去年底到现在的2019-nCoV感染肆虐中，有关病毒感染的发生，发展，预防，救护，隔离措施直到患者的治疗，相关资料的收集，分析，实验室病毒鉴定研究，学术文献的整理和发表都是以极快的速度进行的。这种快速学术文献和信息的发表速度，对抵抗冠状病毒感染和传播，具有极重大的意义。可以说这是在此次对抗瘟疫中的一个亮点。而快速发表的这些文献中都会看到bioRxiv预印本的身影。

              到现在为止，BioRxiv发表的与2019-nCoV有关的预印本文献已有33篇，其中中国学者的文章18篇，占55%。看来中国学者已经懂得如何善用BioRxiv平台了。(更新，一天多的时间又增加了6篇。迄今为止总共39篇）。目前bioRxiv 发表的2019-nCoV 39篇文章List在后边，以便各位学者查阅：

那么什么是bioRxiv预印本呢？bioRxiv提供的是生命科学领域中免费的尚未正式发表的预印本（也可权当是原始草稿）的在线存储和发布服务。它由非营利的研究和教育机构：美国著名的冷泉港实验室运行。当在bioRxiv上发布预印本后，作者可使其研究成果对全球整个科研群体公布，并在选定杂志最终投稿之前收到对草稿的反馈意见。 

目前，生命科学领域的研究人员正在以前所未有的速度和不断增长的速度将其工作发布到预印服务器上，在同行评审期刊上发表论文之前，先在线共享论文。目前的趋势看来，预印本的受欢迎程度和实用性正在推动期刊和资助机构的政策变化。 

2019年初对上传到bioRxiv.org的全部37,648篇预印本数据一个分析，显示bioRxiv是目前最大的，以生物学为重点的预印本服务器。BioRxiv上的预印本比以往任何时候都得到了更多的阅读（仅2018年10月就有110万次下载）。预印本的发布率统计截止到2019年初，已提高到每月2100多个的近期高点。 

此外，2016年或更早之前发布的bioRxiv预印本中有三分之二后来在同行评审期刊上发表，并且大多数已出版的预印本在发表后不到六个月就出现在期刊上。评估还发现下载量更大的预印本可能会在影响因子较高的期刊上发表。开发的Rxivist.org网站即可以下载预印本，也可以通过编程方式与Rxivist上的索引元数据互动。 

       我知道bioRxiv时间也是不长。去年在耶鲁大学一个朋友，发表的有关数学模型预测生理年龄的一篇文章，就发表在bioRxiv上，当时查了一下印象分（impact index），发现bioRxiv是著名冷泉港实验室编辑出版的，作为预印本没有impact index。当时还奇怪，论文不错，怎么发在没有impact index的杂志上了？以后知道了，bioRxiv可以帮助或者不影响预印本文章转移发表到其他同行评议的杂志上。

1. Xiaolong Tian, et al.  Potent binding of 2019 novel coronavirus spike protein by a SARS coronavirus-specific human monoclonal antibody.  bioRxiv 2020.01.28.92301

2. Xin Liu, Xiu-Jie. Wang. Potential inhibitors for 2019-nCoV coronavirus M protease from clinically approved medicines.  bioRxiv 2020.01.29.924100 

3. Tao Liu, et al. Transmission dynamics of 2019 novel coronavirus (2019-nCoV). bioRxiv 2020.01.25.919787

4. D. Paraskevis, et al.   Full-genome evolutionary analysis of the novel corona virus (2019-nCoV) rejects the hypothesis of emergence as a result of a recent recombination event. bioRxiv 2020.01.26.920249

5. Domenico Benvenuto, et al. The 2019-new coronavirus epidemic: evidence for virus evolution.  bioRxiv 2020.01.24.915157 

6. Arunachalam Ramaiah, et al. Insights into Cross-species Evolution of Novel Human Coronavirus 2019-nCoV and Defining Immune Determinants for Vaccine Development. bioRxiv 2020.01.29.925867

7. Ning Dong, et al. Genomic and protein structure modelling analysis depicts the origin and infectivity of 2019-nCoV, a new coronavirus which caused a pneumonia outbreak in Wuhan, China bioRxiv 2020.01.20.913368

8. Julien Riou, et al. Althaus. Pattern of early human-to-human transmission of Wuhan 2019-nCoV.  bioRxiv 2020.01.23.917351

9. Markus Hoffmann, et al. The novel coronavirus 2019 (2019-nCoV) uses the SARS-coronavirus receptor ACE2 and the cellular protease TMPRSS2 for entry into target cells. bioRxiv 2020.01.31.929042

10. Qiang Huang, Andreas Herrmann. Fast assessment of human receptor-binding capability of 2019 novel coronavirus (2019-nCoV).  bioRxiv 2020.02.01.930537

11. Changhai Lei, et al. Potent neutralization of 2019 novel coronavirus by recombinant ACE2-Ig. bioRxiv 2020.02.01.929976

12. Shi Zhao, et al. Preliminary estimation of the basic reproduction number of novel coronavirus (2019-nCoV) in China, from 2019 to 2020: A data-driven analysis in the early phase of the outbreak. bioRxiv 2020.01.23.916395

13. Tianmu Chen, et al. A mathematical model for simulating the transmission of Wuhan novel Coronavirus.  bioRxiv 2020.01.19.911669

14. Qian Guo, et al. Host and infectivity prediction of Wuhan 2019 novel coronavirus using deep learning algorithm.  bioRxiv 2020.01.21.914044

15. Bo Ram Beck, et al. Predicting commercially available antiviral drugs that may act on the novel coronavirus (2019-nCoV), Wuhan, China through a drug-target interaction deep learning model. bioRxiv 2020.01.31.929547

16. Yan Li, et al. Therapeutic Drugs Targeting 2019-nCoV Main Protease by High-Throughput Screening.  bioRxiv 2020.01.28.922922

17. R.N. Thompson. 2019-20 Wuhan coronavirus outbreak: Intense surveillance is vital for preventing sustained transmission in new locations.  bioRxiv 2020.01.24.919159

18. Chi Zhang, Mei Wang. MRCA time and epidemic dynamics of the 2019 novel coronavirus. bioRxiv 2020.01.25.919688

19. Sara Cleemput, et al. Genome Detective Coronavirus Typing Tool for rapid identification and characterization of novel coronavirus genomes.  bioRxiv 2020.01.31.928796

20. Jacob Beal, et al. Highly Distinguished Amino Acid Sequences of 2019-nCoV (Wuhan Coronavirus).  bioRxiv 2020.01.31.929497

21. Peng Shao, Yingji Shan. Beware of asymptomatic transmission: Study on 2019-nCoV prevention and control measures based on extended SEIR model.  bioRxiv 2020.01.28.923169

22. Jingyue Ju, Shiv Kumar, et al. Nucleotide Analogues as Inhibitors of Viral Polymerases. bioRxiv 2020.01.30.927574

23. Wai-kit Ming, et al. Breaking down of healthcare system: Mathematical modelling for controlling the novel coronavirus (2019-nCoV) outbreak in Wuhan, China. bioRxiv 2020.01.27.922443

24. Chenglong Xiong, et al. Evolution and variation of 2019-novel coronavirus. bioRxiv 2020.01.30.926477

25. Michael Letko, Vincent Munster. Functional assessment of cell entry and receptor usage for lineage B β-coronaviruses, including 2019-nCoV.  bioRxiv 2020.01.22.915660

26. Hao Zhang, et al. The digestive system is a potential route of 2019-nCov infection: a bioinformatics analysis based on single-cell transcriptomes. bioRxiv 2020.01.30.927806

27. Mingwang Shen, et al. Modelling the epidemic trend of the 2019 novel coronavirus outbreak in China.  bioRxiv 2020.01.23.916726

28. Jakub M Bartoszewicz, et al. Interpretable detection of novel human viruses from genome sequencing data.  bioRxiv 2020.01.29.925354

29. Shen Lin, et al. Molecular Modeling Evaluation of the Binding Abilities of Ritonavir and Lopinavir to Wuhan Pneumonia Coronavirus Proteases.  bioRxiv 2020.01.31.929695

30. Zhijian Xu, et al. Nelfinavir was predicted to be a potential inhibitor of 2019-nCov main protease by an integrative approach combining homology modelling, molecular docking and binding free energy calculation.  bioRxiv 2020.01.27.921627

31. Wioletta Rut, et al. Engineered unnatural ubiquitin for optimal detection of deubiquitinating enzymes.  bioRxiv 2020.01.30.926881

32. Peng Zhou, et al. Discovery of a novel coronavirus associated with the recent pneumonia outbreak in humans and its potential bat origin. bioRxiv 2020.01.22.914952

33. Fan Wu, et al. Complete genome characterisation of a novel coronavirus associated with severe human respiratory disease in Wuhan, China. bioRxiv 2020.01.24.919183

34. Duc Duy Nguyen, et al. Machine intelligence design of 2019-nCoV drugs. bioRxiv 2020.01.30.927889

35. Gurjit S Randhawa, et al. Machine learning-based analysis of genomes suggests associations between Wuhan 2019-nCoV and bat Betacoronaviruses.  bioRxiv 2020.02.03.932350

36. Syed Faraz Ahmed, et al. Preliminary identification of potential vaccine targets for 2019-nCoV based on SARS-CoV immunological studies . bioRxiv 2020.02.03.933226

37. Carmine Ceraolo, Federico M Giorgi. Phylogenomic analysis of the 2019-nCoV coronavirus. bioRxiv 2020.02.02.931162

38. Xiaoqiang Chai, et al. Specific ACE2 Expression in Cholangiocytes May Cause Liver Damage After 2019-nCoV Infection.  bioRxiv 2020.02.03.931766

39. Xufang Deng, Yafang Chen, Anna M Mielech, et al. Structure-Guided Mutagenesis Alters Deubiquitinating Activity 2 and Attenuates Pathogenesis of a Murine Coronavirus. bioRxiv 782409