The impact of social and emotional learning lessons on career and technical students

Social emotional learning (SEL) has had a rich history in educating the whole child, yet schools still do not have (SEL) as part of their curriculum. Today's educators have a renewed perspective on what common sense always suggested: when schools attend systematically to students' social and emotional skills, the academic achievement of children increases, the incidence of problem behaviors decreases, and the quality of the relationships surrounding each child improves. (Elias et al., 1997, p.1)Students have lost a considerable number of social skills during the pandemic, and many students are having difficulty expressing themselves in a healthy, positive manner. Many soft skills that schools routinely taught were abandoned during the COVID-19 era when many educational platforms moved to remote learning. The students attending Gloucester County Institute of Technology are no exception. These students are learning how to work towards attaining certificates in an array of careers, but soft skills and some social skills are still lacking. The purpose of this study was to expose the students to social emotional learning and determine if this instruction had positive benefits for students. Students in the Automotive and Construction programs received instruction and lessons on understanding healthy ways to express self-awareness, self-management, responsible decision-making, relationship skills, and social awareness. The students were also taught how to apply these skills to aspects of their personal and professional life. Using a pre- and post-survey, students in the study showed growth in understanding skills associated with social emotional learning and how these skills will help as the students move forward through life. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Residual Lung Abnormalities Following COVID-19 Hospitalization: Interim Analysis of the UKILD Post-COVID Study.

RATIONALE: Shared symptoms and genetic architecture between COVID-19 and lung fibrosis suggests SARS-CoV-2 infection may lead to progressive lung damage. OBJECTIVES: The UKILD Post-COVID study interim analysis was planned to estimate the prevalence of residual lung abnormalities in people hospitalized with COVID-19 based on risk strata. METHODS: The Post-HOSPitalisation COVID Study (PHOSP-COVID) was used for capture of routine and research follow-up within 240 days from discharge. Thoracic CTs linked by PHOSP-COVID identifiers were scored for percentage of residual lung abnormalities (ground glass opacities and reticulations). Risk factors in linked CT were estimated with Bayesian binomial regression and risk strata were generated. Numbers within strata were used to estimate post-hospitalization prevalence using Bayesian binomial distributions. Sensitivity analysis was restricted to participants with protocol driven research follow-up. MEASUREMENTS AND MAIN RESULTS: The interim cohort comprised 3700 people. Of 209 subjects with linked CTs (median 119 days, interquartile range 83-155), 166 people (79.4%) had >10% involvement of residual lung abnormalities. Risk factors included abnormal chest X-ray (RR 1·21 95%CrI 1·05; 1·40), percent predicted DLco<80% (RR 1·25 95%CrI 1·00; 1·56) and severe admission requiring ventilation support (RR 1·27 95%CrI 1·07; 1·55). In the remaining 3491 people, moderate to very-high risk of residual lung abnormalities was classified in 7·8%, post-hospitalization prevalence was estimated at 8.5% (95%CrI 7.6%; 9.5%) rising to 11.7% (95%CrI 10.3%; 13.1%) in sensitivity analysis. CONCLUSIONS: Residual lung abnormalities were estimated in up to 11% of people discharged following COVID-19 related hospitalization. Health services should monitor at-risk individuals to elucidate long-term functional implications. This article is open access and distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

Chest radiograph classification and severity of suspected COVID-19 by different radiologist groups and attending clinicians: multi-reader, multi-case study.

OBJECTIVES: To quantify reader agreement for the British Society of Thoracic Imaging (BSTI) diagnostic and severity classification for COVID-19 on chest radiographs (CXR), in particular agreement for an indeterminate CXR that could instigate CT imaging, from single and paired images. METHODS: Twenty readers (four groups of five individuals)-consultant chest (CCR), general consultant (GCR), and specialist registrar (RSR) radiologists, and infectious diseases clinicians (IDR)-assigned BSTI categories and severity in addition to modified Covid-Radiographic Assessment of Lung Edema Score (Covid-RALES), to 305 CXRs (129 paired; 2 time points) from 176 guideline-defined COVID-19 patients. Percentage agreement with a consensus of two chest radiologists was calculated for (1) categorisation to those needing CT (indeterminate) versus those that did not (classic/probable, non-COVID-19); (2) severity; and (3) severity change on paired CXRs using the two scoring systems. RESULTS: Agreement with consensus for the indeterminate category was low across all groups (28-37%). Agreement for other BSTI categories was highest for classic/probable for the other three reader groups (66-76%) compared to GCR (49%). Agreement for normal was similar across all radiologists (54-61%) but lower for IDR (31%). Agreement for a severe CXR was lower for GCR (65%), compared to the other three reader groups (84-95%). For all groups, agreement for changes across paired CXRs was modest. CONCLUSION: Agreement for the indeterminate BSTI COVID-19 CXR category is low, and generally moderate for the other BSTI categories and for severity change, suggesting that the test, rather than readers, is limited in utility for both deciding disposition and serial monitoring. KEY POINTS: • Across different reader groups, agreement for COVID-19 diagnostic categorisation on CXR varies widely. • Agreement varies to a degree that may render CXR alone ineffective for triage, especially for indeterminate cases. • Agreement for serial CXR change is moderate, limiting utility in guiding management.

The impact of social and emotional learning lessons on career and technical students

Social emotional learning (SEL) has had a rich history in educating the whole child, yet schools still do not have (SEL) as part of their curriculum. Today's educators have a renewed perspective on what common sense always suggested: when schools attend systematically to students' social and emotional skills, the academic achievement of children increases, the incidence of problem behaviors decreases, and the quality of the relationships surrounding each child improves. (Elias et al., 1997, p.1)Students have lost a considerable number of social skills during the pandemic, and many students are having difficulty expressing themselves in a healthy, positive manner. Many soft skills that schools routinely taught were abandoned during the COVID-19 era when many educational platforms moved to remote learning. The students attending Gloucester County Institute of Technology are no exception. These students are learning how to work towards attaining certificates in an array of careers, but soft skills and some social skills are still lacking. The purpose of this study was to expose the students to social emotional learning and determine if this instruction had positive benefits for students. Students in the Automotive and Construction programs received instruction and lessons on understanding healthy ways to express self-awareness, self-management, responsible decision-making, relationship skills, and social awareness. The students were also taught how to apply these skills to aspects of their personal and professional life. Using a pre- and post-survey, students in the study showed growth in understanding skills associated with social emotional learning and how these skills will help as the students move forward through life. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Joint patient and clinician priority setting to identify 10 key research questions regarding the long-term sequelae of COVID-19.

Given the large numbers of people infected and high rates of ongoing morbidity, research is clearly required to address the needs of adult survivors of COVID-19 living with ongoing symptoms (long COVID). To help direct resource and research efforts, we completed a research prioritisation process incorporating views from adults with ongoing symptoms of COVID-19, carers, clinicians and clinical researchers. The final top 10 research questions were agreed at an independently mediated workshop and included: identifying underlying mechanisms of long COVID, establishing diagnostic tools, understanding trajectory of recovery and evaluating the role of interventions both during the acute and persistent phases of the illness.

Vascular Thrombosis in Severe COVID-19 Requiring Extracorporeal Membrane Oxygenation: A Multicenter Study.

OBJECTIVES: Coronavirus disease 2019 has been reported to be a prothrombotic condition; however, multicenter data comparing this with other viral pneumonias in those requiring extracorporeal membrane oxygenation are lacking. We conducted a multicenter study using whole-body CT to examine the prevalence, severity, and nature of vascular complications in coronavirus disease 2019 in comparison with patients with other viral pneumonias. DESIGN: We analyzed whole-body CT scans for the presence of vascular thrombosis (defined as pulmonary artery thrombus, venous thrombus, systemic arterial thrombus, or end-organ infarct). The severity, distribution, and morphology of pulmonary artery thrombus were characterized. Competing risk cumulative incidence analysis was used to compare survival with discharge. SETTING: Three centers of the English national extracorporeal membrane oxygenation service. PATIENTS: Consecutive patients admitted with either coronavirus disease 2019 or noncoronavirus disease 2019 viral pneumonia admitted from January 2019. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: One-hundred thirty-six patients (45.2 ± 10.6 yr old, 39/146 [27%] female) requiring extracorporeal membrane oxygenation support underwent whole-body CT scans at admission. Of these, 86 had coronavirus disease 2019 pneumonia, and 50 had noncoronavirus disease 2019 viral pneumonia. Vascular thrombosis was seen more often in patients with coronavirus disease 2019 (odds ratio, 12.9 [95% CI 4.5-36.8]). In those with coronavirus disease 2019, 57 (73%) demonstrated pulmonary artery thrombus or pulmonary perfusion defects. Eighty-two percent of thrombus exhibited emboli-like morphology. The location of pulmonary artery thrombus and parenchymal perfusion defects was only concordant in 30% of cases. The risk of mortality was higher in those with coronavirus disease 2019 compared with noncoronavirus disease 2019 pneumonia (χ2 = 3.94; p = 0.047). Mortality was no different in coronavirus disease 2019 patients with or without vascular thrombosis (χ2 = 0.44; p = 0.51). CONCLUSIONS: In patients who received extracorporeal membrane oxygenation, coronavirus disease 2019 is associated with a higher prevalence of vascular thrombosis compared with noncoronavirus disease viral pneumonias. The pattern of pulmonary vascular changes suggests concurrent embolic disease and small vessel disease. Despite this, vascular thrombosis was not linked to poorer short-term prognosis in those with coronavirus disease 2019.

A Novel and Automated Approach to Classify Radiation Induced Lung Tissue Damage on CT Scans.

Radiation-induced lung damage (RILD) is a common side effect of radiotherapy (RT). The ability to automatically segment, classify, and quantify different types of lung parenchymal change is essential to uncover underlying patterns of RILD and their evolution over time. A RILD dedicated tissue classification system was developed to describe lung parenchymal tissue changes on a voxel-wise level. The classification system was automated for segmentation of five lung tissue classes on computed tomography (CT) scans that described incrementally increasing tissue density, ranging from normal lung (Class 1) to consolidation (Class 5). For ground truth data generation, we employed a two-stage data annotation approach, akin to active learning. Manual segmentation was used to train a stage one auto-segmentation method. These results were manually refined and used to train the stage two auto-segmentation algorithm. The stage two auto-segmentation algorithm was an ensemble of six 2D Unets using different loss functions and numbers of input channels. The development dataset used in this study consisted of 40 cases, each with a pre-radiotherapy, 3-, 6-, 12-, and 24-month follow-up CT scans (n = 200 CT scans). The method was assessed on a hold-out test dataset of 6 cases (n = 30 CT scans). The global Dice score coefficients (DSC) achieved for each tissue class were: Class (1) 99% and 98%, Class (2) 71% and 44%, Class (3) 56% and 26%, Class (4) 79% and 47%, and Class (5) 96% and 92%, for development and test subsets, respectively. The lowest values for the test subsets were caused by imaging artefacts or reflected subgroups that occurred infrequently and with smaller overall parenchymal volumes. We performed qualitative evaluation on the test dataset presenting manual and auto-segmentation to a blinded independent radiologist to rate them as 'acceptable', 'minor disagreement' or 'major disagreement'. The auto-segmentation ratings were similar to the manual segmentation, both having approximately 90% of cases rated as acceptable. The proposed framework for auto-segmentation of different lung tissue classes produces acceptable results in the majority of cases and has the potential to facilitate future large studies of RILD.

Healthcare understanding of COVID-19 antibody.

OBJECTIVE: : The SARS-CoV-2 pandemic has shed light on the difficulties in spreading uniform information. We rely on national and international organizations to provide scientifically accurate information to the public at large. With so many different sources of information, often not scientific, there appears to be an incomplete understanding of many aspects of SARS-CoV-2 infection. We sought to gain information about healthcare worker understanding of the implications of a positive serum COVID-19 antibody test result. We identified a broad range of responses among all categories of healthcare workers in our facility. Most notably we found that there was not complete understanding that there can be asymptomatic spread of COVID-19 infection. METHODS: : We provided health literacy and opinion questions to the healthcare workers of our facility. RESULTS: : Upon analysis of the data, we identified many differences in level of understanding among our healthcare workers. CONCLUSION: : We identified a lack of consensus on important details leading to potentially growing uncertainty with respect to SARS-COV-2 antibody. A diminished health literacy with respect to antibody testing could potentially suggest future issues with understanding the importance of vaccination benefits.

Polygenic predisposition to venous thromboembolism is associated with increased COVID-19 positive testing rates (preprint)

Genetic predisposition to venous thrombosis may impact COVID-19 infection and its sequelae. Participants in the ongoing prospective cohort study, Million Veteran Program (MVP), who were tested for COVID-19, with European ancestry, were evaluated for associations with polygenic venous thromboembolic risk, Factor V Leiden mutation (FVL) (rs6025) and prothrombin gene 3 -UTR mutation (F2 G20210A)(rs1799963), and their interactions. Logistic regression models assessed genetic associations with VTE diagnosis, COVID-19 (positive) testing rates and outcome severity (modified WHO criteria), and post-test conditions, adjusting for outpatient anticoagulation medication usage, age, sex, and genetic principal components. 108,437 out of 464,961 European American MVP participants were tested for COVID-19 with 9786 (9%) positive. PRS(VTE), FVL, F2 G20210A were not significantly associated with the propensity of being tested for COVID-19. PRS(VTE) was significantly associated with a positive COVID-19 test in F5 wild type (WT) individuals (OR 1.05; 95% CI [1.02-1.07]), but not in FVL carriers (0.97, [0.91-1.94]). There was no association with severe outcome for FVL, F2 G20210A or PRS(VTE). Outpatient anticoagulation usage in the two years prior to testing was associated with worse clinical outcomes. PRS(VTE) was associated with prevalent VTE diagnosis among both FVL carriers or F5 wild type individuals as well as incident VTE in the two years prior to testing. Increased genetic propensity for VTE in the MVP was associated with increased COVID-19 positive testing rates, suggesting a role of coagulation in the initial steps of COVID-19 infection. Key PointsO_LIIncreased genetic predisposition to venous thrombosis is associated with increased COVID-19 positive testing rates. C_LIO_LIPRS for VTE further risk stratifies factor V Leiden carriers regarding their VTE risk. C_LI

Towards nationally curated data archives for clinical radiology image analysis at scale: Learnings from national data collection in response to a pandemic.

The prevalence of the coronavirus SARS-CoV-2 disease has resulted in the unprecedented collection of health data to support research. Historically, coordinating the collation of such datasets on a national scale has been challenging to execute for several reasons, including issues with data privacy, the lack of data reporting standards, interoperable technologies, and distribution methods. The coronavirus SARS-CoV-2 disease pandemic has highlighted the importance of collaboration between government bodies, healthcare institutions, academic researchers and commercial companies in overcoming these issues during times of urgency. The National COVID-19 Chest Imaging Database, led by NHSX, British Society of Thoracic Imaging, Royal Surrey NHS Foundation Trust and Faculty, is an example of such a national initiative. Here, we summarise the experiences and challenges of setting up the National COVID-19 Chest Imaging Database, and the implications for future ambitions of national data curation in medical imaging to advance the safe adoption of artificial intelligence in healthcare.

An overview of the National COVID-19 Chest Imaging Database: data quality and cohort analysis.

BACKGROUND: The National COVID-19 Chest Imaging Database (NCCID) is a centralized database containing mainly chest X-rays and computed tomography scans from patients across the UK. The objective of the initiative is to support a better understanding of the coronavirus SARS-CoV-2 disease (COVID-19) and the development of machine learning technologies that will improve care for patients hospitalized with a severe COVID-19 infection. This article introduces the training dataset, including a snapshot analysis covering the completeness of clinical data, and availability of image data for the various use-cases (diagnosis, prognosis, longitudinal risk). An additional cohort analysis measures how well the NCCID represents the wider COVID-19-affected UK population in terms of geographic, demographic, and temporal coverage. FINDINGS: The NCCID offers high-quality DICOM images acquired across a variety of imaging machinery; multiple time points including historical images are available for a subset of patients. This volume and variety make the database well suited to development of diagnostic/prognostic models for COVID-associated respiratory conditions. Historical images and clinical data may aid long-term risk stratification, particularly as availability of comorbidity data increases through linkage to other resources. The cohort analysis revealed good alignment to general UK COVID-19 statistics for some categories, e.g., sex, whilst identifying areas for improvements to data collection methods, particularly geographic coverage. CONCLUSION: The NCCID is a growing resource that provides researchers with a large, high-quality database that can be leveraged both to support the response to the COVID-19 pandemic and as a test bed for building clinically viable medical imaging models.

Physical, cognitive, and mental health impacts of COVID-19 after hospitalisation (PHOSP-COVID): a UK multicentre, prospective cohort study.

BACKGROUND: The impact of COVID-19 on physical and mental health and employment after hospitalisation with acute disease is not well understood. The aim of this study was to determine the effects of COVID-19-related hospitalisation on health and employment, to identify factors associated with recovery, and to describe recovery phenotypes. METHODS: The Post-hospitalisation COVID-19 study (PHOSP-COVID) is a multicentre, long-term follow-up study of adults (aged ≥18 years) discharged from hospital in the UK with a clinical diagnosis of COVID-19, involving an assessment between 2 and 7 months after discharge, including detailed recording of symptoms, and physiological and biochemical testing. Multivariable logistic regression was done for the primary outcome of patient-perceived recovery, with age, sex, ethnicity, body-mass index, comorbidities, and severity of acute illness as covariates. A post-hoc cluster analysis of outcomes for breathlessness, fatigue, mental health, cognitive impairment, and physical performance was done using the clustering large applications k-medoids approach. The study is registered on the ISRCTN Registry (ISRCTN10980107). FINDINGS: We report findings for 1077 patients discharged from hospital between March 5 and Nov 30, 2020, who underwent assessment at a median of 5·9 months (IQR 4·9-6·5) after discharge. Participants had a mean age of 58 years (SD 13); 384 (36%) were female, 710 (69%) were of white ethnicity, 288 (27%) had received mechanical ventilation, and 540 (50%) had at least two comorbidities. At follow-up, only 239 (29%) of 830 participants felt fully recovered, 158 (20%) of 806 had a new disability (assessed by the Washington Group Short Set on Functioning), and 124 (19%) of 641 experienced a health-related change in occupation. Factors associated with not recovering were female sex, middle age (40-59 years), two or more comorbidities, and more severe acute illness. The magnitude of the persistent health burden was substantial but only weakly associated with the severity of acute illness. Four clusters were identified with different severities of mental and physical health impairment (n=767): very severe (131 patients, 17%), severe (159, 21%), moderate along with cognitive impairment (127, 17%), and mild (350, 46%). Of the outcomes used in the cluster analysis, all were closely related except for cognitive impairment. Three (3%) of 113 patients in the very severe cluster, nine (7%) of 129 in the severe cluster, 36 (36%) of 99 in the moderate cluster, and 114 (43%) of 267 in the mild cluster reported feeling fully recovered. Persistently elevated serum C-reactive protein was positively associated with cluster severity. INTERPRETATION: We identified factors related to not recovering after hospital admission with COVID-19 at 6 months after discharge (eg, female sex, middle age, two or more comorbidities, and more acute severe illness), and four different recovery phenotypes. The severity of physical and mental health impairments were closely related, whereas cognitive health impairments were independent. In clinical care, a proactive approach is needed across the acute severity spectrum, with interdisciplinary working, wide access to COVID-19 holistic clinical services, and the potential to stratify care. FUNDING: UK Research and Innovation and National Institute for Health Research.

Remote Assessment of Lung Disease and Impact on Physical and Mental Health (RALPMH): Protocol for a Prospective Observational Study.

BACKGROUND: Chronic lung disorders like chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) are characterized by exacerbations. They are unpleasant for patients and sometimes severe enough to cause hospital admission and death. Moreover, due to the COVID-19 pandemic, vulnerable populations with these disorders are at high risk, and their routine care cannot be done properly. Remote monitoring offers a low cost and safe solution for gaining visibility into the health of people in their daily lives, making it useful for vulnerable populations. OBJECTIVE: The primary objective is to assess the feasibility and acceptability of remote monitoring using wearables and mobile phones in patients with pulmonary diseases. The secondary objective is to provide power calculations for future studies centered around understanding the number of exacerbations according to sample size and duration. METHODS: Twenty participants will be recruited in each of three cohorts (COPD, IPF, and posthospitalization COVID). Data collection will be done remotely using the RADAR-Base (Remote Assessment of Disease And Relapse) mobile health (mHealth) platform for different devices, including Garmin wearable devices and smart spirometers, mobile app questionnaires, surveys, and finger pulse oximeters. Passive data include wearable-derived continuous heart rate, oxygen saturation, respiration rate, activity, and sleep. Active data include disease-specific patient-reported outcome measures, mental health questionnaires, and symptom tracking to track disease trajectory. Analyses will assess the feasibility of lung disorder remote monitoring (including data quality, data completeness, system usability, and system acceptability). We will attempt to explore disease trajectory, patient stratification, and identification of acute clinical events such as exacerbations. A key aspect is understanding the potential of real-time data collection. We will simulate an intervention to acquire responses at the time of the event to assess model performance for exacerbation identification. RESULTS: The Remote Assessment of Lung Disease and Impact on Physical and Mental Health (RALPMH) study provides a unique opportunity to assess the use of remote monitoring in the evaluation of lung disorders. The study started in the middle of June 2021. The data collection apparatus, questionnaires, and wearable integrations were setup and tested by the clinical teams prior to the start of recruitment. While recruitment is ongoing, real-time exacerbation identification models are currently being constructed. The models will be pretrained daily on data of previous days, but the inference will be run in real time. CONCLUSIONS: The RALPMH study will provide a reference infrastructure for remote monitoring of lung diseases. It specifically involves information regarding the feasibility and acceptability of remote monitoring and the potential of real-time data collection and analysis in the context of chronic lung disorders. It will help plan and inform decisions in future studies in the area of respiratory health. TRIAL REGISTRATION: ISRCTN Registry ISRCTN16275601; https://www.isrctn.com/ISRCTN16275601. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/28873.

A MUC5B gene polymorphism, rs35705950-T, confers protective effects in COVID-19 infection (preprint)

RationaleA common MUC5B gene polymorphism, rs35705950-T, is associated with idiopathic pulmonary fibrosis, but its role in the SARS-CoV-2 infection and disease severity is unclear. ObjectivesTo assess whether rs35705950-T confers differential risk for clinical outcomes associated with COVID-19 infection among participants in the Million Veteran Program (MVP) and COVID-19 Host Genetics Initiative (HGI). MethodsMVP participants were examined for an association between the incidence or severity of COVID-19 and the presence of a MUC5B rs35705950-T allele. Comorbidities and clinical events were extracted from the electronic health records (EHR). The analysis was performed within each ancestry group in the MVP, adjusting for sex, age, age2, and first twenty principal components followed by a trans-ethnic meta-analysis. We then pursued replication and performed a meta-analysis with the trans-ethnic summary statistics from the HGI. A phenome-wide association study (PheWAS) of the rs35705950-T was conducted to explore associated pathophysiologic conditions. Measurements and Main ResultsA COVID-19 severity scale was modified from the World Health Organization criteria, and phenotypes derived from the International Classification of Disease-9/10 were extracted from EHR. Presence of rs35705950-T was associated with fewer hospitalizations (Ncases=25353, Ncontrols=631,024; OR=0.86 [0.80-0.93], p=7.4 x 10-5) in trans-ethnic meta-analysis within MVP and joint meta-analyses with the HGI (N=1641311; OR=0.89 [0.85-0.93], p =1.9 x 10-6). Moreover, individuals of European Ancestry with at least one copy of rs35705950-T had fewer post-COVID-19 pneumonia events (OR=0.85 [0.76-0.96], p =0.008). PheWAS exclusively revealed pulmonary involvement. ConclusionsThe MUC5B variant rs35705950-T is protective in COVID-19 infection.

Actionable druggable genome-wide Mendelian randomization identifies repurposingopportunities for COVID-19 (preprint)

Drug repurposing provides a rapid approach to meet the urgent need for therapeutics to address COVID-19. To identify therapeutic targets relevant to COVID-19, we conducted Mendelian randomization (MR) analyses, deriving genetic instruments based on transcriptomic and proteomic data for 1,263 actionable proteins that are targeted by approved drugs or in clinical phase of drug development. Using summary statistics from the Host Genetics Initiative and the Million Veteran Program, we studied 7,554 patients hospitalized with COVID-19 and >1 million controls. We found significant Mendelian randomization results for three proteins (ACE2: P=1.6x10^-6, IFNAR2: P=9.8x10^-11, and IL-10RB: P=1.9x10^-14) using cis-eQTL genetic instruments that also had strong evidence for colocalization with COVID-19 hospitalization. To disentangle the shared eQTL signal for IL10RB and IFNAR2, we conducted phenome-wide association scans and pathway enrichment analysis, which suggested that IFNAR2 is more likely to play a role in COVID-19 hospitalization. Our findings prioritize trials of drugs targeting IFNAR2 and ACE2 for early management of COVID-19.

'Long-COVID': a cross-sectional study of persisting symptoms, biomarker and imaging abnormalities following hospitalisation for COVID-19.

Large numbers of people are being discharged from hospital following COVID-19 without assessment of recovery. In 384 patients (mean age 59.9 years; 62% male) followed a median 54 days post discharge, 53% reported persistent breathlessness, 34% cough and 69% fatigue. 14.6% had depression. In those discharged with elevated biomarkers, 30.1% and 9.5% had persistently elevated d-dimer and C reactive protein, respectively. 38% of chest radiographs remained abnormal with 9% deteriorating. Systematic follow-up after hospitalisation with COVID-19 identifies the trajectory of physical and psychological symptom burden, recovery of blood biomarkers and imaging which could be used to inform the need for rehabilitation and/or further investigation.

The challenges of deploying artificial intelligence models in a rapidly evolving pandemic

The attention and resources of AI researchers have been captured by COVID-19. However, successful adoption of AI models in the fight against the pandemic is facing various challenges, including moving clinical needs as the epidemic progresses and the necessity to translate models to local healthcare situations.