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Objectives: In the midst of the coronavirus disease 2019 (COVID-19) outbreak, chest X-ray (CXR) imaging is playing an important role in diagnosis and monitoring of patients with COVID-19. We propose a deep learning model for detection of COVID-19 from CXRs, as well as a tool for retrieving similar patients according to the model's results on their CXRs. For training and evaluating our model, we collected CXRs from inpatients hospitalized in four different hospitals., Methods: In this retrospective study, 1384 frontal CXRs, of COVID-19 confirmed patients imaged between March and August 2020, and 1024 matching CXRs of non-COVID patients imaged before the pandemic, were collected and used to build a deep learning classifier for detecting patients positive for COVID-19. The classifier consists of an ensemble of pre-trained deep neural networks (DNNS), specifically, ReNet34, ReNet50¸ ReNet152, and vgg16, and is enhanced by data augmentation and lung segmentation. We further implemented a nearest-neighbors algorithm that uses DNN-based image embeddings to retrieve the images most similar to a given image., Results: Our model achieved accuracy of 90.3%, (95% CI: 86.3-93.7%) specificity of 90% (95% CI: 84.3-94%), and sensitivity of 90.5% (95% CI: 85-94%) on a test dataset comprising 15% (350/2326) of the original images. The AUC of the ROC curve is 0.96 (95% CI: 0.93-0.97)., Conclusion: We provide deep learning models, trained and evaluated on CXRs that can assist medical efforts and reduce medical staff workload in handling COVID-19., Key Points: • A machine learning model was able to detect chest X-ray (CXR) images of patients tested positive for COVID-19 with accuracy and detection rate above 90%. • A tool was created for finding existing CXR images with imaging characteristics most similar to a given CXR, according to the model's image embeddings., (© 2021. European Society of Radiology.)

On July 24, 2020, a workshop sponsored by the National Brain Tumor Society was held on innovating brain tumor clinical trials based on lessons learned from the COVID-19 experience. Various stakeholders from the brain tumor community participated including the US Food and Drug Administration (FDA), academic and community clinicians, researchers, industry, clinical research organizations, patients and patient advocates, and representatives from the Society for Neuro-Oncology and the National Cancer Institute. This report summarizes the workshop and proposes ways to incorporate lessons learned from COVID-19 to brain tumor clinical trials including the increased use of telemedicine and decentralized trial models as opportunities for practical innovation with potential long-term impact on clinical trial design and implementation., (© The Author(s) 2021. Published by Oxford University Press on behalf of the Society for Neuro-Oncology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Depression in older adults may result from a variety of reasons such as loneliness feelings and malnutrition., Objective: To examine the direct and indirect effect of loneliness feelings on depressive symptoms, mediated by malnutrition, among older adults from different cultures during the Coronavirus disease 2019 (Covid-19) pandemic quarantine., Method: A convenience sample of 101 Arabs and 100 Jewish older adults aged 65 and over was interviewed. Using bootstrapping, we tested the strength and significance of the conditional indirect effect of malnutrition (mediator) on the relationship between loneliness feelings and depressive symptoms., Results: The relationship between loneliness feelings and depressive symptoms was mediated by malnutrition and Arab older adults reported a higher level than Jewish older adults of loneliness, depression, and malnutrition during the Covid-19 pandemic quarantine., Conclusions and Implications: To reduce loneliness feelings, depressive symptoms, and malnutrition in times of crisis like the Covid-19 pandemic, it is essential to develop new communication methods for and with older adults in general, with particular attention paid to ethnic differences, that will be effective in reducing loneliness and in promoting nutrition intervention. Possible solutions include new social network technologies for reducing loneliness, with continued reliance on phone communication for combined intervention that includes psychological support accompanied by instructions for a healthy lifestyle and malnutrition prevention., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Objectives: We examined the effect of loneliness and the role of two mediating factors, depressive symptoms and malnutrition on subjective age among older adults during the 2020 COVID-19 pandemic, and explored how the pandemic is affecting subjective age. Design: A convenience sample of 201 older adults aged 65 and over was interviewed. Using bootstrapping, we tested the strength and significance of the indirect effect of depressive symptoms and malnutrition (mediators) on the relationship between feelings of loneliness and subjective age. Results: The relationship between feelings of loneliness and subjective age during the COVID-19 pandemic was mediated by malnutrition, but not by depressive symptoms. In addition, the participants felt older during the COVID-19 pandemic compared with the preceding period. Conclusions : An association was found among feelings of loneliness, malnutrition, and subjective age. To overcome these feelings in times of crisis like the pandemic, it is essential to develop new communication methods (technologies for managing and addressing the needs of the older population; technologies to encourage social engagement, and technologies for managing and providing remote medical services) for and with older adults that are effective in reducing loneliness, and to promote good nutrition. Possible practical solutions include new social network technologies for reducing loneliness combined with continued reliance on phone communication as an intervention of psychological support to promote a healthy lifestyle and prevent malnutrition.

Abstract Background The Randomised Evaluation of COVID-19 Therapy (RECOVERY) trial is aimed at addressing the urgent need to find effective treatments for patients hospitalised with suspected or confirmed COVID-19. The trial has had many successes, including discovering that dexamethasone is effective at reducing COVID-19 mortality, the first treatment to reach this milestone in a randomised controlled trial. Despite this, it continues to use standard or ‘fixed’ randomisation to allocate patients to treatments. We assessed the impact of implementing response adaptive randomisation within RECOVERY using an array of performance measures, to learn if it could be beneficial going forward. This design feature has recently been implemented within the REMAP-CAP platform trial. Methods Trial data was simulated to closely match the data for patients allocated to standard care, dexamethasone, hydroxychloroquine, or lopinavir-ritonavir in the RECOVERY trial from March-June 2020, representing four out of five arms tested throughout this period. Trials were simulated in both a two-arm trial setting using standard care and dexamethasone, and a four-arm trial setting utilising all above treatments. Two forms of fixed randomisation and two forms of response-adaptive randomisation were tested. In the two-arm setting, response-adaptive randomisation was implemented across both trial arms, whereas in the four-arm setting it was implemented in the three non-standard care arms only. In the two-arm trial, randomisation strategies were performed at the whole trial level as well as within three pre-specified patient subgroups defined by patients’ respiratory support level. Results All response-adaptive randomisation strategies led to more patients being given dexamethasone and a lower mortality rate in the trial. Subgroup specific response-adaptive randomisation reduced mortality rates even further. In the two-arm trial, response-adaptive randomisation reduced statistical power compared to FR, with subgroup level adaptive randomisation exhibiting the largest power reduction. In the four-arm trial, response-adaptive randomisation increased statistical power in the dexamethasone arm but reduced statistical power in the lopinavir arm. Response-adaptive randomisation did not induce any meaningful bias in treatment effect estimates nor did it cause any inflation in the type 1 error rate. Conclusions Using response-adaptive randomisation within RECOVERY could have increased the number of patients receiving the optimal COVID-19 treatment during the trial, while reducing the number of patients needed to attain the same study power as the original study. This would likely have reduced patient deaths during the trial and lead to dexamethasone being declared effective sooner. Deciding how to balance the needs of patients within a trial and future patients who have yet to fall ill is an important ethical question for the trials community to address. Response-adaptive randomisation deserves to be considered as a design feature in future trials of COVID-19 and other diseases.