The current outbreak of viral pneumonia in the city of Wuhan, China, was caused by a novel coronavirus designated 2019-nCoV by the World Health Organization, as determined by sequencing the viral RNA genome. Many initial patients were exposed to wildlife animals at the Huanan seafood wholesale market, where poultry, snake, bats, and other farm animals were also sold. To investigate possible virus reservoir, we have carried out comprehensive sequence analysis and comparison in conjunction with relative synonymous codon usage (RSCU) bias among different animal species based on the 2019-nCoV sequence. Results obtained from our analyses suggest that the 2019-nCoV may appear to be a recombinant virus between the bat coronavirus and an origin-unknown coronavirus. The recombination may occurred within the viral spike glycoprotein, which recognizes a cell surface receptor. Additionally, our findings suggest that 2019-nCoV has most similar genetic information with bat coronovirus and most similar codon usage bias with snake. Taken together, our results suggest that homologous recombination may occur and contribute to the 2019-nCoV cross-species transmission. K E Y W O R D S 2019-nCoV, codon usage bias, cross-species transmission, phylogenetic analysis, recombination

To investigate the time origin, genetic diversity, and transmission dynamics of the recent 2019-nCoV outbreak in China and beyond, a total of 32 genomes of virus strains sampled from China, Thailand, and the USA with sampling dates between 24 December 2019 and 23 January 2020 were analyzed. Phylogenetic, transmission network, and likelihood-mapping analyses of the genome sequences were performed. On the basis of the likelihood-mapping analysis, the increasing tree-like signals (from 0% to 8.2%, 18.2%, and 25.4%) over time may be indicative of increasing genetic diversity of 2019-nCoV in human hosts. We identified three phylogenetic clusters using the Bayesian inference framework and three transmission clusters using transmission network analysis, with only one cluster identified by both methods using the above genome sequences of 2019-nCoV strains. The estimated mean evolutionary rate for 2019-nCoV ranged from 1.7926 × 10 −3 to 1.8266 × 10 −3 substitutions per site per year. On the basis of our study, undertaking epidemiological investigations and genomic data surveillance could positively impact public health in terms of guiding prevention efforts to reduce 2019-nCOV transmission in real-time. K E Y W O R D S 2019-nCoV, evolutionary rate, phylogenetic cluster, time to most recent common ancestor, TMRCA, transmission cluster

We developed a clinical model and laboratory model for predicting the in-hospital mortality of COVID-19 patients, the AUCs (95% CI) were 0.88 (0.80, 0.95) and 0.98 (0.92, 0.99) in training cohort, and 0.83 (0.68, 0.93) and 0.88 (0.77, 0.95) in validation cohort, respectively. ABSTRACT BackgroundThis study aimed to develop mortality-prediction models for patients with Coronavirus disease 2019 .baseline clinical and laboratory data through the stepwise Akaike information criterion and ensemble XGBoost model to build mortality-prediction models. We then validated these models by randomly collecting COVID-19 patients in the Infection department of Union Results296 patients with COVID-19 were enrolled in the training cohort, 19 of whom died during hospitalization and 277 were discharged from the hospital. The clinical model developed with age, history of hypertension and coronary heart disease showed AUC of 0.88 (95% CI, 0.80-0.95); threshold, -2.6551; sensitivity, 92.31%; specificity, 77.44% and negative predictive value (NPV), 99.34%. The laboratory model developed with age, high-sensitivity C-reactive protein (hsCRP), peripheral capillary oxygen saturation (SpO2), neutrophil and lymphocyte count, D-dimer, aspartate aminotransferase (AST) and glomerular filtration rate (GFR) had a significantly stronger discriminatory power than the clinical model (p=0.0157), with AUC of 0.98 (95% CI, 0.92-0.99); threshold, -2.998; sensitivity, 100.00%; specificity, 92.82% and NPV, 100.00%. In the subsequent validation cohort (N=44), the AUCs (95% CI) were 0.83 (0.68, 0.93) and 0.88 (0.75, 0.96) for clinical model and laboratory model, respectively. ConclusionsWe developed two predictive models for the in-hospital mortality of patients with COVID-19 in Wuhan and validated in patients from another center.

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Background Changes in platelet count are common in COVID-19 patients. The platelet count reflects the thrombocyte turnover, acting as a sensitive indicator of illness severity that is of great clinical utility to monitor a quickly changing health condition of patients affected by aggressive viral infections. This study aims to investigate the significance of platelet count during the progression of the disease in COVID-19 patients.

Although many techniques can be used to generate multitype-induced pluripotent stem (iPS) cells from multitype seed cells, improving the efficiency and shortening the period of cell reprogramming remain troublesome issues. In this study, to generate iPS cells, CD34⁺ cells, isolated from human amniotic fluid cells (HuAFCs) by flow cytometry, were infected with retroviruses carrying only one reprogramming factor (Oct4) and cultured on human amniotic epithelial cell (HuAEC) feeder layers. Approximately 4 to 5 days after viral infection, some embryonic stem cell (ESC)-like colonies appeared among the feeder cells. These colonies were positive for alkaline phosphatase and expressed high levels of ESC pluripotent markers (Nanog, Sox2, Oct4, CD133, and Rex1). Moreover, these iPS cells exhibited high levels of telomerase activity and had normal karyotypes. Additionally, these cells could differentiate into cell types from all 3 germ layers in vivo and in teratomas. In summary, we report a novel way of iPS generation that uses CD34⁺ HuAFCs as seed cells. Using this method, we can generate human iPS cells with greater efficiency and safety (the oncogenic factors, c-Myc and Klf4, were not used), and using the minimum number of reprogramming factors (only one factor, Oct4). Besides, HuAECs were used as feeder layers to culture human iPS cells, which could not only avoid contamination with heterogeneous proteins, but also maintain iPS cells in a self-renewing and undifferentiated state for a long time.

Although autophagy is most critical for survival of cancer cells, especially in fast-growing tumors, the mechanism remains to be fully characterized. Herein we report that PSMD10/gankyrin promotes autophagy in hepatocellular carcinoma (HCC) in response to starvation or stress through 2 complementary routes. PSMD10 was physically associated with ATG7 in the cytoplasm, and this association was enhanced by initial nutrient deprivation. Subsequently, PSMD10 translocated into the nucleus and bound cooperatively with nuclear HSF1 (heat shock transcription factor 1) onto the ATG7 promoter, upregulated ATG7 expression in the advanced stage of starvation. Intriguingly, the type of PSMD10-mediated autophagy was independent of the proteasome system, although PSMD10 has been believed to be an indispensable chaperone for assembly of the 26S proteasome. A significant correlation between PSMD10 expression and ATG7 levels was detected in human HCC biopsies, and the combination of these 2 parameters is a powerful predictor of poor prognosis. The median survival of sorafenib-treated HCC patients with high expression of PSMD10 was much shorter than those with low expression of PSMD10. Furthermore, PSMD10 augmented autophagic flux to resist sorafenib or conventional chemotherapy, and inhibition of autophagy suppressed PSMD10-mediated resistance. We conclude that these results present a novel mechanism involving modulation of ATG7 by PSMD10 in sustaining autophagy, promoting HCC cell survival against starvation or chemotherapy. Targeting of PSMD10 might therefore be an attractive strategy in HCC treatment by suppressing autophagy and inducing HCC cell sensitivity to drugs.

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