ABSTRACT
The relationship between gut microbes and COVID-19 or H1N1 flu is not fully understood. Here, we compared gut mycobiota of 67 COVID-19 patients, 35 H1N1 patients and 48 healthy controls (HCs) using internal transcribed spacer (ITS) 3-ITS4 sequencing. Fungal richness decreased in COVID-19 and H1N1 patients compared to HCs, but fungal diversity decreased in only H1N1 patients. Fungal mycobiota dysbiosis in both COVID-19 and H1N1 patients was mainly characterized by depletions of fungi such as Aspergillus, Penicillium, but several fungi, such as Candida parapsilosis, and Malassezia yamatoensis, were enriched in H1N1 patients. The altered fungal taxa were strongly associated with clinical features such as the incidence of diarrhoea, albumin. Gut mycobiota between COVID-19 patients with mild and severity symptoms are not different, as well as between COVID-19 patients in and out hospital. Therefore, gut mycobiota dysbiosis occur in covid-19 or H1N1 patients and do not improve until discharge.
Subject(s)
COVID-19 , Mycoses , Dysbiosis , DiarrheaABSTRACT
BackgroundSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection swept through Wuhan and spread across China and overseas beginning in December 2019. To identify predictors associated with disease progression, we evaluated clinical risk factors for exacerbation of SARS-CoV-2 infection.MethodsA retrospective analysis was used for PCR-confirmed COVID-19 (coronavirus disease 2019)-diagnosed hospitalized cases between January 19, 2020, and February 19, 2020, in Zhejiang, China. We systematically analysed the clinical characteristics of the patients and predictors of clinical deterioration.ResultsOne hundred patients with COVID-19, with a median age of 54 years, were included. Among them, 49 patients (49%) had severe and critical disease. Age ([36-58] vs [51-70], P=0.0001); sex (49% vs 77.6%, P=0.0031); Body Mass Index (BMI ) ([21.53-25.51] vs [23.28-27.01], P=0.0339); hypertension (17.6% vs 57.1%, P<0.0001); IL-6 ([6.42-30.46] vs [16.2-81.71], P=0.0001); IL-10 ([2.16-5.82] vs [4.35-9.63], P<0.0001); T lymphocyte count ([305- 1178] vs [167.5-440], P=0.0001); B lymphocyte count ([91-213] vs [54.5-163.5], P=0.0001); white blood cell count ([3.9-7.6] vs [5.5-13.6], P=0.0002); D2 dimer ([172-836] vs [408-953], P=0.005), PCT ([0.03-0.07] vs [0.04-0.15], P=0.0039); CRP ([3.8-27.9] vs [17.3-58.9], P<0.0001); AST ([16, 29] vs [18, 42], P=0.0484); artificial liver therapy (2% vs 16.3%, P=0.0148); and glucocorticoid therapy (64.7% vs 98%, P<0.0001) were associated with the severity of the disease. Age and weight were independent risk factors for disease severity.ConclusionDeterioration among COVID-19-infected patients occurred rapidly after hospital admission. In our cohort, we found that multiple factors were associated with the severity of COVID19. Early detection and monitoring of these indicators may reduce the progression of the disease. Removing these factors may halt the progression of the disease. In addition, Oxygen support, early treatment with low doses of glucocorticoids and liver therapy, when necessary, may help reduce mortality in critically ill patients.
Subject(s)
Coronavirus Infections , Critical Illness , Hypertension , COVID-19ABSTRACT
Objective@#To study the effect of low-to-moderate dose glucocorticoid therapy on viral clearance time in patients with COVID-19.@*Methods@#A total of 72 patients diagnosed with COVID-19 from January 19 to February 17, 2020 at the First Affiliated Hospital, School of Medicine, Zhejiang University were recruited. All patients received oral abidol and/or combined lopinavir/ritonavir, darunavir antiviral, and symptomatic supportive care. Among them, 51 patients received methylprednisolone (0.75-1.50 mg·kg-1·d-1) (glucocorticoid treatment group), and 21 patients who did not use glucocorticoid were the control group. The time of stable virologic conversion insputumand the time of radiologic recovery in lungsince onset were compared between the two groups and among the normal patients.The Kruskal-Wallis test or Fisher exact test was used to compare the difference between groups.@*Results@#The median ages of the glucocorticoid group and the control group were 52 [interquartile range (IQR):45, 62] years and 46 (IQR: 32, 56)years, and the differences were significant (P<0.05). The clinical conditions at hospital admission were different between the two groups (P<0.01). There were 52.0% critical ill patients in the glucocorticoid treatment group, compared to that of 71.4% normal patients in the control group. The median times from the onset tostable virologic conversion to negative in the two groups were 15 (IQR:13,20) days and 14 (IQR:12,20) days (P>0.05), and the difference was no statistically significant. The median times from onset to radiologic recovery were 13 (IQR: 11,15) days and 13 (IQR:12,17) days in the two groups, and there was no difference (P>0.05). In ordinary patients, the median timesfrom the onset tostable virologic conversion insputum were no difference (P>0.05), with 13 (IQR:11,18) days in the glucocorticoid group and 13 (IQR:12,15) days in the control group; The median times from onset to radiologic recovery in lungwere also no difference (P>0.05), with 12 (IQR: 10,15)days in the glucocorticoid group and 13 (IQR: 12,17) days inthe control group.@*Conclusions@#Low-to-moderate glucocorticoid treatment has no effect on the time of virus clearance in patients with different clinical types of COVID-19. The glucocorticoid is not recommended since no effectiveness on accelerating the improvement of radiologic recovery in lung has been observed.
ABSTRACT
We found that the real time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab has a relatively low positive rate in the early stage to determine COVID-19 (named by the World Health Organization). The manifestations of computed tomography (CT) imaging of COVID-19 had their own characteristics, which are different from other types of viral pneumonia, such as Influenza-A viral pneumonia. Therefore, clinical doctors call for another early diagnostic criteria for this new type of pneumonia as soon as possible.This study aimed to establish an early screening model to distinguish COVID-19 pneumonia from Influenza-A viral pneumonia and healthy cases with pulmonary CT images using deep learning techniques. The candidate infection regions were first segmented out using a 3-dimensional deep learning model from pulmonary CT image set. These separated images were then categorized into COVID-19, Influenza-A viral pneumonia and irrelevant to infection groups, together with the corresponding confidence scores using a location-attention classification model. Finally the infection type and total confidence score of this CT case were calculated with Noisy-or Bayesian function.The experiments result of benchmark dataset showed that the overall accuracy was 86.7 % from the perspective of CT cases as a whole.The deep learning models established in this study were effective for the early screening of COVID-19 patients and demonstrated to be a promising supplementary diagnostic method for frontline clinical doctors.