Datasets:

Uri-ka

/

CLUSTREC-COVID

like 0

coronavirus recovery

34

Recovery from the coronavirus disease-2019 (COVID-19) in two patients with coexisted (HIV) infection

dafuxkbf

coronavirus recovery

34

WITHDRAWN: Analysis of thin-section CT in patients with coronavirus disease (COVID-19) after hospital discharge

7i75vs1i

coronavirus recovery

34

Long-term conditions and severe acute respiratory syndrome SARS-CoV-2 (COVID-19)

Observation of infection trends through the course of the ongoing COVID-19 pandemic has indicated that those with certain pre-existing chronic conditions, such as hypertension, chronic obstructive pulmonary disease and obesity, are particularly likely to develop severe infection and experience disastrous sequelae, including near-fatal pneumonia. This article aims to outline how SARS-CoV-2 affects people and to consider why individuals living with long-term conditions are at increased risk from infection caused by this virus. A summary of available clinical guidelines with recommendations is presented, to provide community nurses with the up-to-date information required for protecting individuals living with a number of long-term conditions. Additionally, special measures required are outlined, so that community nurses may reflect on how to best provide nursing care for individuals living with long-term conditions and understand protection measures for individuals at increased risk from severe COVID-19.

s6oi477q

coronavirus recovery

34

Follow-up studies in COVID-19 recovered patients - is it mandatory?

The novel Coronavirus disease 2019 (COVID-19) is an illness caused due to Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). The World Health Organization (WHO) has declared this outbreak a global health emergency and as on April 24, 2020, it has spread to 213 countries, with 25,91,015 confirmed cases and 742,855 cases have been recovered from COVID-19. In this dreadful situation our team has already published an article in the Science of the Total Environment, which elaborates the various aspects of the SARS-CoV-2 infection. In this situation, it is imperative to understand the possible outcome of COVID-19 recovered patients and determine if they have any other detrimental illnesses by longitudinal analysis to safeguard their life in future. It is necessary to follow-up these recovered patients and performs comprehensive assessments for detection and appropriate management towards their psychological, physical, and social realm. This urges us to suggest that it is highly important to provide counselling, moral support as well as a few recommended guidelines to the recovered patients and society to restore to normalcy. Epidemiological, clinical and immunological studies from COVID-19 recovered patients are particularly important to understand the disease and to prepare better for potential outbreaks in the future. Longitudinal studies on a larger cohort would help us to understand the in-depth prognosis as well as the pathogenesis of COVID-19. Also, follow-up studies will help us provide more information for the development of vaccines and drugs for these kinds of pandemics in the future. Hence, we recommend more studies are required to unravel the possible mechanism of COVID-19 infection and the after-effects of it to understand the characteristics of the virus and to develop the necessary precautionary measures to prevent it.

uqhaxqqh

coronavirus recovery

34

Neuropathogenesis and Neurologic Manifestations of the Coronaviruses in the Age of Coronavirus Disease 2019: A Review

Importance: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in December 2019, causing human coronavirus disease 2019 (COVID-19), which has now spread into a worldwide pandemic. The pulmonary manifestations of COVID-19 have been well described in the literature. Two similar human coronaviruses that cause Middle East respiratory syndrome (MERS-CoV) and severe acute respiratory syndrome (SARS-CoV-1) are known to cause disease in the central and peripheral nervous systems. Emerging evidence suggests COVID-19 has neurologic consequences as well. Observations: This review serves to summarize available information regarding coronaviruses in the nervous system, identify the potential tissue targets and routes of entry of SARS-CoV-2 into the central nervous system, and describe the range of clinical neurological complications that have been reported thus far in COVID-19 and their potential pathogenesis. Viral neuroinvasion may be achieved by several routes, including transsynaptic transfer across infected neurons, entry via the olfactory nerve, infection of vascular endothelium, or leukocyte migration across the blood-brain barrier. The most common neurologic complaints in COVID-19 are anosmia, ageusia, and headache, but other diseases, such as stroke, impairment of consciousness, seizure, and encephalopathy, have also been reported. Conclusions and Relevance: Recognition and understanding of the range of neurological disorders associated with COVID-19 may lead to improved clinical outcomes and better treatment algorithms. Further neuropathological studies will be crucial to understanding the pathogenesis of the disease in the central nervous system, and longitudinal neurologic and cognitive assessment of individuals after recovery from COVID-19 will be crucial to understand the natural history of COVID-19 in the central nervous system and monitor for any long-term neurologic sequelae.

1z6l12ks

coronavirus recovery

34

Clinical characteristics of fatal and recovered cases of coronavirus disease 2019 in Wuhan, China: a retrospective study

BACKGROUND: The 2019 novel coronavirus has caused the outbreak of the acute respiratory disease in Wuhan, Hubei Province of China since December 2019. This study was performed to analyze the clinical characteristics of patients who succumbed to and who recovered from 2019 novel coronavirus disease (COVID-19). METHODS: Clinical data were collected from two tertiary hospitals in Wuhan. A retrospective investigation was conducted to analyze the clinical characteristics of fatal cases of COVID-19 (death group) and we compare them with recovered patients (recovered group). Continuous variables were analyzed using the Mann-Whitney U test. Categorical variables were analyzed by &#967; test or Fisher exact test as appropriate. RESULTS: Our study enrolled 109 COVID-19 patients who died during hospitalization and 116 recovered patients. The median age of the death group was older than the recovered group (69 [62, 74] vs. 40 [33, 57] years, Z = 9.738, P < 0.001). More patients in the death group had underlying diseases (72.5% vs. 41.4%, &#967; = 22.105, P < 0.001). Patients in the death group had a significantly longer time of illness onset to hospitalization (10.0 [6.5, 12.0] vs. 7.0 [5.0, 10.0] days, Z = 3.216, P = 0.001). On admission, the proportions of patients with symptoms of dyspnea (70.6% vs. 19.0%, &#967; = 60.905, P < 0.001) and expectoration (32.1% vs. 12.1%, &#967; = 13.250, P < 0.001) were significantly higher in the death group. The blood oxygen saturation was significantly lower in the death group (85 [77, 91]% vs. 97 [95, 98]%, Z = 10.625, P < 0.001). The white blood cell (WBC) in death group was significantly higher on admission (7.23 [4.87, 11.17] vs. 4.52 [3.62, 5.88] ×10/L, Z = 7.618, P < 0.001). Patients in the death group exhibited significantly lower lymphocyte count (0.63 [0.40, 0.79] vs. 1.00 [0.72, 1.27] ×10/L, Z = 8.037, P < 0.001) and lymphocyte percentage (7.10 [4.45, 12.73]% vs. 23.50 [15.27, 31.25]%, Z = 10.315, P < 0.001) on admission, and the lymphocyte percentage continued to decrease during hospitalization (7.10 [4.45, 12.73]% vs. 2.91 [1.79, 6.13]%, Z = 5.242, P < 0.001). Alanine transaminase (22.00 [15.00, 34.00] vs. 18.70 [13.00, 30.38] U/L, Z = 2.592, P = 0.010), aspartate transaminase (34.00 [27.00, 47.00] vs. 22.00 [17.65, 31.75] U/L, Z = 7.308, P < 0.001), and creatinine levels (89.00 [72.00, 133.50] vs. 65.00 [54.60, 78.75] µmol/L, Z = 6.478, P < 0.001) were significantly higher in the death group than those in the recovered group. C-reactive protein (CRP) levels were also significantly higher in the death group on admission (109.25 [35.00, 170.28] vs. 3.22 [1.04, 21.80] mg/L, Z = 10.206, P < 0.001) and showed no significant improvement after treatment (109.25 [35.00, 170.28] vs. 81.60 [27.23, 179.08] mg/L, Z = 1.219, P = 0.233). The patients in the death group had more complications such as acute respiratory distress syndrome (ARDS) (89.9% vs. 8.6%, &#967; = 148.105, P < 0.001), acute cardiac injury (59.6% vs. 0.9%, &#967; = 93.222, P < 0.001), acute kidney injury (18.3% vs. 0%, &#967; = 23.257, P < 0.001), shock (11.9% vs. 0%, &#967; = 14.618, P < 0.001), and disseminated intravascular coagulation (DIC) (6.4% vs. 0%, &#967; = 7.655, P = 0.006). CONCLUSIONS: Compared to the recovered group, more patients in the death group exhibited characteristics of advanced age, pre-existing comorbidities, dyspnea, oxygen saturation decrease, increased WBC count, decreased lymphocytes, and elevated CRP levels. More patients in the death group had complications such as ARDS, acute cardiac injury, acute kidney injury, shock, and DIC.

en413zlr

coronavirus recovery

34

Influencia de la infección SARS-CoV-2 sobre enfermedades neurodegenerativas y neuropsiquiátricas: ¿una pandemia demorada?/ Influencia de la infección SARS-CoV-2 sobre enfermedades neurodegenerativas y neuropsiquiátricas: ¿una pandemia demorada?/ Impact of SARS-CoV-2 infection on neurodegenerative and neuropsychiatric diseases: a delayed pandemic?

INTRODUCTION: SARS-CoV-2 was first detected in December 2019 in the Chinese city of Wuhan and has since spread across the world. At present, the virus has infected over 1.7 million people and caused over 100 000 deaths worldwide. Research is currently focused on understanding the acute infection and developing effective treatment strategies. In view of the magnitude of the epidemic, we conducted a speculative review of possible medium- and long-term neurological consequences of SARS-CoV-2 infection, with particular emphasis on neurodegenerative and neuropsychiatric diseases of neuroinflammatory origin, based on the available evidence on neurological symptoms of acute SARS-CoV-2 infection. DEVELOPMENT: We systematically reviewed the available evidence about the pathogenic mechanisms of SARS-CoV-2 infection, the immediate and lasting effects of the cytokine storm on the central nervous system, and the consequences of neuroinflammation for the central nervous system. CONCLUSIONS: SARS-CoV-2 is a neuroinvasive virus capable of triggering a cytokine storm, with persistent effects in specific populations. Although our hypothesis is highly speculative, the impact of SARS-CoV-2 infection on the onset and progression of neurodegenerative and neuropsychiatric diseases of neuroinflammatory origin should be regarded as the potential cause of a delayed pandemic that may have a major public health impact in the medium to long term. Cognitive and neuropsychological function should be closely monitored in COVID-19 survivors.

0xkz36bj

coronavirus recovery

34

Behavioral Health and Response for COVID-19

Research from financial stress, disasters, pandemics, and other extreme events, suggests that behavioral health will suffer, including anxiety, depression, and posttraumatic stress symptoms. Furthermore, these symptoms are likely to exacerbate alcohol or drug use, especially for those vulnerable to relapse. The nature of coronavirus disease 2019 (COVID-19) and vast reach of the virus, leave many unknows for the repercussions on behavioral health, yet existing research suggests that behavioral health concerns should take a primary role in response to the pandemic. We propose a 4-step services system designed for implementation with a variety of different groups and reserves limited clinical services for the most extreme reactions. While we can expect symptoms to remit overtime, many will also have longer-term or more severe concerns. Behavioral health interventions will likely need to change overtime and different types of interventions should be considered for different target groups, such as for those who recover from COVID-19, health-care professionals, and essential personnel; and the general public either due to loss of loved ones or significant life disruption. The important thing is to have a systematic plan to support behavioral health and to engage citizens in prevention and doing their part in recovery by staying home and protecting others.

8kp07f7i

coronavirus recovery

34

SARS-CoV-2 turned positive in a discharged patient with COVID-19 arouses concern regarding the present standards for discharge

An outbreak of coronavirus disease (COVID-19) in Wuhan, China caused by SARS-CoV-2 has led to a serious epidemic in China and other countries, resulting in worldwide concern. With active efforts of prevention and control, more and more patients are being discharged. However, how to manage these patients normatively is still challenging. This paper reports an asymptomatic discharged patient with COVID-19 who retested positive for SARS-CoV-2, which arouses concern regarding the present discharge standards of COVID-19.

7jakv2ge

coronavirus recovery

34

Establishment of a COVID-19 Recovery Unit in a Veteran Affairs (VA) Post-Acute Facility

Coronavirus disease 19 (COVID-19) is now an epidemic of global proportion with major adverse impacts on older adults, persons with chronic diseases, and especially residents of long-term care facilities. This health catastrophe has challenged healthcare facilities' capacity to deliver care to not only COVID-19 patients but all patients who need hospital care. We report on a novel approach of utilizing long-term care beds at a Department of Veterans Affairs healthcare facility for managing recovering COVID-19 patients.

3thnu3kk

coronavirus recovery

34

The impact of nutrition on COVID-19 susceptibility and long-term consequences

While all groups are affected by the COVID-19 pandemic, the elderly, underrepresented minorities, and those with underlying medical conditions are at the greatest risk. The high rate of consumption of diets high in saturated fats, sugars, and refined carbohydrates (collectively called Western diet, WD) worldwide, contribute to the prevalence of obesity and type 2 diabetes, and could place these populations at an increased risk for severe COVID-19 pathology and mortality. WD consumption activates the innate immune system and impairs adaptive immunity, leading to chronic inflammation and impaired host defense against viruses. Furthermore, peripheral inflammation caused by COVID-19 may have long-term consequences in those that recover, leading to chronic medical conditions such as dementia and neurodegenerative disease, likely through neuroinflammatory mechanisms that can be compounded by an unhealthy diet. Thus, now more than ever, wider access to healthy foods should be a top priority and individuals should be mindful of healthy eating habits to reduce susceptibility to and long-term complications from COVID-19.

b999y89f

coronavirus recovery

34

Proyección epidemiológica de COVID-19 en Chile basado en el modelo SEIR generalizado y el concepto de recuperado./ [An epidemiological forecast of COVID-19 in Chile based on the generalized SEIR model and the concept of recovered]

The COVID-19 pandemic declared by the World Health Organization (WHO) has generated a wide-ranging debate regarding epidemiological forecasts and the global implications. With the data obtained from the Chilean Ministry of Health (MINSAL), a prospective study was carried out using the generalized SEIR model to estimate the course of COVID-19 in Chile. Three scenarios were estimated: Scenario 1 with official MINSAL data; scenario 2 with official MINSAL data and recovery criteria proposed by international organizations of health; and scenario 3 with official MINSAL data, recovery criteria proposed by international organizations of health, and without considering deaths in the total recovered. There are considerable differences between scenario 1 compared to 2 and 3 in the number of deaths, active patients, and duration of the disease. Scenario 3, considered the most adverse, estimates a total of 11,000 infected people, 1,151 deaths, and that the peak of the disease will occur in the first days of May. We concluded that the concept of recovered may be decisive for the epidemiological forecasts of COVID-19 in Chile.

agsowv3x

coronavirus recovery

34

La réhabilitation: indispensable pour les survivants d'un COVID-19 sévère./ [Rehabilitation is crucial for severe COVID-19 survivors]

COVID-19 survivors can have serious complications from this viral infection, particularly respiratory and cardiovascular with severe asthenia and fatigue. Several studies have already demonstrated the benefit of early rehabilitation after the acute phase, especially in patients who have been in intensive care. The authors present a rehabilitation program including interdisciplinary care with simple and reproducible clinical criteria.

6ztf2n5w

coronavirus recovery

34

Involvement of the Nervous System in SARS-CoV-2 Infection

As a severe and highly contagious infectious disease, coronavirus disease 2019 (COVID-19) has caused a global pandemic. Several case reports have demonstrated that the respiratory system is the main target in patients with COVID-19, but the disease is not limited to the respiratory system. Case analysis indicated that the nervous system can be invaded by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and that 36.4% of COVID-19 patients had neurological symptoms. Importantly, the involvement of the CNS may be associated with poor prognosis and disease worsening. Here, we discussed the symptoms and evidence of nervous system involvement (directly and indirectly) caused by SARS-CoV-2 infection and possible mechanisms. CNS symptoms could be a potential indicator of poor prognosis; therefore, the prevention and treatment of CNS symptoms are also crucial for the recovery of COVID-19 patients.

9fwttpfb

coronavirus recovery

34

Rehab facilities face COVID-19 crunch as more patients recover

mrjd1lp0

coronavirus recovery

34

A Contemporary Review of Neurological Sequelae of COVID-19

Coronavirus 2019 (COVID-19) is currently the center of what has become a public health crisis. While the virus is well-known for its trademark effects on respiratory function, neurological damage has been reported to affect a considerable proportion of severe cases. To characterize the neuro-invasive potential of this disease, a contemporary review of COVID-19 and its neurological sequelae was conducted using the limited, but growing, literature that is available. These neurological squeal are based on the manifestations that the virus has on normal central and peripheral nervous system function. The authors present the virology of the SARS-CoV-2 agent by analyzing its classification as an enveloped, positive-stranded RNA virus. A comprehensive timeline is then presented, indicating the progression of the disease as a public health threat. Furthermore, underlying chronic neurological conditions potentially lead to more adverse cases of COVID-19. SARS-CoV-2 may reach ACE2 receptors on neuronal tissue through mode of the general circulation. The CNS may also be susceptible to an immune response where a “cytokine storm” can manifest into neural injury. Histological evidence is provided, while symptoms such as headache and vertigo are highlighted as CNS manifestations of COVID-19. Treatment of these symptoms is addressed with paracetamol being recommended as a possible, but not conclusive, treatment to some CNS symptoms. The authors then discuss the peripheral nervous system sequelae and COVID's impact on causing chemosensory dysfunction starting with viral attack on olfactory sensory neurons and cells types within the lining of the nose. Histological evidence is also provided while symptoms such as anosmia and ageusia are characterized as PNS manifestations. Possible treatment options for these symptoms are then addressed as a major limitation, as anecdotal, and not conclusive evidence can be made. Finally, preventive measures of the neurological sequelae are addressed using a multidirectional approach. Postmortem examinations of the brains of COVID-19 patients are suggested as being a possible key to formulating new understandings of its neuropathology. Lastly, the authors suggest a more comprehensive neurological follow-up of recovered patients, in order to better characterize the neurological sequelae of this illness.

411qyubx

coronavirus recovery

34

Anxiety persists after recovery from acquired COVID-19 in anaesthesiologists

bv6g9px3

coronavirus recovery

34

Positive SARS-CoV-2 RNA recurs repeatedly in a case recovered from COVID-19: dynamic results from 108 days of follow-up

The evidence of long-term clinical dynamic on Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) RNA re-positive case are less. We performed a 108 days follow-up on dynamic clinical presentations in a case, who hospitalized three times due to the positive recurrence of SARS-CoV-2 RNA after discharge, to understand the prognosis of the 2019-Coronavirus disease (COVID-19). In this case, positive SARS-CoV-2 recurred even after apparent recovery (normal CT imaging, no clinical symptoms, negative SARS-CoV-2 on stool sample and negative serum IgM test) from COVID-19, viral shedding duration lasted for 65 days, the time from symptom onset to disappearance was up to 95 days. Erythrocyte-associated indicators, liver function and serum lipid metabolism presented abnormal throughout during the observation period. Awareness of atypical presentations such as this one is important to prompt the improvement of the management of COVID-19.

pen037ms

coronavirus recovery

34

Liver Injury in Critically Ill and Non-critically Ill COVID-19 Patients: A Multicenter, Retrospective, Observational Study

Background: Liver injury commonly occurs in patients with COVID-19. There is limited data describing the course of liver injury occurrence in patients with different disease severity, and the causes and risk factors are unknown. We aim to investigate the incidence, characteristics, risk factors, and clinical outcomes of liver injury in patients with COVID-19. Methods: This retrospective observational study was conducted in three hospitals (Zhejiang, China). From January 19, 2020 to February 20, 2020, patients confirmed with COVID-19 (≥18 years) and without liver injury were enrolled and divided into non-critically ill and critically ill groups. The incidence and characteristics of liver injury were compared between the two groups. Demographics, clinical characteristics, treatments, and treatment outcomes between patients with or without liver injury were compared within each group. The multivariable logistic regression model was used to explore the risk factors for liver injury. Results: The mean age of 131 enrolled patients was 51.2 years (standard deviation [SD]: 16.1 years), and 70 (53.4%) patients were male. A total of 76 patients developed liver injury (mild, 40.5%; moderate, 15.3%; severe, 2.3%) with a median occurrence time of 10.0 days. Critically ill patients had higher and earlier occurrence (81.5 vs. 51.9%, 12.0 vs. 5.0 days; p < 0.001), greater injury severity (p < 0.001), and slower recovery (50.0 vs. 61.1%) of liver function than non-critically ill patients. Multivariable regression showed that the number of concomitant medications (odds ratio [OR]: 1.12, 95% confidence interval [CI]: 1.05–1.21) and the combination treatment of lopinavir/ritonavir and arbidol (OR: 3.58, 95% CI: 1.44–9.52) were risk factors for liver injury in non-critically ill patients. The metabolism of arbidol can be significantly inhibited by lopinavir/ritonavir in vitro (p < 0.005), which may be the underlying cause of drug-related liver injury. Liver injury was related to increased length of hospital stay (mean difference [MD]: 3.2, 95% CI: 1.3–5.2) and viral shedding duration (MD: 3.0, 95% CI: 1.0–4.9). Conclusions: Critically ill patients with COVID-19 suffered earlier occurrence, greater injury severity, and slower recovery from liver injury than non-critically ill patients. Drug factors were related to liver injury in non-critically ill patients. Liver injury was related to prolonged hospital stay and viral shedding duration in patients with COVID-19. Clinical Trial Registration: World Health Organization International Clinical Trials Registry Platform, ChiCTR2000030593. Registered March 8, 2020.

2jv7xkfn

coronavirus recovery

34

Comment on “Hearing loss and COVID-19: A note”

axddll4d

coronavirus recovery

34

Potentially irreversible olfactory and gustatory impairments in COVID-19: indolent vs. fulminant SARS-CoV-2 neuroinfection

jhv8mtvn

coronavirus recovery

34

Subjective smell and taste changes during the COVID-19 pandemic: Short term recovery

Since the COVID-19 pandemic began, many individuals have reported acute loss of smell and taste. In order to better characterize all patients with these symptoms, a longitudinal national survey was created. Since April 10, 2020, 549 completed the initial survey, with 295 completing 14-day, and 202 completing 1-month follow up surveys. At 1-month follow-up, 67.1% reported a return to “very good” or “good” smell, and 73.1% reported a return to “very good” or “good” taste. Chemosensory changes are a cardinal sign of COVID-19. Fortunately, our data, representing a large longitudinal study of patients experiencing smell and taste losses during the COVID-19 pandemic, indicates that the majority appear to recover within a month.

hwd9xjnn

coronavirus recovery

34

Concern coronavirus may trigger post-viral fatigue syndromes

r9y9acz4

coronavirus recovery

34

COVID-19: The potential treatment of pulmonary fibrosis associated with SARS-CoV-2 infection

In December 2019, a novel coronavirus, SARS-CoV-2, appeared, causing a wide range of symptoms, mainly respiratory infection. In March 2020, the World Health Organization (WHO) declared Coronavirus Disease 2019 (COVID-19) a pandemic, therefore the efforts of scientists around the world are focused on finding the right treatment and vaccine for the novel disease. COVID-19 has spread rapidly over several months, affecting patients across all age groups and geographic areas. The disease has a diverse course; patients may range from asymptomatic to those with respiratory failure, complicated by acute respiratory distress syndrome (ARDS). One possible complication of pulmonary involvement in COVID-19 is pulmonary fibrosis, which leads to chronic breathing difficulties, long-term disability and affects patients’ quality of life. There are no specific mechanisms that lead to this phenomenon in COVID-19, but some information arises from previous severe acute respiratory syndrome (SARS) or Middle East respiratory syndrome (MERS) epidemics. The aim of this narrative review is to present the possible causes and pathophysiology of pulmonary fibrosis associated with COVID-19 based on the mechanisms of the immune response, to suggest possible ways of prevention and treatment.

4b45gh77

coronavirus recovery

34

The COVID-19 rehabilitation pandemic(1)

The coronavirus disease 2019 (COVID-19) pandemic and the response to the pandemic are combining to produce a tidal wave of need for rehabilitation. Rehabilitation will be needed for survivors of COVID-19, many of whom are older, with underlying health problems. In addition, rehabilitation will be needed for those who have become deconditioned as a result of movement restrictions, social isolation, and inability to access healthcare for pre-existing or new non-COVID-19 illnesses. Delivering rehabilitation in the same way as before the pandemic will not be practical, nor will this approach meet the likely scale of need for rehabilitation. This commentary reviews the likely rehabilitation needs of older people both with and without COVID-19 and discusses how strategies to deliver effective rehabilitation at scale can be designed and implemented in a world living with COVID-19.

j6kis5b5

coronavirus recovery

34

Olfactory dysfunction in recovered COVID‐19 patients

133u377v

coronavirus recovery

34

A Case of COVID-19 Infection With Delayed Thromboembolic Complication on Warfarin

Novel coronavirus disease 2019 (COVID-19) pandemic has posed an unprecedented threat to humanity with more than eight million infections and 450,000 deaths reported worldwide so far. The spectrum of the disease varies from mild asymptomatic infection to severe disease with rapid progression to acute respiratory distress syndrome and multiorgan failure. It is associated with a prothrombotic state and hence there is a risk of thromboembolic complications in critically ill patients, even after recovery. However, the duration of prothrombotic risk after recovery is yet to be determined. We present the case of a 78-year-old man with a history of atrial fibrillation on warfarin who had been recently discharged to a nursing home after recovering from COVID-19 pneumonia and presented to the emergency department a month later with worsening shortness of breath and cough. He was found to have worsening respiratory failure with multiple segmental pulmonary emboli, despite being on warfarin, and supratherapeutic international normalized ratio (INR). He required mechanical ventilation and was started on steroids and therapeutic enoxaparin anticoagulation. This case highlights the risk of delayed thromboembolic complications in patients with COVID-19 infection and the need to identify the subgroup of patients with a higher risk of thromboembolism, such as discharges to nursing homes and those in need of oxygen requirement; and those with underlying comorbid conditions that may require anticoagulation for a longer duration. The role of heparin is being increasingly investigated in patients with COVID-19 infection; however, the role of other anticoagulants such as warfarin is yet to be defined.

rhoo2k3r

coronavirus recovery

34

Analysis of thin-section CT in patients with coronavirus disease (COVID-19) after hospital discharge

PURPOSE: To analyze clinical and thin-section computed tomographic (CT) data from the patients with coronavirus disease (COVID-19) to predict the development of pulmonary fibrosis after hospital discharge. MATERIALS AND METHODS: Fifty-nine patients (31 males, 28 females; mean age: 41 years, range: 25 to 70 years) with confirmed COVID-19 infection performed follow-up thin-section CT of the thorax. After 31.5 days of hospital admission, the results of thin-section CT were analyzed for parenchymal abnormality (ground-glass opacification, interstitial thickening, and consolidation) and evidence of fibrosis (parenchymal band, traction bronchiectasis, and irregular interfaces). Patients were analyzed based on the evidence of fibrosis and divided into two groups, group A (with CT evidence of fibrosis) and group B (without CT evidence of fibrosis). Patient demographics, length of stay (LOS), rate of intensive care unit (ICU) admission, peak C-reactive protein level, and CT score were compared between the two groups. RESULTS: Among the 59 patients, 89.8% (53/59) patients had a typical transition from early phase to advanced phase and advanced phase to dissipating phase. Out of 59 patients, 39% (23/59) patients developed fibrosis (group A), whereas 61% (36/59) patients did not show definite fibrosis (group B). Patients in group A were older (mean age, 45.4 vs. 33.8 years), with longer LOS (19.1 vs. 15.0 days), higher rate of ICU admission (21.7% (5/23) vs. 5.6% (2/36)), higher peak C-reactive protein level (30.7 vs. 18.1 mg/L), and higher maximal CT score (5.2 vs. 4.0) than those in group B. CONCLUSIONS: Pulmonary fibrosis may develop early in patients with COVID-19 after hospital discharge. Older patients with severe illness during treatment were more prone to develop fibrosis according to thin-section CT results.

ptlh8oqx

coronavirus public datasets

35

The American Heart Association COVID-19 CVD Registry powered by Get With The Guidelines®.

Background: In response to the public health emergency created by the COVID-19 pandemic, American Heart Association volunteers and staff aimed to rapidly develop and launch a resource for the medical and research community to expedite scientific advancement through shared learning, quality improvement, and research. In less than 4 weeks after it was first announced on April 3, 2020, AHA's COVID-19 CVD Registry powered by Get With The Guidelines® (GWTG) received its first clinical records. Methods and Results: Participating hospitals are enrolling consecutive hospitalized patients with active COVID-19 disease, regardless of CVD status. This hospital quality improvement program will allow participating hospitals and health systems to evaluate patient-level data including mortality rates, intensive care unit (ICU) bed days, and ventilator days from individual review of electronic medical records of sequential adult patients with active COVID-19 infection. Participating sites can leverage these data for onsite, rapid quality improvement and benchmarking versus other institutions. After 9 weeks, more than 130 sites have enrolled in the program and more than 4,000 records have been abstracted in the national dataset. Additionally, the aggregate dataset will be a valuable data resource for the medical research community. Conclusions: The AHA COVID-19 CVD Registry will support greater understanding of the impact of COVID-19 on cardiovascular disease and will inform best practices for evaluation and management of patients with COVID-19.

2071y2x8

coronavirus public datasets

35

COVID-19 Government Response Event Dataset (CoronaNet v.1.0).

Governments worldwide have implemented countless policies in response to the COVID-19 pandemic. We present an initial public release of a large hand-coded dataset of over 13,000 such policy announcements across more than 195 countries. The dataset is updated daily, with a 5-day lag for validity checking. We document policies across numerous dimensions, including the type of policy, national versus subnational enforcement, the specific human group and geographical region targeted by the policy, and the time frame within which each policy is implemented. We further analyse the dataset using a Bayesian measurement model, which shows the quick acceleration of the adoption of costly policies across countries beginning in mid-March 2020 through 24 May 2020. We believe that these data will be instrumental for helping policymakers and researchers assess, among other objectives, how effective different policies are in addressing the spread and health outcomes of COVID-19.

4dgvuaxr

coronavirus public datasets

35

A New Resource for Genomics and Precision Health Information and Publications on the Investigation and Control of COVID-19 and other Coronaviruses

Summary We developed a new online database that contains the most updated published scientific literature, online news and reports, CDC and National Institutes of Health (NIH) resources. The tool captures emerging discoveries and applications of genomics, molecular, and other precision medicine and precision public health tools in the investigation and control of coronavirus diseases, including COVID-19, MERS-CoV, and SARS. Availability Coronavirus Disease Portal (CDP) can be freely accessed via https://phgkb.cdc.gov/PHGKB/coVInfoStartPage.action. Contact wyu@cdc.gov

1s0hhx71

coronavirus public datasets

35

FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19

In this paper, we present a first multilingual cross-domain dataset of 5182 fact-checked news articles for COVID-19, collected from 04/01/2020 to 15/05/2020. We have collected the fact-checked articles from 92 different fact-checking websites after obtaining references from Poynter and Snopes. We have manually annotated articles into 11 different categories of the fact-checked news according to their content. The dataset is in 40 languages from 105 countries. We have built a classifier to detect fake news and present results for the automatic fake news detection and its class. Our model achieves an F1 score of 0.76 to detect the false class and other fact check articles. The FakeCovid dataset is available at Github.

9it9pgq1

coronavirus public datasets

35

Benchmarking Deep Learning Models and Automated Model Design for COVID-19 Detection with Chest CT Scans

COVID-19 pandemic has spread all over the world for months. As its transmissibility and high pathogenicity seriously threaten people's lives, the accurate and fast detection of the COVID-19 infection is crucial. Although many recent studies have shown that deep learning based solutions can help detect COVID-19 based on chest CT scans, there lacks a consistent and systematic comparison and evaluation on these techniques. In this paper, we first build a clean and segmented CT dataset called Clean-CC-CCII by fixing the errors and removing some noises in a large CT scan dataset CC-CCII with three classes: novel coronavirus pneumonia (NCP), common pneumonia (CP), and normal controls (Normal). After cleaning, our dataset consists of a total of 340,190 slices of 3,993 scans from 2,698 patients. Then we benchmark and compare the performance of a series of state-of-the-art (SOTA) 3D and 2D convolutional neural networks (CNNs). The results show that 3D CNNs outperform 2D CNNs in general. With extensive effort of hyperparameter tuning, we find that the 3D CNN model DenseNet3D121 achieves the highest accuracy of 88.63% (F1-score is 88.14% and AUC is 0.940), and another 3D CNN model ResNet3D34 achieves the best AUC of 0.959 (accuracy is 87.83% and F1-score is 86.04%). We further demonstrate that the mixup data augmentation technique can largely improve the model performance. At last, we design an automated deep learning methodology to generate a lightweight deep learning model MNas3DNet41 that achieves an accuracy of 87.14%, F1-score of 87.25%, and AUC of 0.957, which are on par with the best models made by AI experts. The automated deep learning design is a promising methodology that can help health-care professionals develop effective deep learning models using their private data sets. Our Clean-CC-CCII dataset and source code are available at: https://github.com/arthursdays/HKBU_HPML_COVID-19.

c079r94n

coronavirus public datasets

35

COVID-19 Mobility Data Collection of Seoul, South Korea

The relationship between pandemic and human mobility has received considerable attention from scholars, as investigating such relationship can provide an indication of how human mobility changes in response to a public health crisis or whether reduced mobility contributes to preventing the spread of an infectious disease. While several studies attempted to unveil this relationship, no studies have focused on changes in mobility pattern at a finer scale utilizing high-resolution datasets. To address the complex association between pandemic's spread and human mobility, this paper presents two categories of mobility datasets-trip mode and trip purpose-that concern nearly 10 million citizens' movements during the first 100 days of COVID-19 in Seoul, South Korea, where no major lockdown has been imposed. We curate hourly data of subway ridership, traffic volume and population present count at selected points of interests. The results to be derived from the presented datasets can be used as an important reference for public health decision making in the post COVID-19 era.

r0j0368k

coronavirus public datasets

35

Exploring Epidemiological Behavior of Novel Coronavirus Outbreak through the Development and Analysis of COVID-19 Daily Dataset in Bangladesh

Globally, there is an obvious concern about the fact that the evolving 2019-nCoV coronavirus is a worldwide public health threat. The appearance of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China at the end of 2019 triggered a major global epidemic, which is now a major community health issue. As of April 17, 2020, according to Institute of Epidemiology, Disease Control and Research (IEDCR) Bangladesh has reported 1838 confirmed cases in between 8 March to 17 April 2020, with > 4.08% of mortality rate and >3.15% of recovery rate. COVID-19 outbreak is evolving so rapidly in Bangladesh; therefore, the availability of epidemiological data and its sensible analysis are essential to direct strategies for situational awareness and intervention. This article presents an exploratory data analysis approach to collect and analyze COVID-19 data on epidemiological outbreaks based on first publicly available COVID-19 Daily Dataset of Bangladesh. Various publicly open data sources on the outbreak of COVID-19 provided by the IEDCR, World Health Organization (WHO), Directorate General of Health Services (DGHS), and Ministry of Health and Family Welfare (MHFW) of Bangladesh have been used in this research. A Visual Exploratory Data Analysis (V-EDA) techniques have been followed in this research to understand the epidemiological characteristics of COVID-19 outbreak in different districts of Bangladesh in between 8 March 2020 to 12 April 2020 and these findings were compared with those of other countries. In all, this is extremely important to promptly spread information to understand the risks of this pandemic and begin containment activities in the country.

bce1oeyl

coronavirus public datasets

35

A structured open dataset of government interventions in response to COVID-19

In response to the COVID-19 pandemic, governments have implemented a wide range of nonpharmaceutical interventions (NPIs). Monitoring and documenting government strategies during the COVID-19 crisis is crucial to understand the progression of the epidemic. Following a content analysis strategy of existing public information sources, we developed a specific hierarchical coding scheme for NPIs. We generated a comprehensive structured dataset of government interventions and their respective timelines of implementation. To improve transparency and motivate collaborative validation process, information sources are shared via an open library. We also provide codes that enable users to visualise the dataset. Standardization and structure of the dataset facilitate inter-country comparison and the assessment of the impacts of different NPI categories on the epidemic parameters, population health indicators, the economy, and human rights, among others. This dataset provides an in-depth insight of the government strategies and can be a valuable tool for developing relevant preparedness plans for pandemic. We intend to further develop and update this dataset on a weekly basis until the end of December 2020.

byr1qy54

coronavirus public datasets

35

Associations between COVID-19 infection, tobacco smoking and nicotine use, common respiratory conditions and inhaled corticosteroids: a prospective QResearch-Case Mix Programme data linkage study January-May 2020

Introduction Epidemiological and laboratory research seems to suggest that smoking and perhaps nicotine alone could reduce the severity of COVID-19. Likewise, there is some evidence that inhaled corticosteroids could also reduce its severity, opening the possibility that nicotine and inhaled steroids could be used as treatments. Methods In this prospective cohort study, we will link English general practice records from the QResearch database to Public Health England's database of SARS-CoV-2 positive tests, Hospital Episode Statistics, admission to intensive care units, and death from COVID-19 to identify our outcomes: hospitalisation, ICU admission, and death due to COVID. Using Cox regression, we will perform sequential adjustment for potential confounders identified by separate directed acyclic graphs to: 1. Assess the association between smoking and COVID-19 disease severity, and how that changes on adjustment for smoking-related comorbidity. 2. More closely characterise the association between smoking and severe COVID-19 disease by assessing whether the association is modified by age (as a proxy of length of smoking), gender, ethnic group, and whether people have asthma or COPD. 3. Assess for evidence of a dose-response relation between smoking intensity and disease severity, which would help create a case for causality. 4. Examine the association between former smokers who are using NRT or are vaping and disease severity. 5. Examine whether pre-existing respiratory disease is associated with severe COVID-19 infection. 6. Assess whether the association between chronic obstructive pulmonary disease (COPD) and asthma and COVID-19 disease severity is modified by age, gender, ethnicity, and smoking status. 7. Assess whether the use of inhaled corticosteroids is associated with severity of COVID-19 disease. 8. To assess whether the association between use of inhaled corticosteroids and severity of COVID-19 disease is modified by the number of other airways medications used (as a proxy for severity of condition) and whether people have asthma or COPD. Conclusions This representative population sample will, to our knowledge, present the first comprehensive examination of the association between smoking, nicotine use without smoking, respiratory disease, and severity of COVID-19. We will undertake several sensitivity analyses to examine the potential for bias in these associations.

hlqqkn31

coronavirus public datasets

35

MosMedData: Chest CT Scans with COVID-19 Related Findings

This dataset contains anonymised human lung computed tomography (CT) scans with COVID-19 related findings, as well as without such findings. A small subset of studies has been annotated with binary pixel masks depicting regions of interests (ground-glass opacifications and consolidations). CT scans were obtained between 1st of March, 2020 and 25th of April, 2020, and provided by municipal hospitals in Moscow, Russia. Permanent link: https://mosmed.ai/datasets/covid19_1110. This dataset is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) License.

doazmz7c

coronavirus public datasets

35

Mining Twitter Data on COVID-19 for Sentiment analysis and frequent patterns Discovery

A study with a societal objective was carried out on people exchanging on social networks and more particularly on Twitter to observe their feelings on the COVID-19. A dataset of more than 600,000 tweets with hashtags like COVID and coronavirus posted between February 27, 2020 and March 25, 2020 was built. An exploratory treatment of the number of tweets posted by country, by language and other parameters revealed an overview of the apprehension of the pandemic around the world. A sentiment analysis was elaborated on the basis of the tweets posted in English because these constitute the great majority. On the other hand, the FP-Growth algorithm was adapted to the tweets in order to discover the most frequent patterns and its derived association rules, in order to highlight the tweeters insights relatively to COVID-19.

iah8zka4

coronavirus public datasets

35

On the Generation of Medical Dialogues for COVID-19

Under the pandemic of COVID-19, people experiencing COVID19-related symptoms or exposed to risk factors have a pressing need to consult doctors. Due to hospital closure, a lot of consulting services have been moved online. Because of the shortage of medical professionals, many people cannot receive online consultations timely. To address this problem, we aim to develop a medical dialogue system that can provide COVID19-related consultations. We collected two dialogue datasets - CovidDialog - (in English and Chinese respectively) containing conversations between doctors and patients about COVID-19. On these two datasets, we train several dialogue generation models based on Transformer, GPT, and BERT-GPT. Since the two COVID-19 dialogue datasets are small in size, which bear high risk of overfitting, we leverage transfer learning to mitigate data deficiency. Specifically, we take the pretrained models of Transformer, GPT, and BERT-GPT on dialog datasets and other large-scale texts, then finetune them on our CovidDialog datasets. Experiments demonstrate that these approaches are promising in generating meaningful medical dialogue about COVID-19. But more advanced approaches are needed to build a fully useful dialogue system that can offer accurate COVID-related consultations. The data and code are available at https://github.com/UCSD-AI4H/COVID-Dialogue

tprgbl51

coronavirus public datasets

35

Online COVID-19 diagnosis with chest CT images: Lesion-attention deep neural networks

Chest (computed tomography) CT scanning is one of the most important technologies for COVID-19 diagnosis in the current clinical practice, which motivates more concerted efforts in developing AI-based diagnostic tools to alleviate the enormous burden on the medical system. We develop a lesion-attention deep neural network (LA-DNN) to predict COVID-19 positive or negative with a richly annotated chest CT image dataset. The CT image dataset contains 746 public chest CT images of COVID-19 patients collected from over 760 preprints, and the data annotations are accompanied with the textual radiology reports. We extract two types of important information from these annotations: One is the flag of whether an image indicates a positive or negative case of COVID-19, and the other is the description of five lesions on the CT images associated with the positive cases. The proposed data-driven LA-DNN model focuses on the primary task of binary classification for COVID-19 diagnosis, while an auxiliary multi-label learning task is implemented simultaneously to draw the model's attention to the five lesions of COVID-19 during the training. The joint task learning process makes it a highly sample-efficient deep model that can learn COVID-19 radiology features effectively with very limited samples. The experimental results show that the area under the curve (AUC) and sensitivity (recall) for the diagnosis of COVID-19 patients are 91.2% and 85.7% respectively, which reach the clinical standards for practical use. An online system has been developed for fast online diagnoses using CT images at the web address https://www.covidct.cn/.

nvmon1sm

coronavirus public datasets

35

Sample-Efficient Deep Learning for COVID-19 Diagnosis Based on CT Scans

Coronavirus disease 2019 (COVID-19) has infected more than 1.3 million individuals all over the world and caused more than 106,000 deaths. One major hurdle in controlling the spreading of this disease is the inefficiency and shortage of medical tests. There have been increasing efforts on developing deep learning methods to diagnose COVID-19 based on CT scans. However, these works are difficult to reproduce and adopt since the CT data used in their studies are not publicly available. Besides, these works require a large number of CTs to train accurate diagnosis models, which are difficult to obtain. In this paper, we aim to address these two problems. We build a publicly-available dataset containing hundreds of CT scans positive for COVID-19 and develop sample-efficient deep learning methods that can achieve high diagnosis accuracy of COVID-19 from CT scans even when the number of training CT images are limited. Specifically, we propose an Self-Trans approach, which synergistically integrates contrastive self-supervised learning with transfer learning to learn powerful and unbiased feature representations for reducing the risk of overfitting. Extensive experiments demonstrate the superior performance of our proposed Self-Trans approach compared with several state-of-the-art baselines. Our approach achieves an F1 of 0.85 and an AUC of 0.94 in diagnosing COVID-19 from CT scans, even though the number of training CTs is just a few hundred.

l3f469ht

coronavirus public datasets

35

A citizen science initiative for open data and visualization of COVID-19 outbreak in Kerala, India

India, the second most populated country in the world, reported its first COVID-19 case in the state of Kerala with a travel history from Wuhan. Subsequently, a surge of cases was observed in the state mainly through the individuals who traveled from Europe and the Middle East to Kerala, thus initiating an outbreak. Since public awareness through dissemination of reliable information plays a significant role in controlling the spread of the disease, the Department of Health Services, Government of Kerala initially released daily updates through daily textual bulletins. However, this unstructured data requires refinement and enrichment for upstream applications, such as visualization, and/or analysis. Here we reported a citizen science initiative that leveraged publicly available and crowd-verified data on COVID-19 outbreak in Kerala from the government bulletins, supplemented with the information from media outlets to generate reusable datasets. This data was further used to provide real-time analysis, and daily updates of COVID-19 cases in Kerala, through a user-friendly bilingual dashboard (https://covid19kerala.info/) for non-specialists. We ensured longevity and reusability of the dataset by depositing it in a public repository, aligning with open source principles for future analytical efforts. Finally, to show the scope of the sourced data, we also provided a snapshot of outbreak trends and demographic characteristics of the individuals affected with COVID-19 in Kerala during the first 99 days of the outbreak.

nbzbmrsd

coronavirus public datasets

35

Public Opinions towards COVID-19 in California and New York on Twitter

Background: With the pandemic of COVID-19 and the release of related policies, discussions about the COVID-19 are widespread online. Social media becomes a reliable source for understanding public opinions toward this virus outbreak. Objective: This study aims to explore public opinions toward COVID-19 on social media by comparing the differences in sentiment changes and discussed topics between California and New York in the United States. Methods: A dataset with COVID-19-related Twitter posts was collected from March 5, 2020 to April 2, 2020 using Twitter streaming API. After removing any posts unrelated to COVID-19, as well as posts that contain promotion and commercial information, two individual datasets were created based on the geolocation tags with tweets, one containing tweets from California state and the other from New York state. Sentiment analysis was conducted to obtain the sentiment score for each COVID-19 tweet. Topic modeling was applied to identify top topics related to COVID-19. Results: While the number of COVID-19 cases increased more rapidly in New York than in California in March 2020, the number of tweets posted has a similar trend over time in both states. COVID-19 tweets from California had more negative sentiment scores than New York. There were some fluctuations in sentiment scores in both states over time, which might correlate with the policy changes and the severity of COVID-19 pandemic. The topic modeling results showed that the popular topics in both California and New York states are similar, with "protective measures" as the most prevalent topic associated with COVID-19 in both states. Conclusions: Twitter users from California had more negative sentiment scores towards COVID-19 than Twitter users from New York. The prevalent topics about COVID-19 discussed in both states were similar with some slight differences.

onipxf2z

coronavirus public datasets

35

CoV2ID: Detection and Therapeutics Oligo Database for SARS-CoV-2

The ability to detect the SARS-CoV-2 in a widespread epidemic is crucial for screening of carriers and for the success of quarantine efforts. Methods based on real-time reverse transcription polymerase chain reaction (RT-qPCR) and sequencing are being used for virus detection and characterization. However, RNA viruses are known for their high genetic diversity which poses a challenge for the design of efficient nucleic acid-based assays. The first SARS-CoV-2 genomic sequences already showed novel mutations, which may affect the efficiency of available screening tests leading to false-negative diagnosis or inefficient therapeutics. Here we describe the CoV2ID (http://covid.portugene.com/), a free database built to facilitate the evaluation of molecular methods for detection of SARS-CoV-2 and treatment of COVID-19. The database evaluates the available oligonucleotide sequences (PCR primers, RT-qPCR probes, etc.) considering the genetic diversity of the virus. Updated sequences alignments are used to constantly verify the theoretical efficiency of available testing methods. Detailed information on available detection protocols are also available to help laboratories implementing SARS-CoV-2 testing.

mg2dziuw

coronavirus public datasets

35

CORD-19: The Covid-19 Open Research Dataset.

The Covid-19 Open Research Dataset (CORD-19) is a growing resource of scientific papers on Covid-19 and related historical coronavirus research. CORD-19 is designed to facilitate the development of text mining and information retrieval systems over its rich collection of metadata and structured full text papers. Since its release, CORD-19 has been downloaded over 75K times and has served as the basis of many Covid-19 text mining and discovery systems. In this article, we describe the mechanics of dataset construction, highlighting challenges and key design decisions, provide an overview of how CORD-19 has been used, and preview tools and upcoming shared tasks built around the dataset. We hope this resource will continue to bring together the computing community, biomedical experts, and policy makers in the search for effective treatments and management policies for Covid-19.

pt8nh7wx

coronavirus public datasets

35

A large-scale COVID-19 Twitter chatter dataset for open scientific research -- an international collaboration.

As the COVID-19 pandemic continues its march around the world, an unprecedented amount of open data is being generated for genetics and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated in the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique world-wide event into biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 152 million tweets, growing daily, related to COVID-19 chatter generated from January 1st to April 4th at the time of writing. This open dataset will allow researchers to conduct a number of research projects relating to the emotional and mental responses to social distancing measures, the identification of sources of misinformation, and the stratified measurement of sentiment towards the pandemic in near real time.

1lisdjpm

coronavirus public datasets

35

COVID-19-CT-CXR: a freely accessible and weakly labeled chest X-ray and CT image collection on COVID-19 from biomedical literature.

The latest threat to global health is the COVID-19 outbreak. Although there exist large datasets of chest X-rays (CXR) and computed tomography (CT) scans, few COVID-19 image collections are currently available due to patient privacy. At the same time, there is a rapid growth of COVID-19-relevant articles in the biomedical literature. Here, we present COVID-19-CT-CXR, a public database of COVID-19 CXR and CT images, which are automatically extracted from COVID-19-relevant articles from the PubMed Central Open Access (PMC-OA) Subset. We extracted figures, associated captions, and relevant figure descriptions in the article and separated compound figures into subfigures. We also designed a deep-learning model to distinguish them from other figure types and to classify them accordingly. The final database includes 1,327 CT and 263 CXR images (as of May 9, 2020) with their relevant text. To demonstrate the utility of COVID-19-CT-CXR, we conducted four case studies. (1) We show that COVID-19-CT-CXR, when used as additional training data, is able to contribute to improved DL performance for the classification of COVID-19 and non-COVID-19 CT. (2) We collected CT images of influenza and trained a DL baseline to distinguish a diagnosis of COVID-19, influenza, or normal or other types of diseases on CT. (3) We trained an unsupervised one-class classifier from non-COVID-19 CXR and performed anomaly detection to detect COVID-19 CXR. (4) From text-mined captions and figure descriptions, we compared clinical symptoms and clinical findings of COVID-19 vs. those of influenza to demonstrate the disease differences in the scientific publications. We believe that our work is complementary to existing resources and hope that it will contribute to medical image analysis of the COVID-19 pandemic. The dataset, code, and DL models are publicly available at https://github.com/ncbi-nlp/COVID-19-CT-CXR.

uqfygev4

coronavirus public datasets

35

Neurological and neuropsychiatric complications of COVID-19 in 153 patients: a UK-wide surveillance study

BACKGROUND: Concerns regarding potential neurological complications of COVID-19 are being increasingly reported, primarily in small series. Larger studies have been limited by both geography and specialty. Comprehensive characterisation of clinical syndromes is crucial to allow rational selection and evaluation of potential therapies. The aim of this study was to investigate the breadth of complications of COVID-19 across the UK that affected the brain. METHODS: During the exponential phase of the pandemic, we developed an online network of secure rapid-response case report notification portals across the spectrum of major UK neuroscience bodies, comprising the Association of British Neurologists (ABN), the British Association of Stroke Physicians (BASP), and the Royal College of Psychiatrists (RCPsych), and representing neurology, stroke, psychiatry, and intensive care. Broad clinical syndromes associated with COVID-19 were classified as a cerebrovascular event (defined as an acute ischaemic, haemorrhagic, or thrombotic vascular event involving the brain parenchyma or subarachnoid space), altered mental status (defined as an acute alteration in personality, behaviour, cognition, or consciousness), peripheral neurology (defined as involving nerve roots, peripheral nerves, neuromuscular junction, or muscle), or other (with free text boxes for those not meeting these syndromic presentations). Physicians were encouraged to report cases prospectively and we permitted recent cases to be notified retrospectively when assigned a confirmed date of admission or initial clinical assessment, allowing identification of cases that occurred before notification portals were available. Data collected were compared with the geographical, demographic, and temporal presentation of overall cases of COVID-19 as reported by UK Government public health bodies. FINDINGS: The ABN portal was launched on April 2, 2020, the BASP portal on April 3, 2020, and the RCPsych portal on April 21, 2020. Data lock for this report was on April 26, 2020. During this period, the platforms received notification of 153 unique cases that met the clinical case definitions by clinicians in the UK, with an exponential growth in reported cases that was similar to overall COVID-19 data from UK Government public health bodies. Median patient age was 71 years (range 23-94; IQR 58-79). Complete clinical datasets were available for 125 (82%) of 153 patients. 77 (62%) of 125 patients presented with a cerebrovascular event, of whom 57 (74%) had an ischaemic stroke, nine (12%) an intracerebral haemorrhage, and one (1%) CNS vasculitis. 39 (31%) of 125 patients presented with altered mental status, comprising nine (23%) patients with unspecified encephalopathy and seven (18%) patients with encephalitis. The remaining 23 (59%) patients with altered mental status fulfilled the clinical case definitions for psychiatric diagnoses as classified by the notifying psychiatrist or neuropsychiatrist, and 21 (92%) of these were new diagnoses. Ten (43%) of 23 patients with neuropsychiatric disorders had new-onset psychosis, six (26%) had a neurocognitive (dementia-like) syndrome, and four (17%) had an affective disorder. 18 (49%) of 37 patients with altered mental status were younger than 60 years and 19 (51%) were older than 60 years, whereas 13 (18%) of 74 patients with cerebrovascular events were younger than 60 years versus 61 (82%) patients older than 60 years. INTERPRETATION: To our knowledge, this is the first nationwide, cross-specialty surveillance study of acute neurological and psychiatric complications of COVID-19. Altered mental status was the second most common presentation, comprising encephalopathy or encephalitis and primary psychiatric diagnoses, often occurring in younger patients. This study provides valuable and timely data that are urgently needed by clinicians, researchers, and funders to inform immediate steps in COVID-19 neuroscience research and health policy. FUNDING: None.

2i3iv0sz

coronavirus public datasets

35

Dataset on dynamics of Coronavirus on Twitter

In this data article, we provide a dataset of 8,982,694 Twitter posts around the coronavirus health global crisis. The data were collected through the Twitter REST API search. We used the rtweet R package to download raw data. The term searched was "Coronavirus" which included the word itself and its hashtag version. We collected the data over 23 days, from January 21 to February 12, 2020. The dataset is multilingual, prevailing English, Spanish, and Portuguese. We include a new variable created from other four variables; it is called "type" of tweets, which is useful for showing the diversity of tweets and the dynamics of users on Twitter. The dataset comprises seven databases which can be analysed separately. On the other hand, they can be crossed to set other researches, among them, trends and relevance of different topics, types of tweets, the embeddedness of users and their profiles, the retweets dynamics, hashtag analysis, as well as to perform social network analysis. This dataset can attract the attention of researchers related to different fields on knowledge, such as data science, social science, network science, health informatics, tourism, infodemiology, and others.

lwipyymp

coronavirus public datasets

35

Monitoring Depression Trend on Twitter during the COVID-19 Pandemic

The COVID-19 pandemic has severely affected people's daily lives and caused tremendous economic loss worldwide. However, its influence on people's mental health conditions has not received as much attention. To study this subject, we choose social media as our main data resource and create by far the largest English Twitter depression dataset containing 2,575 distinct identified depression users with their past tweets. To examine the effect of depression on people's Twitter language, we train three transformer-based depression classification models on the dataset, evaluate their performance with progressively increased training sizes, and compare the model's"tweet chunk"-level and user-level performances. Furthermore, inspired by psychological studies, we create a fusion classifier that combines deep learning model scores with psychological text features and users' demographic information and investigate these features' relations to depression signals. Finally, we demonstrate our model's capability of monitoring both group-level and population-level depression trends by presenting two of its applications during the COVID-19 pandemic. We hope this study can raise awareness among researchers and the general public of COVID-19's impact on people's mental health.

qgjtnpd3

coronavirus public datasets

35

Conversations and Medical News Frames on Twitter: Infodemiological Study on COVID-19 in South Korea

BACKGROUND: SARS-CoV-2 (severe acute respiratory coronavirus 2) was spreading rapidly in South Korea at the end of February 2020 following its initial outbreak in China, making Korea the new center of global attention. The role of social media amid the current coronavirus disease (COVID-19) pandemic has often been criticized, but little systematic research has been conducted on this issue. Social media functions as a convenient source of information in pandemic situations. OBJECTIVE: Few infodemiology studies have applied network analysis in conjunction with content analysis. This study investigates information transmission networks and news-sharing behaviors regarding COVID-19 on Twitter in Korea. The real time aggregation of social media data can serve as a starting point for designing strategic messages for health campaigns and establishing an effective communication system during this outbreak. METHODS: Korean COVID-19-related Twitter data were collected on February 29, 2020. Our final sample comprised of 43,832 users and 78,233 relationships on Twitter. We generated four networks in terms of key issues regarding COVID-19 in Korea. This study comparatively investigates how COVID-19-related issues have circulated on Twitter through network analysis. Next, we classified top news channels shared via tweets. Lastly, we conducted a content analysis of news frames used in the top-shared sources. RESULTS: The network analysis suggests that the spread of information was faster in the Coronavirus network than in the other networks (Corona19, Shincheon, and Daegu). People who used the word "Coronavirus" communicated more frequently with each other. The spread of information was faster, and the diameter value was lower than for those who used other terms. Many of the news items highlighted the positive roles being played by individuals and groups, directing readers' attention to the crisis. Ethical issues such as deviant behavior among the population and an entertainment frame highlighting celebrity donations also emerged often. There was a significant difference in the use of nonportal (n=14) and portal news (n=26) sites between the four network types. The news frames used in the top sources were similar across the networks (P=.89, 95% CI 0.004-0.006). Tweets containing medically framed news articles (mean 7.571, SD 1.988) were found to be more popular than tweets that included news articles adopting nonmedical frames (mean 5.060, SD 2.904; N=40, P=.03, 95% CI 0.169-4.852). CONCLUSIONS: Most of the popular news on Twitter had nonmedical frames. Nevertheless, the spillover effect of the news articles that delivered medical information about COVID-19 was greater than that of news with nonmedical frames. Social media network analytics cannot replace the work of public health officials; however, monitoring public conversations and media news that propagates rapidly can assist public health professionals in their complex and fast-paced decision-making processes.

8ravbor6

coronavirus public datasets

35

Open access institutional and news media tweet dataset for COVID-19 social science research

As COVID-19 quickly became one of the most concerned global crisis, the demand for data in academic research is also increasing. Currently, there are several open access Twitter datasets, but none of them is dedicated to the institutional and news media Twitter data collection, to fill this blank, we retrieved data from 69 institutional/news media Twitter accounts, 17 of them were related to government and international organizations, 52 of them were news media across North America, Europe and Asia. We believe our open access data can provide researchers more availability to conduct social science research.

5o12mbut

coronavirus public datasets

35

Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release

Researchers across the globe are seeking to rapidly repurpose existing drugs or discover new drugs to counter the the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). One promising approach is to train machine learning (ML) and artificial intelligence (AI) tools to screen large numbers of small molecules. As a contribution to that effort, we are aggregating numerous small molecules from a variety of sources, using high-performance computing (HPC) to computer diverse properties of those molecules, using the computed properties to train ML/AI models, and then using the resulting models for screening. In this first data release, we make available 23 datasets collected from community sources representing over 4.2 B molecules enriched with pre-computed: 1) molecular fingerprints to aid similarity searches, 2) 2D images of molecules to enable exploration and application of image-based deep learning methods, and 3) 2D and 3D molecular descriptors to speed development of machine learning models. This data release encompasses structural information on the 4.2 B molecules and 60 TB of pre-computed data. Future releases will expand the data to include more detailed molecular simulations, computed models, and other products.

h23w89h2

coronavirus public datasets

35

TICO-19: the Translation Initiative for Covid-19

The COVID-19 pandemic is the worst pandemic to strike the world in over a century. Crucial to stemming the tide of the SARS-CoV-2 virus is communicating to vulnerable populations the means by which they can protect themselves. To this end, the collaborators forming the Translation Initiative for COvid-19 (TICO-19) have made test and development data available to AI and MT researchers in 35 different languages in order to foster the development of tools and resources for improving access to information about COVID-19 in these languages. In addition to 9 high-resourced,"pivot"languages, the team is targeting 26 lesser resourced languages, in particular languages of Africa, South Asia and South-East Asia, whose populations may be the most vulnerable to the spread of the virus. The same data is translated into all of the languages represented, meaning that testing or development can be done for any pairing of languages in the set. Further, the team is converting the test and development data into translation memories (TMXs) that can be used by localizers from and to any of the languages.

31ybxjjx

coronavirus public datasets

35

Chaos game representation dataset of SARS-CoV-2 genome

As of April 16, 2020, the novel coronavirus disease (called COVID-19) spread to more than 185 countries/regions with more than 142,000 deaths and more than 2,000,000 confirmed cases. In the bioinformatics area, one of the crucial points is the analysis of the virus nucleotide sequences using approaches such as data stream, digital signal processing, and machine learning techniques and algorithms. However, to make feasible this approach, it is necessary to transform the nucleotide sequences string to numerical values representation. Thus, the dataset provides a chaos game representation (CGR) of SARS-CoV-2 virus nucleotide sequences. The dataset provides the CGR of 100 instances of SARS-CoV-2 virus, 11540 instances of other viruses from the Virus-Host DB dataset, and three instances of Riboviria viruses from NCBI (Betacoronavirus RaTG13, bat-SL-CoVZC45, and bat-SL-CoVZXC21).

luhvbwgv

coronavirus public datasets

35

Early Outbreak Detection for Proactive Crisis Management Using Twitter Data: COVID-19 a Case Study in the US

During a disease outbreak, timely non-medical interventions are critical in preventing the disease from growing into an epidemic and ultimately a pandemic. However, taking quick measures requires the capability to detect the early warning signs of the outbreak. This work collects Twitter posts surrounding the 2020 COVID-19 pandemic expressing the most common symptoms of COVID-19 including cough and fever, geolocated to the United States. Through examining the variation in Twitter activities at the state level, we observed a temporal lag between the rises in the number of symptom reporting tweets and officially reported positive cases which varies between 5 to 19 days.

ad5avzd6

coronavirus public datasets

35

Document Classification for COVID-19 Literature

The global pandemic has made it more important than ever to quickly and accurately retrieve relevant scientific literature for effective consumption by researchers in a wide range of fields. We provide an analysis of several multi-label document classification models on the LitCovid dataset, a growing collection of 8,000 research papers regarding the novel 2019 coronavirus. We find that pre-trained language models fine-tuned on this dataset outperform all other baselines and that the BioBERT and novel Longformer models surpass all others with almost equivalent micro-F1 and accuracy scores of around 81% and 69% on the test set. We evaluate the data efficiency and generalizability of these models as essential features of any system prepared to deal with an urgent situation like the current health crisis. Finally, we explore 50 errors made by the best performing models on LitCovid documents and find that they often (1) correlate certain labels too closely together and (2) fail to focus on discriminative sections of the articles; both of which are important issues to address in future work. Both data and code are available on GitHub.

elphxl9s

coronavirus public datasets

35

Viral and host factors related to the clinical outcome of COVID-19

In December 2019, coronavirus disease 2019 (COVID-19), which is caused by the new coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), was identified in Wuhan (Hubei province, China)1; it soon spread across the world. In this ongoing pandemic, public health concerns and the urgent need for effective therapeutic measures require a deep understanding of the epidemiology, transmissibility and pathogenesis of COVID-19. Here we analysed clinical, molecular and immunological data from 326 patients with confirmed SARS-CoV-2 infection in Shanghai. The genomic sequences of SARS-CoV-2, assembled from 112 high-quality samples together with sequences in the Global Initiative on Sharing All Influenza Data (GISAID) dataset, showed a stable evolution and suggested that there were two major lineages with differential exposure history during the early phase of the outbreak in Wuhan. Nevertheless, they exhibited similar virulence and clinical outcomes. Lymphocytopenia, especially reduced CD4+ and CD8+ T cell counts upon hospital admission, was predictive of disease progression. High levels of interleukin (IL)-6 and IL-8 during treatment were observed in patients with severe or critical disease and correlated with decreased lymphocyte count. The determinants of disease severity seemed to stem mostly from host factors such as age and lymphocytopenia (and its associated cytokine storm), whereas viral genetic variation did not significantly affect outcomes.

vhwghd5n

coronavirus public datasets

35

An Interactive Online Dashboard for Tracking COVID-19 in U.S. Counties, Cities, and States in Real Time

OBJECTIVE: To create an online resource that informs the public of COVID-19 outbreaks in their area. MATERIALS AND METHODS: This R Shiny application aggregates data from multiple resources that track COVID-19 and visualizes them through an interactive, online dashboard. RESULTS: The web resource, called the COVID-19 Watcher, can be accessed at https://covid19watcher.research.cchmc.org/. It displays COVID-19 data from every county and 188 metropolitan areas in the U.S. Features include rankings of the worst affected areas and auto-generating plots that depict temporal changes in testing capacity, cases, and deaths. DISCUSSION: The Centers for Disease Control and Prevention (CDC) do not publish COVID-19 data for local municipalities, so it is critical that academic resources fill this void so the public can stay informed. The data used have limitations and likely underestimate the scale of the outbreak. CONCLUSIONS: The COVID-19 Watcher can provide the public with real-time updates of outbreaks in their area.

9kb1tt5d

coronavirus public datasets

35

Emergence of a Novel Coronavirus (COVID-19): Protocol for Extending Surveillance Used by the Royal College of General Practitioners Research and Surveillance Centre and Public Health England

BACKGROUND: The Royal College of General Practitioners (RCGP) Research and Surveillance Centre (RSC) and Public Health England (PHE) have successfully worked together on the surveillance of influenza and other infectious diseases for over 50 years, including three previous pandemics. With the emergence of the international outbreak of the coronavirus infection (COVID-19), a UK national approach to containment has been established to test people suspected of exposure to COVID-19. At the same time and separately, the RCGP RSC's surveillance has been extended to monitor the temporal and geographical distribution of COVID-19 infection in the community as well as assess the effectiveness of the containment strategy. OBJECTIVES: The aims of this study are to surveil COVID-19 in both asymptomatic populations and ambulatory cases with respiratory infections, ascertain both the rate and pattern of COVID-19 spread, and assess the effectiveness of the containment policy. METHODS: The RCGP RSC, a network of over 500 general practices in England, extract pseudonymized data weekly. This extended surveillance comprises of five components: (1) Recording in medical records of anyone suspected to have or who has been exposed to COVID-19. Computerized medical records suppliers have within a week of request created new codes to support this. (2) Extension of current virological surveillance and testing people with influenza-like illness or lower respiratory tract infections (LRTI)-with the caveat that people suspected to have or who have been exposed to COVID-19 should be referred to the national containment pathway and not seen in primary care. (3) Serology sample collection across all age groups. This will be an extra blood sample taken from people who are attending their general practice for a scheduled blood test. The 100 general practices currently undertaking annual influenza virology surveillance will be involved in the extended virological and serological surveillance. (4) Collecting convalescent serum samples. (5) Data curation. We have the opportunity to escalate the data extraction to twice weekly if needed. Swabs and sera will be analyzed in PHE reference laboratories. RESULTS: General practice clinical system providers have introduced an emergency new set of clinical codes to support COVID-19 surveillance. Additionally, practices participating in current virology surveillance are now taking samples for COVID-19 surveillance from low-risk patients presenting with LRTIs. Within the first 2 weeks of setup of this surveillance, we have identified 3 cases: 1 through the new coding system, the other 2 through the extended virology sampling. CONCLUSIONS: We have rapidly converted the established national RCGP RSC influenza surveillance system into one that can test the effectiveness of the COVID-19 containment policy. The extended surveillance has already seen the use of new codes with 3 cases reported. Rapid sharing of this protocol should enable scientific critique and shared learning. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/18606.

vchsm0cr

coronavirus public datasets

35

CORD19STS: COVID-19 Semantic Textual Similarity Dataset

In order to combat the COVID-19 pandemic, society can benefit from various natural language processing applications, such as dialog medical diagnosis systems and information retrieval engines calibrated specifically for COVID-19. These applications rely on the ability to measure semantic textual similarity (STS), making STS a fundamental task that can benefit several downstream applications. However, existing STS datasets and models fail to translate their performance to a domain-specific environment such as COVID-19. To overcome this gap, we introduce CORD19STS dataset which includes 13,710 annotated sentence pairs collected from COVID-19 open research dataset (CORD-19) challenge. To be specific, we generated one million sentence pairs using different sampling strategies. We then used a finetuned BERT-like language model, which we call Sen-SCI-CORD19-BERT, to calculate the similarity scores between sentence pairs to provide a balanced dataset with respect to the different semantic similarity levels, which gives us a total of 32K sentence pairs. Each sentence pair was annotated by five Amazon Mechanical Turk (AMT) crowd workers, where the labels represent different semantic similarity levels between the sentence pairs (i.e. related, somewhat-related, and not-related). After employing a rigorous qualification tasks to verify collected annotations, our final CORD19STS dataset includes 13,710 sentence pairs.

eozy4ng5

coronavirus public datasets

35

Open access epidemiological data from the COVID-19 outbreak

4c0vh2h1

coronavirus public datasets

35

COVID-19 Image Data Collection

This paper describes the initial COVID-19 open image data collection. It was created by assembling medical images from websites and publications and currently contains 123 frontal view X-rays.

53aq480d

coronavirus public datasets

35

Weibo-COV: A Large-Scale COVID-19 Social Media Dataset from Weibo

With the rapid development of COVID-19, people are asked to maintain"social distance"and"stay at home". In this scenario, more and more social interactions move online, especially on social media like Twitter and Weibo. People post tweets to share information, express opinions and seek help during the pandemic, and these tweets on social media are valuable for studies against COVID19, such as early warning and outbreaks detection. Therefore, in this paper, we release a novel large-scale COVID-19 social media dataset from Weibo called Weibo-COV, covering more than 40 million tweets from 1 December 2019 to 30 April 2020. Moreover, the field information of the dataset is very rich, including basic tweets information, interactive information, location information and retweet network. We hope this dataset can promote studies of COVID-19 from multiple perspectives and enable better and faster researches to suppress the spread of this disease.

1bjt64o7

coronavirus public datasets

35

Dashboard of sentiment in Austrian social media during COVID-19

To track online emotional expressions of the Austrian population close to real-time during the COVID-19 pandemic, we build a self-updating monitor of emotion dynamics using digital traces from three different data sources. This enables decision makers and the interested public to assess issues such as the attitude towards counter-measures taken during the pandemic and the possible emergence of a (mental) health crisis early on. We use web scraping and API access to retrieve data from the news platform derstandard.at, Twitter and a chat platform for students. We document the technical details of our workflow in order to provide materials for other researchers interested in building a similar tool for different contexts. Automated text analysis allows us to highlight changes of language use during COVID-19 in comparison to a neutral baseline. We use special word clouds to visualize that overall difference. Longitudinally, our time series show spikes in anxiety that can be linked to several events and media reporting. Additionally, we find a marked decrease in anger. The changes last for remarkably long periods of time (up to 12 weeks). We discuss these and more patterns and connect them to the emergence of collective emotions. The interactive dashboard showcasing our data is available online under http://www.mpellert.at/covid19_monitor_austria/. Our work has attracted media attention and is part of an web archive of resources on COVID-19 collected by the Austrian National Library.

mjtlhh5e

coronavirus public datasets

35

MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset

This dataset contains anonymised human lung computed tomography (CT) scans with COVID-19 related findings, as well as without such findings. A small subset of studies has been annotated with binary pixel masks depicting regions of interests (ground-glass opacifications and consolidations). CT scans were obtained between 1st of March, 2020 and 25th of April, 2020, and provided by municipal hospitals in Moscow, Russia. Permanent link: https://mosmed.ai/datasets/covid19_1110. This dataset is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported (CC BY-NC-ND 3.0) License. Key words: artificial intelligence, COVID-19, machine learning, dataset, CT, chest, imaging

0b8250y7

coronavirus public datasets

35

Understanding the perception of COVID-19 policies by mining a multilanguage Twitter dataset

The objective of this work is to explore popular discourse about the COVID-19 pandemic and policies implemented to manage it. Using Natural Language Processing, Text Mining, and Network Analysis to analyze corpus of tweets that relate to the COVID-19 pandemic, we identify common responses to the pandemic and how these responses differ across time. Moreover, insights as to how information and misinformation were transmitted via Twitter, starting at the early stages of this pandemic, are presented. Finally, this work introduces a dataset of tweets collected from all over the world, in multiple languages, dating back to January 22nd, when the total cases of reported COVID-19 were below 600 worldwide. The insights presented in this work could help inform decision makers in the face of future pandemics, and the dataset introduced can be used to acquire valuable knowledge to help mitigate the COVID-19 pandemic.

uwrotzhk

coronavirus public datasets

35

Coswara -- A Database of Breathing, Cough, and Voice Sounds for COVID-19 Diagnosis

The COVID-19 pandemic presents global challenges transcending boundaries of country, race, religion, and economy. The current gold standard method for COVID-19 detection is the reverse transcription polymerase chain reaction (RT-PCR) testing. However, this method is expensive, time-consuming, and violates social distancing. Also, as the pandemic is expected to stay for a while, there is a need for an alternate diagnosis tool which overcomes these limitations, and is deployable at a large scale. The prominent symptoms of COVID-19 include cough and breathing difficulties. We foresee that respiratory sounds, when analyzed using machine learning techniques, can provide useful insights, enabling the design of a diagnostic tool. Towards this, the paper presents an early effort in creating (and analyzing) a database, called Coswara, of respiratory sounds, namely, cough, breath, and voice. The sound samples are collected via worldwide crowdsourcing using a website application. The curated dataset is released as open access. As the pandemic is evolving, the data collection and analysis is a work in progress. We believe that insights from analysis of Coswara can be effective in enabling sound based technology solutions for point-of-care diagnosis of respiratory infection, and in the near future this can help to diagnose COVID-19.

mza4x7h1

coronavirus public datasets

35

Psychometric Analysis and Coupling of Emotions Between State Bulletins and Twitter in India during COVID-19 Infodemic

COVID-19 infodemic has been spreading faster than the pandemic itself. The misinformation riding upon the infodemic wave poses a major threat to people's health and governance systems. Since social media is the largest source of information, managing the infodemic not only requires mitigating of misinformation but also an early understanding of psychological patterns resulting from it. During the COVID-19 crisis, Twitter alone has seen a sharp 45% increase in the usage of its curated events page, and a 30% increase in its direct messaging usage, since March 6th 2020. In this study, we analyze the psychometric impact and coupling of the COVID-19 infodemic with the official bulletins related to COVID-19 at the national and state level in India. We look at these two sources with a psycho-linguistic lens of emotions and quantified the extent and coupling between the two. We modified path, a deep skip-gram based open-sourced lexicon builder for effective capture of health-related emotions. We were then able to capture the time-evolution of health-related emotions in social media and official bulletins. An analysis of lead-lag relationships between the time series of extracted emotions from official bulletins and social media using Granger's causality showed that state bulletins were leading the social media for some emotions such as Medical Emergency. Further insights that are potentially relevant for the policymaker and the communicators actively engaged in mitigating misinformation are also discussed. Our paper also introduces CoronaIndiaDataset2, the first social media based COVID-19 dataset at national and state levels from India with over 5.6 million national and 2.6 million state-level tweets. Finally, we present our findings as COVibes, an interactive web application capturing psychometric insights captured upon the CoronaIndiaDataset, both at a national and state level.

g1u3xyzj

coronavirus public datasets

35

Spatial-Temporal Dataset of COVID-19 Outbreak in China

We present Coronavirus disease 2019 (COVID-19) statistics in China dataset: daily statistics of the COVID-19 outbreak in China at the city/county level. For each city/country, we include the six most important numbers for epidemic research: daily new infections, accumulated infections, daily new recoveries, accumulated recoveries, daily new deaths, and accumulated deaths. We cross validate the dataset and the estimate error rate is about 0.04%. We then give several examples to show how to trace the spreading in particular cities or provinces, and also contrast the development of COVID-19 in all cities in China at the early, middle and late stages. We hope this dataset can help researchers around the world better understand the spreading dynamics of COVID-19 at a regional level, to inform intervention and mitigation strategies for policymakers.

0is1vyhy

coronavirus public datasets

35

Can AI help in screening Viral and COVID-19 pneumonia?

Coronavirus disease (COVID-19) is a pandemic disease, which has already caused thousands of causalities and infected several millions of people worldwide. Any technological tool enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI) in the rapid and accurate detection of COVID-19 from chest X-ray images. The aim of this paper is to propose a robust technique for automatic detection of COVID-19 pneumonia from digital chest X-ray images applying pre-trained deep-learning algorithms while maximizing the detection accuracy. A public database was created by the authors combining several public databases and also by collecting images from recently published articles. The database contains a mixture of 423 COVID-19, 1485 viral pneumonia, and 1579 normal chest X-ray images. Transfer learning technique was used with the help of image augmentation to train and validate several pre-trained deep Convolutional Neural Networks (CNNs). The networks were trained to classify two different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and COVID-19 pneumonia with and without image augmentation. The classification accuracy, precision, sensitivity, and specificity for both the schemes were 99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%, respectively.

iilujjvc

coronavirus public datasets

35

Knowledge synthesis from 100 million biomedical documents augments the deep expression profiling of coronavirus receptors

The COVID-19 pandemic demands assimilation of all available biomedical knowledge to decode its mechanisms of pathogenicity and transmission. Despite the recent renaissance in unsupervised neural networks for decoding unstructured natural languages, a platform for the real-time synthesis of the exponentially growing biomedical literature and its comprehensive triangulation with deep omic insights is not available. Here, we present the nferX platform for dynamic inference from over 45 quadrillion possible conceptual associations extracted from unstructured biomedical text, and their triangulation with Single Cell RNA-sequencing based insights from over 25 tissues. Using this platform, we identify intersections between the pathologic manifestations of COVID-19 and the comprehensive expression profile of the SARS-CoV-2 receptor ACE2. We find that tongue keratinocytes and olfactory epithelial cells are likely under-appreciated targets of SARS-CoV-2 infection, correlating with reported loss of sense of taste and smell as early indicators of COVID-19 infection, including in otherwise asymptomatic patients. Airway club cells, ciliated cells and type II pneumocytes in the lung, and enterocytes of the gut also express ACE2. This study demonstrates how a holistic data science platform can leverage unprecedented quantities of structured and unstructured publicly available data to accelerate the generation of impactful biological insights and hypotheses.

dtnuet6c

coronavirus public datasets

35

An updated analysis of turning point, duration and attack rate of COVID-19 outbreaks in major Western countries with data of daily new cases

As coronavirus spreads around the world, the study of its effects is of great practical significance. We collated data on daily new cases of the COVID-19 outbreaks in the six Western countries of the Group of Seven and the dates of governments' interventions. We studied the periods before and after the dates of major governments' interventions integrally based on a segmented Poisson model. The relevant results are published in the paper of "Predicting turning point, duration and attack rate of COVID - 19 outbreaks in major Western countries" [1]. Our method can be used to update prediction daily as COVID-19 outbreaks evolve. In this article, we illustrate an updated analysis with our method to facilitate reproducibility. Both datasets used and updated are provided.

bycyzejg

coronavirus public datasets

35

Racism is a Virus: Anti-Asian Hate and Counterhate in Social Media during the COVID-19 Crisis

The spread of COVID-19 has sparked racism, hate, and xenophobia in social media targeted at Chinese and broader Asian communities. However, little is known about how racial hate spreads during a pandemic and the role of counterhate speech in mitigating the spread. Here we study the evolution and spread of anti-Asian hate speech through the lens of Twitter. We create COVID-HATE, the largest dataset of anti-Asian hate and counterhate spanning three months, containing over 30 million tweets, and a social network with over 87 million nodes. By creating a novel hand-labeled dataset of 2,400 tweets, we train a text classifier to identify hate and counterhate tweets that achieves an average AUROC of 0.852. We identify 891,204 hate and 200,198 counterhate tweets in COVID-HATE. Using this data to conduct longitudinal analysis, we find that while hateful users are less engaged in the COVID-19 discussions prior to their first anti-Asian tweet, they become more vocal and engaged afterwards compared to counterhate users. We find that bots comprise 10.4% of hateful users and are more vocal and hateful compared to non-bot users. Comparing bot accounts, we show that hateful bots are more successful in attracting followers compared to counterhate bots. Analysis of the social network reveals that hateful and counterhate users interact and engage extensively with one another, instead of living in isolated polarized communities. Furthermore, we find that hate is contagious and nodes are highly likely to become hateful after being exposed to hateful content. Importantly, our analysis reveals that counterhate messages can discourage users from turning hateful in the first place. Overall, this work presents a comprehensive overview of anti-Asian hate and counterhate content during a pandemic. The COVID-HATE dataset is available at http://claws.cc.gatech.edu/covid.

l9mutkby

coronavirus public datasets

35

TweetsCOV19 -- A Knowledge Base of Semantically Annotated Tweets about the COVID-19 Pandemic

Publicly available social media archives facilitate research in the social sciences and provide corpora for training and testing a wide range of machine learning, NLP and information retrieval methods. With respect to the recent outbreak of COVID-19, online discourse on Twitter reflects public opinion and perception related to the pandemic itself as well as mitigating measures and their societal impact. Understanding such discourse, its evolution and interdependencies with real-world events or (mis)information can foster valuable insights. On the other hand, such corpora are crucial facilitators for computational methods addressing tasks such as sentiment analysis, event detection or entity recognition. However, obtaining, archiving and semantically annotating large amounts of tweets is costly. In this paper, we describe TweetsCOV19, a publicly available knowledge base of currently more than 8 million tweets, spanning the period Oct'19-Apr'20. Metadata about the tweets as well as extracted entities, hashtags, user mentions, sentiments, and URLs are exposed using established RDF/S vocabularies, providing an unprecedented knowledge base for a range of knowledge discovery tasks. Next to a description of the dataset and its extraction and annotation process, we present an initial analysis, use cases and usage of the corpus.

aj2kscs9

coronavirus public datasets

35

The COronavirus Pandemic Epidemiology (COPE) Consortium: A Call to Action

The rapid pace of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; COVID-19) pandemic presents challenges to the real-time collection of population-scale data to inform near-term public health needs as well as future investigations. We established the COronavirus Pandemic Epidemiology (COPE) consortium to address this unprecedented crisis on behalf of the epidemiology research community. As a central component of this initiative, we have developed a COVID Symptom Study (previously known as the COVID Symptom Tracker) mobile application as a common data collection tool for epidemiologic cohort studies with active study participants. This mobile application collects information on risk factors, daily symptoms, and outcomes through a user-friendly interface that minimizes participant burden. Combined with our efforts within the general population, data collected from nearly 3 million participants in the United States and United Kingdom are being used to address critical needs in the emergency response, including identifying potential hot spots of disease and clinically actionable risk factors. The linkage of symptom data collected in the app with information and biospecimens already collected in epidemiology cohorts will position us to address key questions related to diet, lifestyle, environmental, and socioeconomic factors on susceptibility to COVID-19, clinical outcomes related to infection, and long-term physical, mental health, and financial sequalae. We call upon additional epidemiology cohorts to join this collective effort to strengthen our impact on the current health crisis and generate a new model for a collaborative and nimble research infrastructure that will lead to more rapid translation of our work for the betterment of public health.

8byzhm1d

coronavirus public datasets

35

NAIST COVID: Multilingual COVID-19 Twitter and Weibo Dataset

Since the outbreak of coronavirus disease 2019 (COVID-19) in the late 2019, it has affected over 200 countries and billions of people worldwide. This has affected the social life of people owing to enforcements, such as"social distancing"and"stay at home."This has resulted in an increasing interaction through social media. Given that social media can bring us valuable information about COVID-19 at a global scale, it is important to share the data and encourage social media studies against COVID-19 or other infectious diseases. Therefore, we have released a multilingual dataset of social media posts related to COVID-19, consisting of microblogs in English and Japanese from Twitter and those in Chinese from Weibo. The data cover microblogs from January 20, 2020, to March 24, 2020. This paper also provides a quantitative as well as qualitative analysis of these datasets by creating daily word clouds as an example of text-mining analysis. The dataset is now available on Github. This dataset can be analyzed in a multitude of ways and is expected to help in efficient communication of precautions related to COVID-19.

o8b1rtux

SARS-CoV-2 spike structure

36

Rapid Structure-Based Screening Informs Potential Agents for Coronavirus Disease (COVID-19) Outbreak

Coronavirus Disease 2019 (COVID-19), caused by the novel coronavirus, has spread rapidly across China. Consequently, there is an urgent need to sort and develop novel agents for the prevention and treatment of viral infections. A rapid structure-based virtual screening is used for the evaluation of current commercial drugs, with structures of human angiotensin converting enzyme II (ACE2), and viral main protease, spike, envelope, membrane and nucleocapsid proteins. Our results reveal that the reported drugs Arbidol, Chloroquine and Remdesivir may hinder the entry and release of virions through the bindings with ACE2, spike and envelope proteins. Due to the similar binding patterns, NHC (β-d-N4-hydroxycytidine) and Triazavirin are also in prospects for clinical use. Main protease (3CLpro) is likely to be a feasible target of drug design. The screening results to target 3CL-pro reveal that Mitoguazone, Metformin, Biguanide Hydrochloride, Gallic acid, Caffeic acid, Sulfaguanidine and Acetylcysteine seem be possible inhibitors and have potential application in the clinical therapy of COVID-19.

dog7i13o

SARS-CoV-2 spike structure

36

Neutralizing nanobodies bind SARS-CoV-2 spike RBD and block interaction with ACE2.

The SARS-CoV-2 virus is more transmissible than previous coronaviruses and causes a more serious illness than influenza. The SARS-CoV-2 receptor binding domain (RBD) of the spike protein binds to the human angiotensin-converting enzyme 2 (ACE2) receptor as a prelude to viral entry into the cell. Using a naive llama single-domain antibody library and PCR-based maturation, we have produced two closely related nanobodies, H11-D4 and H11-H4, that bind RBD (KD of 39 and 12 nM, respectively) and block its interaction with ACE2. Single-particle cryo-EM revealed that both nanobodies bind to all three RBDs in the spike trimer. Crystal structures of each nanobody-RBD complex revealed how both nanobodies recognize the same epitope, which partly overlaps with the ACE2 binding surface, explaining the blocking of the RBD-ACE2 interaction. Nanobody-Fc fusions showed neutralizing activity against SARS-CoV-2 (4-6 nM for H11-H4, 18 nM for H11-D4) and additive neutralization with the SARS-CoV-1/2 antibody CR3022.

47z1p8c9

SARS-CoV-2 spike structure

36

Covid-19, Coronavirus, SARS-CoV-2 and the small bowel.

Although SARS-CoV-2 may primarily enter the cells of the lungs, the small bowel may also be an important entry or interaction site, as the enterocytes are rich in angiotensin converting enzyme (ACE)-2 receptors. The initial gastrointestinal symptoms that appear early during the course of Covid-19 support this hypothesis. Furthermore, SARS-CoV virions are preferentially released apically and not at the basement of the airway cells. Thus, in the setting of a productive infection of conducting airway epithelia, the apically released SARS-CoV may be removed by mucociliary clearance and gain access to the GI tract via a luminal exposure. In addition, post-mortem studies of mice infected by SARS-CoV have demonstrated diffuse damage to the GI tract, with the small bowel showing signs of enterocyte desquamation, edema, small vessel dilation and lymphocyte infiltration, as well as mesenteric nodes with severe hemorrhage and necrosis. Finally, the small bowel is rich in furin, a serine protease which can separate the S-spike of the coronavirus into two "pinchers" (S1 and 2). The separation of the S-spike into S1 and S2 is essential for the attachment of the virion to both the ACE receptor and the cell membrane. In this special review, we describe the interaction of SARS-CoV-2 with the cell and enterocyte and its potential clinical implications.

0cvh83zu

SARS-CoV-2 spike structure

36

Structural basis of a shared antibody response to SARS-CoV-2.

Molecular understanding of neutralizing antibody responses to SARS-CoV-2 could accelerate vaccine design and drug discovery. We analyzed 294 anti-SARS-CoV-2 antibodies and found that IGHV3-53 is the most frequently used IGHV gene for targeting the receptor-binding domain (RBD) of the spike protein. Co-crystal structures of two IGHV3-53 neutralizing antibodies with RBD, with or without Fab CR3022, at 2.33 to 3.20 Å resolution revealed that the germline-encoded residues dominate recognition of the ACE2 binding site. This binding mode limits the IGHV3-53 antibodies to short CDR H3 loops, but accommodates light-chain diversity. These IGHV3-53 antibodies show minimal affinity maturation and high potency, which is promising for vaccine design. Knowledge of these structural motifs and binding mode should facilitate design of antigens that elicit this type of neutralizing response.

12o2r9zx

SARS-CoV-2 spike structure

36

Considerations around the SARS-CoV-2 Spike Protein with particular attention to COVID-19 brain infection and neurological symptoms.

Spike protein (S protein) is the virus 'key' to infect cells being able to strongly bind to the human angiotensin-converting enzyme2 (ACE2), as it has been reported. In fact, Spike structure and function is known to be highly important for cell infection as well as entering the brain. Growing evidence indicates that different types of coronaviruses not only affect the respiratory system, but they might also invade the central nervous system (CNS). However, very few evidence have been so far reported on the presence of COVID-19 in the brain and the potential exploitation, by this virus, of lung to brain axis to reach neurons has not completely understood. In this article we assessed the SARS-CoV and SARS-CoV-2 Spike protein sequence, structure and electrostatic potential using computational approaches. Our results showed that the S proteins of SARS-CoV-2 and SARS-CoV are highly similar, sharing a sequence identity of 77%. In addition, we found that the SARS-CoV-2 S protein is slightly more positively charged than that of SARS-CoV since it contains four more positively charged residues and five less negatively charged residues which may lead to an increased affinity to bind to negatively charged regions of other molecules through non-specific and specific interactions. Analyzing of the S protein binds to the host ACE2 receptor showed a 30% higher binding energy for SARS-CoV-2 than the SARS-CoV S protein. These results might be useful for understanding the mechanism of cell entry, blood brain barrier crossing and clinical features related to the CNS infection by SARS-CoV-2.

2bz78yl1

SARS-CoV-2 spike structure

36

Molecular simulation of SARS-CoV-2 spike protein binding to pangolin ACE2 or human ACE2 natural variants reveals altered susceptibility to infection.

We constructed complex models of SARS-CoV-2 spike protein binding to pangolin or human ACE2, the receptor for virus transmission, and estimated the binding free energy changes using molecular dynamics simulation. SARS-CoV-2 can bind to both pangolin and human ACE2, but has a significantly lower binding affinity for pangolin ACE2 due to the increased binding free energy (9.5 kcal mol-1). Human ACE2 is among the most polymorphous genes, for which we identified 317 missense single-nucleotide variations (SNVs) from the dbSNP database. Three SNVs, E329G (rs143936283), M82I (rs267606406) and K26R (rs4646116), had a significant reduction in binding free energy, which indicated higher binding affinity than wild-type ACE2 and greater susceptibility to SARS-CoV-2 infection for people with them. Three other SNVs, D355N (rs961360700), E37K (rs146676783) and I21T (rs1244687367), had a significant increase in binding free energy, which indicated lower binding affinity and reduced susceptibility to SARS-CoV-2 infection.

d4rekhom

SARS-CoV-2 spike structure

36

Binding of the SARS-CoV-2 Spike Protein to Glycans

The pandemic of SARS-CoV-2 has caused a high number of deaths in the world. To combat it, it is necessary to develop a better understanding of how the virus infects host cells. Infection normally starts with the attachment of the virus to cell-surface glycans like heparan sulfate (HS) and sialic acid-containing oligosaccharides. In this study, we examined and compared the binding of the subunits and spike (S) proteins of SARS-CoV-2 and SARS-CoV, MERS-CoV to these glycans. Our results revealed that the S proteins and subunits can bind to HS in a sulfation-dependent manner, the length of HS is not a critical factor for the binding, and no binding with sialic acid residues was detected. Overall, this work suggests that HS binding may be a general mechanism for the attachment of these coronaviruses to host cells, and supports the potential importance of HS in infection and in the development of antiviral agents against these viruses.

6e3j0pn7

SARS-CoV-2 spike structure

36

A graph-based approach identifies dynamic H-bond communication networks in spike protein S of SARS-CoV-2

Corona virus spike protein S is a large homo-trimeric protein embedded in the membrane of the virion particle. Protein S binds to angiotensin-converting-enzyme 2, ACE2, of the host cell, followed by proteolysis of the spike protein, drastic protein conformational change with exposure of the fusion peptide of the virus, and entry of the virion into the host cell. The structural elements that govern conformational plasticity of the spike protein are largely unknown. Here, we present a methodology that relies upon graph and centrality analyses, augmented by bioinformatics, to identify and characterize large H-bond clusters in protein structures. We apply this methodology to protein S ectodomain and find that, in the closed conformation, the three protomers of protein S bring the same contribution to an extensive central network of H-bonds, has a relatively large H-bond cluster at the receptor binding domain, and a cluster near a protease cleavage site. Markedly different H-bonding at these three clusters in open and pre-fusion conformations suggest dynamic H-bond clusters could facilitate structural plasticity and selection of a protein S protomer for binding to the host receptor, and proteolytic cleavage. From analyses of spike protein sequences we identify patches of histidine and carboxylate groups that could be involved in transient proton binding.

f4z91s03

SARS-CoV-2 spike structure

36

Potent synthetic nanobodies against SARS-CoV-2 and molecular basis for neutralization

SARS-CoV-2, the Covid-19 causative virus, adheres to human cells through binding of its envelope Spike protein to the receptor ACE2. The Spike receptor-binding domain (S-RBD) mediates this key event and thus is a primary target for therapeutic neutralizing antibodies to mask the ACE2-interacting interface. Here, we generated 99 synthetic nanobodies (sybodies) using ribosome and phage display. The best sybody MR3 binds the RBD with KD of 1.0 nM and neutralizes SARS-CoV-2 pseudovirus with IC50 of 0.40 μg mL-1. Crystal structures of two sybody-RBD complexes reveal a common neutralizing mechanism through which the RBD-ACE2 interaction is competitively inhibited by sybodies. The structures allowed the rational design of a mutant with higher affinity and improved neutralization efficiency by ∼24-folds, lowering the IC50 from 12.32 to 0.50 μg mL-1. Further, the structures explain the selectivity of sybodies between SARS-CoV strains. Our work presents an alternative approach to generate neutralizers against newly emerged viruses. One sentence summary Structural and biochemical studies revealed the molecular basis for the neutralization mechanism of in vitro-selected and rationally designed nanobody neutralizers for SARS-CoV-2 pseudovirus.

88wfcc3y

SARS-CoV-2 spike structure

36

Computational methods to develop potential neutralizing antibody Fab region against SARS-CoV-2 as therapeutic and diagnostic tool

SARS-CoV-2, a global pandemic originated from Wuhan city of China in the month of December 2019. There is an urgency to identify potential antibodies to neutralize the virus and also as a diagnostic tool candidate. At present palliative treatments using existing antiviral drugs are under trails to treat SARS-CoV-2.Whole Genome sequence of Wuhan market sample of SARS-CoV-2 was obtained from NCBI Gene ID MN908947.3.Spike protein sequence PDB ID 6VSB obtained from RCSB database. Spike protein sequence had shown top V gene match with IGLV1-44*01, IGLV1-47*02 and has VL type chain. Whole Genome sequence had shown top V gene match with IGHV1-38-4*01 and has VH type chain. VD chain had shown link to allele HLA-A0206 80%, HLA-A0217 80%, HLA-A2301 75%, HLA-A0203 75%, HLA-A0202 70% and HLA-A0201 55% of binding levels. Some conserved regions of spike protein had shown strong binding affinity with HLA-A-0*201, HLA-A24, HLA-B-5701 and HLA-B-5703 alpha chains. Synthetic Fab construct BCR type antibody IgG (CR5840) had shown Polyspecific binding activity with spike glycoprotein when compared with available Anti-SARS antibody CR3022.Thus we propose CR5840 Fab constructed antibody as potential neutralizing antibody for SARS-CoV-2. Based on germline analysis we also propose cytotoxic T lymphocyte epitope peptide selective system as effective tool for the development of SARS-CoV-2 vaccine.

2jrichn5

SARS-CoV-2 spike structure

36

Conformational dynamics of SARS-CoV-2 trimeric spike glycoprotein in complex with receptor ACE2 revealed by cryo-EM

The recent outbreaks of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its rapid international spread pose a global health emergency. The trimeric spike (S) glycoprotein interacts with its receptor human ACE2 to mediate viral entry into host-cells. Here we present cryo-EM structures of an uncharacterized tightly closed SARS-CoV-2 S-trimer and the ACE2-bound-S-trimer at 2.7-Å and 3.8-Å-resolution, respectively. The tightly closed S-trimer with inactivated fusion peptide may represent the ground prefusion state. ACE2 binding to the up receptor-binding domain (RBD) within S-trimer triggers continuous swing-motions of ACE2-RBD, resulting in conformational dynamics of S1 subunits. Noteworthy, SARS-CoV-2 S-trimer appears much more sensitive to ACE2-receptor than SARS-CoV S-trimer in terms of receptor-triggered transformation from the closed prefusion state to the fusion-prone open state, potentially contributing to the superior infectivity of SARS-CoV-2. We defined the RBD T470-T478 loop and residue Y505 as viral determinants for specific recognition of SARS-CoV-2 RBD by ACE2, and provided structural basis of the spike D614G-mutation induced enhanced infectivity. Our findings offer a thorough picture on the mechanism of ACE2-induced conformational transitions of S-trimer from ground prefusion state towards postfusion state, thereby providing important information for development of vaccines and therapeutics aimed to block receptor binding.

4y37676n

SARS-CoV-2 spike structure

36

Extracellular vesicles containing ACE2 efficiently prevent infection by SARS-CoV-2 Spike protein-containing virus

SARS-CoV-2 entry is mediated by binding of the spike protein (S) to the surface receptor ACE2 and subsequent priming by TMPRRS2 allowing membrane fusion. Here, we produced extracellular vesicles (EVs) exposing ACE2 and demonstrate that ACE2-EVs are efficient decoys for SARS-CoV-2 S protein-containing lentivirus. Reduction of infectivity positively correlates with the level of ACE2, is 500 to 1500 times more efficient than with soluble ACE2 and further enhanced by the inclusion of TMPRSS2.

4dadwwtc

SARS-CoV-2 spike structure

36

A potent neutralizing human antibody reveals the N-terminal domain of the Spike protein of SARS-CoV-2 as a site of vulnerability

The pandemic of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents a global public health threat. Most research on therapeutics against SARS-CoV-2 focused on the receptor binding domain (RBD) of the Spike (S) protein, whereas the vulnerable epitopes and functional mechanism of non-RBD regions are poorly understood. Here we isolated and characterized monoclonal antibodies (mAbs) derived from convalescent COVID-19 patients. An mAb targeting the N-terminal domain (NTD) of the SARS-CoV-2 S protein, named 4A8, exhibits high neutralization potency against both authentic and pseudotyped SARS-CoV-2, although it does not block the interaction between angiotensin-converting enzyme 2 (ACE2) receptor and S protein. The cryo-EM structure of the SARS-CoV-2 S protein in complex with 4A8 has been determined to an overall resolution of 3.1 Angstrom and local resolution of 3.4 Angstrom for the 4A8-NTD interface, revealing detailed interactions between the NTD and 4A8. Our functional and structural characterizations discover a new vulnerable epitope of the S protein and identify promising neutralizing mAbs as potential clinical therapy for COVID-19.

b3l4sy1u

SARS-CoV-2 spike structure

36

In silico detection of SARS-CoV-2 specific B-cell epitopes and validation in ELISA for serological diagnosis of COVID-19

Rapid generation of diagnostics is paramount to understand epidemiology and to control the spread of emerging infectious diseases such as COVID-19. Computational methods to predict serodiagnostic epitopes that are specific for the pathogen could help accelerate the development of new diagnostics. A systematic survey of 27 SARS-CoV-2 proteins was conducted to assess whether existing B-cell epitope prediction methods, combined with comprehensive mining of sequence databases and structural data, could predict whether a particular protein would be suitable for serodiagnosis. Nine of the predictions were validated with recombinant SARS-CoV-2 proteins in the ELISA format using plasma and sera from patients with SARS-CoV-2 infection, and a further 11 predictions were compared to the recent literature. Results appeared to be in agreement with 12 of the predictions, in disagreement with 3, while a further 5 were deemed inconclusive. We showed that two of our top five candidates, the N-terminal fragment of the nucleoprotein and the receptor-binding domain of the spike protein, have the highest sensitivity and specificity and signal-to-noise ratio for detecting COVID-19 sera/plasma by ELISA. Mixing the two antigens together for coating ELISA plates led to a sensitivity of 94% (N=80 samples from persons with RT-PCR confirmed SARS-CoV2 infection), and a specificity of 97.2% (N=106 control samples).

90a9rhsh

SARS-CoV-2 spike structure

36

Understanding the B and T cells epitopes of spike protein of severe respiratory syndrome coronavirus-2: A computational way to predict the immunogens

The 2019 novel severe respiratory syndrome coronavirus-2 (SARS-CoV-2) outbreak has caused a large number of deaths with thousands of confirmed cases worldwide. The present study followed computational approaches to identify B- and T-cell epitopes for spike glycoprotein of SARS-CoV-2 by its interactions with the human leukocyte antigen alleles. We identified twenty-four peptide stretches on the SARS-CoV-2 spike protein that are well conserved among the reported strains. The S protein structure further validated the presence of predicted peptides on the surface. Out of which twenty are surface exposed and predicted to have reasonable epitope binding efficiency. The work could be useful for understanding the immunodominant regions in the surface protein of SARS-CoV-2 and could potentially help in designing some peptide-based diagnostics.

ch004jxy

SARS-CoV-2 spike structure

36

De novo design of high-affinity antibody variable regions (Fv) against the SARS-CoV-2 spike protein

The emergence of SARS-CoV-2 is responsible for the pandemic of respiratory disease known as COVID-19, which emerged in the city of Wuhan, Hubei province, China in late 2019. Both vaccines and targeted therapeutics for treatment of this disease are currently lacking. Viral entry requires binding of the viral spike receptor binding domain (RBD) with the human angiotensin converting enzyme (hACE2). In an earlier paper1, we report on the specific residue interactions underpinning this event. Here we report on the de novo computational design of high affinity antibody variable regions through the recombination of VDJ genes targeting the most solvent-exposed hACE2-binding residues of the SARS-CoV-2 spike protein using the software tool OptMAVEn-2.02. Subsequently, we carry out computational affinity maturation of the designed prototype variable regions through point mutations for improved binding with the target epitope. Immunogenicity was restricted by preferring designs that match sequences from a 9-mer library of “human antibodies” based on H-score (human string content, HSC)3. We generated 106 different designs and report in detail on the top five that trade-off the greatest affinity for the spike RBD epitope (quantified using the Rosetta binding energies) with low H-scores. By grafting the designed Heavy (VH) and Light (VL) chain variable regions onto a human framework (Fc), high-affinity and potentially neutralizing full-length monoclonal antibodies (mAb) can be constructed. Having a potent antibody that can recognize the viral spike protein with high affinity would be enabling for both the design of sensitive SARS-CoV-2 detection devices and for their deployment as therapeutic antibodies.

crz52oo8

SARS-CoV-2 spike structure

36

Unfractionated heparin potently inhibits the binding of SARS-CoV-2 spike protein to a human cell line

The SARS-CoV-2 spike protein is known to bind to the receptor, ACE2, on the surface of target cells. The spike protein is processed by membrane proteases, including TMPRSS2, and is either internalised or fuses directly with the cell, leading to infection. We identified a human cell line that expresses both ACE2 and TMPRSS2, the RT4 urinary bladder transitional carcinoma, and used it to develop a proxy assay for viral interactions with host cells. A tagged recombinant form of the spike protein, containing both the S1 and S2 domains, binds strongly to RT4 cells as determined by flow cytometry. Binding is temperature dependent and increases sharply at 37°C, suggesting that processing of the spike protein is likely to be important in the interaction. As the spike protein has previously been shown to bind heparin, a soluble glycosaminoglycan, we used a flow cytometry assay to determine the effect of heparin on spike protein binding to RT4 cells. Unfractionated heparin inhibited spike protein binding with an IC50 value of <0.05U/ml whereas two low molecular weight heparins were much less effective. This suggests that heparin, particularly unfractionated forms, could be considered to reduce clinical manifestations of COVID-19 by inhibiting continuing viral infection. Despite the sensitivity to heparin, we found no evidence that host cell glycosaminoglycans such as heparan and chondroitin sulphates play a major role in spike protein attachment.

3w5fvbts

SARS-CoV-2 spike structure

36

AI334 and AQ806 antibodies recognize the spike S protein from SARS-CoV-2 by ELISA

We tested 10 recombinant antibodies directed against the spike S protein from SARS-CoV-1. Among them, antibodies AI334 and AQ806 detect by ELISA the spike S protein from SARS-CoV-2.

5xm7cwjz

SARS-CoV-2 spike structure

36

Structures of potent and convergent neutralizing antibodies bound to the SARS-CoV-2 spike unveil a unique epitope responsible for exceptional potency

Understanding the mechanism of neutralizing antibodies (NAbs) against SARS-CoV-2 is critical for effective vaccines and therapeutics development. We recently reported an exceptionally potent NAb, BD-368-2, and revealed the existence of VH3-53/VH3-66 convergent NAbs in COVID-19. Here we report the 3.5-Å cryo-EM structure of BD-368-2’s Fabs in complex with a mutation-induced prefusion-state-stabilized spike trimer. Unlike VH3-53/VH3-66 NAbs, BD-368-2 fully blocks ACE2 binding by occupying all three receptor-binding domains (RBDs) simultaneously, regardless of their “up” and “down” positions. BD-368-2 also triggers fusogenic-like structural rearrangements of the spike trimer, which could impede viral entry. Moreover, BD-368-2 completely avoids the common epitope of VH3-53/VH3-66 NAbs, evidenced by multiple crystal structures of their Fabs in tripartite complexes with RBD, suggesting a new way of pairing potent NAbs to prevent neutralization escape. Together, these results rationalize a unique epitope that leads to exceptional neutralization potency, and provide guidance for NAb therapeutics and vaccine designs against SARS-CoV-2.

60gscdyu

SARS-CoV-2 spike structure

36

Structure of the nCoV trimeric spike

5yke4jkv

SARS-CoV-2 spike structure

36

Local computational methods to improve the interpretability and analysis of cryo-EM maps

Cryo-electron microscopy (cryo-EM) maps usually show heterogeneous distributions of B-factors and electron density occupancies and are typically B-factor sharpened to improve their contrast and interpretability at high-resolutions. However, ‘over-sharpening’ due to the application of a single global B-factor can distort processed maps causing connected densities to appear broken and disconnected. This issue limits the interpretability of cryo-EM maps, i.e. ab initio modelling. In this work, we propose 1) approaches to enhance high-resolution features of cryo-EM maps, while preventing map distortions and 2) methods to obtain local B-factors and electron density occupancy maps. These algorithms have as common link the use of the spiral phase transformation and are called LocSpiral, LocBSharpen, LocBFactor and LocOccupancy. Our results, which include improved maps of recent SARS-CoV-2 structures, show that our methods can improve the interpretability and analysis of obtained reconstructions.

4kslllaq

SARS-CoV-2 spike structure

36

Prefusion spike protein stabilization through computational mutagenesis

A novel severe acute respiratory syndrome (SARS)-like coronavirus (SARS-CoV-2) has emerged as a human pathogen, causing global pandemic and resulting in over 400,000 deaths worldwide. The surface spike protein of SARS-CoV-2 mediates the process of coronavirus entry into human cells by binding angiotensin-converting enzyme 2 (ACE2). Due to the critical role in viral-host interaction and the exposure of spike protein, it has been a focus of most vaccines’ developments. However, the structural and biochemical studies of the spike protein are challenging because it is thermodynamically metastable1. Here, we develop a new pipeline that automatically identifies mutants that thermodynamically stabilize the spike protein. Our pipeline integrates bioinformatics analysis of conserved residues, motion dynamics from molecular dynamics simulations, and other structural analysis to identify residues that significantly contribute to the thermodynamic stability of the spike protein. We then utilize our previously developed protein design tool, Eris, to predict thermodynamically stabilizing mutations in proteins. We validate the ability of our pipeline to identify protein stabilization mutants through known prefusion spike protein mutants. We finally utilize the pipeline to identify new prefusion spike protein stabilization mutants.

86hv27vh