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1.
Proc Natl Acad Sci U S A ; 119(11)2022 03 15.
Article in English | MEDLINE | ID: covidwho-1713294

ABSTRACT

The impacts of interferon (IFN) signaling on COVID-19 pathology are multiple, with both protective and harmful effects being documented. We report here a multiomics investigation of systemic IFN signaling in hospitalized COVID-19 patients, defining the multiomics biosignatures associated with varying levels of 12 different type I, II, and III IFNs. The antiviral transcriptional response in circulating immune cells is strongly associated with a specific subset of IFNs, most prominently IFNA2 and IFNG. In contrast, proteomics signatures indicative of endothelial damage and platelet activation associate with high levels of IFNB1 and IFNA6. Seroconversion and time since hospitalization associate with a significant decrease in a specific subset of IFNs. Additionally, differential IFN subtype production is linked to distinct constellations of circulating myeloid and lymphoid immune cell types. Each IFN has a unique metabolic signature, with IFNG being the most associated with activation of the kynurenine pathway. IFNs also show differential relationships with clinical markers of poor prognosis and disease severity. For example, whereas IFNG has the strongest association with C-reactive protein and other immune markers of poor prognosis, IFNB1 associates with increased neutrophil to lymphocyte ratio, a marker of late severe disease. Altogether, these results reveal specialized IFN action in COVID-19, with potential diagnostic and therapeutic implications.


Subject(s)
Blood/metabolism , COVID-19/immunology , Interferons/blood , Proteome , Transcriptome , COVID-19/blood , Case-Control Studies , Datasets as Topic , Humans , Inpatients
2.
Sci Rep ; 12(1): 3212, 2022 02 25.
Article in English | MEDLINE | ID: covidwho-1713208

ABSTRACT

Novel Coronavirus disease (COVID-19) is a highly contagious respiratory infection that has had devastating effects on the world. Recently, new COVID-19 variants are emerging making the situation more challenging and threatening. Evaluation and quantification of COVID-19 lung abnormalities based on chest Computed Tomography (CT) images can help determining the disease stage, efficiently allocating limited healthcare resources, and making informed treatment decisions. During pandemic era, however, visual assessment and quantification of COVID-19 lung lesions by expert radiologists become expensive and prone to error, which raises an urgent quest to develop practical autonomous solutions. In this context, first, the paper introduces an open-access COVID-19 CT segmentation dataset containing 433 CT images from 82 patients that have been annotated by an expert radiologist. Second, a Deep Neural Network (DNN)-based framework is proposed, referred to as the [Formula: see text], that autonomously segments lung abnormalities associated with COVID-19 from chest CT images. Performance of the proposed [Formula: see text] framework is evaluated through several experiments based on the introduced and external datasets. Third, an unsupervised enhancement approach is introduced that can reduce the gap between the training set and test set and improve the model generalization. The enhanced results show a dice score of 0.8069 and specificity and sensitivity of 0.9969 and 0.8354, respectively. Furthermore, the results indicate that the [Formula: see text] model can efficiently segment COVID-19 lesions in both 2D CT images and whole lung volumes. Results on the external dataset illustrate generalization capabilities of the [Formula: see text] model to CT images obtained from a different scanner.


Subject(s)
COVID-19/diagnostic imaging , Image Processing, Computer-Assisted/methods , Neural Networks, Computer , Radiography, Thoracic , Tomography, X-Ray Computed , Datasets as Topic , Female , Humans , Male , Middle Aged
3.
J Immunol Res ; 2022: 1433323, 2022.
Article in English | MEDLINE | ID: covidwho-1697599

ABSTRACT

We performed a database mining on 102 transcriptomic datasets for the expressions of 29 m6A-RNA methylation (epitranscriptomic) regulators (m6A-RMRs) in 41 diseases and cancers and made significant findings: (1) a few m6A-RMRs were upregulated; and most m6A-RMRs were downregulated in sepsis, acute respiratory distress syndrome, shock, and trauma; (2) half of 29 m6A-RMRs were downregulated in atherosclerosis; (3) inflammatory bowel disease and rheumatoid arthritis modulated m6A-RMRs more than lupus and psoriasis; (4) some organ failures shared eight upregulated m6A-RMRs; end-stage renal failure (ESRF) downregulated 85% of m6A-RMRs; (5) Middle-East respiratory syndrome coronavirus infections modulated m6A-RMRs the most among viral infections; (6) proinflammatory oxPAPC modulated m6A-RMRs more than DAMP stimulation including LPS and oxLDL; (7) upregulated m6A-RMRs were more than downregulated m6A-RMRs in cancer types; five types of cancers upregulated ≥10 m6A-RMRs; (8) proinflammatory M1 macrophages upregulated seven m6A-RMRs; (9) 86% of m6A-RMRs were differentially expressed in the six clusters of CD4+Foxp3+ immunosuppressive Treg, and 8 out of 12 Treg signatures regulated m6A-RMRs; (10) immune checkpoint receptors TIM3, TIGIT, PD-L2, and CTLA4 modulated m6A-RMRs, and inhibition of CD40 upregulated m6A-RMRs; (11) cytokines and interferons modulated m6A-RMRs; (12) NF-κB and JAK/STAT pathways upregulated more than downregulated m6A-RMRs whereas TP53, PTEN, and APC did the opposite; (13) methionine-homocysteine-methyl cycle enzyme Mthfd1 downregulated more than upregulated m6A-RMRs; (14) m6A writer RBM15 and one m6A eraser FTO, H3K4 methyltransferase MLL1, and DNA methyltransferase, DNMT1, regulated m6A-RMRs; and (15) 40 out of 165 ROS regulators were modulated by m6A eraser FTO and two m6A writers METTL3 and WTAP. Our findings shed new light on the functions of upregulated m6A-RMRs in 41 diseases and cancers, nine cellular and molecular mechanisms, novel therapeutic targets for inflammatory disorders, metabolic cardiovascular diseases, autoimmune diseases, organ failures, and cancers.


Subject(s)
Atherosclerosis/genetics , Epigenesis, Genetic , Neoplasms/genetics , RNA, Messenger/metabolism , Reactive Oxygen Species/metabolism , Adenosine/analogs & derivatives , Adenosine/metabolism , Autoimmune Diseases/genetics , Datasets as Topic , Gene Expression Profiling , Humans , Inflammation/genetics , Metabolic Diseases/genetics , Methylation
4.
Sci Rep ; 12(1): 1849, 2022 02 03.
Article in English | MEDLINE | ID: covidwho-1671632

ABSTRACT

India is a hotspot of the COVID-19 crisis. During the first wave, several lockdowns (L) and gradual unlock (UL) phases were implemented by the government of India (GOI) to curb the virus spread. These phases witnessed many challenges and various day-to-day developments such as virus spread and resource management. Twitter, a social media platform, was extensively used by citizens to react to these events and related topics that varied temporally and geographically. Analyzing these variations can be a potent tool for informed decision-making. This paper attempts to capture these spatiotemporal variations of citizen reactions by predicting and analyzing the sentiments of geotagged tweets during L and UL phases. Various sentiment analysis based studies on the related subject have been done; however, its integration with location intelligence for decision making remains a research gap. The sentiments were predicted through a proposed hybrid Deep Learning (DL) model which leverages the strengths of BiLSTM and CNN model classes. The model was trained on a freely available Sentiment140 dataset and was tested over manually annotated COVID-19 related tweets from India. The model classified the tweets with high accuracy of around 90%, and analysis of geotagged tweets during L and UL phases reveal significant geographical variations. The findings as a decision support system can aid in analyzing citizen reactions toward the resources and events during an ongoing pandemic. The system can have various applications such as resource planning, crowd management, policy formulation, vaccination, prompt response, etc.


Subject(s)
COVID-19/prevention & control , Communicable Disease Control/methods , Decision Support Techniques , Deep Learning , Social Media , Spatio-Temporal Analysis , COVID-19/epidemiology , Datasets as Topic , Decision Making , Female , Health Policy , Health Resources , Humans , India/epidemiology , Male , Pandemics , Vaccination
5.
Sci Rep ; 12(1): 1847, 2022 02 03.
Article in English | MEDLINE | ID: covidwho-1671622

ABSTRACT

Automatic segmentation of infected lesions from computed tomography (CT) of COVID-19 patients is crucial for accurate diagnosis and follow-up assessment. The remaining challenges are the obvious scale difference between different types of COVID-19 lesions and the similarity between the lesions and normal tissues. This work aims to segment lesions of different scales and lesion boundaries correctly by utilizing multiscale and multilevel features. A novel multiscale dilated convolutional network (MSDC-Net) is proposed against the scale difference of lesions and the low contrast between lesions and normal tissues in CT images. In our MSDC-Net, we propose a multiscale feature capture block (MSFCB) to effectively capture multiscale features for better segmentation of lesions at different scales. Furthermore, a multilevel feature aggregate (MLFA) module is proposed to reduce the information loss in the downsampling process. Experiments on the publicly available COVID-19 CT Segmentation dataset demonstrate that the proposed MSDC-Net is superior to other existing methods in segmenting lesion boundaries and large, medium, and small lesions, and achieves the best results in Dice similarity coefficient, sensitivity and mean intersection-over-union (mIoU) scores of 82.4%, 81.1% and 78.2%, respectively. Compared with other methods, the proposed model has an average improvement of 10.6% and 11.8% on Dice and mIoU. Compared with the existing methods, our network achieves more accurate segmentation of lesions at various scales and lesion boundaries, which will facilitate further clinical analysis. In the future, we consider integrating the automatic detection and segmentation of COVID-19, and conduct research on the automatic diagnosis system of COVID-19.


Subject(s)
COVID-19/diagnostic imaging , Neural Networks, Computer , Radiography, Thoracic/methods , Tomography, X-Ray Computed/methods , Datasets as Topic , Female , Humans , Imaging, Three-Dimensional/methods , Male
6.
Comput Math Methods Med ; 2022: 7672196, 2022.
Article in English | MEDLINE | ID: covidwho-1666503

ABSTRACT

SARS-CoV-2 is a novel virus, responsible for causing the COVID-19 pandemic that has emerged as a pandemic in recent years. Humans are becoming infected with the virus. In 2019, the city of Wuhan reported the first-ever incidence of COVID-19. COVID-19 infected people have symptoms that are related to pneumonia, and the virus affects the body's respiratory organs, making breathing difficult. A real-time reverse transcriptase-polymerase chain reaction (RT-PCR) kit is used to diagnose the disease. Due to a shortage of kits, suspected patients cannot be treated promptly, resulting in disease spread. To develop an alternative, radiologists looked at the changes in radiological imaging, like CT scans, that produce comprehensive pictures of the body of excellent quality. The suspected patient's computed tomography (CT) scan is used to distinguish between a healthy individual and a COVID-19 patient using deep learning algorithms. A lot of deep learning methods have been proposed for COVID-19. The proposed work utilizes CNN architectures like VGG16, DeseNet121, MobileNet, NASNet, Xception, and EfficientNet. The dataset contains 3873 total CT scan images with "COVID" and "Non-COVID." The dataset is divided into train, test, and validation. Accuracies obtained for VGG16 are 97.68%, DenseNet121 is 97.53%, MobileNet is 96.38%, NASNet is 89.51%, Xception is 92.47%, and EfficientNet is 80.19%, respectively. From the obtained analysis, the results show that the VGG16 architecture gives better accuracy compared to other architectures.


Subject(s)
COVID-19/diagnosis , COVID-19/pathology , Deep Learning , Datasets as Topic , Humans , Pandemics , Tomography, X-Ray Computed/methods
7.
PLoS One ; 17(1): e0262609, 2022.
Article in English | MEDLINE | ID: covidwho-1643269

ABSTRACT

BACKGROUND: The use of linked healthcare data in research has the potential to make major contributions to knowledge generation and service improvement. However, using healthcare data for secondary purposes raises legal and ethical concerns relating to confidentiality, privacy and data protection rights. Using a linkage and anonymisation approach that processes data lawfully and in line with ethical best practice to create an anonymous (non-personal) dataset can address these concerns, yet there is no set approach for defining all of the steps involved in such data flow end-to-end. We aimed to define such an approach with clear steps for dataset creation, and to describe its utilisation in a case study linking healthcare data. METHODS: We developed a data flow protocol that generates pseudonymous datasets that can be reversibly linked, or irreversibly linked to form an anonymous research dataset. It was designed and implemented by the Comprehensive Patient Records (CPR) study in Leeds, UK. RESULTS: We defined a clear approach that received ethico-legal approval for use in creating an anonymous research dataset. Our approach used individual-level linkage through a mechanism that is not computer-intensive and was rendered irreversible to both data providers and processors. We successfully applied it in the CPR study to hospital and general practice and community electronic health record data from two providers, along with patient reported outcomes, for 365,193 patients. The resultant anonymous research dataset is available via DATA-CAN, the Health Data Research Hub for Cancer in the UK. CONCLUSIONS: Through ethical, legal and academic review, we believe that we contribute a defined approach that represents a framework that exceeds current minimum standards for effective pseudonymisation and anonymisation. This paper describes our methods and provides supporting information to facilitate the use of this approach in research.


Subject(s)
Biomedical Research/methods , Confidentiality , Data Anonymization , Biomedical Research/ethics , Datasets as Topic , Electronic Data Processing/ethics , Electronic Data Processing/methods , Electronic Health Records/organization & administration , Humans , Information Storage and Retrieval , United Kingdom
8.
PLoS One ; 17(1): e0259958, 2022.
Article in English | MEDLINE | ID: covidwho-1643239

ABSTRACT

The COVID-19 pandemic has been widely spread and affected millions of people and caused hundreds of deaths worldwide, especially in patients with comorbilities and COVID-19. This manuscript aims to present models to predict, firstly, the number of coronavirus cases and secondly, the hospital care demand and mortality based on COVID-19 patients who have been diagnosed with other diseases. For the first part, I present a projection of the spread of coronavirus in Mexico, which is based on a contact tracing model using Bayesian inference. I investigate the health profile of individuals diagnosed with coronavirus to predict their type of patient care (inpatient or outpatient) and survival. Specifically, I analyze the comorbidity associated with coronavirus using Machine Learning. I have implemented two classifiers: I use the first classifier to predict the type of care procedure that a person diagnosed with coronavirus presenting chronic diseases will obtain (i.e. outpatient or hospitalised), in this way I estimate the hospital care demand; I use the second classifier to predict the survival or mortality of the patient (i.e. survived or deceased). I present two techniques to deal with these kinds of unbalanced datasets related to outpatient/hospitalised and survived/deceased cases (which occur in general for these types of coronavirus datasets) to obtain a better performance for the classification.


Subject(s)
COVID-19/epidemiology , Diabetes Mellitus/epidemiology , Hypertension/epidemiology , Machine Learning , Obesity/epidemiology , Bayes Theorem , COVID-19/mortality , COVID-19/physiopathology , COVID-19/transmission , Comorbidity , Contact Tracing , Datasets as Topic , Diabetes Mellitus/mortality , Diabetes Mellitus/physiopathology , Hospitalization , Humans , Hypertension/mortality , Hypertension/physiopathology , Incidence , Mexico/epidemiology , Models, Statistical , Obesity/mortality , Obesity/physiopathology , Outpatients , SARS-CoV-2/pathogenicity , Survival Analysis
10.
Nat Biotechnol ; 40(1): 30-41, 2022 01.
Article in English | MEDLINE | ID: covidwho-1585828

ABSTRACT

Gaining a better understanding of the immune cell subsets and molecular factors associated with protective or pathological immunity against severe acute respiratory syndrome coronavirus (SARS-CoV)-2 could aid the development of vaccines and therapeutics for coronavirus disease 2019 (COVID-19). Single-cell technologies, such as flow cytometry, mass cytometry, single-cell transcriptomics and single-cell multi-omic profiling, offer considerable promise in dissecting the heterogeneity of immune responses among individual cells and uncovering the molecular mechanisms of COVID-19 pathogenesis. Single-cell immune-profiling studies reported to date have identified innate and adaptive immune cell subsets that correlate with COVID-19 disease severity, as well as immunological factors and pathways of potential relevance to the development of vaccines and treatments for COVID-19. For facilitation of integrative studies and meta-analyses into the immunology of SARS-CoV-2 infection, we provide standardized, download-ready versions of 21 published single-cell sequencing datasets (over 3.2 million cells in total) as well as an interactive visualization portal for data exploration.


Subject(s)
COVID-19/immunology , COVID-19/pathology , Data Visualization , Datasets as Topic , Immunity, Innate , SARS-CoV-2/immunology , Single-Cell Analysis , Animals , COVID-19/genetics , Data Analysis , Humans , Transcriptome
12.
Nature ; 600(7890): 695-700, 2021 12.
Article in English | MEDLINE | ID: covidwho-1562062

ABSTRACT

Surveys are a crucial tool for understanding public opinion and behaviour, and their accuracy depends on maintaining statistical representativeness of their target populations by minimizing biases from all sources. Increasing data size shrinks confidence intervals but magnifies the effect of survey bias: an instance of the Big Data Paradox1. Here we demonstrate this paradox in estimates of first-dose COVID-19 vaccine uptake in US adults from 9 January to 19 May 2021 from two large surveys: Delphi-Facebook2,3 (about 250,000 responses per week) and Census Household Pulse4 (about 75,000 every two weeks). In May 2021, Delphi-Facebook overestimated uptake by 17 percentage points (14-20 percentage points with 5% benchmark imprecision) and Census Household Pulse by 14 (11-17 percentage points with 5% benchmark imprecision), compared to a retroactively updated benchmark the Centers for Disease Control and Prevention published on 26 May 2021. Moreover, their large sample sizes led to miniscule margins of error on the incorrect estimates. By contrast, an Axios-Ipsos online panel5 with about 1,000 responses per week following survey research best practices6 provided reliable estimates and uncertainty quantification. We decompose observed error using a recent analytic framework1 to explain the inaccuracy in the three surveys. We then analyse the implications for vaccine hesitancy and willingness. We show how a survey of 250,000 respondents can produce an estimate of the population mean that is no more accurate than an estimate from a simple random sample of size 10. Our central message is that data quality matters more than data quantity, and that compensating the former with the latter is a mathematically provable losing proposition.


Subject(s)
COVID-19 Vaccines/administration & dosage , Health Care Surveys , Vaccination/statistics & numerical data , Benchmarking , Bias , Big Data , COVID-19/epidemiology , COVID-19/prevention & control , Centers for Disease Control and Prevention, U.S. , Datasets as Topic/standards , Female , Health Care Surveys/standards , Humans , Male , Research Design , Sample Size , Social Media , United States/epidemiology , /statistics & numerical data
13.
Sci Rep ; 11(1): 8304, 2021 04 15.
Article in English | MEDLINE | ID: covidwho-1545653

ABSTRACT

COVID-19, a viral infection originated from Wuhan, China has spread across the world and it has currently affected over 115 million people. Although vaccination process has already started, reaching sufficient availability will take time. Considering the impact of this widespread disease, many research attempts have been made by the computer scientists to screen the COVID-19 from Chest X-Rays (CXRs) or Computed Tomography (CT) scans. To this end, we have proposed GraphCovidNet, a Graph Isomorphic Network (GIN) based model which is used to detect COVID-19 from CT-scans and CXRs of the affected patients. Our proposed model only accepts input data in the form of graph as we follow a GIN based architecture. Initially, pre-processing is performed to convert an image data into an undirected graph to consider only the edges instead of the whole image. Our proposed GraphCovidNet model is evaluated on four standard datasets: SARS-COV-2 Ct-Scan dataset, COVID-CT dataset, combination of covid-chestxray-dataset, Chest X-Ray Images (Pneumonia) dataset and CMSC-678-ML-Project dataset. The model shows an impressive accuracy of 99% for all the datasets and its prediction capability becomes 100% accurate for the binary classification problem of detecting COVID-19 scans. Source code of this work can be found at GitHub-link .


Subject(s)
COVID-19/diagnostic imaging , Neural Networks, Computer , Radiography, Thoracic/methods , Tomography, X-Ray Computed/methods , COVID-19/virology , Datasets as Topic , Humans , SARS-CoV-2/isolation & purification
14.
PLoS One ; 16(11): e0258649, 2021.
Article in English | MEDLINE | ID: covidwho-1528716

ABSTRACT

Pandemic scenarios like SARS-Cov-2 require rapid information aggregation. In the age of eHealth and data-driven medicine, publicly available symptom tracking tools offer efficient and scalable means of collecting and analyzing large amounts of data. As a result, information gains can be communicated to front-line providers. We have developed such an application in less than a month and reached more than 500 thousand users within 48 hours. The dataset contains information on basic epidemiological parameters, symptoms, risk factors and details on previous exposure to a COVID-19 patient. Exploratory Data Analysis revealed different symptoms reported by users with confirmed contacts vs. no confirmed contacts. The symptom combination of anosmia, cough and fatigue was the most important feature to differentiate the groups, while single symptoms such as anosmia, cough or fatigue alone were not sufficient. A linear regression model from the literature using the same symptom combination as features was applied on all data. Predictions matched the regional distribution of confirmed cases closely across Germany, while also indicating that the number of cases in northern federal states might be higher than officially reported. In conclusion, we report that symptom combinations anosmia, fatigue and cough are most likely to indicate an acute SARS-CoV-2 infection.


Subject(s)
Anosmia/epidemiology , COVID-19/diagnosis , Cough/epidemiology , Datasets as Topic , Fatigue/epidemiology , Adult , Aged , COVID-19/epidemiology , Data Interpretation, Statistical , Female , Humans , Male , Middle Aged
15.
BMC Med Imaging ; 21(1): 174, 2021 11 22.
Article in English | MEDLINE | ID: covidwho-1528681

ABSTRACT

BACKGROUND: With the rapid spread of COVID-19 worldwide, quick screening for possible COVID-19 patients has become the focus of international researchers. Recently, many deep learning-based Computed Tomography (CT) image/X-ray image fast screening models for potential COVID-19 patients have been proposed. However, the existing models still have two main problems. First, most of the existing supervised models are based on pre-trained model parameters. The pre-training model needs to be constructed on a dataset with features similar to those in COVID-19 X-ray images, which limits the construction and use of the model. Second, the number of categories based on the X-ray dataset of COVID-19 and other pneumonia patients is usually imbalanced. In addition, the quality is difficult to distinguish, leading to non-ideal results with the existing model in the multi-class classification COVID-19 recognition task. Moreover, no researchers have proposed a COVID-19 X-ray image learning model based on unsupervised meta-learning. METHODS: This paper first constructed an unsupervised meta-learning model for fast screening of COVID-19 patients (UMLF-COVID). This model does not require a pre-trained model, which solves the limitation problem of model construction, and the proposed unsupervised meta-learning framework solves the problem of sample imbalance and sample quality. RESULTS: The UMLF-COVID model is tested on two real datasets, each of which builds a three-category and four-category model. And the experimental results show that the accuracy of the UMLF-COVID model is 3-10% higher than that of the existing models. CONCLUSION: In summary, we believe that the UMLF-COVID model is a good complement to COVID-19 X-ray fast screening models.


Subject(s)
COVID-19/diagnostic imaging , Deep Learning , Tomography, X-Ray Computed/methods , Algorithms , Datasets as Topic/statistics & numerical data , Humans , Image Processing, Computer-Assisted , SARS-CoV-2
16.
Sci Data ; 8(1): 297, 2021 11 22.
Article in English | MEDLINE | ID: covidwho-1528020

ABSTRACT

The Covid Symptom Study, a smartphone-based surveillance study on COVID-19 symptoms in the population, is an exemplar of big data citizen science. As of May 23rd, 2021, over 5 million participants have collectively logged over 360 million self-assessment reports since its introduction in March 2020. The success of the Covid Symptom Study creates significant technical challenges around effective data curation. The primary issue is scale. The size of the dataset means that it can no longer be readily processed using standard Python-based data analytics software such as Pandas on commodity hardware. Alternative technologies exist but carry a higher technical complexity and are less accessible to many researchers. We present ExeTera, a Python-based open source software package designed to provide Pandas-like data analytics on datasets that approach terabyte scales. We present its design and capabilities, and show how it is a critical component of a data curation pipeline that enables reproducible research across an international research group for the Covid Symptom Study.


Subject(s)
COVID-19/epidemiology , Citizen Science , Data Curation , Big Data , Data Science , Datasets as Topic , Epidemiological Monitoring , Humans , Mobile Applications , Smartphone , Software
17.
Nature ; 600(7887): 121-126, 2021 12.
Article in English | MEDLINE | ID: covidwho-1521756

ABSTRACT

Mental health is an important component of public health, especially in times of crisis. However, monitoring public mental health is difficult because data are often patchy and low-frequency1-3. Here we complement established approaches by using data from helplines, which offer a real-time measure of 'revealed' distress and mental health concerns across a range of topics4-9. We collected data on 8 million calls from 19 countries, focusing on the COVID-19 crisis. Call volumes peaked six weeks after the initial outbreak, at 35% above pre-pandemic levels. The increase was driven mainly by fear (including fear of infection), loneliness and, later in the pandemic, concerns about physical health. Relationship issues, economic problems, violence and suicidal ideation, however, were less prevalent than before the pandemic. This pattern was apparent both during the first wave and during subsequent COVID-19 waves. Issues linked directly to the pandemic therefore seem to have replaced rather than exacerbated underlying anxieties. Conditional on infection rates, suicide-related calls increased when containment policies became more stringent and decreased when income support was extended. This implies that financial relief can allay the distress triggered by lockdown measures and illustrates the insights that can be gleaned from the statistical analysis of helpline data.


Subject(s)
COVID-19/epidemiology , Hotlines/statistics & numerical data , Mental Health/statistics & numerical data , Adult , Behavior, Addictive , Datasets as Topic , Employment , Fear , Female , France/epidemiology , Germany/epidemiology , Health , Health Policy , Humans , Internationality , Loneliness , Male , United States/epidemiology , Violence
18.
Clin Transl Sci ; 14(6): 2348-2359, 2021 11.
Article in English | MEDLINE | ID: covidwho-1526356

ABSTRACT

Coronavirus disease 2019 (COVID-19) global pandemic is caused by severe acute respiratory syndrome-coronavirus 2 (SARS-CoV-2) viral infection, which can lead to pneumonia, lung injury, and death in susceptible populations. Understanding viral dynamics of SARS-CoV-2 is critical for development of effective treatments. An Immune-Viral Dynamics Model (IVDM) is developed to describe SARS-CoV-2 viral dynamics and COVID-19 disease progression. A dataset of 60 individual patients with COVID-19 with clinical viral load (VL) and reported disease severity were assembled from literature. Viral infection and replication mechanisms of SARS-CoV-2, viral-induced cell death, and time-dependent immune response are incorporated in the model to describe the dynamics of viruses and immune response. Disease severity are tested as a covariate to model parameters. The IVDM was fitted to the data and parameters were estimated using the nonlinear mixed-effect model. The model can adequately describe individual viral dynamics profiles, with disease severity identified as a covariate on infected cell death rate. The modeling suggested that it takes about 32.6 days to reach 50% of maximum cell-based immunity. Simulations based on virtual populations suggested a typical mild case reaches VL limit of detection (LOD) by 13 days with no treatment, a moderate case by 17 days, and a severe case by 41 days. Simulations were used to explore hypothetical treatments with different initiation time, disease severity, and drug effects to demonstrate the usefulness of such modeling in informing decisions. Overall, the IVDM modeling and simulation platform enables simulations for viral dynamics and treatment efficacy and can be used to aid in clinical pharmacokinetic/pharmacodynamic (PK/PD) and dose-efficacy response analysis for COVID-19 drug development.


Subject(s)
Antiviral Agents/pharmacology , COVID-19/drug therapy , Drug Development/methods , Host Microbial Interactions/immunology , Models, Biological , Antiviral Agents/therapeutic use , COVID-19/diagnosis , COVID-19/immunology , COVID-19/virology , Cell Death/drug effects , Cell Death/immunology , Datasets as Topic , Dose-Response Relationship, Drug , Host Microbial Interactions/drug effects , Humans , Nonlinear Dynamics , SARS-CoV-2/drug effects , SARS-CoV-2/immunology , Severity of Illness Index , Treatment Outcome , Viral Load
19.
Glycobiology ; 32(1): 60-72, 2022 02 26.
Article in English | MEDLINE | ID: covidwho-1501077

ABSTRACT

Extensive glycosylation of the spike protein of severe acute respiratory syndrome coronavirus 2 virus not only shields the major part of it from host immune responses, but glycans at specific sites also act on its conformation dynamics and contribute to efficient host receptor binding, and hence infectivity. As variants of concern arise during the course of the coronavirus disease of 2019 pandemic, it is unclear if mutations accumulated within the spike protein would affect its site-specific glycosylation pattern. The Alpha variant derived from the D614G lineage is distinguished from others by having deletion mutations located right within an immunogenic supersite of the spike N-terminal domain (NTD) that make it refractory to most neutralizing antibodies directed against this domain. Despite maintaining an overall similar structural conformation, our mass spectrometry-based site-specific glycosylation analyses of similarly produced spike proteins with and without the D614G and Alpha variant mutations reveal a significant shift in the processing state of N-glycans on one specific NTD site. Its conversion to a higher proportion of complex type structures is indicative of altered spatial accessibility attributable to mutations specific to the Alpha variant that may impact its transmissibility. This and other more subtle changes in glycosylation features detected at other sites provide crucial missing information otherwise not apparent in the available cryogenic electron microscopy-derived structures of the spike protein variants.


Subject(s)
COVID-19/epidemiology , Glycopeptides/chemistry , Mutation , Polysaccharides/chemistry , SARS-CoV-2/genetics , Spike Glycoprotein, Coronavirus/chemistry , Angiotensin-Converting Enzyme 2/genetics , Angiotensin-Converting Enzyme 2/metabolism , COVID-19/transmission , COVID-19/virology , Carbohydrate Sequence , Datasets as Topic , Glycopeptides/genetics , Glycopeptides/metabolism , Glycosylation , HEK293 Cells , Humans , Mass Spectrometry , Peptide Mapping , Polysaccharides/metabolism , Protein Binding , Receptors, Virus/genetics , Receptors, Virus/metabolism , Recombinant Proteins/chemistry , Recombinant Proteins/genetics , Recombinant Proteins/metabolism , SARS-CoV-2/pathogenicity , Spike Glycoprotein, Coronavirus/genetics , Spike Glycoprotein, Coronavirus/metabolism
20.
J Cell Biochem ; 123(2): 347-358, 2022 02.
Article in English | MEDLINE | ID: covidwho-1499273

ABSTRACT

As per the World Health Organization report, around 226 844 344 confirmed positive cases and 4 666 334 deaths are reported till September 17, 2021 due to the recent viral outbreak. A novel coronavirus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) is responsible for the associated coronavirus disease (COVID-19), which causes serious or even fatal respiratory tract infection and yet no approved therapeutics or effective treatment is currently available to combat the outbreak. Due to the emergency, the drug repurposing approach is being explored for COVID-19. In this study, we attempt to understand the potential mechanism and also the effect of the approved antiviral drugs against the SARS-CoV-2 main protease (Mpro). To understand the mechanism of inhibition of the malaria drug hydroxychloroquine (HCQ) against SARS-CoV-2, we performed molecular interaction studies. The studies revealed that HCQ docked at the active site of the Human ACE2 receptor as a possible way of inhibition. Our in silico analysis revealed that the three drugs Lopinavir, Ritonavir, and Remdesivir showed interaction with the active site residues of Mpro. During molecular dynamics simulation, based on the binding free energy contributions, Lopinavir showed better results than Ritonavir and Remdesivir.


Subject(s)
Adenosine Monophosphate/analogs & derivatives , Alanine/analogs & derivatives , Angiotensin-Converting Enzyme 2/antagonists & inhibitors , Antiviral Agents/pharmacology , COVID-19/drug therapy , Coronavirus 3C Proteases/antagonists & inhibitors , Hydroxychloroquine/pharmacology , Lopinavir/pharmacology , Receptors, Virus/drug effects , Ritonavir/pharmacology , SARS-CoV-2/drug effects , Adenosine Monophosphate/pharmacology , Adenosine Monophosphate/therapeutic use , Alanine/pharmacology , Alanine/therapeutic use , Angiotensin-Converting Enzyme 2/chemistry , Angiotensin-Converting Enzyme 2/physiology , Antiviral Agents/therapeutic use , Binding Sites , Catalytic Domain/drug effects , Coronavirus 3C Proteases/chemistry , Coronavirus 3C Proteases/physiology , Datasets as Topic , Drug Repositioning , Energy Transfer , Humans , Hydroxychloroquine/therapeutic use , Lopinavir/therapeutic use , Models, Molecular , Molecular Docking Simulation , Molecular Dynamics Simulation , Protein Binding , Protein Conformation , Receptors, Virus/physiology , Ritonavir/therapeutic use
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