BillionCOV: An enriched billion-scale collection of COVID-19 tweets for efficient hydration.

The COVID-19 pandemic has introduced new norms, such as social distancing, face masks, quarantine, lockdowns, travel restrictions, work/study from home, and business closures, to name a few. The pandemic's seriousness has made people vocal on social media, especially on microblogs such as Twitter. Since the early days of the outbreak, researchers have been collecting and sharing large-scale datasets of COVID-19 tweets. However, the existing datasets carry issues related to proportion and redundancy. We report that more than 500 million tweet identifiers point to deleted or protected tweets. To address these issues, this paper introduces an enriched global billion-scale English-language COVID-19 tweets dataset, BillionCOV, which contains 1.4 billion tweets originating from 240 countries and territories between October 2019 and April 2022. Importantly, BillionCOV facilitates researchers to filter tweet identifiers for efficient hydration. We anticipate that the dataset of this scale with global scope and extended temporal coverage will aid in obtaining a thorough understanding of the pandemic's conversational dynamics.

A Twitter narrative of the COVID-19 pandemic in Australia (preprint)

Social media platforms contain abundant data that can provide comprehensive knowledge of historical and real-time events. During crisis events, the use of social media peaks, as people discuss what they have seen, heard, or felt. Previous studies confirm the usefulness of such socially generated discussions for the public, first responders, and decision-makers to gain a better understanding of events as they unfold at the ground level. This study performs an extensive analysis of COVID-19-related Twitter discussions generated in Australia between January 2020, and October 2022. We explore the Australian Twitterverse by employing state-of-the-art approaches from both supervised and unsupervised domains to perform network analysis, topic modeling, sentiment analysis, and causality analysis. As the presented results provide a comprehensive understanding of the Australian Twitterverse during the COVID-19 pandemic, this study aims to explore the discussion dynamics to aid the development of future automated information systems for epidemic/pandemic management.

BillionCOV: An Enriched Billion-scale Collection of COVID-19 tweets for Efficient Hydration (preprint)

The COVID-19 pandemic introduced new norms such as social distancing, face masks, quarantine, lockdowns, travel restrictions, work/study from home, and business closures, to name a few. The pandemic's seriousness made people vocal on social media, especially on microblogs such as Twitter. Researchers have been collecting and sharing large-scale datasets of COVID-19 tweets since the early days of the outbreak. Sharing raw Twitter data with third parties is restricted; users need to hydrate tweet identifiers in a public dataset to re-create the dataset locally. Large-scale datasets that include original tweets, retweets, quotes, and replies have tweets in billions which takes months to hydrate. The existing datasets carry issues related to proportion and redundancy. We report that more than 500 million tweet identifiers point to deleted or protected tweets. In order to address these issues, this paper introduces an enriched global billion-scale English-language COVID-19 tweets dataset, BillionCOV, that contains 1.4 billion tweets originating from 240 countries and territories between October 2019 and April 2022. Importantly, BillionCOV facilitates researchers to filter tweet identifiers for efficient hydration. This paper discusses associated methods to fetch raw Twitter data for a set of tweet identifiers, presents multiple tweets' distributions to provide an overview of BillionCOV, and finally, reviews the dataset's potential use cases.

The role of job insecurity and work-family conflict on mental health evolution during COVID-19 lockdown

The aim of this intensive longitudinal study was (1) to explore the temporal evolution of two mental health indicators (anxiety and depressive symptoms, and insomnia) throughout COVID-19 lockdown in Spain, and (2) to examine its association with two work-related stressors (job insecurity and work-family conflict). A sample of 1519 participants responded to several questionnaires during the lockdown (between 16 March and 29 April 2020). Results of latent growth modelling showed a curvilinear increase of our two mental health indicators over time (a logarithmic growth for anxiety and depression, accentuated during the first part of the lockdown, and a quadratic growth for insomnia, accentuated during the second part). Regarding its association with work-related stressors, we found that higher levels of job insecurity and work-family conflict were related to higher levels of anxiety, depression, and insomnia. Additionally, we found a significant interaction between time and the two forms of work-family conflict (work-to-home and home-to-work), showing that people with more work-family conflict experienced stronger growth in all mental-health indicators. Overall, this study contributes to the description of the temporal dynamics of mental health during the COVID-19 outbreak in Spain, as well as its association with two key work-related stressors.

Twitter conversations predict the daily confirmed COVID-19 cases.

As of writing this paper, COVID-19 (Coronavirus disease 2019) has spread to more than 220 countries and territories. Following the outbreak, the pandemic's seriousness has made people more active on social media, especially on the microblogging platforms such as Twitter and Weibo. The pandemic-specific discourse has remained on-trend on these platforms for months now. Previous studies have confirmed the contributions of such socially generated conversations towards situational awareness of crisis events. The early forecasts of cases are essential to authorities to estimate the requirements of resources needed to cope with the outgrowths of the virus. Therefore, this study attempts to incorporate the public discourse in the design of forecasting models particularly targeted for the steep-hill region of an ongoing wave. We propose a sentiment-involved topic-based latent variables search methodology for designing forecasting models from publicly available Twitter conversations. As a use case, we implement the proposed methodology on Australian COVID-19 daily cases and Twitter conversations generated within the country. Experimental results: (i) show the presence of latent social media variables that Granger-cause the daily COVID-19 confirmed cases, and (ii) confirm that those variables offer additional prediction capability to forecasting models. Further, the results show that the inclusion of social media variables introduces 48.83%-51.38% improvements on RMSE over the baseline models. We also release the large-scale COVID-19 specific geotagged global tweets dataset, MegaGeoCOV, to the public anticipating that the geotagged data of this scale would aid in understanding the conversational dynamics of the pandemic through other spatial and temporal contexts.

Twitter conversations predict the daily confirmed COVID-19 cases (preprint)

As of writing this paper, COVID-19 (Coronavirus disease 2019) has spread to more than 220 countries and territories. Following the outbreak, the pandemic's seriousness has made people more active on social media, especially on the microblogging platforms such as Twitter and Weibo. The pandemic-specific discourse has remained on-trend on these platforms for months now. Previous studies have confirmed the contributions of such socially generated conversations towards situational awareness of crisis events. The early forecasts of cases are essential to authorities to estimate the requirements of resources needed to cope with the outgrowths of the virus. Therefore, this study attempts to incorporate the public discourse in the design of forecasting models particularly targeted for the steep-hill region of an ongoing wave. We propose a sentiment-involved topic-based latent variables search methodology for designing forecasting models from publicly available Twitter conversations. As a use case, we implement the proposed methodology on Australian COVID-19 daily cases and Twitter conversations generated within the country. Experimental results: (i) show the presence of latent social media variables that Granger-cause the daily COVID-19 confirmed cases, and (ii) confirm that those variables offer additional prediction capability to forecasting models. Further, the results show that the inclusion of social media variables introduces 48.83--51.38% improvements on RMSE over the baseline models. We also release the large-scale COVID-19 specific geotagged global tweets dataset, MegaGeoCOV, to the public anticipating that the geotagged data of this scale would aid in understanding the conversational dynamics of the pandemic through other spatial and temporal contexts.

Effectiveness of mRNA COVID-19 Booster Vaccines against Omicron and Delta Variants among US Veterans (preprint)

Background We estimated vaccine effectiveness (VE) of mRNA vaccines among US Veterans during periods of Delta and Omicron variant dominance. Patients included in this study were largely 65 years or older (62,834, 55%), male (101,259, 88%), and non-Hispanic white (66,986, 58%). Methods We used SARS-CoV-2 laboratory test results to conduct a matched test-negative case-control study to estimate VE of three and two doses of mRNA vaccines against infection (regardless of symptoms), and a matched case-control study to estimate VE against COVID-19-related hospitalization and death. We estimated VE as (1-odds ratio) x 100%. Severity of disease was measured using hospital length of stay (LOS) and admission to an intensive care unit (ICU). Results Against infection, booster doses had 7-times higher VE - 59% (95% confidence interval [CI], 57 to 61) - than 2-dose VE (7%; 95% CI, 3 to 10) during the Omicron period. For the Delta period, estimated VE against infection was 90% (95% CI, 88 to 92) among boosted vaccinees, 64% higher than VE among 2-dose vaccinees [55% (95% CI, 51 to 58)]. Against hospitalization, booster dose VE was 87% (95% CI, 80 to 91) during Omicron and 95% (95% CI, 91 to 97) during Delta; the 2-dose VE was 44% (95% CI, 26 to 58) during Omicron and 75% (95% CI, 70 to 80) during Delta. Against death, estimated VE with a booster dose was 94% (95% CI, 85 to 98) during Omicron and 96% (95% CI, 88 to 99) during Delta, while the 2-dose VE was 75% (95% CI, 52 to 87) during Omicron and 93% (95% CI, 85 to 97) during Delta. During the Omicron period, average hospital LOS was 4 days shorter [3 days (95%CI, 3 to 4 days)] than during the Delta period. Conclusions A mRNA vaccine booster is more effective against infection, hospitalization, and death than 2-dose vaccination among an older male population with comorbidities.

Dual Immersion Digital Instruction: A Theoretical Model for Equitable and Inclusive Classrooms: A preliminary study of opportunities and challenges for equitable and inclusive classrooms

Dual immersion programs have proven effective in achieving biliteracy for all students. However, maintaining equitable practices at the core of such programs has become more challenging in remote learning due to the pandemic. It is necessary, therefore, to revise some of the benefits and challenges of digital instruction mediated by technology in these settings. Using a middle school Dual Immersion (di) program in Southern California as a background, and from the perspective of bilingual education teachers and professors, this article presents a theoretical model called Dual Immersion Digital Instruction (di2) that could serve that purpose. The model includes the five dimensions involved in just, equitable, and inclusive education: Technological, content, social, linguistic, and pedagogical. The article also analyzes the pedagogical opportunities and challenges that teachers in di programs face in regards to each of these dimensions when all instruction becomes fully online. Finally, the article discusses how the shift to online teaching in di classrooms could impact bilingual teacher education programs.Alternate :Los programas de doble inmersión han demostrado ser efectivos en lograr la alfabetización bilingüe para todos los estudiantes. Sin embargo, mantener la equidad en tales programas se ha vuelto más complicado por el aprendizaje a distancia debido a la pandemia. Es necesario, por tanto, revisar los beneficios y retos de la Instrucción Digital mediada por la tecnología en contextos bilingües. Con un programa de Doble Inmersión (di) de una escuela media situada al sur de California como base, y desde la perspectiva de maestros de escuela y profesores universitarios, este artículo presenta un modelo teórico llamado Instrucción digital en doble inmersión (di2) que permitiría lograr este objetivo. El modelo aborda las cinco dimensiones necesarias para una instrucción inclusiva, justa y equitativa: tecnológica, social, lingüística, de contenido y pedagógica. El artículo también analiza las oportunidades pedagógicas y los retos que los docentes de programas de DI enfrentan en cuanto a estas dimensiones cuando la enseñanza se vuelve completamente en línea. Finalmente, el artículo presenta una reflexión sobre cómo el cambio a la instrucción en línea en programas de di podría afectar a los programas de preparación docente.Alternate :Les programmes de double immersion se sont avérés efficaces pour atteindre la bilittératie pour tous les élèves. Cependant, le maintien de pratiques équitables, au cœur de ces programmes, est devenu plus difficile dans l’enseignement à distance en raison des épidémies. L’extension de cette phase d’urgence à distance implique la nécessaire révision des avantages et des défis de l’instruction numérique médiée par la technologie. En utilisant un programme de double immersion (di) au collège dans le sud de la Californie comme contexte, et du point de vue des enseignants et des professeurs d’éducation bilingue, cet article présente un modèle théorique (di2 ) qui aborde cinq dimensions impliquées dans une approche juste, équitable et une éducation inclusive: technologique, sociale, linguistique, de contenue et pédagogique. Nous explorons les opportunités pédagogiques di lorsque toutes les instructions deviennent entièrement en ligne, mettant en évidence les pratiques et les implémentations fiables qui devraient améliorer l’enseignement dans les salles de classe di inclusives une fois la phase à distance a terminée. Nous analysons comment les programmes de préparation des enseignants bilingues devraient revoir leurs cadres, le contenu des cours et les outils d’évaluation.

Deficit Water Irrigation in an Almond Orchard Can Reduce Pest Damage

Irrigated almond orchards in Spain are increasing in acreage, and it is pertinent to study the effect of deficit irrigation on the presence of pests, plant damage, and other arthropod communities. In an orchard examined from 2017 to 2020, arthropods and diseases were studied by visual sampling under two irrigation treatments (T1, control and T2, regulated deficit irrigation (RDI)). Univariate analysis showed no influence of irrigation on the aphid Hyalopterus amygdali (Blanchard) (Hemiptera: Aphididae) population and damage, but Tetranychus urticae Koch (Trombidiformes: Tetranychidae) damage on leaves was significantly less (50–60% reduction in damaged leaf area) in the T2 RDI treatment compared to the full irrigation T1 control in 2019 and 2020. Typhlocybinae (principal species Asymmetrasca decedens (Paoli) (Hemiptera: Cicadellidae)) population was also significantly lower under T2 RDI treatment. Chrysopidae and Phytoseiidae, important groups in the biological control of pests, were not affected by irrigation treatment. The most important diseases observed in the orchard were not, in general, affected by irrigation treatment. The multivariate principal response curves show significant differences between irrigation strategies in 2019 and 2020. In conclusion, irrigation schemes with restricted water use (such as T2 RDI) can help reduce the foliar damage of important pests and the abundance of other secondary pests in almond orchards.

A reliable and valid measure of COVID-19 patient-reported symptoms in outpatients: the Symptoms Evolution of COVID-19 (SE-C19) instrument (preprint)

Background: There is no valid and reliable patient self-reported measure assessing symptomology among outpatients with COVID-19. The Symptoms Evolution of COVID-19 (SE-C19) is a self-administered new instrument that includes 23 symptoms, each rated for severity at their worst moment within the last 24 hours. We studied the psychometric properties of SE-C19. Methods: Reliability, validity, and sensitivity to change of the SE-C19 were assessed in 657 outpatients with confirmed COVID-19 enrolled in NCT04425629. SE-C19 and Patient Global Impression of Severity (PGIS) were administered daily from baseline (predose at Day 1) to end of study (Day 29). Findings: Most patients (70.0%) were aged [≤]50 years and white (85.5%). At baseline, patients reported an average (SD) of 6.6 (3.9) symptoms (ie, rated as at least Mild) with 3.8 (3.3) of these symptoms being rated as Moderate or Severe. By Day 29, most symptoms had resolved; 74.4% of patients reported no symptoms and on average, only 0.6 (SD 1.5) were reported as at least Mild. Stable patients according to the PGIS showed scores with intraclass correlation values indicating moderate-to-good test-retest reliability (ie, 0.50-0.90). At baseline, 20 item scores (87%) varied significantly across PGIS defined groups supporting the validity of SE-C19. A symptom resolution endpoint was defined after excluding the item 'Sneezing', due to its low ability to discriminate severity levels, and 'Confusion', 'Rash', and 'Vomiting', due to their low prevalence in this population. Symptoms resolution required complete absence of all remaining items, except 'Cough', 'Fatigue', and 'Headache', which could be Mild or Moderate in severity. Interpretation: We identified 19 items that are valid and reliable to measure disease-related symptoms in COVID-19 outpatients and propose a definition of symptom resolution that could be used in future clinical trials and potentially, also in clinical practice.

Neutrophil Extracellular Traps, Local IL-8 Expression, and Cytotoxic T-Lymphocyte Absence in the Lungs of Fatal COVID-19: A Retrospective Histopathology Cohort Study (preprint)

Background: Excessive inflammation is pathogenic in pneumonitis associated to severe COVID-19. Neutrophils are among the most abundantly present leukocytes in the inflammatory infiltrates and may form neutrophil extracellular traps (NETs) under the local influence of cytokines. NETs constitute a defence mechanism against bacteria but have also been shown to mediate tissue damage in a number of diseases. Methods: In this retrospective cohort study, sixteen immediate post-mortem lung biopsies were methodologically analysed as exploratory and validation cohorts. NETs were quantitatively analysed by multiplexed immunofluorescence and correlated with local levels of IL-8 mRNA expression and the density of CD8+ T-cell infiltration. SARS-CoV-2 presence in tissue was quantified by RT-PCR and immunohistochemistry.Findings: NETs were found in the lung interstitium and surrounding the bronchiolar epithelium with interindividual and spatial heterogeneity. NET density did not correlate with SARS-CoV-2 tissue viral load. NETs were associated with local IL-8 mRNA levels. NETs were also detected in pulmonary thrombi and in only one out of eight liver tissues in spatial fashion. NET focal presence negatively correlated with CD8+ T-cell infiltration in the lungs. Interpretation: Abundant neutrophils undergoing NETosis are found in the lungs of patients with fatal COVID-19, showing no correlation with viral loads. The strong association between NETs and IL-8 focal expression points to this chemokine as the potential causative factor. The function of cytotoxic T-lymphocytes in the immune responses against SARS-CoV-2 may be interfered by the presence of NETs.Funding Information: This study was supported by Banco Bilbao Vizcaya (BBVA) Foundation, “Ayudas a Equipos de Investigación Científica SARS-CoV-2 y COVID-19”. Declaration of Interests: I.M. reports receiving commercial research grants from BMS, Bioncotech, Alligator, Pfizer, Leadartis and Roche; has received speakers bureau honoraria from MSD; and is a consultant or advisory board member for BMS, Roche, Genmab, F-Star, Bioncotech, Bayer, Numab, Pieris, Alligator, and Merck Serono. C.E.A reports research grants from AstraZeneca. All other authors declare no competing interests.Ethics Approval Statement: This study was approved by the ethics committee of the University of Navarra, Spain (Approval 2020.192). Tissue collections were obtained with consent from a first-degree relative, following a protocol approved by the ethics committee of the University of Navarra (Protocol 2020.192p).

Early initiation of corticosteroids in patients hospitalized with COVID-19 not requiring intensive respiratory support: cohort study (preprint)

Objectives: To determine whether early oral or parenteral corticosteroids compared to no corticosteroids are associated with decreased mortality in patients hospitalized with coronavirus disease 2019 (COVID-19) who are not on intensive respiratory support (IRS) within 48 hours of admission. Design: Observational cohort study Setting: Nationwide cohort of patients receiving care in the Department of Veterans Affairs, a large integrated US national healthcare system Participants: 9,058 patients admitted to a Veterans Affairs Medical Center between June 7, 2020-December 5, 2020 within 14-days after SARS-CoV-2 positive test; exclusion criteria include less than a 48 hour stay, receipt of prior systemic corticosteroids, and no indication of acute medical care for COVID-19. Main outcome measure: 90-day all-cause mortality Results: Of 9,058 total patients (95% men, median age 71 years, 27% black), 6,825 (75%) were not on IRS within 48 hours. Among the 3,025 patients on no oxygen, 598 (20%) received corticosteroids and 283 (9%) died; of 3,800 patients on low-flow nasal cannula oxygen (NC), 2,808 (74%) received corticosteroids and 514 (13%) died. In stratified, inverse probability weighted Cox proportional hazards models comparing those who did and did not receive corticosteroids, patients on no oxygen experienced an 89% increased risk for 90-day mortality (hazard ratio [HR] 1.89, 95% confidence interval [CI] 1.33 to 2.68); there was weak evidence of increased mortality among patients on NC (HR 1.21, 95% CI 0.94 to 1.57). Results were robust in subgroup analyses including restricting corticosteroids to dexamethasone, and in sensitivity analyses employing different modeling approaches. Conclusions: In patients hospitalized with COVID-19, we found no evidence of a mortality benefit associated with early initiation of corticosteroids among those on no oxygen or NC in the first 48 hours, though there was evidence of potential harm. These real-world findings support that clinicians should consider withholding corticosteroids in these populations and further clinical trials may be warranted.

Data-driven molecular design for discovery and synthesis of novel ligands: a case study on SARS-CoV-2

Bridging systems biology and drug design, we propose a deep learning framework for de novo discovery of molecules tailored to bind with given protein targets. Our methodology is exemplified by the task of designing antiviral candidates to target SARS-CoV-2 related proteins. Crucially, our framework does not require fine-tuning for specific proteins but is demonstrated to generalize in proposing ligands with high predicted binding affinities against unseen targets. Coupling our framework with the automatic retrosynthesis prediction of IBM RXN for Chemistry, we demonstrate the feasibility of swift chemical synthesis of molecules with potential antiviral properties that were designed against a specific protein target. In particular, we synthesize an antiviral candidate designed against the host protein angiotensin converting enzyme 2 (ACE2);a surface receptor on human respiratory epithelial cells that facilitates SARS-CoV-2 cell entry through its spike glycoprotein. This is achieved as follows. First, we train a multimodal ligand–protein binding affinity model on predicting affinities of bioactive compounds to target proteins and couple this model with pharmacological toxicity predictors. Exploiting this multi-objective as a reward function of a conditional molecular generator that consists of two variational autoencoders (VAE), our framework steers the generation toward regions of the chemical space with high-reward molecules. Specifically, we explore a challenging setting of generating ligands against unseen protein targets by performing a leave-one-out-cross-validation on 41 SARS-CoV-2-related target proteins. Using deep reinforcement learning, it is demonstrated that in 35 out of 41 cases, the generation is biased towards sampling binding ligands, with an average increase of 83% comparing to an unbiased VAE. The generated molecules exhibit favorable properties in terms of target binding affinity, selectivity and drug-likeness. We use molecular retrosynthetic models to provide a synthetic accessibility assessment of the best generated hit molecules. Finally, with this end-to-end framework, we synthesize 3-Bromobenzylamine, a potential inhibitor of the host ACE2 protein, solely based on the recommendations of a molecular retrosynthesis model and a synthesis protocol prediction model. We hope that our framework can contribute towards swift discovery of de novo molecules with desired pharmacological properties.

SLAMP: A Rapid Fluorometric RT-LAMP Assay for Sensitive and Specific Detection of SARS-CoV-2 from Human Saliva (preprint)

Rapid testing methods can identify outbreaks and trigger preventive strategies for slowing the spread of SARS-CoV-2, the virus that causes COVID-19. The gold-standard detection method for SARS-CoV-2 is reverse transcription quantitative polymerase chain reaction (RT-qPCR) performed on samples collected using a nasopharyngeal (NP) swab. While NP RT-qPCR provides high sensitivity, it requires trained personnel to administer and suffers from lengthy time-to-result. Recently, the testing community has turned to rapid saliva-based screening methods including saliva-to-RT-qPCR and/or saliva-to-RT-LAMP (reverse transcription loop-mediated isothermal amplification) to identify infected individuals regardless of symptomatic presentation. Here, we report a simple and rapid RT-LAMP fluorometric assay performed directly on heat-inactivated saliva, without the addition of buffers or proteinase K treatments we call saliva LAMP (SLAMP). Over the course of two days, a total of 243 individuals were tested using NP RT-qPCR, saliva-based qPCR, and saliva-based RT-LAMP. Of the 243 NP RT-qPCR tests, 65 were positive, 178 were negative, and SLAMP demonstrated a 91% sensitivity and 98% specificity. SLAMP sensitivity becomes 95% when samples negative in saliva tests while positive in NP RT-qPCR are excluded from evaluation, potentially indicating significant differences in viral titer between collection sites on the body. SLAMP is performed in triplicates and takes 45 min to run in the laboratory, requiring less technician time and instrument run time than NP RT-qPCR. These results demonstrate that saliva-based RT-LAMP can enable frequent and rapid screening of large numbers of people to identify pre-symptomatic and asymptomatic individuals thereby controlling outbreaks.

Meaningful Activities During COVID-19 and Association With Mental Health in Belgian Adults. (preprint)

Background: The spread of COVID-19 has affected people’s daily lives and the lockdown may have led to a disruption of daily activities and a decrease of people’s mental health. Aim: To identify correlates of adults’ mental health during the COVID-19 lockdown in Belgium and to assess the role of meaningful activities in particular. Methods: A cross-sectional web survey for assessing mental health (General Health Questionnaire), resilience (Connor-Davidson Resilience Scale), meaning in activities (Engagement in Meaningful Activities Survey) and demographics was conducted between April 24 and May 4, 2020. Hierarchical linear regression was used to identify key correlates. Results: Participants (N=1781) reported low mental health (M=14.85/36). In total, 42.4% of the variance in mental health could be explained by variables such as gender, having children, living space, marital status, health condition, and resilience (β= -.33). Loss of meaningful activities was strongly related to mental health (β= -.36) and explained 9% incremental variance (R2 change= .092, p<.001) above control variables.Conclusions: The extent of performing meaningful activities during the COVID-19 lockdown in Belgium is positively related to adults’ mental health. Insights from this study can be taken into account during future lockdown measures in case of pandemics.

Influence of the COVID-19 Pandemic on Mental Workload and Burnout of Fashion Retailing Workers in Spain (preprint)

This study analyzed the levels of mental workload and the presence of burnout on a sample of fashion retailing workers from Spain and its relationship with the actual Covid-19 pandemic by exploring Covid-19 pandemic predictors of burnout and mental workload. We established a prospective cross-sectional design. Participants (n = 360) answered an online survey including questions about sociodemographic data, perception of Covid-19, CarMen-Q questionnaire (workload), and MBI (burnout syndrome). We obtained data throughout October-November 2020. The results showed that participants exhibit deep concern about the Covid-19 pandemic and its influence at the work level. Although the mental workload was near the middle point of the scale, participants showed moderate to high burnout levels, revealing that the sample is at risk of experiencing higher burnout levels over time as the pandemic and associated economic crisis continue. The multidimensional regression analysis results indicated that environmental changes, work overload, somatic symptoms, insomnia, negative job expectations, and uncertainty constituted significant mental workload predictors. Insomnia, somatic symptoms, and negative job expectations constituted significant predictors for burnout. In conclusion, the uncertainty at work derived from the Covid-19 pandemic is harming the psychological wellbeing of fashion retailing workers in Spain.

Group Membership and Social and Personal Identities as Psychosocial Coping Resources to Psychological Consequences of the COVID-19 Confinement

The confinement imposed by measures to deal with the COVID-19 pandemic may in the short and medium term have psychological and psychosocial consequences affecting the well-being and mental health of individuals. This study aims to explore the role played by group membership and social and personal identities as coping resources to face the experience of the COVID-19 confinement and radical disruption of social, work, family and personal life in a sample of 421 people who have experienced a month of strict confinement in the Region of Madrid. Our results show that identity-resources (membership continuity/new group memberships, and personal identity strength) are positively related to process-resources (social support and perceived personal control), and that both are related to better perceived mental health, lower levels of anxiety and depression, and higher well-being (life satisfaction and resilience) during confinement. These results, in addition to providing relevant information about the psychological consequences of this experience, constitute a solid basis for the design of psychosocial interventions based on group memberships and social identity as coping resources.

Kinetics of Biomarkers Associated with Organ Dysfunction and Inflammatory Status and Their Association with Disease Outcomes in Severe COVID-19: A Longitudinal Cohort Study (preprint)

BACKGROUND: COVID-19 can induce pulmonary and systemic inflammation and subsequent multi-organ dysfunction. In patients with severe COVID-19, no data are available on the longitudinal evolution of biochemical abnormalities and their ability to predict disease outcomes. METHODS: Using a retrospective, longitudinal cohort study design on consecutive patients with severe COVID-19, we monitored biomarker kinetics to estimate the occurrence of organ dysfunction and the severity of the inflammatory reaction and their association with acute respiratory failure (ARF) and death through multilevel modeling adapted for repeated measures. FINDINGS: A total of 162 patients were assessed and did not receive antiviral therapy against SARS-CoV-2. During the study period, 1151 biochemical explorations were carried out for up to 59 biochemical markers in blood and urine, totaling 15,260 biochemical values. The spectrum of biochemical abnormalities and their kinetics were consistent with a multi-organ involvement, including lung, kidney, heart, liver (major cytolysis, cholestasis, conjugated hyperbilirubinemia), muscle (major cytolysis), and pancreas (hyperlipasemia) along with a severe inflammatory syndrome. On the 20 more representative biochemical markers (>250 iterations), only CRP >90 mg/L (odds ratio [OR] 6·87, 95% CI, 2·36–20·01) and urea nitrogen >0·36 g/L (OR 3·91, 95% CI, 1·15–13·29) were independently associated with the risk of ARF. Urea nitrogen >0·42 g/L was the only marker associated with the risk of COVID-19 related death. The proportion of patients who developed an acute kidney injury (AKI) stage 3, increased significantly during follow-up (0·9%, day 0; 21·4%, day 14; P <0·001). INTERPRETATION: Our results point out the lack of the association between the inflammatory markers and the risk of death but rather highlight a significant association between renal dysfunction and the risk of COVID-19 related acute respiratory failure and death. Further studies should address the significance of acute kidney injury in the prediction of COVID-19 related death. FUNDING: No funding.DECLARATION OF INTERESTS: The authors who have taken part in this study declare that they do not have anything to disclose regarding conflicts of interest concerning this manuscript.ETHICS APPROVAL STATEMENT: The “Nancy Biochemical Database” is registered at the French National Commission on Informatics and Liberty, CNIL, under the record N°1763197v0. The Ethics committee of the University Hospital of Nancy approved the study (ID: 2020/264).