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INTRODUCTION: The prevalence and shedding of fecal severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA indicate coronavirus disease 2019 (COVID-19) infection in the gastrointestinal (GI) tract and likely infectivity. We performed a systemic review and meta-analysis to evaluate the prevalence and the duration of shedding of fecal RNA in patients with COVID-19 infection. METHODS: PubMed, Embase, Web of Science, and Chinese databases Chinese National Knowledge Infrastructure and Wanfang Data up to June 2020 were searched for studies evaluating fecal SARS-CoV-2 RNA, including anal and rectal samples, in patients with confirmed COVID-19 infection. The pooled prevalence of fecal RNA in patients with detectable respiratory RNA was estimated. The days of shedding and days to loss of fecal and respiratory RNA from presentation were compared. RESULTS: Thirty-five studies (N = 1,636) met criteria. The pooled prevalence of fecal RNA in COVID-19 patients was 43% (95% confidence interval [CI] 34%–52%). Higher proportion of patients with GI symptoms (52.4% vs 25.9%, odds ratio = 2.4, 95% CI 1.2–4.7) compared with no GI symptoms, specifically diarrhea (51.6% vs 24.0%, odds ratio = 3.0, 95% CI 1.9–4.8), had detectable fecal RNA. After loss of respiratory RNA, 27% (95% CI 15%–44%) of the patients had persistent shedding of fecal RNA. Days of RNA shedding in the feces were longer than respiratory samples (21.8 vs 14.7 days, mean difference = 7.1 days, 95% CI 1.2–13.0). Furthermore, days to loss of fecal RNA lagged respiratory RNA by a mean of 4.8 days (95% CI 2.2–7.5). DISCUSSION: Fecal SARS-CoV-2 RNA is commonly detected in COVID-19 patients with a 3-fold increased risk with diarrhea. Shedding of fecal RNA lasted more than 3 weeks after presentation and a week after last detectable respiratory RNA.

Computer-aided polyp detection in gastric gastroscopy has been the subject of research over the past few decades. However, despite significant advances, automatic polyp detection in real time is still an unsolved problem. In this paper, we report on a convolutional neural network (CNN) for polyp detection that is constructed based on Single Shot MultiBox Detector (SSD) architecture and which we call SSD for Gastric Polyps (SSD-GPNet). To take full advantages of feature maps’ information from the feature pyramid and to acquire higher accuracy, we re-use information that is abandoned by Max-Pooling layers. In other words, we reuse the lost data from the pooling layers and concatenate that data as extra feature maps to contribute to classification and detection. Meanwhile, in the feature pyramid, we concatenate feature maps of the lower layers and feature maps that are deconvolved from upper layers to make explicit relationships between layers and to effectively increase the number of channels. The results show that our enhanced SSD for gastric polyp detection can realize real-time polyp detection with 50 frames per second (FPS) and can improve the mean average precision (mAP) from 88.5% to 90.4%, with only a little loss in time-performance. And the further experiment shows that SSD-GPNet has excellent performance in improving polyp detection recalls over 10% (p = 0.00053), especially in small polyp detection. This can help endoscopic physicians more easily find missed polyps and decrease the gastric polyp miss rate. It may be applicable in daily clinical practice to reduce the burden on physicians.