Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2), has become a pandemic, infecting more than 4,000,000 people worldwide. This review describes the main clinical features of COVID-19 and potential role of microbiota in COVID-19. SARS-CoV and SARS-CoV-2 have 79.5% nucleotide sequence identity and use angiotensin-converting enzyme 2 (ACE2) receptors to enter host cells. The distribution of ACE2 may determine how SARS-CoV-2 infects the respiratory and digestive tract. SARS and COVID-19 share similar clinical features, although the estimated fatality rate of COVID-19 is much lower. The communication between the microbiota and SARS-CoV-2 and the role of this association in diagnosis and treatment are unclear. Changes in the lung microbiota were identified in COVID-19 patients, and the enrichment of the lung microbiota with bacteria found in the intestinal tract is correlated with the onset of acute respiratory distress syndrome and long-term outcomes. ACE2 regulates the gut microbiota by indirectly controlling the secretion of antimicrobial peptides. Moreover, the gut microbiota enhances antiviral immunity by increasing the number and function of immune cells, decreasing immunopathology, and stimulating interferon production. In turn, respiratory viruses are known to influence microbial composition in the lung and intestine. Therefore, the analysis of changes in the microbiota during SARS-CoV-2 infection may help predict patient outcomes and allow the development of microbiota-based therapies.

Our results suggest that statin use is associated with a modest reduced risk of CRC; apparent associations were found for lipophilic statin use. However, long-term statin use did not appear to significantly affect the risk of CRC.

BackgroundTo explore the influences of prenatal antibiotic exposure, the intensity of prenatal and postnatal antibiotic exposure on gut microbiota of preterm infants and whether gut microbiota and drug resistant strains in the neonatal intensive care unit (NICU) over a defined period are related.MethodsAmong 28 preterm infants, there were two groups, the PAT (prenatal antibiotic therapy) group (12 cases), and the PAF (prenatal antibiotic free) group (12 cases). Fecal samples from both groups were collected on days 7 and 14. According to the time of prenatal and postnatal antibiotic exposure, cases were divided into two groups, H (high) group (11 cases) and L (low) group (11 cases), and fecal samples on day 14 were collected. Genomic DNA was extracted from the fecal samples and was subjected to high throughput 16S rRNA amplicon sequencing. Bioinformatics methods were used to analyze the sequencing results.ResultsPrenatal and postnatal antibiotic exposure exercised influence on the early establishment of intestinal microflora of preterm infants. Bacteroidetes decreased significantly in the PAT group (p < 0.05). The number of Bifidobacterium significantly decreased in the PAT group and H group (p < 0.05). The early gut microbiota of preterm infants with prenatal and postnatal antibiotic exposure was similar to resistant bacteria in NICU during the same period.ConclusionPrenatal and postnatal antibiotic exposure may affect the composition of early gut microbiota in preterm infants. Antibiotic-resistant bacteria in NICU may play a role in reshaping the early gut microbiota of preterm infants with prenatal and postnatal antibiotic exposure.Electronic supplementary materialThe online version of this article (10.1186/s12941-018-0264-y) contains supplementary material, which is available to authorized users.

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Objectives: Coronavirus disease 2019 (COVID-19) is sweeping the globe and has resulted in infections in millions of people. Patients with COVID-19 face a high fatality risk once symptoms worsen; therefore, early identification of severely ill patients can enable early intervention, prevent disease progression, and help reduce mortality. This study aims to develop an artificial intelligence-assisted tool using computed tomography (CT) imaging to predict disease severity and further estimate the risk of developing severe disease in patients suffering from COVID-19. Materials and Methods: Initial CT images of 408 confirmed COVID-19 patients were retrospectively collected between January 1, 2020 and March 18, 2020 from hospitals in Honghu and Nanchang. The data of 303 patients in the People's Hospital of Honghu were assigned as the training data, and those of 105 patients in The First Affiliated Hospital of Nanchang University were assigned as the test dataset. A deep learning based-model using multiple instance learning and residual convolutional neural network (ResNet34) was developed and validated. The discrimination ability and prediction accuracy of the model were evaluated using the receiver operating characteristic curve and confusion matrix, respectively. Results: The deep learning-based model had an area under the curve (AUC) of 0.987 (95% confidence interval [CI]: 0.968-1.00) and an accuracy of 97.4% in the training set, whereas it had an AUC of 0.892 (0.828-0.955) and an accuracy of 81.9% in the test set. In the subgroup analysis of patients who had non-severe COVID-19 on admission, the

Whole genome profiling such as array comparative genomic hybridization has identified novel genomic imbalances. Many of these genomic imbalances have since been shown to associate with developmental delay, intellectual disability and congenital malformation. Here we identified five unrelated individuals who have a recurrent 1.71 Mb deletion/duplication at 2q13 (Human Genome Build 19: 111,392,197-113,102,594). Four of these individuals have developmental issues, four have cranial dysmorphism. Literature review revealed 14 more cases that had similar genomic imbalances at 2q13. Many of them had developmental delay and dysmorphism. Taken together, 93% and 63% of individuals with this genomic imbalance displayed impaired developmental skills and/or abnormal facial features respectively. This copy number variant (CNV) has not been reported in normal control databases. We, therefore, propose that CNV in this region is a risk factor for developmental delay and dysmorphism.

Our results indicate that elevated IL-27 strongly correlates with EOS and may provide additional diagnostic value along with PCT.