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1. Pain and Laboratory Animals: Publication Practices for Better Data Reproducibility and Better Animal Welfare

   https://doi.org/10.1371/journal. pone.0155001  

   Larry Carbone, Jamie Austin ( May 2016 , PloS one)

   Scientists who perform major survival surgery on laboratory animals face a dual welfare and methodological challenge: how to choose surgical anesthetics and post-operative analgesics that will best control animal suffering, knowing that both pain and the drugs that manage pain can all affect research outcomes. Scientists who publish full descriptions of animal procedures allow critical and systematic reviews of data, demonstrate their adherence to animal welfare norms, and guide other scientists on how to conduct their own studies in the field. We investigated what information on animal pain management a reasonably diligent scientist might find in planning for a successful experiment. To explore how scientists in a range of fields describe their management of this ethical and methodological concern, we scored 400 scientific articles that included major animal survival surgeries as part of their experimental methods, for the completeness of information on anesthesia and analgesia. The 400 articles (250 accepted for publication pre-2011, and 150 in 2014–15, along with 174 articles they reference) included thoracotomies, craniotomies, gonadectomies, organ transplants, peripheral nerve injuries, spinal laminectomies and orthopedic procedures in dogs, primates, swine, mice, rats and other rodents. We scored articles for Publication Completeness (PC), which was any mention of use ofmore »anesthetics or analgesics; Analgesia Use (AU) which was any use of post-surgical analgesics, and Analgesia Completeness (a composite score comprising intra-operative analgesia, extended post-surgical analgesia, and use of multimodal analgesia). 338 of 400 articles were PC. 98 of these 338 were AU, with some mention of analgesia, while 240 of 338 mentioned anesthesia only but not postsurgical analgesia. Journals’ caliber, as measured by their 2013 Impact Factor, had no effect on PC or AU. We found no effect of whether a journal instructs authors to consult the ARRIVE publishing guidelines published in 2010 on PC or AC for the 150 mouse and rat articles in our 2014–15 dataset. None of the 302 articles that were silent about analgesic use included an explicit statement that analgesics were withheld, or a discussion of how pain management or untreated pain might affect results. We conclude that current scientific literature cannot be trusted to present full detail on use of animal anesthetics and analgesics. We report that publication guidelines focus more on other potential sources of bias in experimental results, under-appreciate the potential for pain and pain drugs to skew data, PLOS ONE | DOI:10.1371/journal.pone.0155001 May 12, 2016 1 / 24 a11111 OPEN ACCESS Citation: Carbone L, Austin J (2016) Pain and Laboratory Animals: Publication Practices for Better Data Reproducibility and Better Animal Welfare. PLoS ONE 11(5): e0155001. doi:10.1371/journal. pone.0155001 Editor: Chang-Qing Gao, Central South University, CHINA Received: December 29, 2015 Accepted: April 22, 2016 Published: May 12, 2016 Copyright: © 2016 Carbone, Austin. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. Authors may be contacted for further information. Funding: This study was funded by the United States National Science Foundation Division of Social and Economic Sciences. Award #1455838. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. and thus mostly treat pain management as solely an animal welfare concern, in the jurisdiction of animal care and use committees. At the same time, animal welfare regulations do not include guidance on publishing animal data, even though publication is an integral part of the cycle of research and can affect the welfare of animals in studies building on published work, leaving it to journals and authors to voluntarily decide what details of animal use to publish. We suggest that journals, scientists and animal welfare regulators should revise current guidelines and regulations, on treatment of pain and on transparent reporting of treatment of pain, to improve this dual welfare and data-quality deficiency.« less
2. Undergraduate Surveys Reveal That Instructors Are Key in Students Overcoming Classroom Struggles During the COVID-19 Pandemic

   https://doi.org/10.3389/feduc.2022.841060  

   Cannon, Randi L. ; Clance, Lauren R. ; Correia, Kelly M. ; Morrison, Blair H. ; Nelson, Madison T. ; Henning, Jeremiah A. ( May 2022 , Frontiers in Education)

   Many undergraduate students encounter struggle as they navigate academic, financial, and social contexts of higher education. The transition to emergency online instruction during the Spring of 2020 due to the COVID-19 pandemic exacerbated these struggles. To assess college students’ struggles during the transition to online learning in undergraduate biology courses, we surveyed a diverse collection of students ( n = 238) at an R2 research institution in the Southeastern United States. Students were asked if they encountered struggles and whether they were able to overcome them. Based on how students responded, they were asked to elaborate on (1) how they persevered without struggle, (2) how they were able to overcome their struggles, or (3) what barriers they encountered that did not allow them to overcome their struggles. Each open-ended response was thematically coded to address salient patterns in students’ ability to either persevere or overcome their struggle. We found that during the transition to remote learning, 67% of students experienced struggle. The most reported struggles included: shifts in class format, effective study habits, time management, and increased external commitments. Approximately, 83% of those struggling students were able to overcome their struggle, most often citing their instructor’s support and resources offeredmore »during the transition as reasons for their success. Students also cited changes in study habits, and increased confidence or belief that they could excel within the course as ways in which they overcame their struggles. Overall, we found no link between struggles in the classroom and any demographic variables we measured, which included race/ethnicity, gender expression, first-generation college students, transfer student status, and commuter student status. Our results highlight the critical role that instructors play in supporting student learning during these uncertain times by promoting student self-efficacy and positive-growth mindset, providing students with the resources they need to succeed, and creating a supportive and transparent learning environment.« less
3. Predictive Models of Mortality for Hospitalized Patients With COVID-19: Retrospective Cohort Study

   https://doi.org/10.2196/21788  

   Wang, Taiyao ; Paschalidis, Aris ; Liu, Quanying ; Liu, Yingxia ; Yuan, Ye ; Paschalidis, Ioannis Ch ( January 2020 , JMIR Medical Informatics)

   Background The novel coronavirus SARS-CoV-2 and its associated disease, COVID-19, have caused worldwide disruption, leading countries to take drastic measures to address the progression of the disease. As SARS-CoV-2 continues to spread, hospitals are struggling to allocate resources to patients who are most at risk. In this context, it has become important to develop models that can accurately predict the severity of infection of hospitalized patients to help guide triage, planning, and resource allocation. Objective The aim of this study was to develop accurate models to predict the mortality of hospitalized patients with COVID-19 using basic demographics and easily obtainable laboratory data. Methods We performed a retrospective study of 375 hospitalized patients with COVID-19 in Wuhan, China. The patients were randomly split into derivation and validation cohorts. Regularized logistic regression and support vector machine classifiers were trained on the derivation cohort, and accuracy metrics (F1 scores) were computed on the validation cohort. Two types of models were developed: the first type used laboratory findings from the entire length of the patient’s hospital stay, and the second type used laboratory findings that were obtained no later than 12 hours after admission. The models were further validated on a multicenter external cohortmore »of 542 patients. Results Of the 375 patients with COVID-19, 174 (46.4%) died of the infection. The study cohort was composed of 224/375 men (59.7%) and 151/375 women (40.3%), with a mean age of 58.83 years (SD 16.46). The models developed using data from throughout the patients’ length of stay demonstrated accuracies as high as 97%, whereas the models with admission laboratory variables possessed accuracies of up to 93%. The latter models predicted patient outcomes an average of 11.5 days in advance. Key variables such as lactate dehydrogenase, high-sensitivity C-reactive protein, and percentage of lymphocytes in the blood were indicated by the models. In line with previous studies, age was also found to be an important variable in predicting mortality. In particular, the mean age of patients who survived COVID-19 infection (50.23 years, SD 15.02) was significantly lower than the mean age of patients who died of the infection (68.75 years, SD 11.83; P<.001). Conclusions Machine learning models can be successfully employed to accurately predict outcomes of patients with COVID-19. Our models achieved high accuracies and could predict outcomes more than one week in advance; this promising result suggests that these models can be highly useful for resource allocation in hospitals.« less
4. Remote Worker Communication During COVID-19: The Role of Quantity, Quality, and Supervisor Expectation-Setting

   https://doi.org/https://doi.org/10.1037/apl0000970  

   Shockley, K. M. ; Allen, T. D. ; Dodd, H. ; Waiwood, M. M. ( January 2021 , Journal of applied psychology)

   Given the huge increase in remote work that has accompanied the Coronavirus disease (COVID-19) pandemic, understanding predictors of performance and wellbeing among remote workers has never been more timely. Effective communication is commonly cited as key to remote worker success, yet communication variables are rarely incorporated into remote work research. In the present study, we examined the relationship between communication frequency, communication quality, and supervisor- set communication expectations with daily job performance and burnout in an occupationally-diverse sample of employees. We used an experience sampling design and our hypotheses were tested with data collected over a 4-week period with a sample of 471 employees who shifted to full-time remote work due to COVID-19. Results indicated that daily communication quality was associated with daily performance and burnout. In addition, the extent to which supervisors established expectations about communication practices (e.g., expected response times to email) at the onset of the transition to remote work was positively associated with performance, but not burnout. Task interdependence was also tested as a moderator. Task interdependence moderated the relationship between communication quality and performance, such that the relationship was stronger when task interdependence was higher than when it was lower. Task interdependence also moderated themore »relationship between supervisor-set expectations and performance such that the relationship was stronger when task interdependence was lower than when it was higher. Expected curvilinear relationships between communication frequency and outcomes were not detected. Based on our findings, we provide recommendations for practice and future research.« less
5. Building an Open Resources Repository for COVID-19 Research

   https://doi.org/10.2478/dim-2020-0012  

   Hu, Tao ; Guan, Weihe Wendy ; Zhu, Xinyan ; Shao, Yuanzheng ; Liu, Lingbo ; Du, Jing ; Liu, Hongqiang ; Zhou, Huan ; Wang, Jialei ; She, Bing ; et al ( July 2020 , Data and Information Management)

   Abstract The COVID-19 outbreak is a global pandemic declared by the World Health Organization, with rapidly increasing cases in most countries. A wide range of research is urgently needed for understanding the COVID-19 pandemic, such as transmissibility, geographic spreading, risk factors for infections, and economic impacts. Reliable data archive and sharing are essential to jump-start innovative research to combat COVID-19. This research is a collaborative and innovative effort in building such an archive, including the collection of various data resources relevant to COVID-19 research, such as daily cases, social media, population mobility, health facilities, climate, socioeconomic data, research articles, policy and regulation, and global news. Due to the heterogeneity between data sources, our effort also includes processing and integrating different datasets based on GIS (Geographic Information System) base maps to make them relatable and comparable. To keep the data files permanent, we published all open data to the Harvard Dataverse ( https://dataverse.harvard.edu/dataverse/2019ncov ), an online data management and sharing platform with a permanent Digital Object Identifier number for each dataset. Finally, preliminary studies are conducted based on the shared COVID-19 datasets and revealed different spatial transmission patterns among mainland China, Italy, and the United States.