coronavirus disease 2019 (COVID-19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has produced a worldwide panic. Beyond the principal human-to-human transmission method by droplet and contact, there is still limited knowledge about possible alternate transmission methods to guide clinical care. Recent clinical studies have observed digestive symptoms in patients with COVID-19, 1 possibly because of the enrichment and infection of SARS-CoV-2 in the gastrointestinal tract, mediated by virus receptor of angiotensin converting enzyme 2 (ACE2), 2 which suggests the potential for a fecal-oral route of SARS-CoV-2 transmission. 3,4 However, there is still a large gap in the biological knowledge of COVID-19. In this study, via a bulkto-cell strategy focusing on ACE2, we performed an integrated omics analysis at the genome, transcriptome, and proteome levels in bulk tissues and single cells across species to decipher the potential routes for SARS-CoV-2 infection in depth. MethodsClinical and epidemiologic data of patients with COVID-19 were collected from a continually updated resource. 5 The transcriptome and proteome derived from bulk tissues and cells were accessed from multiple databases. A phenome-wide association study data set was supplied for genetic analysis on the ACE2 pathway. P values were calculated from t test and gene set analysis. More details are shown in Supplementary Methods.
This study was to investigate the CT quantification of COVID-19 pneumonia and its impacts on the assessment of disease severity and the prediction of clinical outcomes in the management of COVID-19 patients. Materials Methods: Ninety-nine COVID-19 patients who were confirmed by positive nucleic acid test (NAT) of RT-PCR and hospitalized from January 19, 2020 to February 19, 2020 were collected for this retrospective study. All patients underwent arterial blood gas test, routine blood test, chest CT examination, and physical examination on admission. In addition, follow-up clinical data including the disease severity, clinical treatment, and clinical outcomes were collected for each patient. Lung volume, lesion volume, nonlesion lung volume (NLLV) (lung volume À lesion volume), and fraction of nonlesion lung volume (%NLLV) (nonlesion lung volume / lung volume) were quantified in CT images by using two U-Net models trained for segmentation of lung and COVID-19 lesions in CT images. Furthermore, we calculated 20 histogram textures for lesions volume and NLLV, respectively. To investigate the validity of CT quantification in the management of COVID-19, we built random forest (RF) models for the purpose of classification and regression to assess the disease severity (Moderate, Severe, and Critical) and to predict the need and length of ICU stay, the duration of oxygen inhalation, hospitalization, sputum NAT-positive, and patient prognosis. The performance of RF classifiers was evaluated using the area under the receiver operating characteristic curves (AUC) and that of RF regressors using the root-mean-square error. Results: Patients were classified into three groups of disease severity: moderate (n = 25), severe (n = 47) and critical (n = 27), according to the clinical staging. Of which, a total of 32 patients, 1 (1/25) moderate, 6 (6/47) severe, and 25 critical (25/27), respectively, were admitted to ICU. The median values of ICU stay were 0, 0, and 12 days, the duration of oxygen inhalation 10, 15, and 28 days, the hospitalization 12, 16, and 28 days, and the sputum NAT-positive 8, 9, and 13 days, in three severity groups, respectively. The clinical outcomes were complete recovery (n = 3), partial recovery with residual pulmonary damage (n = 80), prolonged recovery (n = 15), and death (n = 1). The %NLLV in three severity groups were 92.18 § 9.89%, 82.94 § 16.49%, and 66.19 § 24.15% with p value <0.05 among each two groups. The AUCs of RF classifiers using hybrid models were 0.927 and 0.929 in classification of moderate vs (severe + critical), and severe vs critical, respectively, which were significantly higher than either radiomics models or clinical models (p < 0.05). The root-mean-square errors of RF regressors were 0.88 weeks for prediction of duration of hospitalization (mean: 2.60 § 1.01 weeks), 0.92 weeks for duration of oxygen inhalation (mean: 2.44 § 1.08 weeks), 0.90 weeks for duration of sputum NAT-positive (mean: 1.59 § 0.98 weeks), and 0.69 weeks for stay of ICU (mean: 1.32 § 0.67 weeks), respectively....
The audit ‘expectation gap’ is a crucial issue associated with the independent auditing function and has significant implications on the development of auditing standards and practices. Through a questionnaire survey, this study investigated the rise of ‘expectation gap’ and related auditing issues under business and auditing environment in the People's Republic of China. The results reveal that the role and benefits of public accounting (independent auditing) had been positively recognized by Chinese audit beneficiaries and auditors, and there were increasing demands for expanding the applicability of public accounting. This study obtained substantial evidence on the emergence of audit ‘expectation gap’ in China, with respect to audit objectives, auditor's obligation to detect and report fraud, auditor independence, and third party liability of auditors. The causes and practical implications of the ‘expectation gap’ are therefore analyzed contextual to the present practices of public accounting in changing Chinese social and economic conditions. This study should cast light on understanding of the institutional setting and recent development of independent audits in China.
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