Persistent neuropsychiatric symptoms after COVID-19: a systematic review and meta-analysis. (preprint)

BackgroundThe nature and extent of persistent neuropsychiatric symptoms after COVID-19 are not established. To help inform mental health service planning in the pandemic recovery phase, we systematically determined the prevalence of neuropsychiatric symptoms in survivors of COVID-19. MethodsFor this pre-registered systematic review and meta-analysis (PROSPERO ID CRD42021239750) we searched PubMed, EMBASE, CINAHL and PsycINFO to 20th February 2021, plus our own curated database. We included peer-reviewed studies reporting neuropsychiatric symptoms at post-acute or later time-points after COVID-19 infection, and in control groups where available. For each study a minimum of two authors extracted summary data. For each symptom we calculated a primary pooled prevalence using generalised linear mixed models. Heterogeneity was measured with I2. Subgroup analyses were conducted for COVID-19 hospitalisation, severity, and duration of follow-up. FindingsFrom 2,844 unique titles we included 51 studies (n=18,917 patients). The mean duration of follow-up after COVID-19 was 77 days (range 14-182 days). Study quality was generally moderate. The most frequent neuropsychiatric symptom was sleep disturbance (pooled prevalence=27{middle dot}4% [95%CI 21{middle dot}4- 34{middle dot}4%]), followed by fatigue (24{middle dot}4% [17{middle dot}5-32{middle dot}9%]), objective cognitive impairment (20{middle dot}2% [10{middle dot}3-35{middle dot}7%]), anxiety (19{middle dot}1%[13{middle dot}3-26{middle dot}8%]), and post-traumatic stress (15{middle dot}7% [9{middle dot}9-24{middle dot}1%]). Only two studies reported symptoms in control groups, both reporting higher frequencies in Covid-19 survivors versus controls. Between-study heterogeneity was high (I2=79{middle dot}6%-98{middle dot}6%). There was little or no evidence of differential symptom prevalence based on hospitalisation status, severity, or follow-up duration. InterpretationNeuropsychiatric symptoms are common and persistent after recovery from COVID-19. The literature on longer-term consequences is still maturing, but indicates a particularly high frequency of insomnia, fatigue, cognitive impairment, and anxiety disorders in the first six months after infection. FundingJPR is supported by the Wellcome Trust (102186/B/13/Z). IK is funded through the NIHR (Oxford Health Biomedical Research Facility, Development and Skills Enhancement Award) and the Medical Research Council (Dementias Platform UK and Deep and Frequent Phenotyping study project grants). HH is funded by the German Research Foundation (DFG, Grant: HO 1286/16-1). The funders played no role in the design, analysis or decision to publish. RESEARCH IN CONTEXTO_ST_ABSEvidence before this studyC_ST_ABSNeuropsychiatric symptoms like cognitive impairment, fatigue, insomnia, depression and anxiety can be highly disabling. Recently there has been increasing awareness of persistent neuropsychiatric symptoms after COVID-19 infection, but a systematic synthesis of these symptoms is not available. In this review we searched five databases up to 20th February 2021, to establish the pooled prevalence of individual neuropsychiatric symptoms up to six months after COVID-19. Added value of this studyThis study establishes which of a range of neuropsychiatric symptoms are the most common after COVID-19. We found high rates in general, with little convincing evidence that these symptoms lessen in frequency during the follow-up periods studied. ImplicationsPersistent neuropsychiatric symptoms are common and appear to be limited neither to the post-acute phase, nor to recovery only from severe COVID-19. Our results imply that health services should plan for high rates of requirement for multidisciplinary services (including neurological, neuropsychiatric and psychological management) as populations recover from the COVID-19 pandemic.

The neurology and neuropsychiatry of COVID-19: a systematic review and meta-analysis of the early literature reveals frequent CNS manifestations and key emerging narratives (preprint)

ObjectivesThere is accumulating evidence of the neurological and neuropsychiatric features of infection with SARS-CoV-2. In this systematic review and meta-analysis, we aimed to describe the characteristics of the early literature and estimate point prevalences for neurological and neuropsychiatric manifestations. MethodsWe searched MEDLINE, Embase, PsycInfo and CINAHL up to 18 July 2020 for randomised controlled trials, cohort studies, case-control studies, cross-sectional studies and case series. Studies reporting prevalences of neurological or neuropsychiatric symptoms were synthesised into meta-analyses to estimate pooled prevalence. Results13,292 records were screened by at least two authors to identify 215 included studies, of which there were 37 cohort studies, 15 case-control studies, 80 cross-sectional studies and 83 case series from 30 countries. 147 studies were included in the meta-analysis. The symptoms with the highest prevalence were anosmia (43.1% [35.2--51.3], n=15,975, 63 studies), weakness (40.0% [27.9--53.5], n=221, 3 studies), fatigue (37.8% [31.6--44.4], n=21,101, 67 studies), dysgeusia (37.2% [30.0--45.3], n=13,686, 52 studies), myalgia (25.1% [19.8--31.3], n=66.268, 76 studies), depression (23.0 % [11.8--40.2], n=43,128, 10 studies), headache (20.7% [95% CI 16.1--26.1], n=64,613, 84 studies), anxiety (15.9% [5.6--37.7], n=42,566, 9 studies) and altered mental status (8.2% [4.4--14.8], n=49,326, 19 studies). Heterogeneity for most clinical manifestations was high. ConclusionsNeurological and neuropsychiatric symptoms of COVID-19 in the pandemics early phase are varied and common. The neurological and psychiatric academic communities should develop systems to facilitate high-quality methodologies, including more rapid examination of the longitudinal course of neuropsychiatric complications of newly emerging diseases and their relationship to neuroimaging and inflammatory biomarkers.

Medium-term effects of SARS-CoV-2 infection on multiple vital organs, exercise capacity, cognition, quality of life and mental health, post-hospital discharge (preprint)

BackgroundThe medium-term effects of Coronavirus disease (COVID-19) on multiple organ health, exercise capacity, cognition, quality of life and mental health are poorly understood. MethodsFifty-eight COVID-19 patients post-hospital discharge and 30 comorbidity-matched controls were prospectively enrolled for multiorgan (brain, lungs, heart, liver and kidneys) magnetic resonance imaging (MRI), spirometry, six-minute walk test, cardiopulmonary exercise test (CPET), quality of life, cognitive and mental health assessments. FindingsAt 2-3 months from disease-onset, 64% of patients experienced persistent breathlessness and 55% complained of significant fatigue. On MRI, tissue signal abnormalities were seen in the lungs (60%), heart (26%), liver (10%) and kidneys (29%) of patients. COVID-19 patients also exhibited tissue changes in the thalamus, posterior thalamic radiations and sagittal stratum on brain MRI and demonstrated impaired cognitive performance, specifically in the executive and visuospatial domain relative to controls. Exercise tolerance (maximal oxygen consumption and ventilatory efficiency on CPET) and six-minute walk distance (405{+/-}118m vs 517{+/-}106m in controls, p<0.0001) were significantly reduced in patients. The extent of extra-pulmonary MRI abnormalities and exercise tolerance correlated with serum markers of ongoing inflammation and severity of acute illness. Patients were more likely to report symptoms of moderate to severe anxiety (35% versus 10%, p=0.012) and depression (39% versus 17%, p=0.036) and a significant impairment in all domains of quality of life compared to controls. InterpretationA significant proportion of COVID-19 patients discharged from hospital experience ongoing symptoms of breathlessness, fatigue, anxiety, depression and exercise limitation at 2-3 months from disease-onset. Persistent lung and extra-pulmonary organ MRI findings are common. In COVID-19 survivors, chronic inflammation may underlie multiorgan abnormalities and contribute to impaired quality of life. FundingNIHR Oxford and Oxford Health Biomedical Research Centres, British Heart Foundation Centre for Research Excellence, UKRI, Wellcome Trust, British Heart Foundation.

Adult Stem Cell-derived Complete Lung Organoid Models Emulate Lung Disease in COVID-19 (preprint)

SARS-CoV-2, the virus responsible for COVID-19, causes widespread damage in the lungs in the setting of an overzealous immune response whose origin remains unclear. We present a scalable, propagable, personalized, cost-effective adult stem cell-derived human lung organoid model that is complete with both proximal and distal airway epithelia. Monolayers derived from adult lung organoids (ALOs), primary airway cells, or hiPSC-derived alveolar type-II (AT2) pneumocytes were infected with SARS-CoV-2 to create in vitro lung models of COVID-19. Infected ALO-monolayers best recapitulated the transcriptomic signatures in diverse cohorts of COVID-19 patient-derived respiratory samples. The airway (proximal) cells were critical for sustained viral infection whereas distal alveolar differentiation (AT2[->]AT1) was critical for mounting the overzealous host immune response in fatal disease; ALO monolayers with well-mixed proximodistal airway components recapitulated both. Findings validate a human lung model of COVID-19 which can be immediately utilized to investigate COVID-19 pathogenesis, and vet new therapies and vaccines.

3D reconstruction of SARS-CoV-2 infection in ferrets emphasizes focal infection pattern in the upper respiratory tract (preprint)

The visualization of viral pathogens in infected tissues is an invaluable tool to understand spatial virus distribution, localization, and cell tropism in vivo. Commonly, virus-infected tissues are analyzed using conventional immunohistochemistry in paraffin-embedded thin sections. Here, we demonstrate the utility of volumetric three-dimensional (3D) immunofluorescence imaging using tissue optical clearing and light sheet microscopy to investigate host-pathogen interactions of pandemic SARS-CoV-2 in ferrets at a mesoscopic scale. The superior spatial context of large, intact samples (> 150 mm3) allowed detailed quantification of interrelated parameters like focus-to-focus distance or SARS-CoV-2-infected area, facilitating an in-depth description of SARS-CoV-2 infection foci. Accordingly, we could confirm a preferential infection of the ferret upper respiratory tract by SARS-CoV-2 and emphasize a distinct focal infection pattern in nasal turbinates. Conclusively, we present a proof-of-concept study for investigating critically important respiratory pathogens in their spatial tissue morphology and demonstrate the first specific 3D visualization of SARS-CoV-2 infection.

Molecular mechanism of SARS-CoV-2 inactivation by temperature (preprint)

Recent studies have shown that SARS-CoV-2 virus can be inactivated by effect of heat, even though, little is known about the molecular changes induced by the temperature. Here, we unravel the basics of such inactivation mechanism over the SARS-CoV-2 spike glycoprotein by executing atomistic molecular dynamics simulations. Both the closed down and open up states, which determine the accessibility to the receptor binding domain, were considered. Results suggest that the spike undergoes drastic changes in the topology of the hydrogen bond network while salt bridges are mainly preserved. Reorganization in the hydrogen bonds structure produces conformational variations in the receptor binding subunit and explain the thermal inactivation of the virus. Conversely, the macrostructure of the spike is preserved at high temperature because of the retained salt bridges. The proposed mechanism has important implications for engineering new approaches to inactivate the SARS-CoV-2 virus.

Team Alex at CLEF CheckThat! 2020: Identifying Check-Worthy Tweets With Transformer Models (preprint)

While misinformation and disinformation have been thriving in social media for years, with the emergence of the COVID-19 pandemic, the political and the health misinformation merged, thus elevating the problem to a whole new level and giving rise to the first global infodemic. The fight against this infodemic has many aspects, with fact-checking and debunking false and misleading claims being among the most important ones. Unfortunately, manual fact-checking is time-consuming and automatic fact-checking is resource-intense, which means that we need to pre-filter the input social media posts and to throw out those that do not appear to be check-worthy. With this in mind, here we propose a model for detecting check-worthy tweets about COVID-19, which combines deep contextualized text representations with modeling the social context of the tweet. We further describe a number of additional experiments and comparisons, which we believe should be useful for future research as they provide some indication about what techniques are effective for the task. Our official submission to the English version of CLEF-2020 CheckThat! Task 1, system Team_Alex, was ranked second with a MAP score of 0.8034, which is almost tied with the wining system, lagging behind by just 0.003 MAP points absolute.