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1.
Clin Transl Sci ; 2020 Sep 03.
Article in English | MEDLINE | ID: covidwho-742070

ABSTRACT

On March 11, 2020, the World Health Organization declared its assessment of COVID-19 as a global pandemic. However, specific anti SARS-CoV-2 drugs are still under development, and patients are managed by multiple complementary treatments. We performed a retrospective analysis to compare and evaluate the effect of low molecular weight heparin (LMWH) treatment on disease progression. For this purpose, the clinical records and laboratory indicators were extracted from electronic medical records of forty-two patients with COVID-19 (twenty-one of whom were treated with LMWH, and twenty-one without LMWH) hospitalized (Union Hospital of Huazhong University of Science and Technology) from February 1 to March 15, 2020. Changes in the percentage of lymphocytes before and after LMWH treatment were significantly different from those in the control group (p=0.011). Likewise, changes in the levels of D-dimer and fibrinogen degradation products (FDP) in the LMWH group before and after treatment were significantly different from those in the control group (p=0.035). Remarkably, IL-6 levels were significantly reduced after LMWH treatment (p=0.006), indicating that, besides other beneficial properties, LMWH may exert an anti-inflammatory effect and attenuate in part the 'cytokine storm' induced by the virus. Our results support the use of LMWH as a potential therapeutic drug for the treatment of COVID-19, paving the way for a subsequent well-controlled clinical study.

2.
Preprint | medRxiv | ID: ppmedrxiv-20176776

ABSTRACT

Effectively identifying COVID-19 patients using non-PCR clinical data is critical for the optimal clinical outcomes. Currently, there is a lack of comprehensive understanding of various biomedical features and appropriate technical approaches to accurately detecting COVID-19 patients. In this study, we recruited 214 confirmed COVID-19 patients in non-severe (NS) and 148 in severe (S) clinical type, 198 non-infected healthy (H) participants and 129 non-COVID viral pneumonia (V) patients. The participants' clinical information (23 features), lab testing results (10 features), and thoracic CT scans upon admission were acquired as three input feature modalities. To enable late fusion of multimodality data, we developed a deep learning model to extract a 10-feature high-level representation of the CT scans. Exploratory analyses showed substantial differences of all features among the four classes. Three machine learning models (k-nearest neighbor kNN, random forest RF, and support vector machine SVM) were developed based on the 43 features combined from all three modalities to differentiate four classes (NS, S, V, and H) at once. All three models had high accuracy to differentiate the overall four classes (95.4%-97.7%) and each individual class (90.6%-99.9%). Multimodal features provided substantial performance gain from using any single feature modality. Compared to existing binary classification benchmarks often focusing on single feature modality, this study provided a novel and effective breakthrough for clinical applications. Findings and the analytical workflow can be used as clinical decision support for current COVID-19 and other clinical applications with high-dimensional multimodal biomedical features.

3.
Preprint | medRxiv | ID: ppmedrxiv-20175950

ABSTRACT

Monitoring the levels of SARS-CoV-2 specific antibodies such as IgG, M and A in COVID-19 patient is an alternative method for diagnosing SARS-CoV-2 infection and an simple way to monitor immune responses in convalescent patients and after vaccination. Here, we assessed the levels of SARS-CoV-2 RBD specific antibodies in twenty-seven COVID-19 convalescent patients over 28-99 days after hospital discharge. Almost all patient who had severe or moderate COVID-19 symptoms and a high-level of IgG during the hospitalization showed a significant reduction at revisit. The remaining patients who had a low-level IgG during hospitalization stayed low at revisit. As expected, IgM levels in almost all convalescent patients reduced significantly or stayed low at revisit. The RBD-specific IgA levels were also reduced significantly at revisit. We also attempted to estimate decline rates of virus-specific antibodies using a previously established exponential decay model of antibody kinetics after infection. The predicted days when convalescent patients' RBD-specific IgG reaches to an undetectable level are approximately 273 days after hospital discharge, while the predicted decay times are 150 days and 108 days for IgM and IgA, respectively. This investigation and report will aid current and future studies to develope SARS-CoV-2 vaccines that are potent and long-lasting.

4.
Preprint | medRxiv | ID: ppmedrxiv-20177204

ABSTRACT

Importance: Serological assays can help diagnose and determine the rate of SARS-CoV-2 infections in a population. Objective: We characterized and compared 11 different lateral flow assays for their performance in diagnostic or epidemiological settings. Design, Setting, Participants: We used two cohorts to determine the speci- ficity: (i) up to 350 blood donor samples from past influenza seasons and (ii) up to 110 samples which tested PCR negative for SARS-CoV-2 during the first wave of SARS-CoV-2 infections in Switzerland. The sensitivity was determined using up to 370 samples which tested PCR positive for SARS-CoV-2 during the same time and is representative for age distribution and severity. Main Outcome: We found a single test usable for epidemiological studies in the current low-prevalence setting, all other tests showed lacking sensitivity or specificity for a usage in either epidemiological or diagnostic setting. However, orthogonal testing by combining two tests without common cross-reactivities makes testing in a low-prevalence setting feasible. Results: Nine out of the eleven tests showed specificities below 99%, only five of eleven tests showed sensitivities comparable to established ELISAs, and only one ful- filled both criteria. Contrary to previous results from lab assays, five tests measured an IgM response in >80% of the samples. We found no common cross-reactivities, which allows orthogonal testing schemes for five tests of sufficient sensitivities. Conclusions and Relevance: This study emphasizes the need for large and diverse negative cohorts when determining specificities, and for diverse and repre- sentative positive samples when determining sensitivities of lateral flow assays for SARS-CoV-2 infections. Failure to adhere to statistically relevant sample sizes or cohorts exclusively made up of hospitalised patients fails to accurately capture the performance of these assays in epidemiological settings. Our results allow a rational choice between tests for different use cases.

5.
Preprint | medRxiv | ID: ppmedrxiv-20177071

ABSTRACT

Background: COVID-19 has stretched the ability of many institutions to supply needed personal protective equipment, especially N95 respirators. N95 decontamination and reuse programs provide one potential solution to this problem. Unfortunately, a comprehensive evaluation of the effects of decontamination on the integrity of various N95 models using a quantitative fit test (QTFT) approach is lacking. Aims: 1) To investigate the effects of up to eight rounds of vaporized H2O2 (VHP) decontamination on the integrity of N95 respirators currently in use in a hospital setting. 2) To examine if N95 respirators worn by one user can adapt to the face shape of a second user with no compromise of integrity following VHP decontamination. Methods: The PortaCount Pro+ Respirator Fit Tester Model 8038 was used to quantitatively define the integrity, measured by fit, of N95 respirators following decontamination with VHP. Findings: There was an observable downward trend in the integrity of Halyard Fluidshield 46727 N95 respirators throughout eight cycles of decontamination with VHP. The integrity of 3M 1870 N95 respirators was significantly reduced after the respirator was worn, decontaminated with VHP, and then quantitatively fit tested on a second user. Furthermore, we uncovered inconsistencies between qualitative fit test and QTFT results that may have strong implications on the fit testing method used by institutions. Conclusions: Our data revealed variability in the integrity of different N95 models after VHP decontamination and exposed potential limitations of N95 decontamination and reuse programs.

6.
Preprint | medRxiv | ID: ppmedrxiv-20176743

ABSTRACT

Background: Since the outbreak of COVID-19, many put their hopes in the rapid development of effective immunizations. For now patient isolation, physical distancing and good hygiene are the sole measures for prevention. Processed breast milk with antibodies against SaRS-CoV-2 may serve as additional protection. We aimed to determine the presence and neutralization capacity of antibodies against SaRS-CoV-2 in breastmilk of mothers who have recovered from COVID-19. Methods: This prospective case control study included lactating mothers, recovered from (suspected) COVID-19 and healthy controls. Serum and breastmilk was collected. To assess the presence of antibodies in breastmilk and serum, we used multiple complementary assays, namely ELISA with the SARS-CoV-2 spike protein, SARS-CoV-2 receptor binding domain (RBD) and with the SARS-CoV-2 nucleocapsid (N) protein for IgG and bridging ELISA with the SARS-CoV-2 RBD and N protein for total Ig. To assess the effect of pasteurization breastmilk was exposed to Holder Pasteurization and High Pressure Pasteurization. Results: Breastmilk contained antibodies against SARS-CoV-2 using any of the assays in 24 out of 29 (83%) proven cases, in six out of nine (67%) suspected cases and in none of the 13 controls. In vitro neutralization of SARS-CoV-2 clinical isolate virus strain was successful in a subset of serum (13%) and milk samples (26%). Although after pasteurization of the milk SARS-CoV-2 antibodies were detected with both methods of pasteurization, virus neutralizing capacity of those antibodies was only retained with the HPP approach. Conclusion: Breastmilk of mothers who recovered from COVID-19 contains significant amounts of IgA against SARS-CoV-2, both before and after pasteurization.

7.
Preprint | medRxiv | ID: ppmedrxiv-20174623

ABSTRACT

In the early phases of the SARS coronavirus type 2 (SARS-CoV-2) pandemic, testing focused on individuals fitting a strict case definition involving a limited set of symptoms together with an identified epidemiological risk, such as contact with an infected individual or travel to a high-risk area. To assess whether this impaired our ability to detect and control early introductions of the virus into the UK, we PCR-tested archival specimens collected on admission to a large UK teaching hospital who retrospectively were identified as having a clinical presentation compatible with COVID-19. In addition, we screened available archival specimens submitted for respiratory virus diagnosis, and dating back to early January 2020, for the presence of SARS-CoV-2 RNA. Our data provides evidence for widespread community circulation of SARS-CoV2 in early February 2020 and into March that was undetected at the time due to restrictive case definitions informing testing policy. Genome sequence data showed that many of these early cases were infected with a distinct lineage of the virus. Sequences obtained from the first officially recorded case in Nottinghamshire - a traveller returning from Daegu, South Korea - also clustered with these early UK sequences suggesting acquisition of the virus occurred in the UK and not Daegu. Analysis of a larger sample of sequences obtained in the Nottinghamshire area revealed multiple viral introductions, mainly in late February and through March. These data highlight the importance of timely and extensive community testing to prevent future widespread transmission of the virus.

8.
Preprint | medRxiv | ID: ppmedrxiv-20159608

ABSTRACT

Background: Early clinical reports have suggested that the prevalence of thrombotic complications in the pathogenesis of COVID-19 may be as high as 30% in intensive care unit (ICU)-admitted patients and could be a major factor contributing to mortality. However, mechanisms underlying COVID-19-associated thrombo-coagulopathy, and its impact on patient morbidity and mortality, are still poorly understood. Methods: We performed a comprehensive analysis of coagulation and thromboinflammatory factors in plasma from COVID-19 patients with varying degrees of disease severity. Furthermore, we assessed the functional impact of these factors on clot formation and clot lysis. Results: Across all COVID-19 disease severities (mild, moderate and severe) we observed a significant increase (6-fold) in the concentration of ultra-large von Willebrand factor (UL-VWF) multimers compared to healthy controls. This is likely the result of an interleukin (IL)-6 driven imbalance of VWF and the regulatory protease ADAMTS13 (a disintegrin and metalloproteinase with thrombospondin type 1 motifs, member 13). Upregulation of this key pro-coagulant pathway may also be influenced by the observed increase (~6-fold) in plasma -defensins, a consequence of increased numbers of neutrophils and neutrophil activation. Markers of endothelial, platelet and leukocyte activation were accompanied by increased plasma concentrations of Factor XIII (FXIII) and plasminogen activator inhibitor (PAI)-1. In patients with high FXIII we observed alteration of the fibrin network structure in in vitro assays of clot formation, which coupled with increased PAI-1, prolonged the time to clot lysis by the t-PA/plasmin fibrinolytic pathway by 52% across all COVID-19 patients (n=23). Conclusions: We show that an imbalance in the VWF/ADAMTS13 axis causing increased VWF reactivity may contribute to the formation of platelet-rich thrombi in the pulmonary vasculature of COVID-19 patients. Through immune and inflammatory responses, COVID-19 also alters the balance of factors involved in fibrin generation and fibrinolysis which accounts for the persistent fibrin deposition previously observed in post-mortem lung tissue.

9.
Preprint | medRxiv | ID: ppmedrxiv-20176560

ABSTRACT

Background: The Coronavirus Disease 2019 (COVID-19) pandemic has caused negative impacts on the physical and mental health of the population worldwide. Pregnant and puerperal women comprise the population most vulnerable to impacts on mental health. Objective: To synthesize the scientific evidence on the repercussions of the COVID-19 pandemic on the mental health of pregnant and puerperal women. Methods: systematic review focused on answering the question what is the impact of the COVID-19 pandemic on the mental health of pregnan and puerperal women?. In order to perform the search of the studies, we used combinations among the keywords: pregnan*, puerper*, prenatal, perinatal, mental health, COVID-19, SARS-CoV-2. In total, we identified 150 studies from the databases and 14 studies were selected from preprints. We identified another four studies through manual search, totaling 18 studies to compose the final sample of this review. Results: Anxiety and depression were the main outcomes found, being shown in 15 and 11 studies, respectively. Other outcomes found in more than one study were: concerns related to several factors, loneliness, stress and fear. Conclusion: From this review, we can infer that the COVID-19 pandemic has impacted the mental health of pregnant and puerperal women, with depression and anxiety being the most frequent changes. The social detachment, the media pressure, the fear of contracting the infection, the economic scenario and the rupture of family rituals are shown as intensifying factors of psychological distress, thus causing changes in the mental health of these women.

10.
Preprint | medRxiv | ID: ppmedrxiv-20176594

ABSTRACT

To facilitate containment of the COVID-19 pandemic currently active in the United States and across the world, options for easy, non-invasive antibody testing are required. Here we have adapted a commercially available, serum-based ELISA for use with saliva samples, which will enable widespread, affordable testing for patients who experienced this disease.

11.
Preprint | medRxiv | ID: ppmedrxiv-20176917

ABSTRACT

Purpose: Early identification of a potentially deteriorating clinical course in hospitalized COVID-19 patients is critical since there exists a resource-demand gap for the ventilators. Materials: We aimed to develop and validate a deep learning-based approach to predict the need for mechanical ventilation as early as at the time of initial radiographic evaluation. We exploited the well-established DenseNet121 deep learning architecture for this purpose on 663 X-ray images derived from 528 hospitalized COVID-19 patients. Two Pulmonary and Critical Care experts blindly and independently evaluated the same X-ray images for purpose of validation. Results: We found that our deep learning model predicted the need for ventilation with a high accuracy, sensitivity and specificity (90.06%, 86.34% and 84.38%, respectively). This prediction was done approximately three days ahead of the actual intubation event. Our model also outperformed two Pulmonary and Critical Care experts who evaluated the same X-ray images and provided an incremental accuracy of 7.24-13.25%. Conclusion: Our deep learning model accurately predicted the need for mechanical ventilation early during hospitalization of COVID-19 patients. Until effective preventive or treatment measures become widely available for COVID-19 patients, prognostic stratification as provided by our model is likely to be highly valuable.

12.
Preprint | medRxiv | ID: ppmedrxiv-20175158

ABSTRACT

Background: Adaptive Biotechnologies has built an immune medicine platform based on the sequencing of immune receptors (immunoglobulins, B-cell receptors [BCRs] and T-cell receptors [TCRs]) with myriad applications in health and disease. This broad platform technology can be used to assess the diversity of the cellular adaptive immune system and track disease-associated TCRs and BCRs during the course of infection. The SARS-CoV-2 virus is spreading rapidly throughout the world, causing significant morbidity and mortality. Researchers, governments, and biotechnology companies are mobilizing to develop and distribute diagnostic and therapeutic alternatives to try to curb this global pandemic. Methods: In collaboration with our partners LabCorp/Covance, Adaptive Biotechnologies has opened the ImmuneRACE study to prospectively collect samples from individuals who have been infected with SARS-CoV-2, who have recovered from SARS-CoV-2 infection, or who have been exposed to someone infected with SARS-CoV-2. Discussion: We believe that the information contained within the genetics of the adaptive immune response to SARS-CoV-2 can improve our understanding of the immunobiology of this devasting virus and may inform efforts to improve current diagnostic and therapeutic approaches. To facilitate scientific and clinical advancement in the fight against COVID-19, the TCR sequence data resulting from the primary aims of this study will be made publicly available to scientists and researchers across the globe, an effort made possible through a collaboration with Microsoft. Trial registration: ImmunoRACE is registered with the US National Institutes of Health and can be accessed at ClinicalTrials.gov (NCT04494893).

13.
Preprint | medRxiv | ID: ppmedrxiv-20176602

ABSTRACT

A fundamental problem dealing with the Covid-19 pandemic has been to estimate the rate of infection, since so many cases are asymptomatic and contagious just for a few weeks. For example, in the US, estimate the proportion P(t) = N/330 where N is the US total who have ever been infected (in millions)at time t (months, t =0 being March 20). This is important for decisions on social restrictions, and allocation of medical resources, etc. However, the demand for extensive testing has not produced good estimates. In the US, the CDC has used the blood supply to sample for anti-bodies. Anti-bodies do not tell the whole picture, according to the Karolinska Instituet , many post infection cases show T-cell immunity, but no anti-bodies. We introduce a method based on a difference-differential equation (dde) for P(t). We emphasize that this is just for the present, with no prediction on how the pandemic will evolve. The dde uses only x=x(s), which is the number/million testing positive, and y=y(s), the number/million who have been tested for all time 0 < s < t (months), with no assumptions on the dynamics of the pandemic. However, we need two parameters. First, R , the ratio of asymptomatic to symptomatic infected cases. Second, T , the period of active infection when the virus can be detected. Both are random variables with distribution which can be estimated. For fixed R, we prove uniform bounds (1+ R) x/(y +1) < P(t) < (1+ R) x(t) , are best possible, with range depending on T . One advantage of our theory is being able to estimate P for many regions and countries where x and y is the only information available.

14.
Preprint | medRxiv | ID: ppmedrxiv-20176537

ABSTRACT

Background and Objective: The covid-19 epidemic is rapidly escalating in India and unlike developed countries there is no evidence of plateau or decline in the past 6 months. To evaluate association of state-level sociodemographics with incident cases and deaths we performed an ecological study. Methods: Publicly available data sources were used. Absolute number of covid-19 cases and deaths were obtained and cases and deaths/million in each state calculated from February to July 2020. To assess association of state level disease burden with sociodemographic variables (urbanization, human development, healthcare availability, healthcare access and quality etc.) we determined Pearson correlation and logarithmic trends. Results: Covid-19 in India has led to more than 2,000,000 cases and 45,000 deaths by end July 2020. There is large variation in state-level cases/million ranging from 7247 (Delhi), 3728 (Goa) and 3427 (Maharashtra) to less than 300/million in a few. Deaths/million range from 212 (Delhi), 122 (Maharashtra) and 51 (Tamilnadu) to 2 in north-eastern states. Most of the high burden states (except Delhi) are reporting increasing burden and deaths with the largest increase in July 2020. There is a significant positive correlation of urbanization with covid-19 cases (r= 0.65, R squared= 0.35) and deaths (r= 0.60, R squared= 0.28) and weaker correlation with other sociodemographic variables. From March to July 2020, stable R squared value for urbanization is observed with cases (0.37 to 0.39) while it is increasing for deaths (0.10 to 0.28). Conclusions: Covid-19 epidemic is escalating in India and cases as well as deaths are significantly greater in more urbanized states. Prevention, control and treatment should focus on urban health systems.

15.
Preprint | medRxiv | ID: ppmedrxiv-20177089

ABSTRACT

Background: As of August 15, 2020, Bangladesh lost 3591 lives since the first Coronavirus disease 2019 (COVID-19) case announced on March 8. The objective of the study was to report the clinical manifestation of both symptomatic and asymptomatic COVID-19-positive patients. Methods: A online-based cross-sectional survey was conducted for initial recruitment of participants with subsequent telephone interview by the three trained physicians in 237 adults with confirmed COVID-19 infection in Bangladesh. The study period was between 27 April to 26th May, 2020. Consent was ensured before commencing the interview. Collected data were entered in a predesigned case report form and subsequently analyzed by SPSS 20. Results: The mean age at presentation was 41.59 (13.73 SD) years and most of the cases were male (73%). A total of 90.29% of patients reside in urban areas. Among the positive cases, 13.1% (n=31) were asymptomatic. Asymptomatic cases were significantly more common in households with 2 to 4 members (p=.008). Both symptomatic and asymptomatic patients shared similar ages of presentation (p=0.23), gender differences (p=0.30), and comorbidities (p=0.11). Only 5.3% of patients received ICU care during their treatment. The most frequent presentation was fever (88.3%), followed by cough (69.9%), chest pain (34.5%), body ache (31.1%), and sore throat (30.1%). Thirty-nine percent (n=92) of the patients had comorbidities, with diabetes and hypertension being the most frequently observed. Conclusion: There has been an upsurge in COVID-19 cases in Bangladesh. Patients were mostly middle-aged and male. Typical presentations were fever and cough. Maintenance of social distancing and increased testing are required to meet the current public health challenge.

16.
Preprint | medRxiv | ID: ppmedrxiv-20176693

ABSTRACT

Background Nasopharyngeal samples (NPS) are the mainstay of COVID-19 diagnosis. However, the extent to which assay signals relate to exhaled virus is unknown. We investigated the use of novel, non-invasive face-mask sampling (FMS) to detect exhaled SARS-CoV-2 RNA in two studies. Methods In an outbreak study (cohort 1), we performed FMS and NPS for 21 consecutive days after diagnosis on six healthcare workers who were screened positive for SARS-CoV-2. In a second hospitalised cohort (cohort 2), we performed FMS on 47 patients within 24 hours of a positive diagnosis. COVID-19 severity was graded according to WHO recommendations. Findings In cohort 1, SARS-COV-2 was detected by FMS in 10/40 (25%) samples (4/6 individuals), with no correlation between NPS and FMS RNA signals. All samples were negative by day 14 post diagnosis. Sustained FMS positivity with higher viral RNA signals showed a trend towards disease severity. In cohort 2, 19/47 (40%) individuals exhaled SARS-CoV-2 RNA extending over five orders of magnitude. FMS positive participants were older (positive: median age [IQR] 71 [61-84] vs negative: 61 [45-73], p=0.04) with more comorbidities (positive: 2 [1-3] vs negative: 1 [0-2], p<0.001) and have active cough (positive: 68% vs negative: 24%, p=0.003) and breathlessness (positive: 74% vs negative: 32%, p=0.005) during sampling, compared to FMS negative patients. Of five patients who were FMS positive and asymptomatic at time of sampling, two died of severe COVID-19 pneumonia within one month of follow up. Interpretation FMS detects exhaled SARS-COV-2, with stronger signals in those who develop severe disease.

17.
Preprint | medRxiv | ID: ppmedrxiv-20172924

ABSTRACT

Wastewater-based epidemiology (WBE) has emerged as an effective environmental surveillance tool in monitoring fecal-oral pathogen infections within a community. Congruently, SARS-CoV-2 virus, the etiologic agent of COVID-19, has been demonstrated to infect the gastrointestinal tissues, and be shed in feces. In the present study, SARS-CoV-2 RNA was concentrated from wastewater, sludge, surface water, ground water, and soil samples of municipal and hospital wastewater systems and related environment in Wuhan during the COVID-19 middle and low risk periods, and the viral RNA copies quantified using RT-qPCR. From the findings of this study, during the middle risk period, one influent sample and three secondary treatment effluents collected from Waste Water Treatment Plant 2 (WWTP2), as well as two influent samples from wastewater system of Hospital 2 were SARS-CoV-2 RNA positive. One sludge sample collected from Hospital 4; which was obtained during low risk period, was positive for SARS-CoV-2 RNA. These study findings demonstrate the significance of WBE in continuous surveilling and monitoring of SARS-CoV-2 at the community level, even when the COVID19 prevalence is low. Therefore, the application of WBE is principally useful in tracking the level of infections in communities and the risk assessment of the secondary environment.

18.
Preprint | bioRxiv | ID: ppbiorxiv-259242

ABSTRACT

Respiratory viruses including Respiratory syncytial virus (RSV), influenza virus and cornaviruses such as Middle Eastern respiratory virus (MERS) and SARS-CoV-2 infect and cause serious and sometimes fatal disease in thousands of people annually. It is critical to understand virus propagation dynamics within the respiratory system because new insights will increase our understanding of virus pathogenesis and enable infection patterns to be more predictable in vivo, which will enhance targeting of vaccines and drug delivery. This study presents a computational model of virus propagation within the respiratory tract network. The model includes the generation network branch structure of the respiratory tract, biophysical and infectivity properties of the virus, as well as air flow models that aid the circulation of the virus particles. The model can also consider the impact of the immune response aim to inhibit virus replication and spread. The model was applied to the SARS-CoV-2 virus by integrating data on its life-cycle, as well as density of Angiotensin Converting Enzyme (ACE2) expressing cells along the respiratory tract network. Using physiological data associated with the respiratory rate and virus load that is inhaled, the model can improve our understanding of the concentration and spatiotemporal dynamics of virus.

19.
American journal of cancer research ; 10(7):2010-2031, 2020.
Article | WHO COVID | ID: covidwho-710041

ABSTRACT

Coronavirus disease 2019 (COVID-19) is a novel, human-infecting β-coronavirus enveloped, positive-sense single-stranded RNA viruses, similar to the severe acute respiratory syndrome (SARS) infection that emerged in November 2002 In traditional Chinese medicine (TCM), the epidemic disease concepts of "febrile epidemics" (wenyi) or "warm diseases" (wenbing) are based on geographic and cultural aspects, and Chinese herbal medicine (CHM) played an important role in the treatment of epidemic diseases CHM was widely used to treat patients suffered with SARS almost two decades ago during outbreak of SARS, with proven safety and potential benefits TCM has also been widely used to treat cancer patients for a long history and much of them associate with immunomodulatory activity and are used to treat coronavirus-related diseases We propose the use of CHM treatment principles for clinical practice, based on four main stages of COVID-19 infection: early, intermediate, severe, and convalescence We suggest corresponding decoctions that exhibit antiviral activity and anti-inflammatory effects in the early stage of infection;preventing the disease from progressing from an intermediate to severe stage of infection;restoring normal lung function and improving consciousness in the severe stage;and ameliorating pulmonary and vascular injury in the convalescent stage We summarize the pharmaceutical mechanisms of CHM for treating coronavirus via antiviral, anti-inflammatory and immunomodulatory effects

20.
Preprint | medRxiv | ID: ppmedrxiv-20105841

ABSTRACT

Effectively and efficiently diagnosing COVID-19 patients with accurate clinical type is essential to achieve optimal outcomes of the patients as well as reducing the risk of overloading the healthcare system. Currently, severe and non-severe COVID-19 types are differentiated by only a few clinical features, which do not comprehensively characterize complicated pathological, physiological, and immunological responses to SARS-CoV-2 invasion in different types. In this study, we recruited 214 confirmed COVID-19 patients in non-severe and 148 in severe type, from Wuhan, China. The patients' comorbidity and symptoms (26 features), and blood biochemistry (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest (RF) models using features in each modality were developed and validated to classify COVID-19 clinical types. Using comorbidity/symptom and biochemistry as input independently, RF models achieved >90% and >95% predictive accuracy, respectively. Input features' importance based on Gini impurity were further evaluated and top five features from each modality were identified (age, hypertension, cardiovascular disease, gender, diabetes; D-Dimer, hsTNI, neutrophil, IL-6, and LDH). Combining top 10 multimodal features, RF model achieved >99% predictive accuracy. These findings shed light on how the human body reacts to SARS-CoV-2 invasion as a unity and provide insights on effectively evaluating COVID-19 patient's severity and developing treatment plans accordingly. We suggest that symptoms and comorbidities can be used as an initial screening tool for triaging, while biochemistry and features combined are applied when accuracy is the priority.

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