BACKGROUND: Assessing the impact of government responses to Covid-19 is crucial to contain the pandemic and improve preparedness for future crises. We investigate here the impact of non-pharmaceutical interventions (NPIs) and infection threats on the daily evolution of cross-border movements of people during the Covid-19 pandemic. We use a unique database on Facebook users mobility, and rely on regression and machine learning models to identify the role of infection threats and containment policies. Permutation techniques allow us to compare the impact and predictive power of these two categories of variables. RESULTS: In contrast with studies on within-border mobility, our models point to a stronger importance of containment policies in explaining changes in cross-border traffic as compared with international travel bans and fears of being infected. The latter are proxied by the numbers of Covid-19 cases and deaths at destination. Although the ranking among coercive policies varies across modelling techniques, containment measures in the destination country (such as cancelling of events, restrictions on internal movements and public gatherings), and school closures in the origin country (influencing parental leaves) have the strongest impacts on cross-border movements. CONCLUSION: While descriptive in nature, our findings have policy-relevant implications. Cross-border movements of people predominantly consist of labor commuting flows and business travels. These economic and essential flows are marginally influenced by the fear of infection and international travel bans. They are mostly governed by the stringency of internal containment policies and the ability to travel.

Most interventions for mental health and psychosocial support (MHPSS) have been developed in contexts and with populations that differ significantly from the realities of migration. There is an urgent need for MHPSS in transit; however, transit-specific aspects of MHPSS provision are often neglected due to the inherent challenges transit poses to traditional conceptualizations of practice. The Delphi method, which consisted of three iterative rounds of surveys, was applied with the goal of identifying challenges to and adaptations of MHPSS in the transit context. Twenty-six MHPSS providers working with refugees in 10 European transit countries participated; 69% of participants completed all three survey rounds. There was consensus that a flexible model of MHPSS, which can balance low intensity interventions and specialized care, is needed. Agreement was high for practice-related and sociopolitical factors impacting MHPSS in transit; however, the mandate of MHPSS providers working in the transit context achieved the lowest consensus and is yet to be defined. There is a need to rethink MHPSS in the refugee transit context. Providing MHPSS to refugees on the move has specificities, most of which are related to the instability and uncertainty of the context. Future directions for improving mental health protection for refugees, asylum seekers, and migrants in transit are highlighted.

The outbreak of the SARS-CoV-2 elicited a surge in publications. Obstetric reports were with few exceptions characterized by small sample sizes with potentially limited generalizability. In this review, evidence suggests increased susceptibility to COVID-19 in pregnancy; common pregnancy comorbidities may help explain worse outcomes. While the risk of death is low, pregnancy may be associated with increased need for ventilation. Prematurity rates seem to be increased but may be accounted for in part by higher cesarean rates, to a large degree accounted for by elective decision to shorten the course of the labor. Though fetal/neonatal complication rates may be higher in the presence of COVID-19 infection, survival rates seem unaffected and vertical transmission is rare. As the outbreak continues in the USA with resurgence in many other western countries that achieved initial success in suppressing the virus, much remains to be learned. For example, the question related to the degree to pregnancy modifying symptomatology remains open. Currently, routine polymerase chain reaction testing remains limited by supply shortages possibly delaying diagnosis until later in the course of the disorder and thus altering the symptom complex at presentation. To add to the knowledge base, we initiated a regional COVID-19 in pregnancy collaborative observational study with a coordinating center, standardized data collection and a shared database. This was facilitated by a longstanding tradition of collaboration among regional obstetric services. Over an anticipated two-year study duration, we expect to study 400 documented and suspected COVID-19 pregnancies with time and site of services controls for cohort effect and high power to detect several adverse maternal/infant outcomes. We include a complete listing of variables in our database, which, along with our experience in setting up our regional collaborative, we hope and believe will be of use in other settings.

In countries with a severe outbreak of COVID-19, most governments are considering whether anti-transmission measures are worth social and economic costs. The seriousness of economic costs such as the closure of some workplaces, unemployment, reduction in production, and social costs such as school closures, disruptions in education could be observable. However, the effect of the measures taken on the spread of the epidemic, such as the number of delayed or prevented cases, could not be observed. For this reason, the direct effects of the measures taken on health, that is, the effects on the course of the epidemic, are important research subjects. For this purpose, in this study, the breakpoint linear regression analysis is performed to analyze the trends of daily active cases, recovered, and deaths in Turkey. The analysis reveals that there has been a remarkable impact on lockdown and other precautions. Using the breakpoint regression model, we also analyze the active cases' trajectory for eight affected countries and compare the patterns in these countries with Turkey.

Background COVID-19 patients on hemodialysis (HD) have high mortality We investigated the value of RT-PCR and the dynamic changes of antibodies (ELISA IgM+IgA and IgG) in a large HD cohort Methods Prospective observational study in ten Madrid HD centers Infection rate, anti- SARS-CoV-2 body dynamics and the incidence of asymptomatic SARS-CoV-2 infection (defined by positive RT-PCR, IgM-IgA or IgG) were assessed Results From March 1 to April 15, 2020, 136 (16 8%) of 808 HD patients were diagnosed of symptomatic COVID-19 by nasopharyngeal RT-PCR and 42/136 (31%) died In the second fortnight of April, RT-PCR and anti-SARS-CoV-2 antibodies were assessed on 763 of the surviving patients At this point, 69/91 (75,8%) symptomatic COVID-19 patients had anti-SARS-CoV-2 antibodies Four weeks later, 15 4% (10/65) of initially antibody positive patients had become negative Among patients without prior symptomatic COVID-19, 9/672 (1 3%) were RT-PCR positive and 101/672 patients (15 0%) were antibody positive Four weeks later, 6224/86 (72 1%) of initially antibody positive patients had become negative Considering only IgG tittles, serology remained positive after four weeks in 90% (54/60) of patients with symptomatic COVID-19 and in 52 5% (21/40) of asymptomatic patients The probability of an adequate serologic response (defined as the development of anti-SARS-CoV2 antibodies that persisted at 4 weeks) was higher in patients who had symptomatic COVID-19 than in asymptomatic SARS-CoV2 infection (OR 4 04 [2 04-7 99] corrected for age, Charlson score and time on HD Living in a nursing home (5 9 [2 3-15 1]) was the main risk factors for SARS-CoV2 infection Conclusion The anti-SARS-CoV-2 antibody immune response in HD patients depends on clinical presentation and the antibody titers decay earlier than previously reported for the general population This inadequate immune response raises questions about the efficacy of future vaccines

We outline a contact-tracing strategy based on proximity sensing using mobile devices. We discuss what an ideal system should look like and what it can do. We show that, when adopted sufficiently broadly, such a contact-tracing strategy can bring COVID-19 under complete control, end the need of social distancing, and return the society to full normalcy. We also review some of the challenges faced by the current generation of proximity-sensing technologies, including Bluetooth Low Energy used by phones, and consider both interim and longer-term solutions. Our main contribution is that we reason through why such a contact-tracing strategy is likely to achieve the stated goal of returning to full normalcy. Using probabilistic models, we show that universal adoption is not necessary to achieve the stated goal, thus there is some room for exceptions;however, the adoption rate needs to be very high, e.g., above $95\%$ depending on the disease parameters. With more vigilance in disease surveillance to detect mild cases earlier, the number may be brought down to about $90\%$. The results call for deployment effort to be led by public authorities at the state or federal level so that the required adoption rate can be reached and the tracing coverage is wide enough to be relevant for disease control.

Social distancing policies such as limits on public gatherings and contact with others were utilized around the world to slow the spread of COVID-19. Yet, decreased social interactions may also threaten peoples well-being. In this project, we sought to understand novelty-relevant experiences surrounding in-home companion robot pets for adults that were living in some degree of social isolation due to the COVID-19 pandemic. After 6-weeks of participants living with the robot companion, we conducted semi-structured interviews (N = 9) and six themes emerged from our iterative analysis (expectations versus reality, ontological comparisons, interactions, third-party influence, identity, and comfort). Findings suggest that novelty is a complex phenomenon consisting of various elements (i.e., imagined novelty, technology novelty, and relational novelty). Each component influences the users experience. Our findings also suggest that our understanding of novelty as a nonlinear resource may hold important implications for how we view human-robot relationships beyond initial encounters.

Nucleoside-modified messenger RNA (mRNA)-lipid nanoparticles (LNPs) are the basis for the first two EUA (Emergency Use Authorization) COVID-19 vaccines. The use of nucleoside-modified mRNA as a pharmacological agent opens immense opportunities for therapeutic, prophylactic, and diagnostic molecular interventions. In particular, mRNA-based drugs may specifically modulate immune cells, such as T lymphocytes, for immunotherapy of oncologic, infectious and other conditions. The key challenge, however, is that T cells are notoriously resistant to transfection by exogenous mRNA. Here, we report that conjugating CD4 antibody to LNPs enables specific targeting and mRNA interventions to CD4+ cells, including T cells. After systemic injection in mice, CD4-targeted radiolabeled mRNA-LNPs accumulated in spleen, providing a??30-fold higher signal of reporter mRNA in T cells isolated from spleen as compared with non-targeted mRNA-LNP. Intravenous injection of CD4-targeted LNP loaded by Cre recombinase-encoding mRNA provided specific dose-dependent loxP-mediated genetic recombination, resulting in reporter gene expression in about 60% and 40% of CD4+ T cells in spleen and lymph nodes, respectively. T cell phenotyping showed uniform transfection of T cell subpopulations, with no variability in uptake of CD4-targeted mRNA-LNP in naive, central memory, and effector cells. The specific and efficient targeting and transfection of mRNA to T cells established in this study provides a platform technology for immunotherapy of devastating conditions and HIV cure.

Tools have been developed to facilitate communication and support information exchange between people diagnosed with cancer and their physicians. Patient-reported outcome measures, question prompt lists, patient-held records, tape recordings of consultations, decision aids, and survivorship care plans have all been promoted as potential tools, and there is extensive literature exploring their impact on patient outcomes. Eleven systematic reviews of studies evaluating tools to facilitate patient-physician communication were reviewed and summarized in this overview of systematic reviews. Across the systematic reviews, 87 publications reported on 84 primary studies involving 15,381 participants. Routine use of patient-reported outcome measures and feedback of results to clinicians can improve pain management, physician-patient communication, and symptom detection and control; increase utilization of supportive care; and increase patient involvement in care. Question prompt lists can increase the number of questions asked by patients without increasing consultation length and may encourage them to reflect and plan questions before the consultation. There is limited benefit in audio recording consultations or using patient-held records during consultations. Physicians should be supported by adequately resourced health services to respond effectively to the range of clinical and broader patient needs identified through the routine use of tools to facilitate communication.

This Research to Practice Work in Progress Paper studies the high attrition rate problem of first-time computer science Freshmen students at most universities. The problem is worsened given the growing demand of Information Technology workers and due to the limited instruction of computer science related content being taught within the high school education curriculum. The result is that incoming college students who are majoring in computer science or in related STEM fields are unprepared. Additionally, the means to adequately meet the employment demand is less likely with the low percentage of workers from under-represented minority (URM) groups in jobs within the computer science related industry. Much research has been done on predicting and improving student's success, particularly with the first programming and algorithms course known as CS1 and being ready to take Calculus. The problem is difficult to understand due to the many factors that exists, such as students having different education backgrounds, not knowing what a computer science education entails, and student support systems at a new school. At our university, for three summers, we offered our incoming engineering students a pre-college 4-week summer experience to better prepare them for their first year. The student population targeted were from under-represented minority groups, first-generation, low-income, and women. The goal of the program was to better prepare the students for success by engaging and advising them with both, computer science and math content by bringing them together as a cohort, which is essential during their first critical year in a computer science engineering field of study. The goal of this paper is to study the attrition rates and gain insight on student success predictors for entering Computer Science students. Research has shown that pre-college programs can benefit student success. By targeting students from underrepresented minority groups our summer program integrates computer science and math concepts to better prepare students for 'Day 1' of college. The research work employs the student involvement theory to promote student success and retention. With COVID-19 restricting students to online learning, challenges in student-faculty and student-student contact have significantly made an impact. In addition to online survey/interview data, math and computer science course completion rates were collected from our 87 summer cohort participants to compare with the rest of the students. Triangulation of all the data collected yielded some insights and confirmed others on predictors for student success and persistence. Specifically, the summer students were disproportionately affected by COVID-19, compared to the general population (i.e., they were readily not able to collaborate with their peers and approach faculty). ? 2021 IEEE.

BACKGROUND: In a pandemic, it is important for clinicians to stratify patients and decide who receives limited medical resources. Machine learning (ML) models have been proposed to accurately predict COVID-19 disease severity. Previous studies have typically tested only one ML algorithm and limited performance evaluation to "area under the curve" (AUC). To obtain best results possible, it may be important to try multiple algorithms using machine learning (ML) to optimize performance. OBJECTIVE: In this study, we used automated machine learning (autoML) to train various machine learning (ML) algorithms. We selected the model that best predicted the chance of patient survival from COVID-19 infection. In addition, we investigated which variables (i.e. vital signs, biomarkers, comorbidities, etc.) were most influential in generating an accurate model. METHODS: The data was retrospectively collected at our institution on all patients testing positive for COVID-19 between 3/1/2020-7/3/2020. We collected 48 variables from each patient within 36 hours before or after the index time: RT-PCR positivity. Patients were followed up for 30 days or death. This data was used for autoML to build 20 ML models with various algorithms. The main performance of ML models was measured by area under the precision recall curve (AUCPR). Subsequently, we established model interpretability to identify and rank variables that drove model predictions using Shapley additive explanations (SHAP) and partial dependence plot (PDP). Finally, dimensionality reduction was conducted to extract the 10 most influential variables. AutoML was retrained using only these 10 variables and its output models was evaluated against the model that used 48 variables. RESULTS: Input from 4313 patients was used. The best model that autoML generated using 48 variables was the stacked ensemble models (AUCPR = 0.807). The two best independent models were the Gradient Boost Models (GBM) and Extreme Gradient Boost (XGBoost) models with AUCPR of 0.803 and 0.793, respectively. Deep learning models were significantly inferior with AUCPR = 0.73. The ten most influential variables in generating high performing models were systolic and diastolic blood pressure, age, pulse oximetry, blood urea nitrogen, lactate dehydrogenase, D-dimer, troponin, respiratory rate, and Charlson comorbidity score. When the autoML was retrained with these 10 variables, the stacked ensemble model again performed the best with AUCPR of 0.791. CONCLUSIONS: By using autoML, we have developed high-performing models that predict survival from COVID-19 infection. In addition, we identified important variables that correlated with mortality. This is proof of concept that autoML is an efficient, effective, and informative method to generate ML based clinical decision supporting tools.

India, the second-most populous country in the world is witnessing a daily surge in the COVID-19 infected cases. India is currently among the worst-hit nations worldwide due to the COVID-19 pandemic and ranks just behind Brazil and the USA. The prediction of the future course of the pandemic is thus of utmost importance in order to prevent further worsening of the situation. In this paper, we develop models for the past trajectory (March 01, 2020-July 25, 2020) and also make a month-long (July 26, 2020-August 24, 2020) forecast of the future evolution of the COVID-19 pandemic in India by using an autoregressive integrated moving average (ARIMA) model. We determine the most optimal ARIMA model (ARIMA(7,2,2)) based on the statistical parameters viz. root-mean-squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and the coefficient of determination ( R 2 ). Subsequently, the developed model is used to obtain a one month-long forecast for the cumulative cases, active cases, recoveries, and the number of fatalities. According to our forecasting results, India is likely to have 3800,989 cumulative infected cases, 1634,142 cumulative active cases, 2110,697 cumulative recoveries, and 56,150 cumulative deaths by August 24, 2020, if the current trend of the pandemic continues to prevail. The implications of these forecasts are that in the upcoming month, the infection rate of COVID-19 in India is going to escalate, while the rate of recovery and the case-fatality rate is likely to reduce. In order to avert these possible scenarios, the administration and health-care personnel need to formulate and implement robust control measures, while the general public needs to be more responsible and strictly adhere to the established and newly formulated guidelines in order to slow down the spread of the pandemic and prevent it from transforming into a catastrophe.

This letter highlights the role of macroeconomic and financial uncertainty in predicting US recessions. In-sample forecasts using probit models indicate that the two variables are the best predictors of recessions at short horizons. Macroeconomic uncertainty has the highest predictive power up to 7 months ahead and becomes the second best predictorafter the yield curve slopeat longer horizons. Using data up to end-2018, out-of-sample forecasts show that uncertainty has significantly contributed to lower the probability of a recession in 2019, which indeed did not occur.

As COVID-19 continues to rapidly spread throughout the world, incidence varies greatly among different countries. These differences raise the question whether nations with lower incidence share any medical commonalities that could be used to not only explain that lower incidence, but that could also provide guidance for potential treatments elsewhere. Such treatment would be particularly valuable if it could be used as a prophylactic against COVID-19 transmission, thereby effectively slowing spread of the disease while we await the wide availability of safe and effective vaccines. Here, we show that countries with routine mass drug administration of prophylactic chemotherapy including Ivermectin have significantly lower incidences of COVID-19. Prophylactic use of Ivermectin against parasitic infections is most common in Africa and we hence show that the reported correlation is highly significant both when compared among African nations as well as in a worldwide context. We surmise that this may be connected to Ivermectin's ability to inhibit SARS-CoV-2 replication which likely leads to lower infection rates. However, other pathways must exist to explain persistence of such inhibitory effect after serum levels of Ivermectin have declined. It is suggested that Ivermectin be evaluated for potential off-label prophylactic use in certain cases to help bridge the time until a safe and effective vaccine becomes available.

Importance Cardiometabolic disease is responsible for decreased longevity and poorer cardiovascular outcomes in the modern era. Metabolite profiling provides a specific measure of global metabolic function to examine specific metabolic mechanisms and pathways of cardiometabolic disease beyond its clinical definitions. Objectives To define a molecular basis for cardiometabolic stress and assess its association with cardiovascular prognosis. Design, Setting, and Participants A prospective observational cohort study was conducted in a population-based setting across 2 geographically distinct centers (Boston Puerto Rican Health Study [BPRHS], an ongoing study of individuals enrolled between June 1, 2004, and October 31, 2009; and Atherosclerosis Risk in Communities [ARIC] study, whose participants were originally sampled between November 24, 1986, and February 10, 1990, and followed up through December 31, 2017). Participants in the BPRHS were 668 Puerto Rican individuals with metabolite profiling living in Massachusetts, and participants in the ARIC study were 2152 individuals with metabolite profiling and long-term follow-up for mortality and cardiovascular outcomes. Statistical analysis was performed from October 1, 2018, to March 13, 2020. Exposure The primary exposure was metabolite profiles across both cohorts. Main Outcomes and Measures Outcomes included associations with multisystem cardiometabolic stress and all-cause mortality and incident coronary heart disease (in the ARIC study). Results Participants in the BPRHS (N = 668; 491 women; mean [SD] age, 57.0 [7.4] years; mean [SD] body mass index [calculated as weight in kilograms divided by height in meters squared], 32.0 [6.5]) had higher prevalent cardiometabolic risk relative to those in the ARIC study (N = 2152; 599 African American individuals; 1213 women; mean [SD] age, 54.3 [5.7] years; mean [SD] body mass index, 28.0 [5.5]). Multisystem cardiometabolic stress was defined for 668 Puerto Rican individuals in the BPRHS as a multidimensional composite of hypothalamic-adrenal axis activity, sympathetic activation, blood pressure, proatherogenic dyslipidemia, insulin resistance, visceral adiposity, and inflammation. A total of 260 metabolites associated with cardiometabolic stress were identified in the BPRHS, involving known and novel pathways of cardiometabolic disease (eg, amino acid metabolism, oxidative stress, and inflammation). A parsimonious metabolite-based score associated with cardiometabolic stress in the BPRHS was subsequently created; this score was applied to shared metabolites in the ARIC study, demonstrating significant associations with coronary heart disease and all-cause mortality after multivariable adjustment at a 30-year horizon (per SD increase in metabolomic score: hazard ratio, 1.14; 95% CI, 1.00-1.31; P = .045 for coronary heart disease; and hazard ratio, 1.15; 95% CI, 1.07-1.24; P < .001 for all-cause mortality). Conclusions and Relevance Metabolites associated with cardiometabolic stress identified known and novel pathways of cardiometabolic disease in high-risk, community-based cohorts and were associated with coronary heart disease and survival at a 30-year time horizon. These results underscore the shared molecular pathophysiology of metabolic dysfunction, cardiovascular disease, and longevity and suggest pathways for modification to improve prognosis across all linked conditions.

INTRODUCTION: As the COVID-19 pandemic was spreading in 2020, the government imposed national lockdowns. We considered the effects these lockdowns had on the paediatric population, with a specific focus on lower limb orthopaedic trauma. We hypothesise that these restrictions will have altered the mechanisms of injury and reduced the number of referrals. MATERIALS AND METHODS: We retrospectively analysed data from 28/08/19 to 01/04/21, considering the variations in referrals and operations during these times, and analysed these data using an online statistical calculator. We examined the rate of referrals, types of fractures referred to the centre, mechanism of injury, volume of operations performed, and average wait times to undergo an operation. The data were compared in pre-lockdown and lockdown times. RESULTS: 67 paediatric patients with lower limb fractures were included in this study. Throughout the lockdown periods, the mean age of children referred was younger (6.9 from 11.1) and they were less likely to be injured as a result of sport (p = 0.0493). They were more likely to fracture their lower leg (p = 0.0016) when compared with other anatomical regions. The average weekly rate of referrals dropped (0.84C0.68), but the rate of operations almost quartered (0.39C0.16). The average wait times for operations dropped significantly, with patients waiting 80% less time from the date of their injury. CONCLUSION: This study highlights the impact of the coronavirus pandemic on the prevalence and management of lower limb paediatric trauma. The demographics and mechanisms of injury which presented to the trust over the pandemic and associated national lockdowns were significantly different. There was a drop in the number of referrals and a preference to non-operative management when patients did present.

BACKGROUND AND OBJECTIVE Minimally invasive procedures such as foam sclerotherapy and radiofrequency ablation (RFA) have gained attention for treatment of incompetent great saphenous vein (GSV). The objective of this study was to compare recurrence rate and quality of life between foam sclerotherapy and RFA in patients with incompetent GSV varicose veins. METHODS In this parallel single-blinded randomized clinical trial, 60 adult patients with primary varicose veins due to incompetent GSV (CEAP classes C2-4EPAsPr) were included and randomly divided to receive RFA or foam sclerotherapy. Health-related quality of life (HRQOL) was assessed by the Short Form 36, and the Aberdeen Varicose Vein Questionnaire (AVVQ) was applied to assess the impact of varicose veins on quality of life of the patients. In addition, pain severity after the procedures was investigated by a visual analog scale (VAS) (range, 0 to 10). The patients were followed at 1 week, 1 month, 3 months, and 6 months postoperation. GSV reflux and recurrence was assessed by color Doppler ultrasound examination after 6 months. RESULTS Twenty-eight patients in RFA and 27 patients in foam sclerotherapy remained for the final analyses. The time interval from the procedure and recovery to daily normal activities was 1 day in both groups. Mean (SD) pain VAS score in RFA group decreased from preintervention score of 7.35 (3.28) to 1.21 (0.68); P < .0001. Likewise, this score decreased from 6.64 (2.04) to 1.29 (0.91) in foam sclerotherapy group. HRQOL scores increased gradually at 1, 3, and 6 months after the intervention. AVVQ scores decreased significantly 1 week postintervention in both groups. After 6 months, 17.9% (5 patients) in RFA group and 14.8% (4 patients) in foam sclerotherapy group had recurrence of GSV reflux (P = .52). CONCLUSION Both foam sclerotherapy and RFA were effective in treatment of GSV reflux. Comparable findings were observed between the 2 groups regarding postoperative pain, recovery time, HRQOL, and AVVQ scores.

Interest in investigating organizational resilience has surged due to the increasing number of unexpected shocks and disruptions in the global economy. It is more important than ever to have well defined ways of measuring organizational resilience as a precursor to understanding its antecedents. In this article, we discuss the assumptions (regarding choices of counterfactuals and time intervals) needed to operationalize organizational resilience as a performance outcome and identify the minimal set of variables that can be used to estimate the resilience of an organization. We highlight the importance of the choice of time window (rule-based vs. variable) and counterfactuals (absolute vs. relative) to measure resilience.

The clinical course of infection due to respiratory viruses such as Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV2), the causative agent of Coronavirus Disease 2019 (COVID-19) is thought to be influenced by the community of organisms that colonizes the upper respiratory tract, the oropharyngeal microbiome. In this study, we examined the oropharyngeal microbiome of suspected COVID-19 patients presenting to the Emergency Department and an inpatient COVID-19 unit with symptoms of acute COVID-19. Of 115 enrolled patients, 74 were confirmed COVID-19+ and 50 had symptom duration of 14 days or less; 38 acute COVID-19+ patients (76%) went on to require respiratory support. Although no microbiome features were found to be significantly different between COVID-19+ and COVID-19? patients, when we conducted random forest classification modeling (RFC) to predict the need of respiratory support for the COVID-19+ patients our analysis identified a subset of organisms and metabolic pathways whose relative abundance, when combined with clinical factors (such as age and Body Mass Index), was highly predictive of the need for respiratory support (F1 score 0.857). Microbiome Multivariable Association with Linear Models (MaAsLin2) analysis was then applied to the features identified as predicative of the need for respiratory support by the RFC. This analysis revealed reduced abundance of Prevotella salivae and metabolic pathways associated with lipopolysaccharide and mycolic acid biosynthesis to be the strongest predictors of patients requiring respiratory support. These findings suggest that composition of the oropharyngeal microbiome in COVID-19 may play a role in determining who will suffer from severe disease manifestations.

The COVID-19 Pandemic, SARS-COV-2 virus-form transformations, and ensuing psychosocial stress stemming from environmental change and isolation, has led to the conjecture that there would be a surge in psychosis cases. Intuitively, patients with Serious Mental Illness (SMI), like Schizophrenia Spectrum Disorder and Major Depression, would be particularly susceptible. Existing literature illustrates psychological distress as a primary effect of the Pandemic - on people with/without SMI. We initiated a rapid review to determine the impact of the SARS-COV-2 virus - in symptomatic and asymptomatic cases - on people with/without psychosis. We envisioned that this would provide insights on effective clinical-intervention methods for psychotic-patients, during and after the Pandemic. Our review draws from papers, published in 2020, that considered participants - with/without psychiatric illness and exposure to SARS-COV-2 infection. The Salutogenesis Model was used to comprehend observations from the systematic-review, leading to suggestions and recommendations for preventive and promotive public health strategies.