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1.
Nucleic Acids Res ; 50(D1): D1-D10, 2022 01 07.
Article in English | MEDLINE | ID: covidwho-1607482

ABSTRACT

The 2022 Nucleic Acids Research Database Issue contains 185 papers, including 87 papers reporting on new databases and 85 updates from resources previously published in the Issue. Thirteen additional manuscripts provide updates on databases most recently published elsewhere. Seven new databases focus specifically on COVID-19 and SARS-CoV-2, including SCoV2-MD, the first of the Issue's Breakthrough Articles. Major nucleic acid databases reporting updates include MODOMICS, JASPAR and miRTarBase. The AlphaFold Protein Structure Database, described in the second Breakthrough Article, is the stand-out in the protein section, where the Human Proteoform Atlas and GproteinDb are other notable new arrivals. Updates from DisProt, FuzDB and ELM comprehensively cover disordered proteins. Under the metabolism and signalling section Reactome, ConsensusPathDB, HMDB and CAZy are major returning resources. In microbial and viral genomes taxonomy and systematics are well covered by LPSN, TYGS and GTDB. Genomics resources include Ensembl, Ensembl Genomes and UCSC Genome Browser. Major returning pharmacology resource names include the IUPHAR/BPS guide and the Therapeutic Target Database. New plant databases include PlantGSAD for gene lists and qPTMplants for post-translational modifications. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). Our latest update to the NAR online Molecular Biology Database Collection brings the total number of entries to 1645. Following last year's major cleanup, we have updated 317 entries, listing 89 new resources and trimming 80 discontinued URLs. The current release is available at http://www.oxfordjournals.org/nar/database/c/.


Subject(s)
Databases, Factual , Molecular Biology , Animals , COVID-19 , Databases, Nucleic Acid , Databases, Protein , Genome, Microbial , Genome, Viral , Humans , Mice , Plants/genetics , Protein Processing, Post-Translational , Proteome , SARS-CoV-2/genetics , Signal Transduction
2.
Cell Death Dis ; 12(12): 1156, 2021 12 14.
Article in English | MEDLINE | ID: covidwho-1585874

ABSTRACT

Lots of cell death initiator and effector molecules, signalling pathways and subcellular sites have been identified as key mediators in both cell death processes in cancer. The XDeathDB visualization platform provides a comprehensive cell death and their crosstalk resource for deciphering the signaling network organization of interactions among different cell death modes associated with 1461 cancer types and COVID-19, with an aim to understand the molecular mechanisms of physiological cell death in disease and facilitate systems-oriented novel drug discovery in inducing cell deaths properly. Apoptosis, autosis, efferocytosis, ferroptosis, immunogenic cell death, intrinsic apoptosis, lysosomal cell death, mitotic cell death, mitochondrial permeability transition, necroptosis, parthanatos, and pyroptosis related to 12 cell deaths and their crosstalk can be observed systematically by the platform. Big data for cell death gene-disease associations, gene-cell death pathway associations, pathway-cell death mode associations, and cell death-cell death associations is collected by literature review articles and public database from iRefIndex, STRING, BioGRID, Reactom, Pathway's commons, DisGeNET, DrugBank, and Therapeutic Target Database (TTD). An interactive webtool, XDeathDB, is built by web applications with R-Shiny, JavaScript (JS) and Shiny Server Iso. With this platform, users can search specific interactions from vast interdependent networks that occur in the realm of cell death. A multilayer spectral graph clustering method that performs convex layer aggregation to identify crosstalk function among cell death modes for a specific cancer. 147 hallmark genes of cell death could be observed in detail in these networks. These potential druggable targets are displayed systematically and tailoring networks to visualize specified relations is available to fulfil user-specific needs. Users can access XDeathDB for free at https://pcm2019.shinyapps.io/XDeathDB/ .


Subject(s)
Cell Death/physiology , Regulated Cell Death/physiology , Signal Transduction/physiology , Animals , COVID-19/metabolism , COVID-19/physiopathology , Cluster Analysis , Databases, Factual , Humans , Necroptosis , Neoplasms/metabolism , Neoplasms/physiopathology , Phagocytosis , SARS-CoV-2/metabolism , SARS-CoV-2/physiology , Signal Transduction/drug effects , Software
3.
Sci Rep ; 11(1): 24108, 2021 12 16.
Article in English | MEDLINE | ID: covidwho-1585796

ABSTRACT

Despite the great potential of Virtual Reality (VR) to arouse emotions, there are no VR affective databases available as it happens for pictures, videos, and sounds. In this paper, we describe the validation of ten affective interactive Virtual Environments (VEs) designed to be used in Virtual Reality. These environments are related to five emotions. The testing phase included using two different experimental setups to deliver the overall experience. The setup did not include any immersive VR technology, because of the ongoing COVID-19 pandemic, but the VEs were designed to run on stereoscopic visual displays. We collected measures related to the participants' emotional experience based on six discrete emotional categories plus neutrality and we included an assessment of the sense of presence related to the different experiences. The results showed how the scenarios can be differentiated according to the emotion aroused. Finally, the comparison between the two experimental setups demonstrated high reliability of the experience and strong adaptability of the scenarios to different contexts of use.


Subject(s)
Arousal/physiology , COVID-19/psychology , Databases, Factual/statistics & numerical data , Emotions/physiology , SARS-CoV-2/isolation & purification , Virtual Reality , Adult , COVID-19/epidemiology , COVID-19/virology , Emotions/classification , Empathy , Female , Humans , Male , Pandemics/prevention & control , Photic Stimulation/methods , Reproducibility of Results , SARS-CoV-2/physiology , Young Adult
4.
Sci Rep ; 11(1): 24397, 2021 12 22.
Article in English | MEDLINE | ID: covidwho-1585779

ABSTRACT

Angiotensin-converting enzyme 2 (ACE2) is an important factor in coronavirus disease (COVID-19) interactions. Losartan (LOS) belongs to the angiotensin receptor blocker (ARB) family. Additionally, the protective role of ACE2 restored by LOS has been suggested and clinically examined in the treatment of COVID-19 patients. Furthermore, clinical trials with LOS have been conducted. However, the mechanism through which LOS enhances ACE2 expression remains unclear. In addition, the response of ACE2 to LOS differs among patients. Our LOS-treated patient data revealed a correlated mechanism of ACE2 with components of the renin-angiotensinogen system. We observed a significant positive regulation of MAS1 and ACE2 expression. In the context of LOS treatment of COVID-19, ACE2 expression could depend on LOS regulated MAS1. Thus, MAS1 expression could predict the COVID-19 treatment response of LOS.


Subject(s)
Angiotensin Receptor Antagonists/pharmacology , Angiotensin-Converting Enzyme 2/metabolism , Losartan/pharmacology , Renin-Angiotensin System/drug effects , Angiotensin Receptor Antagonists/therapeutic use , Angiotensin-Converting Enzyme 2/genetics , COVID-19/drug therapy , COVID-19/pathology , COVID-19/virology , Databases, Factual , Humans , Losartan/therapeutic use , /metabolism , Receptor, Angiotensin, Type 1/genetics , Receptor, Angiotensin, Type 1/metabolism , SARS-CoV-2/isolation & purification , Up-Regulation/drug effects
5.
Syst Rev ; 10(1): 317, 2021 12 22.
Article in English | MEDLINE | ID: covidwho-1582006

ABSTRACT

BACKGROUND: In December 2019, a novel coronavirus, severe acute respiratory syndrome coronavirus 2 was identified as the cause of an acute respiratory disease, coronavirus disease 2019 (COVID-19). Given the lack of validated treatments, there is an urgent need for a high-quality management of COVID-19. Clinical practice guidelines (CPGs) are one tool that healthcare providers may use to enhance patient care. As such, it is necessary that they have access to high-quality evidence-based CPGs upon which they may base decisions regarding the management and use of therapeutic interventions (TI) for COVID-19. The purpose of the proposed study is to assess the quality of CPGs that make management or TI recommendations for COVID-19 using the AGREE II instrument. METHODS: The proposed systematic review will identify CPGs for TI use and/or the management of COVID-19. The MEDLINE, EMBASE, CINAHL, and Web of Science databases, as well as the Guidelines International Network, National Institute for Health and Clinical Excellence, Scottish Intercollegiate Guidelines Network, and the World Health Organization websites, will be searched from December 2019 onwards. The primary outcome of this study is the assessed quality of the CPGs. The quality of eligible CPGs will be assessed using the Appraisal of Guidelines, Research and Evaluation II (AGREE II) instrument. Descriptive statistics will be used to quantify the quality of the CPGs. The secondary outcomes of this study are the types of management and/or TI recommendations made. Inconsistent and duplicate TI and/or management recommendations made between CPGs will be compared across guidelines. To summarize and explain the findings related to the included CPGs, a narrative synthesis will also be provided. DISCUSSION: The results of this study will be of utmost importance to enhancing clinical decision-making among healthcare providers caring for patients with COVID-19. Moreover, the results of this study will be relevant to guideline developers in the creation of CPGs or improvement of existing ones, researchers who want to identify gaps in knowledge, and policy-makers looking to encourage and endorse the adoption of CPGs into clinical practice. The results of this review will be published in a peer-reviewed journal and presented at conferences. SYSTEMATIC REVIEW REGISTRATION: International Prospective Register for Systematic Reviews (PROSPERO)- CRD42020219944.


Subject(s)
COVID-19 , Databases, Factual , Evidence-Based Practice , Humans , Review Literature as Topic , SARS-CoV-2 , Systematic Reviews as Topic
6.
PLoS One ; 16(12): e0261328, 2021.
Article in English | MEDLINE | ID: covidwho-1581752

ABSTRACT

The COVID-19 pandemic has been a major source of stress for a majority of people that might have negative long-term effects on mental health and well-being. In recent years, video games and their potential positive effects on stress relief have been researched and "relaxation" has been an important keyword in marketing a certain kind of video game. In a quasi-experimental design, this study investigated the increase of average daily player peak (ADPPs) for the COVID period compared to the pre-COVID period and if this increase was significantly larger for relaxing games in contrast to non-relaxing games. Results showed a medium-sized increase of ADPPs over all types of games but no difference between relaxing games and non-relaxing games. These results are discussed in regards to their potential of presenting gaps between the current theoretical models of the influence of video games on mental health and actual observed player behaviour.


Subject(s)
COVID-19/psychology , Video Games/psychology , Video Games/statistics & numerical data , Case-Control Studies , Databases, Factual , Humans , Relaxation , Stress, Psychological
7.
Int J Mol Sci ; 22(24)2021 Dec 19.
Article in English | MEDLINE | ID: covidwho-1580688

ABSTRACT

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) triggered the pandemic Coronavirus Disease 19 (COVID-19), causing millions of deaths. The elderly and those already living with comorbidity are likely to die after SARS-CoV-2 infection. People suffering from Alzheimer's disease (AD) have a higher risk of becoming infected, because they cannot easily follow health roles. Additionally, those suffering from dementia have a 40% higher risk of dying from COVID-19. Herein, we collected from Gene Expression Omnibus repository the brain samples of AD patients who died of COVID-19 (AD+COVID-19), AD without COVID-19 (AD), COVID-19 without AD (COVID-19) and control individuals. We inspected the transcriptomic and interactomic profiles by comparing the COVID-19 cohort against the control cohort and the AD cohort against the AD+COVID-19 cohort. SARS-CoV-2 in patients without AD mainly activated processes related to immune response and cell cycle. Conversely, 21 key nodes in the interactome are deregulated in AD. Interestingly, some of them are linked to beta-amyloid production and clearance. Thus, we inspected their role, along with their interactors, using the gene ontologies of the biological process that reveals their contribution in brain organization, immune response, oxidative stress and viral replication. We conclude that SARS-CoV-2 worsens the AD condition by increasing neurotoxicity, due to higher levels of beta-amyloid, inflammation and oxidative stress.


Subject(s)
Alzheimer Disease/genetics , COVID-19/complications , COVID-19/genetics , Alzheimer Disease/complications , Alzheimer Disease/virology , Amyloid beta-Peptides/metabolism , Brain/virology , COVID-19/physiopathology , Comorbidity/trends , Databases, Factual , Gene Expression/genetics , Gene Expression Profiling/methods , Humans , Inflammation/metabolism , Neurotoxicity Syndromes/metabolism , Oxidative Stress/physiology , Pandemics , SARS-CoV-2/metabolism , SARS-CoV-2/pathogenicity , Transcriptome/genetics
8.
PLoS One ; 16(3): e0248580, 2021.
Article in English | MEDLINE | ID: covidwho-1575655

ABSTRACT

BACKGROUND: Brazil became the epicenter of the COVID-19 epidemic in a brief period of a few months after the first officially registered case. The knowledge of the epidemiological/clinical profile and the risk factors of Brazilian COVID-19 patients can assist in the decision making of physicians in the implementation of early and most appropriate measures for poor prognosis patients. However, these reports are missing. Here we present a comprehensive study that addresses this demand. METHODS: This data-driven study was based on the Brazilian Ministry of Health Database (SIVEP-Gripe) regarding notified cases of hospitalized COVID-19 patients during the period from February 26th to August 10th, 2020. Demographic data, clinical symptoms, comorbidities and other additional information of patients were analyzed. RESULTS: The hospitalization rate was higher for male gender (56.56%) and for older age patients of both sexes. Overall, the lethality rate was quite high (41.28%) among hospitalized patients, especially those over 60 years of age. Most prevalent symptoms were cough, dyspnoea, fever, low oxygen saturation and respiratory distress. Cardiac disease, diabetes, obesity, kidney disease, neurological disease, and pneumopathy were the most prevalent comorbidities. A high prevalence of hospitalized COVID-19 patients with cardiac disease (65.7%) and diabetes (53.55%) and with a high lethality rate of around 50% was observed. The intensive care unit (ICU) admission rate was 39.37% and of these 62.4% died. 24.4% of patients required invasive mechanical ventilation (IMV), with high mortality among them (82.98%). The main mortality risk predictors were older age and IMV requirement. In addition, socioeconomic conditions have been shown to significantly influence the disease outcome, regardless of age and comorbidities. CONCLUSION: Our study provides a comprehensive overview of the hospitalized Brazilian COVID-19 patients profile and the mortality risk factors. The analysis also evidenced that the disease outcome is influenced by multiple factors, as unequally affects different segments of population.


Subject(s)
COVID-19/mortality , Adolescent , Adult , Aged , Brazil/epidemiology , COVID-19/epidemiology , Child , Child, Preschool , Databases, Factual , Female , Hospitalization , Humans , Infant , Intensive Care Units , Male , Middle Aged , Risk Factors , SARS-CoV-2/isolation & purification , Young Adult
9.
PLoS One ; 16(3): e0247839, 2021.
Article in English | MEDLINE | ID: covidwho-1574949

ABSTRACT

As SARS-CoV-2 has spread quickly throughout the world, the scientific community has spent major efforts on better understanding the characteristics of the virus and possible means to prevent, diagnose, and treat COVID-19. A valid approach presented in the literature is to develop an image-based method to support COVID-19 diagnosis using convolutional neural networks (CNN). Because the availability of radiological data is rather limited due to the novelty of COVID-19, several methodologies consider reduced datasets, which may be inadequate, biasing the model. Here, we performed an analysis combining six different databases using chest X-ray images from open datasets to distinguish images of infected patients while differentiating COVID-19 and pneumonia from 'no-findings' images. In addition, the performance of models created from fewer databases, which may imperceptibly overestimate their results, is discussed. Two CNN-based architectures were created to process images of different sizes (512 × 512, 768 × 768, 1024 × 1024, and 1536 × 1536). Our best model achieved a balanced accuracy (BA) of 87.7% in predicting one of the three classes ('no-findings', 'COVID-19', and 'pneumonia') and a specific balanced precision of 97.0% for 'COVID-19' class. We also provided binary classification with a precision of 91.0% for detection of sick patients (i.e., with COVID-19 or pneumonia) and 98.4% for COVID-19 detection (i.e., differentiating from 'no-findings' or 'pneumonia'). Indeed, despite we achieved an unrealistic 97.2% BA performance for one specific case, the proposed methodology of using multiple databases achieved better and less inflated results than from models with specific image datasets for training. Thus, this framework is promising for a low-cost, fast, and noninvasive means to support the diagnosis of COVID-19.


Subject(s)
COVID-19/diagnostic imaging , Databases, Factual , Neural Networks, Computer , Pneumonia/diagnostic imaging , Algorithms , Bias , Deep Learning , Humans , Image Interpretation, Computer-Assisted , Radiography, Thoracic
10.
PLoS One ; 16(3): e0248438, 2021.
Article in English | MEDLINE | ID: covidwho-1574763

ABSTRACT

OBJECTIVES: Accurate and reliable criteria to rapidly estimate the probability of infection with the novel coronavirus-2 that causes the severe acute respiratory syndrome (SARS-CoV-2) and associated disease (COVID-19) remain an urgent unmet need, especially in emergency care. The objective was to derive and validate a clinical prediction score for SARS-CoV-2 infection that uses simple criteria widely available at the point of care. METHODS: Data came from the registry data from the national REgistry of suspected COVID-19 in EmeRgency care (RECOVER network) comprising 116 hospitals from 25 states in the US. Clinical variables and 30-day outcomes were abstracted from medical records of 19,850 emergency department (ED) patients tested for SARS-CoV-2. The criterion standard for diagnosis of SARS-CoV-2 required a positive molecular test from a swabbed sample or positive antibody testing within 30 days. The prediction score was derived from a 50% random sample (n = 9,925) using unadjusted analysis of 107 candidate variables as a screening step, followed by stepwise forward logistic regression on 72 variables. RESULTS: Multivariable regression yielded a 13-variable score, which was simplified to a 13-point score: +1 point each for age>50 years, measured temperature>37.5°C, oxygen saturation<95%, Black race, Hispanic or Latino ethnicity, household contact with known or suspected COVID-19, patient reported history of dry cough, anosmia/dysgeusia, myalgias or fever; and -1 point each for White race, no direct contact with infected person, or smoking. In the validation sample (n = 9,975), the probability from logistic regression score produced an area under the receiver operating characteristic curve of 0.80 (95% CI: 0.79-0.81), and this level of accuracy was retained across patients enrolled from the early spring to summer of 2020. In the simplified score, a score of zero produced a sensitivity of 95.6% (94.8-96.3%), specificity of 20.0% (19.0-21.0%), negative likelihood ratio of 0.22 (0.19-0.26). Increasing points on the simplified score predicted higher probability of infection (e.g., >75% probability with +5 or more points). CONCLUSION: Criteria that are available at the point of care can accurately predict the probability of SARS-CoV-2 infection. These criteria could assist with decisions about isolation and testing at high throughput checkpoints.


Subject(s)
COVID-19/diagnosis , COVID-19/epidemiology , Emergency Service, Hospital/trends , Adult , Aged , Clinical Decision Rules , Coronavirus Infections/diagnosis , Cough , Databases, Factual , Decision Trees , Emergency Service, Hospital/statistics & numerical data , Female , Fever , Humans , Male , Mass Screening , Middle Aged , Registries , SARS-CoV-2/pathogenicity , United States/epidemiology
11.
CMAJ Open ; 9(4): E1149-E1158, 2021.
Article in English | MEDLINE | ID: covidwho-1575519

ABSTRACT

BACKGROUND: There were large disruptions to health care services after the onset of the COVID-19 pandemic. We sought to describe the extent to which pandemic-related changes in service delivery and access affected use of primary care for children overall and by equity strata in the 9 months after pandemic onset in Manitoba and Ontario. METHODS: We performed a population-based study of children aged 17 years or less with provincial health insurance in Ontario or Manitoba before and during the COVID-19 pandemic (Jan. 1, 2017-Nov. 28, 2020). We calculated the weekly rates of in-person and virtual primary care well-child and sick visits, overall and by age group, neighbourhood material deprivation level, rurality and immigrant status, and assessed changes in visit rates after COVID-19 restrictions were imposed compared to expected baseline rates calculated for the 3 years before pandemic onset. RESULTS: Among almost 3 million children in Ontario and more than 300 000 children in Manitoba, primary care visit rates declined to 0.80 (95% confidence interval [CI] 0.77-0.82) of expected in Ontario and 0.82 (95% CI 0.79-0.84) of expected in Manitoba in the 9 months after the onset of the pandemic. Virtual visits accounted for 53% and 29% of visits in Ontario and Manitoba, respectively. The largest monthly decreases in visits occurred in April 2020. Although visit rates increased slowly after April 2020, they had not returned to prerestriction levels by November 2020 in either province. Children aged more than 1 year to 12 years experienced the greatest decrease in visits, especially for well-child care. Compared to prepandemic levels, visit rates were lowest among rural Manitobans, urban Ontarians and Ontarians in low-income neighbourhoods. INTERPRETATION: During the study period, the pandemic contributed to rapid, immediate and inequitable decreases in primary care use, with some recovery and a substantial shift to virtual care. Postpandemic planning must consider the need for catch-up visits, and the long-term impacts warrant further study.


Subject(s)
COVID-19/epidemiology , Pediatrics/statistics & numerical data , Primary Health Care/statistics & numerical data , Adolescent , Age Distribution , Ambulatory Care/statistics & numerical data , COVID-19/virology , Child , Child, Preschool , Cross-Sectional Studies , Databases, Factual , Emigrants and Immigrants , Female , Humans , Infant , Infant, Newborn , Male , Manitoba/epidemiology , Ontario/epidemiology , Outcome Assessment, Health Care , Pandemics , Population Surveillance , Rural Population
12.
J Infect Dev Ctries ; 15(11): 1578-1583, 2021 11 30.
Article in English | MEDLINE | ID: covidwho-1572714

ABSTRACT

INTRODUCTION: Globally South-East Asia reported 40% of SARS-CoV-2 infected cases in the fourth week of April 2021. It continued to show an increase with India accounting for 50% of cases worldwide and 30% of global deaths. Genomic surveillance should continue at a rapid pace because of the continuously evolving nature of the virus. The time period of sample collection from the Global Initiative on Sharing All Influenza Data database was concurrent with the surge in new cases seen in the Indian subcontinent. METHODOLOGY: 7,415 sequences were downloaded from Global Initiative on Sharing All Influenza Data between January and April 2021; out of which 4,411 were high coverage genome sequences and were considered for analysis. Phylogenetic analysis were carried out using Nextstrain. RESULTS: 21A or B.1.617 or delta was the most prevalent lineage in India accounting for 67.7% of the genomes. Next important clades were 20A, 20B and 20I accounting for 23.6%, 11.8% and 12.1% respectively collected between January 2021 and April 2021. The remaining sequences were assigned to clade 20H, 20J, 20D, 20C, 20G,20E,19A and 19B.The spike mutation frequencies of L452R, E484Q and P681R in Indian state of Maharashtra were 62.4%, 66.5% and 61.5% respectively. Two unique N-terminal domain deletion of spike protein were found at position 67 and 68. CONCLUSIONS: The phylogenomics of the delta variant or 21A emerged in neighboring Asian countries of Thailand, Bangladesh, Indonesia and Japan. We analyzed the SARS-CoV-2 genomes from India for mutation characterization of the spike glycoprotein and the nucleocapsid protein.


Subject(s)
COVID-19/epidemiology , SARS-CoV-2/isolation & purification , Asia, Southeastern/epidemiology , COVID-19/virology , Databases, Factual , Humans , Mutation , Phylogeny , SARS-CoV-2/genetics
13.
J Infect Dev Ctries ; 15(11): 1625-1629, 2021 11 30.
Article in English | MEDLINE | ID: covidwho-1572705

ABSTRACT

INTRODUCTION: This paper aims to measure the performance of early detection methods, which are usually used for infectious diseases. METHODOLOGY: By using real data of confirmed Coronavirus cases from the Kingdom of Saudi Arabia and Italy, the moving epidemic method (MEM) and the moving average cumulative sums (Mov. Avg Cusum) methods are used in our simulation study. RESULTS: Our results suggested that the CUSUM method outperforms the MEM in detecting the start of the Coronavirus outbreak.


Subject(s)
COVID-19/diagnosis , Diagnostic Tests, Routine , Early Diagnosis , SARS-CoV-2 , Benchmarking , COVID-19/epidemiology , Databases, Factual , Disease Outbreaks/prevention & control , Humans , Italy/epidemiology , Saudi Arabia/epidemiology
14.
Proc Natl Acad Sci U S A ; 118(51)2021 12 21.
Article in English | MEDLINE | ID: covidwho-1569345

ABSTRACT

The COVID-19 pandemic presented enormous data challenges in the United States. Policy makers, epidemiological modelers, and health researchers all require up-to-date data on the pandemic and relevant public behavior, ideally at fine spatial and temporal resolution. The COVIDcast API is our attempt to fill this need: Operational since April 2020, it provides open access to both traditional public health surveillance signals (cases, deaths, and hospitalizations) and many auxiliary indicators of COVID-19 activity, such as signals extracted from deidentified medical claims data, massive online surveys, cell phone mobility data, and internet search trends. These are available at a fine geographic resolution (mostly at the county level) and are updated daily. The COVIDcast API also tracks all revisions to historical data, allowing modelers to account for the frequent revisions and backfill that are common for many public health data sources. All of the data are available in a common format through the API and accompanying R and Python software packages. This paper describes the data sources and signals, and provides examples demonstrating that the auxiliary signals in the COVIDcast API present information relevant to tracking COVID activity, augmenting traditional public health reporting and empowering research and decision-making.


Subject(s)
COVID-19/epidemiology , Databases, Factual , Health Status Indicators , Ambulatory Care/trends , Epidemiologic Methods , Humans , Internet/statistics & numerical data , Physical Distancing , Surveys and Questionnaires , Travel , United States/epidemiology
15.
Sci Rep ; 11(1): 23914, 2021 12 13.
Article in English | MEDLINE | ID: covidwho-1569278

ABSTRACT

Chest X-ray (CXR) images have been one of the important diagnosis tools used in the COVID-19 disease diagnosis. Deep learning (DL)-based methods have been used heavily to analyze these images. Compared to other DL-based methods, the bag of deep visual words-based method (BoDVW) proposed recently is shown to be a prominent representation of CXR images for their better discriminability. However, single-scale BoDVW features are insufficient to capture the detailed semantic information of the infected regions in the lungs as the resolution of such images varies in real application. In this paper, we propose a new multi-scale bag of deep visual words (MBoDVW) features, which exploits three different scales of the 4th pooling layer's output feature map achieved from VGG-16 model. For MBoDVW-based features, we perform the Convolution with Max pooling operation over the 4th pooling layer using three different kernels: [Formula: see text], [Formula: see text], and [Formula: see text]. We evaluate our proposed features with the Support Vector Machine (SVM) classification algorithm on four CXR public datasets (CD1, CD2, CD3, and CD4) with over 5000 CXR images. Experimental results show that our method produces stable and prominent classification accuracy (84.37%, 88.88%, 90.29%, and 83.65% on CD1, CD2, CD3, and CD4, respectively).


Subject(s)
COVID-19/diagnostic imaging , Radiographic Image Interpretation, Computer-Assisted/methods , Algorithms , Databases, Factual , Deep Learning , Humans , Support Vector Machine
17.
Comput Math Methods Med ; 2021: 4321131, 2021.
Article in English | MEDLINE | ID: covidwho-1553710

ABSTRACT

The COVID-19 pandemic has had a devastating effect on many people, creating severe anxiety, fear, and complicated feelings or emotions. After the initiation of vaccinations against coronavirus, people's feelings have become more diverse and complex. Our aim is to understand and unravel their sentiments in this research using deep learning techniques. Social media is currently the best way to express feelings and emotions, and with the help of Twitter, one can have a better idea of what is trending and going on in people's minds. Our motivation for this research was to understand the diverse sentiments of people regarding the vaccination process. In this research, the timeline of the collected tweets was from December 21 to July21. The tweets contained information about the most common vaccines available recently from across the world. The sentiments of people regarding vaccines of all sorts were assessed using the natural language processing (NLP) tool, Valence Aware Dictionary for sEntiment Reasoner (VADER). Initializing the polarities of the obtained sentiments into three groups (positive, negative, and neutral) helped us visualize the overall scenario; our findings included 33.96% positive, 17.55% negative, and 48.49% neutral responses. In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time. A recurrent neural network- (RNN-) oriented architecture, including long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM), was used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving 90.83%. Other performance metrics such as precision,, F1-score, and a confusion matrix were also used to validate our models and findings more effectively. This study improves understanding of the public's opinion on COVID-19 vaccines and supports the aim of eradicating coronavirus from the world.


Subject(s)
COVID-19 Vaccines , COVID-19/prevention & control , Deep Learning , Social Media , Attitude , Attitude to Health , Databases, Factual , Humans , Language , Models, Statistical , Neural Networks, Computer , Public Opinion , Reproducibility of Results , Vaccination
19.
JAMA ; 326(19): 1940-1952, 2021 Nov 16.
Article in English | MEDLINE | ID: covidwho-1544160

ABSTRACT

Importance: There has been limited research on patients with ST-segment elevation myocardial infarction (STEMI) and COVID-19. Objective: To compare characteristics, treatment, and outcomes of patients with STEMI with vs without COVID-19 infection. Design, Setting, and Participants: Retrospective cohort study of consecutive adult patients admitted between January 2019 and December 2020 (end of follow-up in January 2021) with out-of-hospital or in-hospital STEMI at 509 US centers in the Vizient Clinical Database (N = 80 449). Exposures: Active COVID-19 infection present during the same encounter. Main Outcomes and Measures: The primary outcome was in-hospital mortality. Patients were propensity matched on the likelihood of COVID-19 diagnosis. In the main analysis, patients with COVID-19 were compared with those without COVID-19 during the previous calendar year. Results: The out-of-hospital STEMI group included 76 434 patients (551 with COVID-19 vs 2755 without COVID-19 after matching) from 370 centers (64.1% aged 51-74 years; 70.3% men). The in-hospital STEMI group included 4015 patients (252 with COVID-19 vs 756 without COVID-19 after matching) from 353 centers (58.3% aged 51-74 years; 60.7% men). In patients with out-of-hospital STEMI, there was no significant difference in the likelihood of undergoing primary percutaneous coronary intervention by COVID-19 status; patients with in-hospital STEMI and COVID-19 were significantly less likely to undergo invasive diagnostic or therapeutic coronary procedures than those without COVID-19. Among patients with out-of-hospital STEMI and COVID-19 vs out-of-hospital STEMI without COVID-19, the rates of in-hospital mortality were 15.2% vs 11.2% (absolute difference, 4.1% [95% CI, 1.1%-7.0%]; P = .007). Among patients with in-hospital STEMI and COVID-19 vs in-hospital STEMI without COVID-19, the rates of in-hospital mortality were 78.5% vs 46.1% (absolute difference, 32.4% [95% CI, 29.0%-35.9%]; P < .001). Conclusions and Relevance: Among patients with out-of-hospital or in-hospital STEMI, a concomitant diagnosis of COVID-19 was significantly associated with higher rates of in-hospital mortality compared with patients without a diagnosis of COVID-19 from the past year. Further research is required to understand the potential mechanisms underlying this association.


Subject(s)
COVID-19/complications , Hospital Mortality , Hospitalization , ST Elevation Myocardial Infarction/mortality , Adult , Aged , Aged, 80 and over , Case-Control Studies , Databases, Factual , Female , Humans , Male , Middle Aged , Out-of-Hospital Cardiac Arrest , Propensity Score , Retrospective Studies , ST Elevation Myocardial Infarction/complications , United States/epidemiology
20.
Comput Math Methods Med ; 2021: 7259414, 2021.
Article in English | MEDLINE | ID: covidwho-1533111

ABSTRACT

In this paper, based on the improved convolutional neural network, in-depth analysis of the CT image of the new coronary pneumonia, using the U-Net series of deep neural networks to semantically segment the CT image of the new coronary pneumonia, to obtain the new coronary pneumonia area as the foreground and the remaining areas as the background of the binary image, provides a basis for subsequent image diagnosis. Secondly, the target-detection framework Faster RCNN extracts features from the CT image of the new coronary pneumonia tumor, obtains a higher-level abstract representation of the data, determines the lesion location of the new coronary pneumonia tumor, and gives its bounding box in the image. By generating an adversarial network to diagnose the lesion area of the CT image of the new coronary pneumonia tumor, obtaining a complete image of the new coronary pneumonia, achieving the effect of the CT image diagnosis of the new coronary pneumonia tumor, and three-dimensionally reconstructing the complete new coronary pneumonia model, filling the current the gap in this aspect, provide a basis to produce new coronary pneumonia prosthesis and improve the accuracy of diagnosis.


Subject(s)
Algorithms , COVID-19/diagnostic imaging , Neural Networks, Computer , Tomography, X-Ray Computed/statistics & numerical data , COVID-19/diagnosis , Computational Biology , Databases, Factual , Deep Learning , Diagnosis, Computer-Assisted/statistics & numerical data , Humans , Imaging, Three-Dimensional/statistics & numerical data , Pandemics , Radiographic Image Interpretation, Computer-Assisted/statistics & numerical data , SARS-CoV-2
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