B.1.1.529 seems to have gone from 0.1% to 50% in just a couple of weeks, when it took Delta several months to achieve that..!! Brace up folks..!! #NuVariant

Busy day on B.1.1.529 - a variant of great concern - The world should provide support to South Africa and Africa and not discriminate or isolate it! By protecting and supporting it, we will protect the world! A plea for billionaires and financial institutions. 1/8 tweets

The “big issue of inequalities” for long COVID patients who face ongoing symptoms and disability needs to be addressed, an expert has said.

‘Issue of inequalities’ for long COVID patients needs to be addressed

Interested in #causalinference? Learn from top experts in the field. Summer courses offered at the Harvard T.H. Chan School of Public Health in June, 2022. Key Topics in Causal Inference Target Trial Emulation Stay tuned for more details. https://causalab.sph.harvard.edu/courses

Not a thread, but key point: 6/6

The @nextstrain http://CoVariants.org focal build for 21K (B.1.1.529) is now live. http://nextstrain.org/groups/neherlab/ncov/21K… As previously, the long branch leading to 21K is clearly visible, making it hard with current sequences to tell much about the evolutionary history.

How it started 3 months ago— How it’s going. #COVID19

Masks, vaccines, boosters, rapid tests and anti-Covid pills will all be essential in the months ahead

We can prevail over Omicron. We just need to use the tools we have

A CONFIRMATORY FACTOR ANALYSIS OF A COGNITIVE MODEL OF COVID-19 RELATED DISTRESS

The mental health impact of the COVID-19 pandemic has been significant, with many regions across the globe reporting significant increases in anxiety, depression, trauma, and insomnia This study aims to validate a potential cognitive model of maintenance factors of COVID-19 related distress by examining psychological predictors of distress, and their goodness-of-fit as a coherent model. Participants from the general population (n=555) were recruited using a cross-sectional on-line survey design, assessing Demographic factors, Anxiety, Depression, Loneliness, COVID-19 related distress, Trauma Cognitions related to COVID-19, Rumination, Safety Behaviours, Personality Factors, and Mental Effort related to COVID-19. A series of stepwise linear regressions found that components of the model were significant and accounted for a large percentage of variance when examining Covid-19 related distress (R2=0.447 Covid Stress Scale), Anxiety (R2=0.536 DASS-Anxiety Subscale) and Depression (R2=0.596 Depression DASS-subscale). In a confirmatory factor analysis, Loneliness, Post-Traumatic Cognitions about Self, Post-Traumatic Cognitions about the World, Emotional Stability, and Mental Effort related to COVID-19 loaded onto a single factor. The final model showed adequate fit (CFI=0.990, TLI=0.983, RMSEA=0.053(.027-.080), GFI=0.986, SRMR=0.0216), χ2=23.087, p=0.006). The results highlight the importance of cognitive factors, such as post-traumatic cognitions, rumination, and mental effort in maintaining COVID-19 related distress.

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The COVID-19-pandemic offers a unique, if tragic, opportunity to assess the impact of a world-wide crisis on religion. Theories from various disciplines including the psychology of religion and cultural evolution suggest that crises cause higher levels of religiosity. However, such theories also predict that levels of religiosity should remain stable in the context of well-functioning governments, secular institutions and norms that might address social, epistemic, and material needs in a crisis. While the relationship between crisis and religion have been examined in countries with higher levels of religiosity, it has yet to be extensively empirically tested in countries with lower levels of religiosity. Here, on the basis of explicit causal assumptions and using Bayesian multilevel modeling, we analyze quasi-representative longitudinal data from Denmark collected over the course of the pandemic from May 2020 to March 2021. Our analysis show that self-reported religiosity did not increase as a result of the pandemic, an inference that is robust to a range of model specifications, including full Bayesian imputation of missing covariates and post-stratification. We discuss possible interpretations of this finding and argue for an emphasis on cultural context going forward in theories of crises and religion.

Does a Pandemic Increase Religiosity in a Secular Nation? A Longitudinal Examination

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Cuteness in the young has long been theorized to elicit care and protection (Lorenz, 1943). Most research on this has focused on human infants, despite theories suggesting that cuteness may elicit broader social interest that could support development beyond infancy (Kringelbach et al., 2016; Sherman & Haidt, 2011). In four experiments (N=531 adults, 98 children), we tested whether ‘kindchenschema’—facial proportions associated with cuteness—and perceived cuteness elicit interest in playing with and caring for children, and whether masks disrupt these processes. Participants viewed images of children’s faces, masked or unmasked. Kindchenschema correlated with perceived cuteness and age, and these variables predicted adults’ interest in playing with and caring for children. Masks did not reduce cuteness ratings or interest in children, although they weakened relations between perceived cuteness and interest, and between perceived age and interest. Cuteness and related signals may shape adults’ interactions with children, and, consequently, children’s development.

Kindchenschema and Cuteness Elicit Interest in Caring for and Playing with Young Children, But Less So in the Presence of Masks

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The political rise of Donald Trump and the ideology of Trumpism has had a major impact on American politics, culture, and its response to the COVID-19 pandemic. We began the process of validating a psychometric model of Trumpism using three waves (~1000 participants per wave) of data from national online surveys of adults conducted in the United States from June 2020 to February 2021 as part of the COVID-19 Adjustment and Behaviors Survey. We found that the covariance among measures of Trump approval and attitudes about race, immigration, policing, guns, and media bias were best accounted for by a single Trumpism factor, and that this factor was strongly related to attitudes about a rigged 2020 Presidential election, the Insurrection of January 6, 2021, and Trump idolatry. Trumpism was also associated with Republican party affiliation and White race, but had only small associations with age, sex, income, and education. Trumpism was associated with increased odds of a positive COVID-19 diagnosis, skepticism about the seriousness of COVID-19, lack of support for government restrictions to reduce the spread of COVID-19, less adherence to social distancing and mask wearing guidelines, anti-vax attitudes, and hesitancy to receive a COVID-19 vaccine. Results indicate these measures provide a valid assessment of Trumpism which is likely to continue to play a major role in American political and cultural life for the foreseeable future.

Politics can be bad for your health: Trumpism and COVID-19 Outcomes

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What are the underlying cognitive mechanisms that support belief in conspiracies? Common dual-process perspectives suggest that deliberation helps people make more accurate decisions and decreases belief in conspiracy theories that have been proven wrong (therefore, bringing people closer to objective accuracy). However, evidence for this stance is i) mostly correlational and ii) existing causal evidence might be influenced by experimental demand effects and/or a lack of suitable control conditions. Furthermore, recent work has found that analytic thinking tends to increase the coherence between prior beliefs and new information, which may not always lead to accurate conclusions. In two studies, participants were asked to evaluate the strength of conspiratorial (or non-conspiratorial) explanations of events. In the first study, which used well-known conspiracy theories, deliberation had no effect. In the second study, which used relatively unknown conspiracy theories, we found that experimentally manipulating deliberation did increase belief accuracy - but only among people with a strong ‘anti-conspiracy’ or strong ‘pro-conspiracy’ mindset from the outset, and not among those with an intermediate conspiratorial mindset. Although these results generally support the idea that encouraging people to deliberate can help to counter the growth of novel conspiracy theories, they also indicate that the effect of deliberation on conspiratorial beliefs is more complicated than previously thought.

Does deliberation decrease belief in conspiracies?

10.31234/osf.io/xya43

Despite the prominence of emotion disturbance in bipolar disorder, few studies have assessed emotion differentiation. The present investigation used an experience-sampling approach to test the utility of emotion differentiation in predicting bipolar mood-related difficulties. Across two studies, emerging adults participated during a normative first year of college (Spring 2019; Study 1; n=136) or during their first year of college marked by a naturalistic global pandemic stressor (Spring 2020; Study 2; n=136). Study 1 results suggested that emotion differentiation was not associated with trait bipolar risk. Study 2 suggested that global emotion differentiation was associated with increased trait bipolar risk, but not current mood symptom severity. These results suggest that relationships between emotion differentiation and dimensions of bipolar risk may vary by context. Discussion focuses on the implications for translational interventions.

Emotion Differentiation and Bipolar Risk in Emerging Adults Before and During the COVID-19 Pandemic

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Background: Test, Trace and Isolate (TTI) is a key strategy in the SARS-CoV-2 pandemic response. There is limited experimental evidence on how to improve uptake of COVID-19 testing. In this study we manipulate test site proximity to evaluate its impact on test uptake. Methods: We conducted two quasi-experimental studies (from February 8th to March 21st, 2021) during a community-wide testing initiative in The Netherlands. In Study 1 we placed a test site in one village (reducing distance to test site from 8.4km to 800m) but not in a matched control village (distance to test site 9.9km). In Study 2 a mobile test bus alternated between two areas codes, changing distance to the test site from 3.5km to 200m and from 1.6km to 850m. The trial is registered at the Netherlands Trial Register (number NL9365). Findings: In Study 1 (n=11,317 eligible inhabitants), a logistic regression controlling for baseline differences (8.6% control vs 10% intervention) found a significant effect of reducing test site proximity (17.5% control vs 29% intervention) [Odds Ratio =1.84 {95% CI 1.68-2.02}]; p < 0.0001]. In Study 2 (n=1,880 eligible inhabitations), a Poisson regression revealed higher test uptake when the mobile test bus was present (33.6% when absent vs 42.6% when present, Incidence Rate Ratio = 1.35, [95% CI 1.14-1.59]; p < 0.0001). Interpretation: Reducing the distance to COVID-19 test facilities increased test uptake, at least when the distance was reduced from an average of 3.5km to 200 metres. Localising test facilities can substantially increase testing for COVID-19, and thus the effectiveness of TTI in general. Funding: The Ministry of Health, Welfare and Sport (VWS) of the Netherlands.

The effect of proximity of COVID-19 test facilities on test uptake: Two quasi-experimental trials

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Background: Digital technologies are creating unprecedented opportunities to improve and increase support to older people with cognitive and mental health problems, and to their family and carers. However, barriers that preclude the implementation of technology driven programs for the assessment and intervention of adults at risk of cognitive decline need to be better understood. This study investigated these outstanding issues, as well as considering the impact that the COVID-19 Pandemic has had on such barriers. Methods: A sample of 105 participants completed an online survey. Their ages ranged from 18 to 92 years. Of these,72%were female,83%had higher education and beyond, 42% were working, 42% were retired, and 14% were unemployed. The questionnaires assessed IT experience alongside awareness, attitudes, and stigmas regarding the use of technologies, particularly those used to support cognitive and mental health. Questionnaires also explored the impact of the COVID-19 pandemic on these technology-related factors. We compared these across groups of young (n=45), middle age (n=12) and older adults (n=48). Results: Relative to younger participants, older participants were less aware of, and held stronger stigmas against healthcare technologies, even though they reported more IT experience. IT awareness was associated to more positive (r=0.619, p<0.001) and less negative IT Attitudes (r=-0.271, p=0.015), more acceptability (r=-0.374, p=0.001) and receptiveness towards technologies (r=-0.610, p<0.001). Male participants appeared to be more aware of such technologies than female participants. However, relative to men, women had increased the number of ways and frequency with which they used technologies since the COVID-19 pandemic started, and older people in general felt more inclined to endorse the need to learn more about healthcare technologies. Conclusions: Having more accumulated IT experience throughout our lives may not necessarily lead to better acceptance of healthcare technologies. More awareness about such specific technologies will help overcome stigmas, and challenging environments such as those imposed by the COVID-19 pandemicmay lead to positive changes in perception and acceptance of such technologies. These are necessary steps towards the personalisation of healthcare technologies to support vulnerable adults at risk of dementia.

Factors influencing acceptance of technology across age: Amid the COVID-19 pandemic

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We apply a novel crowdsourcing approach to provide rapid insights on the most promising interventions to promote uptake of COVID-19 booster vaccines. In the first stage, international experts proposed 46 unique interventions. To reduce noise and potential bias, in the second stage, experts and representative general population samples from the UK and the US rated the proposed interventions on several criteria, including expected effectiveness and acceptability. Sanctions were evaluated as potentially most effective but least accepted. Interventions that received the most positive evaluations regarding both effectiveness and acceptability across evaluation groups were a day off after getting vaccinated, financial incentives, tax benefits, benefit campaigns, and mobile vaccination teams. The results provide useful insights to help governments in their decision which interventions to implement.

Crowdsourcing interventions to promote uptake of COVID-19 booster vaccines

During the COVID-19 pandemic, individuals have depended on risk information to make decisions about everyday behaviors and public policy. In this online informational intervention, we assessed whether an interactive website influenced individuals' risk tolerance to support public health goals. We collected data from 10,891 unique users who interacted with the online COVID-19 Event Risk Tool (https://covid19risk.biosci.gatech.edu/), which featured interactive elements (a dynamic risk map, survey questions, and a risk quiz with accuracy feedback). After learning about the risk of COVID-19 exposure, participants reported being less willing to participate in potentially risky events. This increase in risk aversion was most pronounced for large event sizes and for individuals who had underestimated risk. We also uncovered a bias in risk estimation: Participants tended to overestimate the risk of small events, but underestimate the risk of large events. Our results bear implications for risk communication and insights for broader research on risky decision-making.

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Counteracting COVID-19 Risk Misestimation with an Interactive Website

Covid leads as Pharmaceutical Technology lists the top five terms tweeted on infectious diseases in February 2022, based on data from GlobalData’s Pharmaceuticals Influencer Platform. The top trends are the most mentioned terms or concepts among Twitter discussions of more than 150 infectious diseases experts tracked by GlobalData’s Pharmaceuticals Influencer platform during February 2022.

Infectious diseases trends: Covid-19 most mentioned on Twitter Feb. 2022

For years, I repressed thinking about three things that shaped my life and my body. But the fourth blow of coronavirus pushed it all out into the open

How long Covid forced me to confront my past and my identity

To handle an infectious outbreak, the public must be informed about the infection risk and be motivated to comply with infection control measures. Perceiving the situation as threatening and seeing public benefits to complying may increase the public’s motivation to comply. The current study used a preregistered survey experiment to investigate if emphasizing high infection risk and appealing to societal benefits impacted intention to comply with infection control measures. The results show main effects of risk and of appeals to societal benefits. There was no interaction between risk scenario and motivational emphasis. The results suggest that to maximize compliance, information about disease outbreak should emphasize the individual risk of contracting the disease, and could also underline the public value of limiting infection spread. These findings can inform communication strategies during an infectious disease outbreak and help health authorities limit transmission.

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Risk of infection and appeal to public benefit increase compliance with infection control measures

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To further understand how to combat COVID-19 vaccination hesitancy, we examined the effects of pro-vaccine expert consensus messaging on lay attitudes of vaccine safety and intention to vaccinate. We surveyed N = 729 individuals from four countries. Regardless of its content, consensus messaging had an overall small positive effect. Most critically, the direction of the effect varied depending on the baseline attitudes of participants: consensus information improved the attitude of vaccine sceptics and uncertain individuals, while having no effect on vaccine supporters. We also analysed whether the persuasiveness of expert consensus would increase after puncturing an illusion of explanatory depth in individuals. This further manipulation had no direct effect, nor interacted with the type of expert consensus. We conclude that highlighting expert consensus may be a way to increase support toward COVID-19 vaccination in those already hesitant or sceptical with little risk of side-effects.

COVID-19 vaccine sceptics are persuaded by pro-vaccine expert consensus messaging

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Restrictions, social isolation, and uncertainty related to the global COVID-19 pandemic have disrupted the ways that parents and children maintain family routines, health, and wellbeing. Companion animals (pets) can be a critical source of comfort during traumatic experiences, although changes to family routines, such as those caused by COVID-19, can also bring about challenges like managing undesirable pet behaviours or pet-human interactions. We aimed to examine the relationship between pet attachment and mental health for both parents and their children during the COVID-19 pandemic in Australia. A total of 1,034 parents living with a child under 18 years and a cat or dog completed an online cross-sectional survey. Path analysis using multivariate linear regression was conducted to examine associations between objective COVID-19 impacts, subjective worry about COVID-19, human-pet attachment, and mental health. After adjusting for core demographic factors, stronger pet-child attachment was associated with greater child anxiety (parent-reported, p<.001). Parent-pet attachment was not associated with self-reported psychological distress (p=.42), however, parents who reported a strong emotional closeness with their pet reported greater psychological distress (p=.002). Findings highlight the role of pets during times of change and uncertainty. It is possible that families are turning to animals as a source of comfort, during a time when traditional social supports are less accessible. Alternatively, strong pet attachment is likely to reflect high levels of empathy, which might increase vulnerability to psychological distress. Longitudinal evidence is required to delineate the mechanisms underpinning pet attachment and mental health.

Parent and child mental health during COVID-19 in Australia: The role of pet attachment

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Introduction: Vast available international evidence has investigated the mental health impacts of the COVID-19 pandemic. This review aims to synthesise evidence, identifying populations and characteristics associated with poor mental health. Methods: A meta-review of pooled prevalence of anxiety and depression, with subgroup analyses for the general population, healthcare workers (HCW) and COVID-19 patients; and a meta-synthesis of systematic reviews to collate evidence on associated factors and further mental disorders. Databases searched included Scopus, Embase, PsycINFO, and MEDLINE dated to May 2021. Eligibility criteria included systematic reviews and/or meta-analyses, published post-November 2019, reporting data in English on mental health outcomes during the pandemic. Results: Eighty-one systematic reviews were included, 51 of which incorporated meta-analysis. Meta-review overall anxiety prevalence was 29% (95%CI: 27–31%, I2: 99.83%), with subgroup prevalence as 35% (95%CI: 23–47%, I2: 97.4%) in COVID-19 patients, 29% in HCW (95%CI: 25– 32, I2: 99.8%) and 28% in the general population (95%CI: 25–31%, I2: 99.9%). Meta-review overall depression prevalence was 28% (95%CI: 26–30%, I2: 99.7), with subgroup prevalence as 30% (95%CI: 7–60%, I2: 99.8%) in COVID-19 patients, 28% (95%CI: 25–31%, I2: 99.7%) in HCW and 27% (95%CI: 25–30, I2: 99.8%) in the general population. Meta-synthesis found many experienced psychological distress and PTSD/PTSS during COVID-19, but pooled prevalence ranged substantially. Fear of, proximity to, or confirmed COVID-19 infection; undergoing quarantine; and COVID-19-related news exposure were associated with adverse mental health outcomes. Amongst other factors, people who are younger, female, LGBTIQ, pregnant, parents or experiencing low social support, financial issues or socio-economic disadvantage, tended to have poorer mental health during the pandemic period. Conclusions: Despite high volumes of reviews, the diversity of findings and dearth of longitudinal studies within reviews means clear links between COVID-19 and mental health are not available, although existing evidence indicates probable associations.

A hidden pandemic? An umbrella review of global evidence on mental health in the time of COVID-19

doi: https://doi.org/10.1038/d41586-022-00215-2

The highly transmissible variant emerged with a host of unusual mutations. Now scientists are trying to work out how it evolved.

Where did Omicron come from? Three key theories

Update on growth of Omicron subvariant BA.2 in England from Wellcome Sanger data. Growing in all regions. The main Omicron variant we've had so far is BA.1. There is then its child BA.1.1 with an extra mutation and its brother BA.2 which is pretty different to BA.1. 1/2

Despite Covid hospitalizations trending downward, 80% of hospitals across the country are under ‘high or extreme stress’

As Omicron peaks, the US healthcare system is left ‘broken beyond repair’

https://doi.org/10.1038/s41586-022-04399-5

The Omicron (B.1.1.529) variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was initially identified in November of 2021 in South Africa and Botswana as well as in a sample from a traveler from South Africa in Hong Kong1,2. Since then, B.1.1.529 has been detected globally. This variant seems to be at least equally infectious than B.1.617.2 (Delta), has already caused super spreader events3 and has outcompeted Delta within weeks in several countries and metropolitan areas. B.1.1.529 hosts an unprecedented number of mutations in its spike gene and early reports have provided evidence for extensive immune escape and reduced vaccine effectiveness2,4–6. Here, we investigated the neutralizing and binding activity of sera from convalescent, mRNA double vaccinated, mRNA boosted, convalescent double vaccinated, and convalescent boosted individuals against wild type, B.1.351 and B.1.1.529 SARS-CoV-2 isolates. Neutralizing activity of sera from convalescent and double vaccinated participants was undetectable to very low against B.1.1.529 while neutralizing activity of sera from individuals who had been exposed to spike three or four times was maintained, albeit at significantly reduced levels. Binding to the B.1.1.529 receptor binding domain (RBD) and N-terminal domain (NTD) was reduced in convalescent not vaccinated individuals, but was mostly retained in vaccinated individuals.

Activity of convalescent and vaccine serum against SARS-CoV-2 Omicron

Covid cases in England reached a new high of 113,628 on Christmas Day and 1,281 people were admitted to hospital – the highest daily figure since mid-February.Official data on new Covid cases, which was delayed over the festive period, also showed 98,515 new confirmed cases reported in England on Monday. Data for Boxing Day from England and Wales combined revealed 108,893 daily cases reported.

England could fit Covid air filters to all classrooms for half cost of royal yacht

England could fit an air purifier to every classroom for half the price of the new royal yacht, a move which scientists and campaigners say would significantly reduce the spread of Covid in schools.The move would cost about £140m, according to calculations by the Liberal Democrats. Government sources have said there will be no delay to the start of the school term, despite surging Omicron cases, and that any additional restrictions will not include classroom closures.

Hospitals could see as many as one in six doctors and nurses off sick throughout 2022, according to modelling, as the Omicron wave of Covid fuels burnout, stress and anxiety among NHS staff.

‘A living nightmare’: Burnout could lead to tripling of NHS staff sickness next year

https://doi.org/10.1038/s41582-021-00593-7

The neurological deficits caused by COVID-19, which were first reported in the early months of 2020, continue to intrigue neurologists and health-care professionals worldwide. As two new studies highlight, these manifestations are frequent and are expected to increase the burden of morbidity and mortality in the acute and chronic phases of COVID-19.

Counting the neurological cost of COVID-19

What to KnowNY smashed its all-time daily COVID case record for the eighth time in little more than a week on Sunday as new state data clearly shows vaccine efficacy declines vs. infection but not hospitalization More than 49K NYers tested positive, Gov. Kathy Hochul said, an increase of nearly 5,000 over Friday; most of them were in NYC; hospitalizations statewide topped 4,800 SundayStill, officials say there is no reason to panic; yes, the sheer infection increases alone are astonishing, they say, but vaccines are holding up vs severe omicron infection and state and city hospitals are prepared

NY Pre-Christmas COVID Testing Delivers Record Total Just Shy of 50,000 Cases in Single Day

DOI:10.21203/rs.3.rs-1139035/v1

SARS-CoV-2 infection and persistence throughout the human body and brain

6.Laboratory of Virology, Division of Intramural Research, National Institute of Allergy 23and Infectious Diseases, National Institute of Health, Hamilton, MT, USA 247.Laboratory of Persistence Viral Diseases, Rocky Mountain Laboratories, National 25Institute of Allergy and Infectious Diseases, National Institute of Health, Hamilton, MT, 26USA 278.Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National 28Institutes of Health, Bethesda, MD, USA 299.National Institute of Dental and Craniofacial Research, National Institutes of Health, 30Bethesda, MD, USA 3110.University of Maryland School of Medicine, Baltimore, MD, USA 3211.Postdoctoral Research Associate Training Program, National Institute of General Medical 33Sciences, National Institutes of Health, Bethesda, MD, USA 3412.R Adams Cowley Shock Trauma Center, Department of Medicine and Program in 35Trauma, University of Maryland School of Medicine, Baltimore, MD, USA 3613.R Adams Cowley Shock Trauma Center, Department of Surgery and Program in Trauma, 37University of Maryland School of Medicine, Baltimore, MD, USA 3814.Department of Medicine, Division of Infectious Disease, University of Maryland School 39of Medicine, Baltimore, MD, USA 4015.Institute of Human Virology, University of Maryland School of Medicine, Baltimore, 41MD, USA 4216.Department of Surgery, Division of Cardiac Surgery, University of Maryland School of 43Medicine, Baltimore, MD, USA 4417.Department of Medicine, Division of Pulmonary and Critical Care Medicine, University 45of Maryland School of Medicine, Baltimore, MD, USA 4618.Hospitalist Department, TidalHealth Peninsula Regional, Salisbury, MD, USA 4719.Division of Critical Care Medicine, Department of Medicine, University of Maryland St. 48Joseph Medical Center, Towson, MD, USA 4920.Medical Virology Section, Laboratory of Infectious Diseases, National Institute of 50Allergy and Infectious Diseases, National Institutes of Health, Bethesda MD 51^See Acknowledgements 52*Corresponding author. Email: chertowd@cc.nih.gov 535455565758596061626364656667COVID-19 is known to cause multi-organ dysfunction1-3 in acute infection, with 68prolonged symptoms experienced by some patients, termed Post-Acute Sequelae of SARS-69CoV-2 (PASC)4-5. However, the burden of infection outside the respiratory tract and time 70to viral clearance is not well characterized, particularly in the brain3,6-14. We performed 71complete autopsies on 44 patients with COVID-19 to map and quantify SARS-CoV-2 72distribution, replication, and cell-type specificity across the human body, including brain, 73from acute infection through over seven months following symptom onset. We show that 74SARS-CoV-2 is widely distributed, even among patients who died with asymptomatic to 75mild COVID-19, and that virus replication is present in multiple pulmonary and 76extrapulmonary tissues early in infection. Further, we detected persistent SARS-CoV-2 77RNA in multiple anatomic sites, including regions throughout the brain, for up to 230 days 78following symptom onset. Despite extensive distribution of SARS-CoV-2 in the body, we 79observed a paucity of inflammation or direct viral cytopathology outside of the lungs. Our 80data prove that SARS-CoV-2 causes systemic infection and can persist in the body for 81months. 82Main text:83 Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the 84causative agent of coronavirus disease 2019 (COVID-19), has well described pulmonary and 85extrapulmonary manifestations1-3, including multiorgan failure and shock among severe and fatal 86cases. Some survivors experience Post-Acute Sequelae of SARS-CoV-2 (PASC) – also known as 87Long COVID—with cardiovascular, pulmonary, and neurological manifestations with or without 88functional impairment4-5. While autopsy studies of fatal COVID-19 cases support the ability of 89SARS-CoV-2 to infect multiple organs3,7-12, extra-pulmonary organs often lack histopathological 90evidence of direct virally-mediated injury or inflammation10-14. The paradox of extra-pulmonary 91infection without injury or inflammation raises many pathogen- and host-related questions. 92These questions include, but are not limited to: What is the burden of infection within versus 93outside of the respiratory tract? What cell types are infected across extra-pulmonary tissues, and 94do they support SARS-CoV-2 infection and replication? In the absence of cellular injury and 95inflammation in extra-pulmonary tissues, does SARS-CoV-2 persist, and if so, over what 96interval? Does SARS-CoV-2 evolve as it spreads to and persists in different anatomical 97compartments? 98To inform these pathogen-focused questions and to evaluate for the presence or absence 99of associated histopathology in matched tissue specimens, we performed extensive autopsies on 100a diverse population of 44 individuals who died from or with COVID-19 up to 230 days 101following initial symptom onset. Our approach focused on timely, systematic, and 102comprehensive tissue sampling and preservation of adjacent tissue samples for complementary 103analyses. We performed droplet digital polymerase chain reaction (ddPCR) for sensitive 104detection and quantification of SARS-CoV-2 gene targets in all tissue samples collected. To 105elucidate SARS-CoV-2 cell-type specificity and validate ddPCR findings, we performed in situ106hybridization (ISH) broadly across sampled tissues. Immunohistochemistry (IHC) was used to 107further validate cell-type specificity in the brain where controversy remains on the regional 108distribution and cellular tropism of SARS-CoV-2 infection. In all samples where SARS-CoV-2 109RNA was detected by ddPCR, we performed qRT-PCR to detect subgenomic (sg)RNA, an assay 110suggestive of recent virus replication15. We confirmed the presence of replication-competent 111SARS-CoV-2 in extrapulmonary tissues by virus isolation in cell culture. Lastly, in six 112individuals, we measured the diversity and anatomic distribution of intra-individual SARS-CoV-1132 variants using high-throughput, single-genome amplification and sequencing (HT-SGS). 114We categorized autopsy cases of SARS-CoV-2 infection as “early” (n=17), “mid” 115(n=13), or “late” (n=14) by illness day (D) at the time of death, being ≤D14, D15-D30, or ≥D31, 116respectively. We defined persistence as presence of SARS-CoV-2 RNA among late cases. Due to 117the extensive tissue collection, we analyzed and described the results in terms of grouped tissue 118categories as the following: respiratory tract; cardiovascular; lymphoid; gastrointestinal; renal 119and endocrine; reproductive; muscle, skin, adipose, & peripheral nerves; and brain. 120121Autopsy cohort overview 122Between April 26, 2020 and March 2, 2021, we performed autopsies on 44 PCR-123confirmed cases (Extended Data Fig. 1). SARS-CoV-2 seroconversion was detected in 38 of 124these cases (Supplementary Data 1); three early cases (P27, P36, P37) had not seroconverted and 125perimortem plasma was unavailable for the other three cases (P3, P4, P15). Extensive sampling 126of the brain was accomplished in 11 of the 44 cases (Fig. 1). The cohort was 29.5% female with 127a mean age of 59.2 years and was diverse across race and ethnicity (Extended Data Table 1). 12895.5% of patients had at least one comorbidity, with hypertension (54.5%), obesity (52.3%), and 129chronic respiratory disease (34.1%) being most common. Patients presented to the hospital a 130mean of 9.4 days following symptom onset and were hospitalized a mean of 26.4 days. Overall, 131the mean interval from symptom onset to death was 35.2 days and the mean postmortem interval 132was 26.2 hours. 81.8% of patients required intubation with invasive mechanical ventilation, 13322.7% received extracorporeal membrane oxygenation (ECMO) support, and 40.9% required 134renal replacement therapy. Vasopressors, systemic steroids, systemic anticoagulation, and 135antibiotics were commonly administered (Extended Data Table 1). Individual patient-level 136demographic and clinical information can be found in Extended Data Table 2. 137138Widespread infection and persistence 139SARS-CoV-2 RNA was detected in all 44 cases and across 79 of 85 anatomical locations 140and body fluids sampled (Extended Data Fig. 2, Supplementary Data 1). The highest burden of 141SARS-CoV-2 RNA (i.e., >100,000 N gene copies/ng RNA input) was detected in the respiratory 142tract of early cases (Figure 1), but we detected at least 100 N gene copies/ng RNA input from 143every tissue group besides reproductive tissues from multiple individuals among early cases. The 144mean SARS-CoV-2 N gene copies/ng RNA detected from tissues in each grouping among early 145cases are as follows: 9,210.10 across respiratory tissues; 38.75 across cardiovascular tissues; 14630.01 across lymphoid tissues; 24.68 across gastrointestinal tissues; 12.76 across renal and 147endocrine tissues; 0.36 across reproductive tissues; 27.50 across muscle, peripheral nerve, 148adipose, and skin tissues; 57.40 across ocular tissues; and 32.93 across brain tissues (Extended 149Data Table 3). 150With a few exceptions, the overall burden of SARS-CoV-2 RNA decreased by a log or 151more across tissue categories among mid cases, and further decreased among late cases. 152However, several mid and late cases had high levels (≥5 N gene copies/ng RNA input) detected 153among multiple tissues (Extended Data Fig. 2). Further, persistence of low-level SARS-CoV-2 154RNA (0.0004 to <0.5 N gene copies/ng RNA input) was frequently detected across multiple 155tissue categories among all late cases, despite being undetectable in plasma (Extended Data Fig. 1562, Supplementary Data 1). Notably, SARS-CoV-2 RNA was detected in the brains of all six late 157cases and across most locations evaluated in the brain in five of these six, including P42 who 158died at D230 (Fig. 1). 159Overall, SARS-CoV-2 RNA was detected in respiratory tissue of 43/44 cases (97.7%); 160cardiovascular tissue of 35/44 cases (79.5%); lymphoid tissue of 38/44 cases (86.4%); 161gastrointestinal tissue of 32/44 (72.7%); renal and endocrine tissue of 28/44 cases (63.6%); 162reproductive tissue in 17/40 cases (42.5%); muscle, skin, adipose, and peripheral nervous tissue 163in 30/44 cases (68.2%); ocular tissue and humors of 22/28 cases (57.9%); and brain tissue in 16410/11 cases (90.9%) (Extended Data Table 3). 165We additionally detected SARS-CoV-2 sgRNA across all tissue categories, 166predominately among early cases (14/17, 82.4%), as well as in plasma, pleural fluid, and vitreous 167humor (Fig. 1, Extended Data Fig. 2, Supplementary Data 1). sgRNA was also detected in at 168least one tissue of 61.5% of mid cases and 42.9% of late cases, including across three tissue 169categories in a case at D99 (P20). 170We isolated SARS-CoV-2 in cell culture from multiple pulmonary and extrapulmonary 171tissues, including lung, bronchus, sinus turbinate, heart, mediastinal lymph node, small intestine, 172and adrenal gland from early cases up to D7 (P19, P27, P32, P37; Supplementary Data 1). 173174Intra-individual viral variant diversity 175We used HT-SGS to analyze SARS-CoV-2 spike gene variant sequences from a total of 17646 tissues in six individuals. In five individuals from the early group, predominant spike 177sequences were largely identical across tissues. In P27, P19, and P18, no non-synonymous virus 178genetic diversity was detected in pulmonary and extrapulmonary sites despite a high depth of 179single-molecule sampling (Extended Data Fig. 3). Thus, virus populations that were relatively 180homogeneous had disseminated in these individuals without coding changes in spike. However, 181we also noted important patterns of intra-individual virus diversity in several patients from the 182early group. In P27, although all 4,525 inferred spike amino acid sequences were identical, two 183virus haplotypes, each with a single synonymous substitution, were preferentially detected in 184extrapulmonary sites including right and left ventricles and mediastinal LN. In P38, we observed 185clear virus genetic differences between the lung lobes and the brain, with a D80F residue found 186in 31/31 pulmonary but 0/490 brain sequences and a G1219V residue that was restricted to brain 187minor variants. A similar distinction was observed between sequences from dura mater and other 188sites in P36, albeit at very low sampling depth (n = 2 sequences) from dura mater. Overall, these 189findings suggested no need for alterations in receptor utilization to permit extrapulmonary 190dissemination of SARS-CoV-2, while also revealing genetic compartmentalization between 191viruses in the lung lobes and those in extrapulmonary sites, including the brain. 192193ISH reveals SARS-CoV-2 cellular tropism 194We validated our ddPCR results across all tissue categories via ISH for SARS-CoV-2 195spike RNA across selected early, mid, and late cases (Supplementary Data 3). Overall, we 196detected SARS-CoV-2 RNA via ISH in 36 distinct cell types across all sampled organs 197(Extended Data Table 4, Supplementary Data 3). Spike RNA was detected throughout the 198respiratory tract in early cases, as well as within the sinus turbinate, trachea, lungs, from late 199cases (i.e., P33, P20, P42). 200The heart contained spike RNA within myocytes, endothelium, and smooth muscle of 201vessels of both early (P18, P19) and late (P3 & P42) cases. The pericardium demonstrated a 202positive signal for spike RNA within fibroblasts of the stroma. Intimal cells of the aorta were 203additionally found to contain spike RNA. Mononuclear leukocytes within the lymph node, 204spleen, and appendix of an early case (P19) contained spike RNA, as did colonic epithelium (Fig 2052). 206Epithelial cells along the intestinal tract in early cases (P16, P18, P19) contained viral 207RNA, as well as stratified squamous epithelium of the esophagus. Mononuclear leukocytes were 208again visualized with SARS-CoV-2 RNA in lymphoid aggregates and the interstitium of the 209small and large intestine, with infected cells still present in the colon of late cases (P33, P42). 210Kupffer cells, hepatocytes, and bile duct epithelium within the liver were additionally found to 211contain spike RNA. 212Within the kidney, spike RNA could be visualized within parietal epithelium of 213Bowman’s capsule, collecting duct cells, distal tubule cells, and glomerular endothelium. The 214adrenal glands contained spike RNA within endocrine cells. Endocrine follicular cells of the 215thyroid and glandular cells of the pancreas were also positive for spike RNA (Fig. 2). Among 216reproductive organs, spike RNA was visualized within Leydig and Sertoli cells of the testis, 217germ cells within the testicular tubules, endometrial gland epithelium, endometrial stromal cells, 218uterine smooth muscle cells, and stromal cells of the post-menopause ovary (Fig. 2). 219Myocytes within skeletal muscle contained spike RNA in both early (P18) and late (P20) 220cases. In addition to the organ-specific cell type infection of SARS-CoV-2, endothelium, 221muscularis of atrial vessels, and Schwann cells were identified as infected throughout the body, 222and were similarly positive across early and late cases. 223Spike RNA was found in neurons, glia and ependyma, as well as endothelium of vessels 224across all lobes of the brain of early, mid, and late cases. Within the cerebellum specifically, 225neurons, Purkinje cells, and endothelium of vasculature also contained spike protein via IHC 226(Fig. 3). 227228COVID-19 histological findings 229The histopathology findings from our cohort were similar to those reported in other case 230series (Extended Data Fig. 4). All but five cases were considered to have died from COVID-19 231(Extended Data Table 5), and, of these, 37 (94.5%) had either acute pneumonia or diffuse 232alveolar damage at the time of death (Supplementary Data 2). Phases of diffuse alveolar damage 233showed clear temporal associations, with the exudative phase seen mainly within the first three 234weeks of infection and the fibrosing phase not seen until after a month of infection (Extended 235Data Fig. 5). Pulmonary thromboembolic complications, which were also likely related to 236SARS-CoV-2 infection, with or without infarction, were noted in 10 (23%) cases. Another 237finding likely related to SARS-CoV-2 infection included myocardial infiltrates in four cases, 238including one case of significant myocarditis16 (P3). Some of the cases of microscopic ischemia 239appeared to be associated with fibrin-platelet microthrombi, and may therefore be related to 240COVID-19 thrombotic complications. Within the lymph nodes and spleen, we observed 241lymphodepletion and both follicular and paracortical hyperplasia. 242Outside the lungs, histological changes were mainly related to complications of therapy 243or preexisting co-morbidities: mainly obesity, diabetes, and hypertension. Five cases had old 244ischemic myocardial scars and three had coronary artery bypass grafts in place. Given the 245prevalence of diabetes and obesity in our cohort, it was not surprising to find diabetic 246nephropathy (10 cases, 23%) or steatohepatitis (5 cases, 12%). One case was known to have 247chronic hepatitis C with cirrhosis, but the other cases of advanced hepatic fibrosis were likely 248related to fatty liver disease, even if diagnostic features of steatohepatitis were not present. 249Hepatic necrosis (13 cases, 30%) and changes consistent with acute kidney injury (17 cases, 25039%) were likely related to hypoxic-ischemic injury in these very ill patients. 251In the examination of the 11 brains, we found few histopathologic changes, despite the 252evidence of substantial viral burden.Vascular congestion was an unusual finding that had an 253unclear etiology and could be related to the hemodynamic changes incurred with infection. 254Global hypoxic/ischemic change was seen in two cases, one of which was a juvenile (P36) with a 255seizure disorder who was found to be SARS-CoV-2 positive on hospital admission, but who 256likely died of seizure complications unrelated to viral infection. 257258Discussion 259Here we provide the most comprehensive analysis to date of SARS-CoV-2 cellular 260tropism, quantification, and persistence across the body and brain, in a diverse autopsy cohort 261collected throughout the first year of the pandemic in the United States. Our focus on short post-262mortem intervals, comprehensive approach to tissue collection, and preservation techniques –263RNAlater and flash freezing of fresh tissue – allowed us to detect and quantify viral levels with 264high sensitivity by ddPCR and ISH, as well as culture virus, which are notable differences 265compared to other studies. 266We show SARS-CoV-2 disseminates across the human body and brain early in infection 267at high levels, and provide evidence of virus replication at multiple extrapulmonary sites during 268the first week following symptom onset. We detected sgRNA in at least one tissue in over half of 269cases (14/27) beyond D14, suggesting that prolonged viral replication may occur in extra-270pulmonary tissues as late as D99. While others have questioned if extrapulmonary viral presence 271is due to either residual blood within the tissue8,17 or cross-contamination from the lungs during 272tissue procurement8, our data rule out both theories. Only 12 cases had detectable SARS-CoV-2 273RNA in a perimortem plasma sample, and of these only two early cases also had SARS-CoV-2 274sgRNA in the plasma, which occurred at Ct levels higher than nearly all of their tissues with 275sgRNA. Therefore, residual blood contamination cannot account forRNA levels within tissues. 276Furthermore, blood contamination would not account for the SARS-CoV-2 sgRNA or virus 277isolated from tissues. Contamination of additional tissues during procurement, is likewise ruled 278out by ISH demonstrating widespread SARS-CoV-2 cellular tropism across the sampled organs, 279by IHC detecting viral protein in the brain, and by several cases of virus genetic 280compartmentalization in which spike variant sequences that were abundant in extrapulmonary 281tissues were rare or undetected in lung samples. 282Using both ddPCR and sgRNA analysis to inform our selection of tissue for virus 283isolation and ISH staining allow us to describe a number of novel findings. Others6,8-12,17 have 284previously reported SARS-CoV-2 RNA within the heart, lymph node, small intestine, and 285adrenal gland. We demonstrate conclusively that SARS-CoV-2 is capable of infecting and 286replicating within these tissues. Current literature has also reported absent or controversial 287expression of ACE2 and/or TMPRSS2 in several extrapulmonary tissues, such as the colon, 288lymphoid tissues, and ocular tissues, calling into question if these tissues can become infected by 289SARS-CoV-21-3. However, we observed high levels of SARS-CoV-2 RNA and evidence of 290replication within these organs, as well as SARS-CoV-2 RNA via ISH in colonic mucosal 291epithelium and mononuclear leukocytes within the spleen, thoracic cavity lymph nodes, and GI 292lymphoid aggregates. We believe these ISH positive cells represent either infection or 293phagocytized virus in resident macrophages. Further, we isolated virus from a mediastinal lymph 294node and ocular tissue from two early cases (P19, P32). 295Our use of a single-copy sequencing approach for the SARS-CoV-2 spike allowed us to 296demonstrate homogeneous virus populations in many tissues, while also revealing informative 297virus variants in others. Low intra-individual diversity of SARS-CoV-2 sequences has been 298observed frequently in previous studies18-20, and likely relates to the intrinsic mutation rate of the 299virus as well as lack of early immune pressure to drive virus evolution in new infections. It is 300important to note that our HT-SGS approach has both a high accuracy and a high sensitivity for 301minor variants within each sample, making findings of low virus diversity highly reliable21. The 302virus genetic compartmentalization that we observed between pulmonary and extrapulmonary 303sites in several individuals supports independent replication of the virus at these sites, rather than 304spillover from one site to another. Importantly, lack of compartmentalization between these sites 305in other individuals does not rule out independent virus replication, as independently replicating 306populations may share identical sequences if overall diversity is very low. It was also interesting 307to note several cases where brain-derived virus spike sequences showed non-synonymous 308differences relative to sequences from other tissues. These differences may indicate differential 309selective pressure on spike by antiviral antibodies in brain versus other sites, though further 310studies will be needed to confirm this speculation. 311Our results collectively show while that the highest burden of SARS-CoV-2 is in the 312airways and lung, the virus can disseminate early during infection and infect cells throughout the 313entire body, including widely throughout the brain. While others have posited this viral 314dissemination occurs through cell trafficking11 due to a reported failure to culture virus from 315blood3,22, our data support an early viremic phase, which seeds the virus throughout the body 316following pulmonary infection. Recent work by Jacobs et al.22 in which SARS-CoV-2 virions 317were pelleted and imaged from COVID-19 patient plasma, supports this mechanism of viral 318dissemination. Although our cohort is primarily made up of severe cases of COVID-19, two 319early cases had mild respiratory symptoms (P28; fatal pulmonary embolism occurred at home) or 320no symptoms (P36; diagnosed upon hospitalization for ultimately fatal complications of a 321comorbidity), yet still had SARS-CoV-2 RNA widely detected across the body, including brain, 322with detection of sgRNA in multiple compartments. Our findings, therefore, suggest viremia 323leading to body-wide dissemination, including across the blood-brain barrier, and viral 324replication can occur early in COVID-19, even in asymptomatic or mild cases. Further, P36 was 325a juvenile with no evidence of multisystem inflammatory syndrome in children, suggesting 326infected children without severe COVID-19 can also experience systemic infection with SARS-327CoV-2. 328Finally, a major contribution of our work is a greater understanding of the duration and 329locations at which SARS-CoV-2 can persist. While the respiratory tract was the most common 330location in which SARS-CoV-2 RNA tends to linger, ≥50% of late cases also had persistence in 331the myocardium, thoracic cavity lymph nodes, tongue, peripheral nerves, ocular tissue, and in all 332sampled areas of the brain, except the dura mater. Interestingly, despite having much lower 333levels of SARS-CoV-2 in early cases compared to respiratory tissues, we found similar levels 334between pulmonary and the extrapulmonary tissue categories in late cases. This less efficient 335viral clearance in extrapulmonary tissues is perhaps related to a less robust innate and adaptive 336immune response outside the respiratory tract. 337We detected sgRNA in tissue of over 60% of the cohort. While less definitive than viral 338culture23,24, multiple studies have shown that sgRNA levels correlate with acute infection and can 339be detected in respiratory samples of immunocompromised patients experiencing prolonged 340infection24. These data coupled with ISH suggest that SARS-CoV-2 can replicate within tissue 341for over 3 months after infection in some individuals, with RNA failing to clear from multiple 342compartments for up to D230. This persistence of viral RNA and sgRNA may represent infection 343with defective virus, which has been described in persistent infection with measles virus –344another single-strand enveloped RNA virus—in cases of subacute sclerosing panencephalitis25. 345The mechanisms contributing to PASC are still being investigated; however, ongoing 346systemic and local inflammatory responses have been proposed to play a role5. Our data provide 347evidence for delayed viral clearance, but do not support significant inflammation outside of the 348respiratory tract even among patients who died months after symptom onset. 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Patients are aligned from shortest to longest duration of illness (DOI) 441prior to death, listed at the bottom of the figure, and grouped into early (≤14 days), mid (15-30 442days), and late (≥31 days) DOI. Tissues are grouped by tissue category beginning with the 443respiratory tract at the top and central nervous system at the bottom. Viral RNA levels range 444from 0.002 to 500,000 N gene copies per ng of RNA input, depicted as a gradient from dark blue 445at the lowest level to dark red at the highest level. Tissues that were also positive for sgRNA via 446real-time RT-PCR are shaded with black vertical bars. L/left, LN/lymph node, NA/not acquired, 447R/right, SC/spinal cord. 448449450451452453454455456457458459460Fig. 2 RNA in situ (RNAscope) detection of SARS-CoV-2 in extrapulmonary tissues. 461SARS-CoV-2 virus is localized to the Golgi and endoplasmic, peri-nuclear in appearance, in the 462following organs and cell types (500 X magnifications): A) Thyroid, demonstrating presence of 463virus within follicular cells. B) Esophagus, demonstrating the presence of virus within the 464stratified squamous epithelium (*), as well as signal in capillaries within the stroma (#). C. 465Spleen, demonstrating the presence of mononuclear lymphoid cells within the white pulp. D) 466Appendix, demonstrating the presence of virus in both colonic epithelium (*) and mononuclear 467lymphoid cells in the stroma (#). E) Adrenal demonstrates virus within endocrine secretory cells 468of the adrenal gland. F) Ovary demonstrates the presence of virus in stromal cells of the ovary in 469a post-menopausal ovary. G) Testis demonstrates the presence of virus in both Sertoli cells (*) 470and maturing germ cells within the seminiferous tubules of the testis (#). H) Endometrium 471demonstrates the presence of virus within endometrial gland epithelium (*) and stromal cells (#), 472in a pre-menopausal endometrial sample. 473474475Fig. 3 SARS-CoV-2 protein expression in human cerebellum. Low magnification 476visualization of no-primary control (A) and primary-added adjacent (B) cerebellar sections 477labeled for SARS-CoV-2 (green) and NeuN (magenta) demonstrate viral-specific protein 478expression within the tissue. The locations of the molecular layer (ML), granular layer (GL), and 479white matter (WM) are indicated in (A) and also correspond to (B). Higher magnification images 480demonstrate cell type-specific infection (C-E). Both NeuN positive neurons (yellow arrows) and 481other unidentified cells (white arrows) are associated with viral protein in the GL (C). Purkinje 482cells adjacent to the ML are infected (D, white arrow). In rare instances, blood vessels adjacent 483to the GL and WM were associated with viral protein (E, white arrow). The scale bars in A is 484also associated with B. All immunofluorescent images were obtained by confocal microscopy. 485486487488489490491492493494495496497498499500501502503504505506Methods: 507Autopsies 508Autopsies were performed and tissues were collected as previously described26 in the National 509Cancer Institute’s Laboratory of Pathology at the National Institutes of Health Clinical Center 510following consent of the legal next of kin. 511512Measurement of IgG and IgM antibodies against Nucleocapsid and Spike protein of SARS-513CoV-2 514Fluid-phase luciferase immunoprecipitation systems (LIPS) assays were used to study IgG and 515IgM antibody response to SARS-CoV-2. For IgG LIPS measurements, Renilla luciferase-516nucleocapsid and Gaussia luciferase-spike protein extracts were employed with protein A/G 517beads (Protein A/G UltraLink Resin, Thermo Fisher Scientific) as the IgG capture reagent as 518previously described with microtiter filter plates27. For IgM measurements, anti-human IgM goat 519agarose beads (Sigma) were substituted as the capture reagent using both the microfilter plate 520and microtube format28. The IgM immunoprecipitation assays performed in 1.5 ml microfuge 521tube format containing 1 l sera or plasma, Renilla luciferase-nucleocapsid (10 million light unit 522input per tube) or Gaussia luciferase-spike protein (40 million light input per tube) and buffer A 523(20 mM Tris, pH 7.5, 150 mM NaCl, 5 mM MgCl2, 0.1% Triton X-100) to a total volume of 100 524l. After mixing, the tubes were incubated at room temp for 1 hour. Next 10 l of the anti-human 525IgM agarose bead suspension was added to each tube for additional 60 minutes and tubes were 526placed on a rotating wheel at 4o C. The samples were then washed by brief centrifugation to 527collect the bead pellet at room temperature 3 times with 1.5 ml Buffer A and once with 1.5 ml of 528PBS. After the final wash, the beads were mixed with coelenterazine substrate (100 l) and light 529units measured in a tube luminometer. Known seronegative and seropositive samples for IgG and 530IgM antibodies against nucleocapsid and spike proteins were used for assigning seropositive cut-531off values and for standardization. 532533 SARS-CoV-2 RNA quantification of tissues and body fluids 534Total RNA was extracted from RNAlater (Invitrogen)-preserved tissues and body fluids 535collected at autopsy using the RNeasy Mini, RNeasy Fibrous Tissue Mini, RNeasy Lipid Tissue 536Mini Kit, and QIAamp Viral RNA Mini Kits (Qiagen) according to the manufacturer’s protocols. 537Upstream tissue processing and subsequent RNA quantification have been previously 538described26. The QX200 AutoDG Droplet Digital PCR System (Bio-Rad) was used to detect and 539quantify SARS-CoV-2 RNA in technical replicates of 5.5 uL RNA for fluids and up to 550 ng 540RNA for tissues as previously described26. Results were then normalized to copies of N1, N2, 541and RP per mL of sample input for fluids and per ng of RNA concentration input for tissues. For 542samples to be considered positive for SARS-CoV-2 N1 or N2 genes, they needed to mean the 543manufacturer’s limit of detection of ≥0.1 copies/μ L and ≥2 positive droplets per well. Over 60 544control autopsy tissues from uninfected patients, representing all organs collected for COVID-19 545autopsy cases, were used to validate the manufacturer’s EUA published LOD for nasopharyngeal 546swabs for tissues (Extended Data Table 8). ddPCR data for P316 as well as a portion of tissues 547from the oral cavity26 have been previously reported. 548549sgRNA analysis of ddPCR positive tissues 550Tissues that tested positive for one or both SARS-CoV-2 N gene targets via ddPCR had RNA 551submitted for sgRNA analysis. Briefly, five μ l RNA was used in a one-step real-time RT-PCR 552assay to sgRNA (forward primer 5’- CGATCTCTTGTAGATCTGTTCTC-3'; reverse primer 5’- 553ATATTGCAGCAGTACGCACACA-3'; probe 5’-FAM-554ACACTAGCCATCCTTACTGCGCTTCG-ZEN-IBHQ-3')29 using the Rotor-Gene probe kit 555(Qiagen) according to instructions of the manufacturer. In each run, standard dilutions of counted 556RNA standards were run in parallel to calculate copy numbers in the samples. The limit of 557detection for this assay was determined to be <40 Cq (Supplemental Data 1) using 40 control 558autopsy tissues from uninfected patients, representing all organs collected for COVID-19 559autopsy cases. 560561Viral isolation from select postmortem tissues 562Select tissues with high viral RNA levels via ddPCR and sgRNA PCR measuring at or below a 56330 Cq underwent virus isolation to prove the presence of infectious virus. Virus isolation was 564performed on tissues by homogenizing the tissue in 1ml DMEM and inoculating Vero E6 cells in 565a 24-well plate with 250 μ l of cleared homogenate and a 1:10 dilution thereof. Plates were 566centrifuged for 30 minutes at 1000 rpm and incubated for 30 minutes at 37°C and 5% CO2. The 567inoculum was then removed and replaced with 500 μ l DMEM containing 2% FBS, 50 U/ml 568penicillin and 50 μg/ml streptomycin. Six days after inoculation, cytopathic effect (CPE) was 569scored. A blind passage of samples where no CPE was present, was performed according to the 570same method. Supernatants from plates with CPE present were analyzed via PCR for SARS-571CoV-2 to rule out other causes of CPE. 572573Virus Sequencing Methods 574Patients with duration of illness ≤7 d (P27, P19) and 8-14 d (P18) with multiple body site 575tissues containing sgRNA levels ≤31 Cq value were selected for high throughput, single-genome 576amplification and sequencing (HT-SGS) as previously described21. Presence of variants of 577SARS-CoV-2 were analyzed within and between tissues. 578579SARS-CoV-2 RNA in situ hybridization 580Chromogenic in situ detection was performed using the manual RNAScope 2.5 HD assay (Cat# 581322310, Advanced Cell Diagnostics, Hayward, CA) with a modified pretreatment protocol.582Briefly, formalin-fixed and paraffin-embedded (FFPE) tissue sections were cut at 7 μm, air dried 583overnight, and baked for 2 hrs at 60ºC. The FFPE tissue sections were deparaffinized, 584dehydrated, and then treated with pretreat 1 for 10 min at room temperature. The slides were 585boiled with pretreatment reagent for 15 min, digested with protease at 40ºC for 10 min, then 586hybridized for 2 hours at 40oC with probe-V-nCov2019-S (Cat# 848561, Advanced Cell 587Diagnostics). In addition, probe-Hs-PPIB (Cat# 313901, Advanced Cell Diagnostics) and probe-588dapB (Cat# 310043, Advanced Cell Diagnostics) were used as a positive and negative control, 589respectively. Subsequent amplification was done according to the original protocol. Detection of 590specific probe binding sites were visualized with RNAScope 2.5 HD Reagent kit-brown 591chromogenic labels (Advanced Cell Diagnostics). The slides were counterstained with 592hematoxylin and cover-slipped. 593594SARS-CoV-2 immunohistochemistry 595FFPE cerebellar sections were deparaffinized, rehydrated and subject to 0.01M Citrate buffer 596antigen retrieval for 20min at 120°C. Slides were incubated in 0.1% TritonX100 in PBS for 59730min, washed extensively with PBS and fresh True Black Plus® solution (1:40, Cat#23014, 598Biotium) applied for 7min. Following PBS wash, blocking serum (5% normal donkey 599serum/0.3M glycine) was applied for 30min. Primary antibodies against SARS-CoV-2 NP1 600(1:250, custom made) and NeuN (1:200, Cat#MAB377, Chemicon) were diluted in blocking 601serum and applied to slides overnight at 4°C. Species-specific secondary conjugates (1:500, 602Cat#A32790 and #A32744, ThermoFisher) were applied for 1hr at RT. Hoescht 33342 applied 603for 10min (1:2000, Cat#H3570, ThermoFisher) labeled nuclei. Slides were cover-slipped with 604Prolong Gold (Cat#P36930, ThermoFisher). 605606Data Availability 607The datasets that support the findings of this study are available in Supplementary Data 1, 2 and 6083. Sequence data described in this manuscript have been deposited (database accession numbers 609XXXX). The bioinformatic pipeline for HT-SGS data analysis has been deposited 610(https://github.com/niaid/UMI-pacbio-pipeline). ISH images from our cohort as well as positive 611and negative controls are available in Supplementary Data 3, which is available at 612https://halo.cancer.gov, Authentication method: NIH, username: halocancernci@gmail.com, 613password: covid19N!H. 614615Methods References: 61626.Huang, N., et al. SARS-CoV-2 infection of the oral cavity and saliva. Nat Med. 27, 892–617903 (2021). https://doi.org/10.1038/s41591-021-01296-8. 61827.Burbelo, P. D., et al. Sensitivity in Detection of Antibodies to Nucleocapsid and Spike 619Proteins of Severe Acute Respiratory Syndrome Coronavirus 2 in Patients With 620Coronavirus Disease 2019. J Infect Dis. 222(2), 206-213 (2020). 621https://doi.org/10.1093/infdis/jiaa273. 62228.Burbelo, P. D., Goldman, R., & Mattson, T. L. A simplified immunoprecipitation method 623for quantitatively measuring antibody responses in clinical sera samples by using 624mammalian-produced Renilla luciferase-antigen fusion proteins. BMC Biotechnol. 5, 22 625(2005). https://doi.org/10.1186/1472-6750-5-22. 62629.Wölfel R., et al. Virological assessment of hospitalized patients with COVID-19. Nature. 627581(7809), 465-469 (2020). https://doi.org/10.1038/s41586-020-2196-x. 628629Acknowledgements: 630This study was funded and supported by the Intramural Research Program of the National 631Institutes of Health, Clinical Center, National Institute of Dental and Craniofacial Research, and 632National Institute of Allergy and Infectious Diseases. 633This research was made possible through the NIH Medical Research Scholars Program, a 634public-private partnership supported jointly by the NIH and contributions to the Foundation for 635the NIH from the Doris Duke Charitable Foundation, Genentech, the American Association for 636Dental Research, and the Colgate-Palmolive Company. 637638NIH COVID-19 Autopsy Consortium 639Daniel S. Chertow1,2, Kevin M. Vannella1,2, Sydney R. Stein1,2, Marcos J. Ramos-Benitez1,2,4, 640Andrew P. Platt1,2, James M. Dickey1,2, Ashley L. Babyak1,2, Luis J. Perez Valencia1,2, Sabrina 641C. Ramelli3, Shelly J. Curran3, Mary E. Richert3, David E. Kleiner5, Stephen M. Hewitt5, Martha 642Quezado5, Willie J. Young5, Sarah P. Young5, Billel Gasmi5, Michelly Sampaio De Melo5, 643Sabina Desar5, Saber Tadros5, Nadia Nasir5, Xueting Jin5, Sharika Rajan5, Esra Dikoglu5, Neval 644Ozkaya5, Kris Ylaya5, Joon-Yong Chung5, Stefania Pittaluga5, Grace Smith5, Elizabeth R. 645Emanuel6, Brian L. Kelsall6, Justin A. Olivera7, Megan Blawas7, Robert A. Star7, Alison 646Grazioli8, Nicole Hays9, Madeleine Purcell9, Shreya Singireddy9, Jocelyn Wu9, Katherine Raja9, 647Ryan Curto9, Jean E. Chung10, Amy J. Borth10, Kimberly A. Bowers10, Anne M. Weichold10, 648Paula A. Minor10, Mir Ahmad N. Moshref10, Emily E. Kelly10, Mohammad M. Sajadi11,12, Kapil 649K. Saharia11,12, Daniel L. Herr13, Thomas M. Scalea14, Douglas Tran15, Ronson J. Madathil15, 650Siamak Dahi15, Kristopher B. Deatrick15, Eric M. Krause16, Joseph Rabin17, Joseph A. Herrold18, 651Ali Tabatabai18, Eric S. Hochberg18, Christopher R. Cornachione18, Andrea R. Levine18, Justin E. 652Richards19, John Elder20, Allen P. Burke20, Michael A. Mazzeffi21, Robert H. Christenson22, 653Zackary A. Chancer23, Mustafa Abdulmahdi24, Sabrina Sopha24, Tyler Goldberg24, Shahabuddin 654Soherwardi25, Yashvir Sangwan26, Michael T. McCurdy27,12, Kristen Sudano27, Diane Blume27, 655Bethany Radin27, Madhat Arnouk27, James W. Eagan Jr28, Robert Palermo29, Anthony D. 656Harris30 657658Affiliations: 6591.Emerging Pathogens Section, Department of Critical Care Medicine, Clinical Center, 660National Institutes of Health, Bethesda, MD, USA 6612.Laboratory of Immunoregulation, National Institute of Allergy and Infectious Diseases, 662Bethesda, MD, USA 6633.Critical Care Medicine Department, Clinical Center, National Institutes of Health, 664Bethesda, MD, USA 6654.Postdoctoral Research Associate Training Program, National Institute of General Medical 666Sciences, National Institutes of Health, Bethesda, MD, USA 6675.Laboratory of Pathology, Center for Cancer Research, National Cancer Institute, National 668Institutes of Health, Bethesda, MD, USA 6696.Mucosal Immunobiology Section, Laboratory of Molecular Immunology, National 670Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, 671USA 6727.Renal Diagnostics and Therapeutics Unit, Kidney Diseases Branch, National Institute of 673Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, 674MD, USA 6758.Kidney Disease Section, Kidney Diseases Branch, National Institute of Diabetes and 676Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA 6779.University of Maryland School of Medicine, Baltimore, MD, USA 67810.University of Maryland Medical Center, Baltimore, MD, USA 67911.Institute of Human Virology, University of Maryland School of Medicine, Baltimore, 680MD, USA 68112.Department of Medicine, Division of Pulmonary and Critical Care Medicine, University 682of Maryland School of Medicine, Baltimore, MD, USA 68313.R Adams Cowley Shock Trauma Center, Department of Medicine and Program in 684Trauma, University of Maryland School of Medicine, Baltimore, MD, USA 68514.Department of Shock Trauma Critical Care, University of Maryland School of Medicine, 686Baltimore, MD, USA 68715.Department of Surgery, Division of Cardiac Surgery, University of Maryland School of 688Medicine, Baltimore, MD, USA 68916.Department of Surgery, Division of Thoracic Surgery, University of Maryland School of 690Medicine, Baltimore, MD, USA 69117.R Adams Cowley Shock Trauma Center, Department of Surgery and Program in Trauma, 692University of Maryland School of Medicine, Baltimore, MD, USA 69318.Department of Medicine, Division of Infectious Disease, University of Maryland School 694of Medicine, Baltimore, MD, USA 69519.Department of Anesthesiology, Division of Critical Care Medicine, University of 696Maryland School of Medicine, Baltimore, MD, USA 69720.Department of Autopsy and Thoracic Pathology, University of Maryland School of 698Medicine, Baltimore, MD, USA 69921.Department of Anesthesiology and Critical Care Medicine, George Washington School 700of Medicine and Health Sciences, Washington, DC USA 70122.Department of Laboratory Science, University of Maryland School of Medicine, 702Baltimore, MD, USA 70323.Department of Anesthesiology, University of Southern California Keck School of 704Medicine, Los Angeles, CA, USA 70524.Critical Care Medicine, University of Maryland Baltimore Washington Medical Center, 706Glen Burnie, MD, USA 70725.Hospitalist Department, TidalHealth Peninsula Regional, Salisbury, MD, USA 70826.Department of Interventional Pulmonology, TidalHealth Peninsula Regional, Salisbury, 709MD, USA 71027.Division of Critical Care Medicine, Department of Medicine, University of Maryland St. 711Joseph Medical Center, Towson, MD, USA 71228.Department of Pathology, University of Maryland, St. Joseph Medical Center, Towson, 713MD, USA 71429.Department of Pathology, Greater Baltimore Medical Center, Townson, MD, USA 71530.Department of Epidemiology and Public Health, University of Maryland School of 716Medicine, Baltimore, MD, USA 717718Author Contributions 719DSC, KMV, SRS, MJRB, ALB, LJPV, AG, DLH, SMH & DEK contributed to the study design 720and protocols for autopsy procurement. APP, JMD, MER, AG, NH, MP, SS, JW, KR, RC, JEC, 721AJB, KAB, AMW, PAM, MANM, EEK, MMS, KKS, DLH, TMS, DT, RJM, SD, KBD, EMK, 722JR, JAH, AT, ESH, CRC, ARL, JER, JE, APB, MAM, RHC, ZAC, MA, SS, TG, SS, YS, MTM, 723KS, DB, BR, MA, JWE Jr, RP, and ADH provided care for, recruited, collected samples from, 724and/or procured medical records for the patients in this study. DEK, SMH, MQ, WJY, SPY, BG, 725MSDM, SD, ST, NN, XJ, SR, ED, NO, KY, JYC, SP, and GS conducted the autopsies and/or 726histological and ISH analysis. SRS, MJRB, APP, JMD, ALB, LJPV, SCR, SJC, ERE, BLK, 727JAO, MB, and RAS assisted with procurement and preservation of autopsy specimens. SRS with 728assistance from SCR and JMD performed RNA extraction, ddPCR, and data analysis. MS, CKY, 729VJM, and EDW performed and analyzed data for sgRNA RT-PCR. CWW and KEP conducted 730IHC on cerebellum. PDB and JIC measured antibody responses to SARS-CoV-2 in perimortem 731plasma samples. SHK, FB, and EAB performed viral sequencing. SRS drafted the manuscript 732with critical input from DSC, KMV, SMH, DEK, SCR, APP, MJRB, EDW, VJM, AG, DLH, 733KKS, MMS MTM, PDB, JIC, CWW, KEP, and SJC. All authors approved the submitted version 734of the manuscript. 735Competing Interests: 736The authors declare no competing or conflict of interest. 737Additional Information: 738Supplementary information is available for this paper. 739Correspondence and requests for materials should be addressed to DSC. 740741742743744745746747748749Extended Data Fig. 1 Autopsy procurement relative to Maryland COVID-19 cases, March 75019th, 2020 to March 9th, 2021. Daily COVID-19 reported cases for Maryland (light blue bars) 751with 7-day average (dark blue line) with timing of autopsies (red arrows). 752753754755756757758759760761762763Extended Data Fig. 2 Distribution, quantification, and replication of SARS-CoV-2 across the 764body and brain over time. The heat map depicts the highest average quantification of SARS-765CoV-2 RNA (N) via ddPCR present within all sampled tissues of 44 autopsy cases. Patients are 766aligned from shortest to longest duration of illness (DOI) prior to death, listed at the bottom of 767the figure, and grouped into early (0-14 d), mid (15-30 d), and late (≥31 d) DOI. Tissues are 768grouped by body system beginning with the respiratory tract at the top and CNS at the bottom. 769Viral RNA levels range from 0.0004 to 500,000 copies per ng of RNA input, depicted as a 770gradient from dark blue at the lowest level to dark red at the highest level. Tissues that were also 771positive for sgRNA via real-time RT-PCR are shaded with black vertical bars. 772773774775776777778779780781782783784785786787Extended Data Figure 3: Analysis of SARS-CoV-2 genetic diversity across body 788compartments in patients. (a) P18, (b) P19, (c) P27, (d) P33, (e) P36, (f) P38. Haplotype 789diagrams (left) show SARS-CoV-2 spike single genome sequences detected in multiple organs. 790Spike NH2-terminal domain (NTD), receptor-binding domain (RBD), and furin cleavage site (F) 791regions are shaded grey, and remaining regions of the spike are shaded white. Ticks with 792different colors indicate mutations relative to the WA-1 reference sequence; green indicates non-793synonymous differences from WA-1 detected in all sequences in the individual; blue indicates 794synonymous mutations detected variably within the individual, and pink indicates non-795synonymous mutations detected variably within the individual. Bar graphs (right) show the 796percentage of all single genome sequences in the sample matching each haplotype. 797798799800801802803804805806807808809810811Extended Data Fig. 4 Representative findings in patients in the COVID-19 cohort. A. Lung, 812Subject P22. Exudative phase diffuse alveolar damage with hyaline membranes and mild 813interstitial inflammation (H&E, 100x). B. Lung, Subject P26. Proliferative phase diffuse alveolar 814damage and sparse inflammation. (H&E, 200x). C. Lung, Subject P22. Organizing thrombus in 815medium sized pulmonary artery. (H&E, 40x). D. Lung, Subject P28. Diffuse pulmonary 816hemorrhage. (H&E, 100x). E. Heart, Subject P3. Active lymphocytic myocarditis with 817cardiomyocyte necrosis. (H&E, 400x). F. Heart, Subject P38. Microscopic focus of bland 818myocardial contraction band necrosis. (H&E, 400x). G. Liver, Subject P41. Steatohepatitis with 819mild steatosis and scattered ballooned hepatocytes. (H&E, 400x), H. Liver, Subject P41. Focal 820bridging fibrosis involving central hepatic veins. (Masson trichrome, 40x). I. Kidney, Subject 821P16. Nodular glomerulosclerosis. (Masson trichrome, 600x). J. Spleen, Subject P16. Preservation 822of white pulp and congestion (H&E, 40x) K. Spleen, Subject P14. Lymphoid depletion of white 823pulp with proteinaceous material and red pulp congestion. (H&E, 100x) L. Spleen, Subject P34. 824Relative preservation of white pulp with extramedullary hematopoiesis (inset) in red pulp (H&E, 825200x) M. Lymph node, Subject P25. Follicular hyperplasia with well-defined follicles. (H&E, ) 826N. Lymph node, Subject P25. Marked plasmacytosis in the medullary cord. (H&E, 400x) O. 827Lymph node, Subject P25. Marked plasmacytosis and sinus histiocytosis. (H&E, 400x) P. Brain, 828Subject P35, Focal subarachnoid and intraparenchymal hemorrhage. (H&E, 40x) Q. Brain, 829Subject P44, Vascular congestion. (H&E, 40x) R. Brain, Subject P43, Intravascular platelet 830aggregates. (anti-CD61 stain, 100x) 831832833834835836837838Extended Data Fig. 5 Temporal association of diffuse alveolar damage in patients dying 839from COVID-19. Number of autopsy cases with stages of diffuse alveolar damage via 840histopathologic analysis by duration of illness. Early time points mainly show the initial 841exudative phase of diffuse alveolar damage, while patients dying after prolonged illness are more 842likely to show organizing or fibrosing stages. 843844845846Extended Data Table 1 Autopsy cohort demographics, comorbidities, and clinical 847intervention summary. (a) Summary of demographics and known comorbidities for autopsy 848cases. (b) Summary of illness course and clinical care for autopsy cases. Data compiled from 849available patient medical records. ECMO/extracorporeal membrane oxygenation. 850851852853854Extended Data Table 2 Individual case demographics and clinical summary. Data obtained 855from available medical records. AF/atrial fibrillation, AVAPS/average volume-assured pressure 856support, BiPAP/bilevel positive airway pressure, CAD/coronary artery disease, CHF/congestive 857heart failure, CKD/chronic kidney disease, CML/chronic myeloid leukemia, COPD/chronic 858obstructive pulmonary disease, DAD/diffuse alveolar damage, DM/diabetes mellitus, DVT/deep 859vein thrombosis, ECMO/extracorporeal membrane oxygenation, ESRD/end-stage renal disease, 860HLD/hyperlipidemia, HTN/hypertension, Hx/historical, ILD/interstitial lung disease, LV/left 861ventricular, MS/multiple sclerosis, PE/pulmonary embolism, PVD/peripheral vascular disease, 862PH/pulmonary hypertension, s/p/status post. 863864865Extended Data Table 3 Summary of SARS-CoV-2 RNA and sgRNA by tissue category over 866time. (a) Summary of the average nucleocapsid gene copies/ng RNA across cases by tissue 867category and duration of illness (days). (b) Summary of the number and percentage of cases with 868SARS-CoV-2 RNA detected via droplet digital (dd)PCR by tissue category for all cases and by 869tissue and duration of illness (days). The number and percentage of tissues positive for ddPCR 870that were additionally positive for subgenomic (sg)RNA PCR is listed in the right most column. 871*A tissue positive via ddPCR was not tested via sgRNA PCR. CNS/central nervous system, 872LN/lymph node. 873874875Extended Data Table 4 SARS-CoV-2 cellular tropism. Summary of cell types that were 876identified as SARS-CoV-2 positive by ISH, and the corresponding anatomic sites in which this 877was observed. 878Cell TypeLocationsBile duct epitheliumLiverChondrocytesBronchial cartilage ringsCollecting duct epitheliumKidneyDistal tubule epitheliumKidneyEndocrine cells of adrenalAdrenal glandEndocrine cells of thyroidThyroidEndotheliumVasculature, allEpendymaBrain Exocrine cells of pancreasPancreasFibroblast-like cellsPericardium, heart, trachea, bronchusGerm cellsTestisGlandular epithelumUterus GliaBrain, all locationsHepatocytesLiverHyaline MembraneLungInterstitial cells of endometriumUterusIntimal cellsAortaKupffer cellsLiverLeydig cellsTestisMononuclear leukocytesLung, spleen, lymph nodes, lymphoid aggregates of GIMucosal epitheliumSmall intestine, colonMucus secreting epithelium, salivary typeSalivary glands, trachea, bronchusMyocytes, CardiacHeartMyocytes, StriatedPsoas muscleMyocytes, SmoothUterus, GINeuronsBrain, all locationsParietal cells Kidney, Bowman's capsulePneumocytes, type I & IILungPurkinje cellCerebellumSchwann cellsNerves, allSertoli cellsTestisStratified epithelium (& basal layer)Trachea, esophagusStromal cellsPericardium, uterus, ovaryVascular smooth muscleArteries, all879Extended Data Table 5 Histopathologic findings of COVID-19 autopsy cases. Summary of 880histopathologic findings across organ system across 44 autopsy cases. Central nervous system 881findings are reported for the 11 cases in which consent for sampling was obtained. 1Includes one 882case in which the COVID lungs were transplanted and data from explanted lungs used in table. 8832Individual lung weights were missing in 4 cases. 3Findings missing on 1 case due to extreme 884autolysis. 4Weight missing on one case. 5Lymph node findings missing in 4 cases 885

COVID-19 is known to cause multi-organ dysfunction1-3 in acute infection, with prolonged symptoms experienced by some patients, termed Post-Acute Sequelae of SARS-CoV-2 (PASC)4-5. However, the burden of infection outside the respiratory tract and time to viral clearance is not well characterized, particularly in the brain3,6-14. We performed complete autopsies on 44 patients with COVID-19 to map and quantify SARS-CoV-2 distribution, replication, and cell-type specificity across the human body, including brain, from acute infection through over seven months following symptom onset. We show that SARS-CoV-2 is widely distributed, even among patients who died with asymptomatic to mild COVID-19, and that virus replication is present in multiple extrapulmonary tissues early in infection. Further, we detected SARS-CoV-2 RNA in multiple anatomic sites, including regions throughout the brain, for up to 230 days following symptom onset. Despite extensive distribution of SARS-CoV-2 in the body, we observed a paucity of inflammation or direct viral cytopathology outside of the lungs. Our data prove that SARS-CoV-2 causes systemic infection and can persist in the body for months.

Going into this week, Leonardo Vicente was preparing for the worst.New York’s hospital and nursing home employees were mandated to receive at least one COVID-19 vaccine shot by Monday, September 27th to keep their jobs. But ahead of the deadline, barely half—56%—of the staff at the Highbridge Woodycrest Center, the nursing home Vicente runs in the Bronx, had done so.Highbridge has a five-star rating from the Centers for Medicare and Medicaid Services, which keeps track of the quality of care in nursing homes around the country, and is well-staffed compared to many other facilities in the area. But it was facing a huge exodus of employees under the mandate. Functioning with nearly half the staff out would be difficult, but Vicente figured he’d be able to weather the storm.

NY Nursing Homes See Overnight Surge In Employee COVID Vaccinations Thanks To State Mandate

Just over two months since ‘Freedom Day’ and in many places it feels like Covid-19 is a distant memory. Masks have been abandoned, schools are ‘back to normal’, people are crowding back on tubes; meanwhile you still can’t go into a vet’s consulting room with your pet. So where exactly are we with regards to Covid-19 in England? I’m going to try and tackle some of the questions I get asked regularly. I’ve met people who have been double jabbed who have caught Covid-19 and become very unwell. Is it really true that the vaccines protect against severe illness?

Covid-19: 2 months since ‘Freedom Day’, but where are we now?

More than 4.2 billion people worldwide have received a dose of a Covid-19 vaccine, equal to about 54.7 percent of the world population. This map shows the stark gap between vaccination programs in different countries.

Tracking Coronavirus Vaccinations Around the World

Between March 1 and September 11, 4 per per cent of the people who contracted COVID in NSW were fully vaccinatedThe vaccines are effective at helping people avoid serious illnessHowever, some people remain vulnerable despite being fully vaccinated

A small number of fully vaccinated people with COVID-19 in NSW have died — here's why

The case for universal boosters is weak, and the benefits are unclearThe resurgence of covid-19 in high income countries with advanced vaccine programmes has raised concerns about the durability of vaccine effectiveness, especially against the more transmissible delta variant. This has led some to argue in favour of booster doses for the general population before clear evidence of benefit, which we believe is misguided.

doi: https://doi.org/10.1136/bmj.n2320

Covid-19 vaccination: evidence of waning immunity is overstated