Evaluating ground glass opacities (GGO) in the COVID-19 era. Do autoantibodies help?
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Introduction: COVID-19 is an infectious disease, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and there have been outbreaks worldwide. The presentation may include unspecific and mild symptoms, myalgia, headaches, high fever, dry cough, severe dyspnea and acute respiratory distress syndrome (ARDS).
Case study: We present a rare case of microscopic polyangiitis (MPA) with interstitial lung disease and without renal involvement misdiagnosed as COVID-19.
Conclusions: Differential diagnosis of COVID-19 is extremely important, and must be correctly identified in order to proceed with correct treatment.Keywords:
myalgia
Abstract As the outbreak of coronavirus disease 2019 (COVID-19) has spread globally, determining how to prevent the spread is of paramount importance. We reported the effectiveness of different responses of 4 affected cities in preventing the COVID-19 spread. We expect the Wenzhou anti–COVID-19 measures may provide information for cities around the world that are experiencing this epidemic.
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Starting from December 2019, Wuhan, China, encountered the first outbreak of coronavirus disease 2019 (COVID-19) (1-2).The epidemic was successfully suppressed by strict containment so that the number of infected people was reduced to 0 on April 8, 2020 (3-4).After that, China experienced roughly 3 dozen outbreaks with local transmission caused by imported severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).These outbreaks were then contained by an effective suppression strategy, and the number of infected people has successfully reached zero again.However, local outbreaks of COVID-19 have reappeared in several areas in China recently and were caused by the SARS-CoV-2 Delta variant of concern (VOC) (5).As of August 26, 2021, a total of 1,390 cases of COVID-19 had been reported in 50 cities in 19 provincial-level administrative divisions (PLADs) (Figure 1), all of which were found to involve the Delta variant.Furthermore, these cases were found to stem from 12 genetically distinct Delta variant imported sources that were grouped into 11 related outbreaks or sporadic case incidents, resulting in 10 geographically separated epidemics including the following areas (Figure 1): Nanjing City of Jiangsu
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At the end of 2019, a mysterious outbreak appeared, forming an atypical pneumonia suspected of originatingfrom an animal market in Wuhan China. The outbreak is a new type of coronavirus which is named asCOVID-19 disease (2019 – nCoV, Novel Coronavirus). COVID 19 disease is a viral infection caused bySARS-CoV-2, namely the acute respiratory syndrome coronavirus 2 which emerged in Wuhan, China.Vaccines and antiviral drugs have not been found, meanwhile COVID-19 are prevented by using nonpharmacologicalinterventions, one of the actions taken by several countries is to create policy protocolssuch as lockdowns.The results of this study indicate that lockdown is effective in reducing the spread of COVID -19, it can beseen from the significant decrease in R0 and Rt, <1 in several countries after lockdown such as UK 0.99(0.96–1.02), Italy 0.89 (0.87–0.91), French 0.76 (0.72–0.82) and Spain 0.74 (0.71–0.78) which means thatsomeone who is infected cannot infect other people and the disease will die (disappear). The value of Rt alsoshows a consistent decline Rt to <1 (0.88) after 2 weeks of lockdown in Italy.The conclusion from this literature show that lockdowns can reduce the spread of COVID-19 which isshown by the number of incidents before and after the lockdown which has decreased to zero cases in China.The decline in cases also occurred in Europe although at the beginning of the lockdown it was not significantbut it was increasingly effective and continued to be significant after the lockdown was imposed.
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Palmer, Chris; Dieffenderfer, Brian; Buckman, Sara; Kerby, Paul; Despotovic, Vladimir; Konzen, Lisa; White, Jason; Meyer, Shelley; Bertrand, Jill; Reda, Patricia; Boyle, Walter Author Information
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Chang, Stephanie; Narayanan, Divya; Hwang, Josephine; Chen, Peter; Parimon, Tanyalak Author Information
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Tieu, Alvin; Chwastek, Damian; Casey, Lansdell; Jamieson, Taylor; Carolina, Ilkow; Stewart, Duncan; Lalu, Manoj Author Information
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We wish to thank Dr. Zhao and Dr. Frerichs1 for acknowledging the clinical importance of the findings of our study.2 We agree with them that the correct implementation and, to a lesser extent, the precise methodology description of the electrical impedance tomography technique and analysis procedures are important for further development and clinical use.We accept that referring to “ventilated and perfused pixels” as the denominator in the calculation of shunt and dead space areas may be misleading and we should have clarified that the detected lung size was defined by all pixels ventilated and/or perfused. Nonetheless, we reassure Dr. Zhao and Dr. Frerichs that our calculation was performed correctly, and our mistake was a mere typo rather than a methodologic error.We also agree that the terms “dorsal ventilation” and “dorsal perfusion” are not in agreement with the consensus electrical impedance tomography terminology and definitions,3 as published in 2017. It is worth remarking, however, that we referred to the terms used in a more recent article,4 published in 2021, whose senior author was one of the experts participating in the consensus statement mentioned above. That said, we should probably have better defined our measurements as “dorsal ventilation area” (or “region” or “size”), indicating that we referred just to the pixel counts and not to the pixel values. Indeed, we stated in the Methods description that “dorsal ventilation represented the percentage of total ventilated lung area that is located in the dorsal half of the thorax.”The software kindly provided by Draeger for research purposes included the “homogeneity” rather than “inhomogeneity” index, and we accordingly used that parameter. We are led to believe this criticism should be directed to the company rather than to us. Nevertheless, we believe that the real issue about the inhomogeneity index is the strong dependency on the lung area considered for calculation. In the year 2008, when the inhomogeneity index was first introduced by Dr. Zhao,5 it was probably not considered that some conditions creating large “out-of-phase” variations—such as artifacts due to the heart and the diaphragm or to pleural effusions—would result in negative pixel values at end inspiration. Incorporating those out-of-phase pixels in the calculation of the global inhomogeneity index would lead to inclusion of extrapulmonary areas. Last, if pixels with negative values are excluded or set to 0, the inhomogeneity index cannot be greater than one.We agree that the assessment of lung perfusion with saline bolus has been studied in several animal studies; however, a comprehensive review on pulmonary perfusion with electrical impedance tomography was beyond the aims of our study. In all honesty, however, this limitation was not included in the original version of our article and was strongly and repeatedly required by one reviewer. Finally, while we may agree that an electrical impedance tomography image does not originate from “only the area of the lung surrounded by the belt,” it remains true that it does not cover the whole lung parenchyma, which makes the limitation still valid.Dr. Navalesi receives royalties from Intersurgical SPA (Mirandola, Italy) for the invention of Helmet Next. He also received speaking fees from Draeger (Lubeck, Germany), Intersurgical SPA, Getinge (Cinisello Balsamo, Italy), MSD (Rahway, New Jersey), Gilead (Foster City, California), and Novartis (Basilea, Switzerland). His research lab received research grants and/or research equipment from Intersurgical SPA, Draeger, and Gilead. Dr. Zarantonello and Dr. Sella received speaking fees from Getinge. The other authors declare no competing interests.
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Severe acute respiratory syndrome coronavirus type 2, SARS-CoV-2 is a disease that causes multi-organ failure in humans and causes physiological changes, which are changes in the components of hematology and biochemical biomarkers that are not specific to Covid-19 disease but considered hallmark into SARS COV-2. Globally, researches indicate that the vast majority of COVID-19 cases fall into the least severe category, i.e., mild to moderate: 81%, severe 14%, and critical 5% of all confirmed cases that infected with SARS COV-2.
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A recent study based on the use of experimental artificial intelligence (AI) tool showed 70%-80% accuracy in predicting development of severe disease in coronavirus disease 2019 (COVID-19) based on predictive parameters alanine aminotransferase (ALT), myalgia and hemoglobin, whilst only 5 of 53 patients developed acute respiratory distress syndrome (ARDS), 2 of whom reporting myalgia. [1]It is commonly advocated that myalgia may reflect generalized inflammation and cytokine response. [1]Multiple studies showed that myalgia is a common symptom at onset of COVID-19, seen in up to 36% of such patients. [2]herefore, in this short article we aim to further assess whether myalgia may be a reliable predictor of severe COVID-19 disease.A search in Medline (PubMed interface), Scopus, and Web of Science, utilizing the keywords "myalgia" AND "COVID-19" OR "Coronavirus 2019" OR "SARS-CoV-2" in all fields with no date or language restrictions was conducted.Search results were screened by title, abstract, and full text for those reporting data on prevalence of myalgia in patients developing severe or non-severe COVID-19 disease.Articles fitting the criteria with validated definition of "severe disease" (i.e., patient development of severe respiratory distress, requiring ICU admission, ventilatory support, or death) were incorporated into a pooled analysis.Computation of odds ratio (OR) and 95% confidence interval (CI) of myalgia in severe and non-severe COVID-19 disease was performed with MetaXL, software version 5.
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