[Persistent chest pain after resolution of coronavirus 2019 disease (COVID-19)].
Ontology highlight
ABSTRACT:
SUBMITTER: Tung-Chen Y
PROVIDER: S-EPMC7287471 | biostudies-literature | 2020 Aug
REPOSITORIES: biostudies-literature
Publications
[Persistent chest pain after resolution of coronavirus 2019 disease (COVID-19)].
Tung-Chen Y Y Blanco-Alonso S S Antón-Huguet B B Figueras-López C C Ugueto-Rodrigo C C
Semergen 20200611
Similar Datasets
Project description:The purpose of this study was to identify miRNAs that were dysregulated after the onset of COVID-19 and thus potentially be used for risk stratification (i.e., mortality). Therefore, we conducted a multi-center, retrospective longitudinal cohort study enrolling 142 patients with laboratory-confirmed SARS-CoV-2 infection who presented to two Canadian hospitals from May 2020 – December 2020 along with a cohort of 27 SARS-CoV-2 patients with mild upper respiratory tract symptoms and 69 SARS-CoV-2-negative patients from the ICU. Blood was biobanked from SARS-CoV-2 positive patients in the emergency department (mild), ward (moderate) or intensive care unit (severe). Assessment of miRNA expression and co-regulatory network generation revealed significant transcriptome dyregulation in pateints with severe COVID-19 that was largely different from SARS-CoV-2 negative patients in the ICU.
2022-04-08 | GSE178246 | GEO
Project description: Not available
| S-EPMC8186924 | biostudies-literature
Project description:Coronavirus disease 2019 (COVID-19) outbreak, first reported in Wuhan, China, has rapidly swept around the world just within a month, causing global public health emergency. In diagnosis, chest computed tomography (CT) manifestations can supplement parts of limitations of real-time reverse transcription polymerase chain reaction (RT-PCR) assay. Based on a comprehensive literature review and the experience in the frontline, we aim to review the typical and relatively atypical CT manifestations with representative COVID-19 cases at our hospital, and hope to strengthen the recognition of these features with radiologists and help them make a quick and accurate diagnosis.Key Points• Ground glass opacities, consolidation, reticular pattern, and crazy paving pattern are typical CT manifestations of COVID-19.• Emerging atypical CT manifestations, including airway changes, pleural changes, fibrosis, nodules, etc., were demonstrated in COVID-19 patients.• CT manifestations may associate with the progression and prognosis of COVID-19.
| S-EPMC7088323 | biostudies-literature
Project description:SUMMARYIn recent decades, several new diseases have emerged in different geographical areas, with pathogens including Ebola virus, Zika virus, Nipah virus, and coronaviruses (CoVs). Recently, a new type of viral infection emerged in Wuhan City, China, and initial genomic sequencing data of this virus do not match with previously sequenced CoVs, suggesting a novel CoV strain (2019-nCoV), which has now been termed severe acute respiratory syndrome CoV-2 (SARS-CoV-2). Although coronavirus disease 2019 (COVID-19) is suspected to originate from an animal host (zoonotic origin) followed by human-to-human transmission, the possibility of other routes should not be ruled out. Compared to diseases caused by previously known human CoVs, COVID-19 shows less severe pathogenesis but higher transmission competence, as is evident from the continuously increasing number of confirmed cases globally. Compared to other emerging viruses, such as Ebola virus, avian H7N9, SARS-CoV, and Middle East respiratory syndrome coronavirus (MERS-CoV), SARS-CoV-2 has shown relatively low pathogenicity and moderate transmissibility. Codon usage studies suggest that this novel virus has been transferred from an animal source, such as bats. Early diagnosis by real-time PCR and next-generation sequencing has facilitated the identification of the pathogen at an early stage. Since no antiviral drug or vaccine exists to treat or prevent SARS-CoV-2, potential therapeutic strategies that are currently being evaluated predominantly stem from previous experience with treating SARS-CoV, MERS-CoV, and other emerging viral diseases. In this review, we address epidemiological, diagnostic, clinical, and therapeutic aspects, including perspectives of vaccines and preventive measures that have already been globally recommended to counter this pandemic virus.
| S-EPMC7405836 | biostudies-literature
Project description:OBJECTIVE:This study aimed to investigate the potential parameters associated with imaging progression on chest CT from coronavirus disease 19 (COVID-19) patients. RESULTS:The average age of 273 COVID-19 patients enrolled with imaging progression were older than those without imaging progression (p = 0.006). The white blood cells, platelets, neutrophils and acid glycoprotein were all decreased in imaging progression patients (all p < 0.05), and monocytes were increased (p = 0.025). The parameters including homocysteine, urea, creatinine and serum cystatin C were significantly higher in imaging progression patients (all p < 0.05), while eGFR decreased (p < 0.001). Monocyte-lymphocyte ratio (MLR) was significantly higher in imaging progression patients compared to that in imaging progression-free ones (p < 0.001). Logistic models revealed that age, MLR, homocysteine and period from onset to admission were factors for predicting imaging progression on chest CT at first week from COVID-19 patients (all p < 0.05). CONCLUSION:Age, MLR, homocysteine and period from onset to admission could predict imaging progression on chest CT from COVID-19 patients. METHODS:The primary outcome was imaging progression on chest CT. Baseline parameters were collected at the first day of admission. Imaging manifestations on chest CT were followed-up at (6±1) days.
| S-EPMC7185104 | biostudies-literature
Project description:COVID-19 is an emerging infection caused by a novel coronavirus that is moving so rapidly that on 30 January 2020 the World Health Organization declared the outbreak a Public Health Emergency of International Concern and on 11 March 2020 as a pandemic. An early diagnosis of COVID-19 is crucial for disease treatment and control of the disease spread. Real-time reverse-transcription polymerase chain reaction (RT-PCR) demonstrated a low sensibility; therefore chest computed tomography (CT) plays a pivotal role not only in the early detection and diagnosis, especially for false negative RT-PCR tests, but also in monitoring the clinical course and in evaluating the disease severity. This paper reports the CT findings with some hints on the temporal changes over the course of the disease: the CT hallmarks of COVID-19 are bilateral distribution of ground glass opacities with or without consolidation in the posterior and peripheral lung, but the predominant findings in later phases include consolidations, linear opacities, "crazy-paving" pattern, "reversed halo" sign and vascular enlargement. The CT findings of COVID-19 overlap with the CT findings of other diseases, in particular the viral pneumonia including influenza viruses, parainfluenza virus, adenovirus, respiratory syncytial virus, rhinovirus, human metapneumovirus, etc. There are differences as well as similarities in the CT features of COVID-19 compared with those of the severe acute respiratory syndrome. The aim of this article is to review the typical and atypical CT findings in COVID-19 patients in order to help radiologists and clinicians to become more familiar with the disease.
| S-EPMC7270744 | biostudies-literature
Project description:Coronavirus disease 2019 (COVID-19) is a type of viral pneumonia with an uncommon outbreak in Wuhan, China, in December 2019, which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV2). SARS-CoV-2 is extremely contagious and has resulted in a fast pandemic of COVID-19. Currently, COVID-19 is on the rise around the world, and it poses a severe threat to public health around the world. This review provides an overview about the COVID-19 virus to increase public awareness and understanding of the virus and its consequences in terms of history, epidemiology, structure, genome, clinical symptoms, diagnosis, prevention, and treatment.
| S-EPMC7429403 | biostudies-literature
Project description:To understand and analyse the global impact of COVID-19 on outpatient services, inpatient care, elective surgery, and perioperative colorectal cancer care, a DElayed COloRectal cancer surgery (DECOR-19) survey was conducted in collaboration with numerous international colorectal societies with the objective of obtaining several learning points from the impact of the COVID-19 outbreak on our colorectal cancer patients which will assist us in the ongoing management of our colorectal cancer patients and to provide us safe oncological pathways for future outbreaks.
| 2346925 | ecrin-mdr-crc
Project description:The present study was done to evaluate chest computed tomography (CT) images of patients with 2019 novel coronavirus disease (COVID-19) by multifractal technique as a new method to find a way for comparing lung infection quantitatively and identifying progression pattern of the disease. The multifractal spectra extracted from analysis of CT images showed that these spectra were correlated with lung infection amount and disease progression so that, multifractal parameters (αmin, αmax, ∆α, f(αmin), f(αmax), ∆f(α), α(q = 0), and f(α) max) were strongly dependent on amount of lung infection. The results demonstrated that multifractality of chest CT images was increased with the increase in lung infection in patients. The interesting and promising result was that capacity dimension (D0) as a new diagnostic parameter varied linearly with progression and reduction of lung infection. A critical value was found for D0, according to which patients with D0 lower than 1.4 can be healed by treatment. Therefore, herein, a way was found for quantitative assessment of lung infection of patients with COVID-19 by analyzing chest CT images using the multifractal method. This method can be very effective for physicians in diagnosis and treatment of pneumonia caused by COVID-19 and timely identification of therapeutic effects.
| S-EPMC8784535 | biostudies-literature
Project description:ObjectivesTo utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents.MethodsA deep learning algorithm consisted of lesion detection, segmentation, and location was trained and validated in 14,435 participants with chest CT images and definite pathogen diagnosis. The algorithm was tested in a non-overlapping dataset of 96 confirmed COVID-19 patients in three hospitals across China during the outbreak. Quantitative detection performance of the model was compared with three radiological residents with two experienced radiologists' reading reports as reference standard by assessing the accuracy, sensitivity, specificity, and F1 score.ResultsOf 96 patients, 88 had pneumonia lesions on CT images and 8 had no abnormities on CT images. For per-patient basis, the algorithm showed superior sensitivity of 1.00 (95% confidence interval (CI) 0.95, 1.00) and F1 score of 0.97 in detecting lesions from CT images of COVID-19 pneumonia patients. While for per-lung lobe basis, the algorithm achieved a sensitivity of 0.96 (95% CI 0.94, 0.98) and a slightly inferior F1 score of 0.86. The median volume of lesions calculated by algorithm was 40.10 cm3. An average running speed of 20.3 s ± 5.8 per case demonstrated the algorithm was much faster than the residents in assessing CT images (all p < 0.017). The deep learning algorithm can also assist radiologists make quicker diagnosis (all p < 0.0001) with superior diagnostic performance.ConclusionsThe algorithm showed excellent performance in detecting COVID-19 pneumonia on chest CT images compared with resident radiologists.Key points• The higher sensitivity of deep learning model in detecting COVID-19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis. • The deep learning model improves diagnosis efficiency by shortening processing time. • The deep learning model can automatically calculate the volume of the lesions and whole lung.
| S-EPMC7331494 | biostudies-literature