Highlights d AI system that can diagnose COVID-19 pneumonia using CT scans d Prediction of progression to critical illness d Potential to improve performance of junior radiologists to the senior level d Can assist evaluation of drug treatment effects with CT quantification

The novel coronavirus (2019-nCoV) infection caused pneumonia. we retrospectively analyzed the virus presence in the pharyngeal swab, blood, and the anal swab detected by real-time PCR in the clinical lab. Unexpectedly, the 2109-nCoV RNA was readily detected in the blood (6 of 57 patients) and the anal swabs (11 of 28 patients). Importantly, all of the 6 patients with detectable viral RNA in the blood cohort progressed to severe symptom stage, indicating a strong correlation of serum viral RNA with the disease severity (p-value = 0.0001). Meanwhile, 8 of the 11 patients with annal swab virus-positive was in severe clinical stage. However, the concentration of viral RNA in the anal swab (Ct value = 24 + 39) was higher than in the blood (Ct value = 34 + 39) from patient 2, suggesting that the virus might replicate in the digestive tract. Altogether, our results confirmed the presence of virus RNA in extra-pulmonary sites. ARTICLE HISTORY

Cancer stem cells (CSCs) have been identified in solid tumors and cancer cell lines. In this study, we isolated a series of cancer cell clones, which were heterogeneous in growth rate, cell cycle distribution and expression profile of genes and proteins, from ovarian tumor specimens of a patient and identified a sub-population enriched for ovarian CSCs defined by CD24 phenotype. Experiments in vitro demonstrated CD24þ sub-population possessed stem celllike characteristics of remaining quiescence and more chemoresistant compared with CD24À fraction, as well as a specific capacity for self-renewal and differentiation. In addition, injection of 5 Â 10 3 CD24 þ cells was able to form tumor xenografts in nude mice, whereas equal number of CD24 À cells remained nontumorigenic. We also found that CD24 þ cells expressed higher mRNA levels of some 'stemness' genes, including Nestin, b-catenin, Bmi-1, Oct4, Oct3/4, Notch1 and Notch4 which were involved in modulating many functions of stem cells, and lower E-cadherin mRNA level than CD24 À cells. Altogether, these observations suggest human ovarian tumor cells are organized as a hierarchy and CD24 demarcates an ovarian cancer-initiating cell population. These findings will have important clinical applications for developing effective therapeutic strategies to treat ovarian cancer.

Background-The incidence of thyroid cancer is rising steadily because of overdiagnosis and overtreatment conferred by widespread use of sensitive imaging techniques for screening. This overall incidence growth is especially driven by increased diagnosis of indolent and welldifferentiated papillary subtype and early-stage thyroid cancer, whereas the incidence of advancedstage thyroid cancer has increased marginally. Thyroid ultrasound is frequently used to diagnose thyroid cancer. The aim of this study was to use deep convolutional neural network (DCNN) models to improve the diagnostic accuracy of thyroid cancer by analysing sonographic imaging data from clinical ultrasounds.Methods-We did a retrospective, multicohort, diagnostic study using ultrasound images sets from three hospitals in China. We developed and trained the DCNN model on the training set, 131 731 ultrasound images from 17 627 patients with thyroid cancer and 180 668 images from 25 325 controls from the thyroid imaging database at Tianjin Cancer Hospital. Clinical diagnosis of the training set was made by 16 radiologists from Tianjin Cancer Hospital. Images from anatomical sites that were judged as not having cancer were excluded from the training set and only individuals with suspected thyroid cancer underwent pathological examination to confirm diagnosis. The model's diagnostic performance was validated in an internal validation set from Tianjin Cancer Hospital (8606 images from 1118 patients) and two external datasets in China (the