been found useful for understanding adaptive behavior in the face of unknown risk, and thus offers a framework for examining scenario-specific willingness to respond among local public health workers. We thus aim to use the EPPM as a lens for examining the influences of perceived threat and efficacy on local public health workers' response willingness to pandemic influenza.We administered an online, EPPM-based survey about attitudes/beliefs toward emergency response (Johns Hopkins approximately Public Health Infrastructure Response Survey Tool), to local public health employees in three states between November 2006-December 2007. A total of 1835 responses were collected for an overall response rate of 83%. With some regional variation, overall 16% of the workers in 2006-7 were not willing to "respond to a pandemic flu emergency regardless of its severity". Local health department employees with a perception of high threat and high efficacy--i.e., those fitting a 'concerned and confident' profile in the EPPM analysis--had the highest declared rates of willingness to respond to an influenza pandemic if required by their agency, which was 31.7 times higher than those fitting a 'low threat/low efficacy' EPPM profile.In the context of pandemic influenza planning, the EPPM provides a useful framework to inform nuanced understanding of baseline levels of--and gaps in--local public health workers' response willingness. Within local health departments, 'concerned and confident' employees are most likely to be willing to respond. This finding may allow public health agencies to design, implement, and evaluate training programs focused on emergency response attitudes in health departments

The monoclonal antibody combination REGEN-COV protects against SARS-CoV-2 mutational escape in preclinical and human studies.

Monoclonal antibodies against SARS-CoV-2 are a clinically validated therapeutic option against COVID-19. Because rapidly emerging virus mutants are becoming the next major concern in the fight against the global pandemic, it is imperative that these therapeutic treatments provide coverage against circulating variants and do not contribute to development of treatment-induced emergent resistance. To this end, we investigated the sequence diversity of the spike protein and monitored emergence of virus variants in SARS-COV-2 isolates found in COVID-19 patients treated with the two-antibody combination REGEN-COV, as well as in preclinical in vitro studies using single, dual, or triple antibody combinations, and in hamster in vivo studies using REGEN-COV or single monoclonal antibody treatments. Our study demonstrates that the combination of non-competing antibodies in REGEN-COV provides protection against all current SARS-CoV-2 variants of concern/interest and also protects against emergence of new variants and their potential seeding into the population in a clinical setting

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe