Gastrointestinal symptoms of 95 cases with SARS-CoV-2 infection.

OBJECTIVE: To study the GI symptoms in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infected patients. DESIGN: We analysed epidemiological, demographic, clinical and laboratory data of 95 cases with SARS-CoV-2 caused coronavirus disease 2019. Real-time reverse transcriptase PCR was used to detect the presence of SARS-CoV-2 in faeces and GI tissues. RESULTS: Among the 95 patients, 58 cases exhibited GI symptoms of which 11 (11.6%) occurred on admission and 47 (49.5%) developed during hospitalisation. Diarrhoea (24.2%), anorexia (17.9%) and nausea (17.9%) were the main symptoms with five (5.3%), five (5.3%) and three (3.2%) cases occurred on the illness onset, respectively. A substantial proportion of patients developed diarrhoea during hospitalisation, potentially aggravated by various drugs including antibiotics. Faecal samples of 65 hospitalised patients were tested for the presence of SARS-CoV-2, including 42 with and 23 without GI symptoms, of which 22 (52.4%) and 9 (39.1%) were positive, respectively. Six patients with GI symptoms were subjected to endoscopy, revealing oesophageal bleeding with erosions and ulcers in one severe patient. SARS-CoV-2 RNA was detected in oesophagus, stomach, duodenum and rectum specimens for both two severe patients. In contrast, only duodenum was positive in one of the four non-severe patients. CONCLUSIONS: GI tract may be a potential transmission route and target organ of SARS-CoV-2.

Fecal multi-omics analysis reveals diverse molecular alterations of gut ecosystem in COVID-19 patients.

Gut ecosystem has profound effects on host physiology and health. Gastrointestinal (GI) symptoms were frequently observed in patients with COVID-19. Compared with other organs, gut antiviral response can result in more complicated immune responses because of the interactions between the gut microbiota and host immunity. However, there are still large knowledge gaps in the impact of COVID-19 on gut molecular profiles and commensal microbiome, hindering our comprehensive understanding of the pathogenesis of SARS-CoV-2 and the treatment of COVID-19. We performed longitudinal stool multi-omics profiling to systemically investigate the molecular phenomics alterations of gut ecosystem in COVID-19. Gut proteomes of COVID-19 were characterized by disturbed immune, proteolysis and redox homeostasis. The expression and glycosylation of proteins involved in neutrophil degranulation and migration were suppressed, while those of proteases were upregulated. The variable domains of Ig heavy chains were downregulated and the overall glycosylation of IgA heavy chain constant regions, IgGFc-binding protein, and J chain were suppressed with glycan-specific variations. There was a reduction of beneficial gut bacteria and an enrichment of bacteria derived deleterious metabolites potentially associated with multiple types of diseases (such as ethyl glucuronide). The reduction of Ig heave chain variable domains may contribute to the increase of some Bacteroidetes species. Many bacteria ceramide lipids with a C17-sphingoid based were downregulated in COVID-19. In many cases, the gut phenome did not restore two months after symptom onset. Our study indicates widely disturbed gut molecular profiles which may play a role in the development of symptoms in COVID-19. Our findings also emphasis the need for ongoing investigation of the long-term gut molecular and microbial alterations during COVID-19 recovery process. Considering the gut ecosystem as a potential target could offer a valuable approach in managing the disease.

Risk factors for the COVID-19 severity and its correlation with viral shedding: A retrospective cohort study.

Coronavirus disease 2019 (COVID-19) have become a pandemic in the world. This study is aim to explore risk factors for COVID-19 severity in the early stage and the correlation between the viral shedding and COVID-19 severity. We included inpatient with laboratory confirmed COVID-19 who had been discharged by 9 March 2020. The medical record data and dynamic change of biochemical indicators in-hospital were compared between common and severe patients. Eighty patients were included in this study. Multivariable regression demonstrated increasing odds of severity associated with the duration of fever (odds ratio [OR], 1.42; 95% confidence interval [CI], 1.10-1.82, per day increase; P = .007), C-reactive protein (CRP) (OR, 1.26; 95% CI, 1.04-1.52; P = .02), and PO2 < 80 mm Hg (28.07, 95% CI, 1.50-524.12; P = .026) on admission. We found severe acute respiratory syndrome coronavirus 2 viral RNA could be long-term presence in respiratory tract and fecal sample, up to 43 and 46 days, respectively. However, the duration of viral shedding have no correlation with the COVID-19 severity. The duration of fever, elevated CRP and PO2 < 80 mm Hg on admission were associated with the COVID-19 severity in the early stage and there is no correlation between the viral shedding and COVID-19 severity.

Auxiliary Diagnosis for COVID-19 with Deep Transfer Learning.

To assist physicians identify COVID-19 and its manifestations through the automatic COVID-19 recognition and classification in chest CT images with deep transfer learning. In this retrospective study, the used chest CT image dataset covered 422 subjects, including 72 confirmed COVID-19 subjects (260 studies, 30,171 images), 252 other pneumonia subjects (252 studies, 26,534 images) that contained 158 viral pneumonia subjects and 94 pulmonary tuberculosis subjects, and 98 normal subjects (98 studies, 29,838 images). In the experiment, subjects were split into training (70%), validation (15%) and testing (15%) sets. We utilized the convolutional blocks of ResNets pretrained on the public social image collections and modified the top fully connected layer to suit our task (the COVID-19 recognition). In addition, we tested the proposed method on a finegrained classification task; that is, the images of COVID-19 were further split into 3 main manifestations (ground-glass opacity with 12,924 images, consolidation with 7418 images and fibrotic streaks with 7338 images). Similarly, the data partitioning strategy of 70%-15%-15% was adopted. The best performance obtained by the pretrained ResNet50 model is 94.87% sensitivity, 88.46% specificity, 91.21% accuracy for COVID-19 versus all other groups, and an overall accuracy of 89.01% for the three-category classification in the testing set. Consistent performance was observed from the COVID-19 manifestation classification task on images basis, where the best overall accuracy of 94.08% and AUC of 0.993 were obtained by the pretrained ResNet18 (P < 0.05). All the proposed models have achieved much satisfying performance and were thus very promising in both the practical application and statistics. Transfer learning is worth for exploring to be applied in recognition and classification of COVID-19 on CT images with limited training data. It not only achieved higher sensitivity (COVID-19 vs the rest) but also took far less time than radiologists, which is expected to give the auxiliary diagnosis and reduce the workload for the radiologists.

The impact of new modes of electronic communication in the treatment of severe acute respiratory syndrome coronavirus 2 infection.

BACKGROUND: More than 26,760,000 cases of SARS-CoV-2 infection have been reported globally to date. This study aimed to analyze the impact of new electronic communication tools in the diagnosis and treatment of patients with SARS-CoV-2 infection. METHODS: From January 20 to February 26, 2020, adult patients with laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection who were treated in The Fifth Affiliated Hospital, Sun Yat-sen University, in Zhuhai, China, were recruited. Forty-seven eligible patients were enrolled and randomly classified into either the test group or the control group. All of the patients received the standard therapeutic regimen and routine ward rounds. The test group was subdivided into three subgroups: the first subgroup (5-minute group) was given an extra 5-minute ward round by WeChat voice call once daily for basic disease communication; the second subgroup (10-minute group) received an extra 10-minute ward round by WeChat voice call once daily for further detail; and the third subgroup (20-minute group) was given an extra 10-minute ward round via WeChat voice call once daily, as well as an extra 10 minutes every 3 days. The primary outcome was the duration of positive-to-negative conversion of SARS-CoV-2 nucleic acid diagnosed by the NAT (nucleic acid testing). RESULTS: In the test groups, the median time from diagnosis to the endpoint was 7.0 days [interquartile range (IQR), 3.8-10.8], compared with 10.0 days (IQR, 6.5-14.5) in the control group. It showed significant reduced the duration time of virus from positive to negative by the NAT (nucleic acid testing), (P=0.032) especially between the 10-minute subgroup (3.0 days; IQR, 3.0-7.5) and the control group (P=0.0065). CONCLUSIONS: The use of new modes of electronic communication can benefit patients during the COVID-19 pandemic and could be extremely valuable in addressing the shortage of medical protective equipment and reducing occupational risk of exposure to infection.