Menagerie: A text-mining tool to support animal-human translation in neurodegeneration research


Autoři: Caroline J. Zeiss aff001;  Dongwook Shin aff002;  Brent Vander Wyk aff003;  Amanda P. Beck aff004;  Natalie Zatz aff005;  Charles A. Sneiderman aff002;  Halil Kilicoglu aff002
Působiště autorů: Department of Comparative Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America aff001;  Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland, United States of America aff002;  Department of Internal Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America aff003;  Department of Pathology, Albert Einstein College of Medicine, New York, United States of America aff004;  Department of Ecology and Evolutionary Biology, Yale University, New Haven, Connecticut, United States of America aff005
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226176

Souhrn

Discovery studies in animals constitute a cornerstone of biomedical research, but suffer from lack of generalizability to human populations. We propose that large-scale interrogation of these data could reveal patterns of animal use that could narrow the translational divide. We describe a text-mining approach that extracts translationally useful data from PubMed abstracts. These comprise six modules: species, model, genes, interventions/disease modifiers, overall outcome and functional outcome measures. Existing National Library of Medicine natural language processing tools (SemRep, GNormPlus and the Chemical annotator) underpin the program and are further augmented by various rules, term lists, and machine learning models. Evaluation of the program using a 98-abstract test set achieved F1 scores ranging from 0.75–0.95 across all modules, and exceeded F1 scores obtained from comparable baseline programs. Next, the program was applied to a larger 14,481 abstract data set (2008–2017). Expected and previously identified patterns of species and model use for the field were obtained. As previously noted, the majority of studies reported promising outcomes. Longitudinal patterns of intervention type or gene mentions were demonstrated, and patterns of animal model use characteristic of the Parkinson’s disease field were confirmed. The primary function of the program is to overcome low external validity of animal model systems by aggregating evidence across a diversity of models that capture different aspects of a multifaceted cellular process. Some aspects of the tool are generalizable, whereas others are field-specific. In the initial version presented here, we demonstrate proof of concept within a single disease area, Parkinson’s disease. However, the program can be expanded in modular fashion to support a wider range of neurodegenerative diseases.

Klíčová slova:

Animal models – Animal models of disease – Animal studies – Genetics of disease – Levodopa – Macaque – Neurodegenerative diseases – Parkinson disease


Zdroje

1. Scannell JW, Blanckley A, Boldon H, Warrington B. Diagnosing the decline in pharmaceutical R&D efficiency. Nature reviews Drug discovery. 2012;11(3):191–200. Epub 2012/03/02. doi: 10.1038/nrd3681 22378269.

2. van der Worp HB, Howells DW, Sena ES, Porritt MJ, Rewell S, O'Collins V, et al. Can animal models of disease reliably inform human studies? PLoS medicine. 2010;7(3):e1000245. Epub 2010/04/03. doi: 10.1371/journal.pmed.1000245 20361020.

3. Mullane K, Williams M. Preclinical Models of Alzheimer's Disease: Relevance and Translational Validity. Curr Protoc Pharmacol. 2019;84(1):e57. Epub 2019/02/26. doi: 10.1002/cpph.57 30802363.

4. Begley CG, Ioannidis JP. Reproducibility in science: improving the standard for basic and preclinical research. Circulation research. 2015;116(1):116–26. Epub 2015/01/02. doi: 10.1161/CIRCRESAHA.114.303819 25552691.

5. Ioannidis JP. Acknowledging and Overcoming Nonreproducibility in Basic and Preclinical Research. JAMA: the journal of the American Medical Association. 2017;317(10):1019–20. Epub 2017/02/14. doi: 10.1001/jama.2017.0549 28192565.

6. Landis SC, Amara SG, Asadullah K, Austin CP, Blumenstein R, Bradley EW, et al. A call for transparent reporting to optimize the predictive value of preclinical research. Nature. 2012;490(7419):187–91. Epub 2012/10/13. doi: 10.1038/nature11556 23060188.

7. Harrison RK. Phase II and phase III failures: 2013–2015. Nature reviews Drug discovery. 2016;15(12):817–8. Epub 2016/11/05. doi: 10.1038/nrd.2016.184 27811931.

8. Fogel DB. Factors associated with clinical trials that fail and opportunities for improving the likelihood of success: A review. Contemp Clin Trials Commun. 2018;11:156–64. Epub 2018/08/17. doi: 10.1016/j.conctc.2018.08.001 30112460.

9. Arrowsmith J, Miller P. Trial watch: phase II and phase III attrition rates 2011–2012. Nature reviews Drug discovery. 2013;12(8):569. Epub 2013/08/02. doi: 10.1038/nrd4090 23903212.

10. Sena ES, van der Worp HB, Bath PM, Howells DW, Macleod MR. Publication bias in reports of animal stroke studies leads to major overstatement of efficacy. PLoS biology. 2010;8(3):e1000344. Epub 2010/04/03. doi: 10.1371/journal.pbio.1000344 20361022.

11. Tsilidis KK, Panagiotou OA, Sena ES, Aretouli E, Evangelou E, Howells DW, et al. Evaluation of excess significance bias in animal studies of neurological diseases. PLoS biology. 2013;11(7):e1001609. Epub 2013/07/23. doi: 10.1371/journal.pbio.1001609 23874156.

12. ter Riet G, Korevaar DA, Leenaars M, Sterk PJ, Van Noorden CJ, Bouter LM, et al. Publication bias in laboratory animal research: a survey on magnitude, drivers, consequences and potential solutions. PloS one. 2012;7(9):e43404. Epub 2012/09/08. doi: 10.1371/journal.pone.0043404 22957028.

13. Boutron I, Ravaud P. Misrepresentation and distortion of research in biomedical literature. Proceedings of the National Academy of Sciences of the United States of America. 2018;115(11):2613–9. Epub 2018/03/14. doi: 10.1073/pnas.1710755115 29531025.

14. Pound P, Bracken MB. Is animal research sufficiently evidence based to be a cornerstone of biomedical research? BMJ (Clinical research ed). 2014;348:g3387. Epub 2014/06/01. doi: 10.1136/bmj.g3387 24879816.

15. Ransohoff RM. All (animal) models (of neurodegeneration) are wrong. Are they also useful? The Journal of experimental medicine. 2018;215(12):2955–8. Epub 2018/11/22. doi: 10.1084/jem.20182042 30459159.

16. Pistollato F, Ohayon EL, Lam A, Langley GR, Novak TJ, Pamies D, et al. Alzheimer disease research in the 21st century: past and current failures, new perspectives and funding priorities. Oncotarget. 2016;7(26):38999–9016. Epub 2016/10/26. doi: 10.18632/oncotarget.9175 27229915.

17. Perrin S. Preclinical research: Make mouse studies work. Nature. 2014;507(7493):423–5. Epub 2014/03/29. doi: 10.1038/507423a 24678540.

18. Cristea IA, Ioannidis JPA. P values in display items are ubiquitous and almost invariably significant: A survey of top science journals. PloS one. 2018;13(5):e0197440. Epub 2018/05/16. doi: 10.1371/journal.pone.0197440 29763472.

19. Ware JJ, Munafo MR. Significance chasing in research practice: causes, consequences and possible solutions. Addiction. 2015;110(1):4–8. Epub 2014/07/22. doi: 10.1111/add.12673 25040652.

20. Buckholtz NS, Ryan LM, Petanceska S, Refolo LM. NIA commentary: translational issues in Alzheimer's disease drug development. Neuropsychopharmacology: official publication of the American College of Neuropsychopharmacology. 2012;37(1):284–6. Epub 2011/12/14. doi: 10.1038/npp.2011.116 22157857.

21. Curran T. Reproducibility of academic preclinical translational research: lessons from the development of Hedgehog pathway inhibitors to treat cancer. Open Biol. 2018;8(8). Epub 2018/08/03. doi: 10.1098/rsob.180098 30068568.

22. Zeiss CJ, Allore HG, Beck AP. Established patterns of animal study design undermine translation of disease-modifying therapies for Parkinson's disease. PloS one. 2017;12(2):e0171790. Epub 2017/02/10. doi: 10.1371/journal.pone.0171790 28182759.

23. Scott S, Kranz JE, Cole J, Lincecum JM, Thompson K, Kelly N, et al. Design, power, and interpretation of studies in the standard murine model of ALS. Amyotrophic lateral sclerosis: official publication of the World Federation of Neurology Research Group on Motor Neuron Diseases. 2008;9(1):4–15. Epub 2008/02/15. doi: 10.1080/17482960701856300 18273714.

24. Kilkenny C, Browne W, Cuthill IC, Emerson M, Altman DG. Animal research: reporting in vivo experiments: the ARRIVE guidelines. British journal of pharmacology. 2010;160(7):1577–9. Epub 2010/07/24. doi: 10.1111/j.1476-5381.2010.00872.x 20649561.

25. Santori G. Research papers: Journals should drive data reproducibility. Nature. 2016;535(7612):355. Epub 2016/07/23. doi: 10.1038/535355b 27443731.

26. Collins FS, Tabak LA. Policy: NIH plans to enhance reproducibility. Nature. 2014;505(7485):612–3. Epub 2014/02/01. doi: 10.1038/505612a 24482835.

27. Leung V, Rousseau-Blass F, Beauchamp G, Pang DSJ. ARRIVE has not ARRIVEd: Support for the ARRIVE (Animal Research: Reporting of in vivo Experiments) guidelines does not improve the reporting quality of papers in animal welfare, analgesia or anesthesia. PloS one. 2018;13(5):e0197882. Epub 2018/05/26. doi: 10.1371/journal.pone.0197882 29795636.

28. Mlinaric A, Horvat M, Supak Smolcic V. Dealing with the positive publication bias: Why you should really publish your negative results. Biochem Med (Zagreb). 2017;27(3):030201. Epub 2017/11/29. doi: 10.11613/bm.2017.030201 29180912.

29. Porter RJ, Boden JM, Miskowiak K, Malhi GS. Failure to publish negative results: A systematic bias in psychiatric literature. Aust N Z J Psychiatry. 2017;51(3):212–4. Epub 2017/02/22. doi: 10.1177/0004867416683816 28218051.

30. Heng HH. The conflict between complex systems and reductionism. JAMA: the journal of the American Medical Association. 2008;300(13):1580–1. Epub 2008/10/02. doi: 10.1001/jama.300.13.1580 18827215.

31. Zeiss CJ. From Reproducibility to Translation in Neurodegenerative Disease. ILAR journal / National Research Council, Institute of Laboratory Animal Resources. 2017;58(1):106–14. Epub 2017/04/27. doi: 10.1093/ilar/ilx006 28444192.

32. Pound P, Ritskes-Hoitinga M. Is it possible to overcome issues of external validity in preclinical animal research? Why most animal models are bound to fail. J Transl Med. 2018;16(1):304. Epub 2018/11/09. doi: 10.1186/s12967-018-1678-1 30404629.

33. Hodge RD, Bakken TE, Miller JA, Smith KA, Barkan ER, Graybuck LT, et al. Conserved cell types with divergent features in human versus mouse cortex. Nature. 2019;573(7772):61–8. Epub 2019/08/23. doi: 10.1038/s41586-019-1506-7 31435019.

34. Bolker J. Model organisms: There's more to life than rats and flies. Nature. 2012;491(7422):31–3. Epub 2012/11/07. doi: 10.1038/491031a 23128209.

35. Khare R, Wei CH, Mao Y, Leaman R, Lu Z. tmBioC: improving interoperability of text-mining tools with BioC. Database: the journal of biological databases and curation. 2014;2014. Epub 2014/07/27. doi: 10.1093/database/bau073 25062914.

36. Soto AJ, Przybyla P, Ananiadou S. Thalia: semantic search engine for biomedical abstracts. Bioinformatics (Oxford, England). 2019;35(10):1799–801. Epub 2018/10/18. doi: 10.1093/bioinformatics/bty871 30329013.

37. Wei CH, Kao HY, Lu Z. PubTator: a web-based text mining tool for assisting biocuration. Nucleic acids research. 2013;41(Web Server issue):W518–22. Epub 2013/05/25. doi: 10.1093/nar/gkt441 23703206.

38. Gopalakrishnan V, Jha K, Jin W, Zhang A. A survey on literature based discovery approaches in biomedical domain. Journal of biomedical informatics. 2019;93:103141. Epub 2019/03/13. doi: 10.1016/j.jbi.2019.103141 30857950.

39. Brembs B. Prestigious Science Journals Struggle to Reach Even Average Reliability. Front Hum Neurosci. 2018;12:37. Epub 2018/03/09. doi: 10.3389/fnhum.2018.00037 29515380.

40. Bezard E, Yue Z, Kirik D, Spillantini MG. Animal models of Parkinson's disease: limits and relevance to neuroprotection studies. Movement disorders: official journal of the Movement Disorder Society. 2013;28(1):61–70. Epub 2012/07/04. doi: 10.1002/mds.25108 22753348.

41. Gubellini P, Kachidian P. Animal models of Parkinson's disease: An updated overview. Revue neurologique. 2015;171(11):750–61. Epub 2015/09/08. doi: 10.1016/j.neurol.2015.07.011 26343921.

42. Ioannidis JP. Extrapolating from animals to humans. Science translational medicine. 2012;4(151):151ps15. Epub 2012/09/14. doi: 10.1126/scitranslmed.3004631 22972841.

43. Snoy PJ. Establishing efficacy of human products using animals: the US food and drug administration's "animal rule". Veterinary pathology. 2010;47(5):774–8. Epub 2010/06/17. doi: 10.1177/0300985810372506 20551476.

44. Bergman H, Wichmann T, DeLong MR. Reversal of experimental parkinsonism by lesions of the subthalamic nucleus. Science (New York, NY). 1990;249(4975):1436–8. Epub 1990/09/21. doi: 10.1126/science.2402638 2402638.

45. DeLong MR. Primate models of movement disorders of basal ganglia origin. Trends in neurosciences. 1990;13(7):281–5. Epub 1990/07/01. doi: 10.1016/0166-2236(90)90110-v 1695404.

46. Olanow CW, Kieburtz K, Schapira AH. Why have we failed to achieve neuroprotection in Parkinson's disease? Annals of neurology. 2008;64 Suppl 2:S101–10. Epub 2009/01/08. doi: 10.1002/ana.21461 19127580.

47. Lang AE, Espay AJ. Disease Modification in Parkinson's Disease: Current Approaches, Challenges, and Future Considerations. Movement disorders: official journal of the Movement Disorder Society. 2018;33(5):660–77. Epub 2018/04/13. doi: 10.1002/mds.27360 29644751.

48. Bove J, Perier C. Neurotoxin-based models of Parkinson's disease. Neuroscience. 2012;211:51–76. Epub 2011/11/24. doi: 10.1016/j.neuroscience.2011.10.057 22108613.

49. Cenci MA, Crossman AR. Animal models of l-dopa-induced dyskinesia in Parkinson's disease. Movement disorders: official journal of the Movement Disorder Society. 2018;33(6):889–99. Epub 2018/03/01. doi: 10.1002/mds.27337 29488257.

50. Zeiss CJ. Improving the predictive value of interventional animal models data. Drug discovery today. 2015;20(4):475–82. Epub 2014/12/03. doi: 10.1016/j.drudis.2014.10.015 25448761.

51. Stenetorp P PS, Topic G, Ohta T, Ananiadou S, Tsujii J. BRAT: a web-based tool for NLP-assisted text annotation. Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics. 2012:102–7

52. Rindflesch TC, Fiszman M. The interaction of domain knowledge and linguistic structure in natural language processing: interpreting hypernymic propositions in biomedical text. Journal of biomedical informatics. 2003;36(6):462–77. Epub 2004/02/05. doi: 10.1016/j.jbi.2003.11.003 14759819.

53. Wei CH, Kao HY, Lu Z. GNormPlus: An Integrative Approach for Tagging Genes, Gene Families, and Protein Domains. BioMed research international. 2015;2015:918710. Epub 2015/09/18. doi: 10.1155/2015/918710 26380306.

54. Zeiss CJ. Improving the predictive value of interventional animal models data. Drug discovery today. 2015;20(4):475–82. Epub 2014/12/03. doi: 10.1016/j.drudis.2014.10.015 25448761.

55. Szeto JY, Lewis SJ. Current Treatment Options for Alzheimer's Disease and Parkinson's Disease Dementia. Curr Neuropharmacol. 2016;14(4):326–38. Epub 2015/12/09. doi: 10.2174/1570159X14666151208112754 26644155.

56. Niu Y, Zhu, X., Li, J., and Hirst, G. Analysis of polarity information in medical text. AMIA annual symposium proceedings. 2005;2005:570.

57. Porter M. An algorithm for suffix stripping. Program. 1980;14(3):130–7.

58. McCray AT, Burgun A, Bodenreider O. Aggregating UMLS semantic types for reducing conceptual complexity. Stud Health Technol Inform. 2001;84(Pt 1):216–20. Epub 2001/10/18. 11604736.

59. Fan RE C K, Hsieh CJ, Wang XR and Lin CJ. LIBLINEAR: A library for large linear classification. Journal of Machine Learning Research. 2008;9:1871–4

60. Johnston TM, Fox SH. Symptomatic Models of Parkinson's Disease and L-DOPA-Induced Dyskinesia in Non-human Primates. Curr Top Behav Neurosci. 2015;22:221–35. Epub 2014/08/28. doi: 10.1007/7854_2014_352 25158623.

61. Cooper JF, Van Raamsdonk JM. Modeling Parkinson's Disease in C. elegans. Journal of Parkinson's disease. 2018;8(1):17–32. Epub 2018/02/27. doi: 10.3233/JPD-171258 29480229.

62. West RJ, Furmston R, Williams CA, Elliott CJ. Neurophysiology of Drosophila models of Parkinson's disease. Parkinson's disease. 2015;2015:381281. Epub 2015/05/12. doi: 10.1155/2015/381281 25960916.

63. Matsui H, Takahashi R. Parkinson's disease pathogenesis from the viewpoint of small fish models. J Neural Transm (Vienna). 2018;125(1):25–33. Epub 2017/08/05. doi: 10.1007/s00702-017-1772-1 28770388.

64. Konnova EA, Swanberg M. Animal Models of Parkinson's Disease. In: Stoker TB, Greenland JC, editors. Parkinson's Disease: Pathogenesis and Clinical Aspects. Brisbane AU: The Authors.; 2018.

65. Grandi LC, Di Giovanni G, Galati S. Animal models of early-stage Parkinson's disease and acute dopamine deficiency to study compensatory neurodegenerative mechanisms. Journal of neuroscience methods. 2018;308:205–18. Epub 2018/08/15. doi: 10.1016/j.jneumeth.2018.08.012 30107207.

66. Dutta G, Zhang P, Liu B. The lipopolysaccharide Parkinson's disease animal model: mechanistic studies and drug discovery. Fundamental & clinical pharmacology. 2008;22(5):453–64. Epub 2008/08/20. doi: 10.1111/j.1472-8206.2008.00616.x 18710400.

67. Visanji NP, Brotchie JM, Kalia LV, Koprich JB, Tandon A, Watts JC, et al. alpha-Synuclein-Based Animal Models of Parkinson's Disease: Challenges and Opportunities in a New Era. Trends in neurosciences. 2016;39(11):750–62. Epub 2016/10/26. doi: 10.1016/j.tins.2016.09.003 27776749.

68. Tenreiro S, Franssens V, Winderickx J, Outeiro TF. Yeast models of Parkinson's disease-associated molecular pathologies. Current opinion in genetics & development. 2017;44:74–83. Epub 2017/02/25. doi: 10.1016/j.gde.2017.01.013 28232272.

69. Imbriani P, Sciamanna G, Santoro M, Schirinzi T, Pisani A. Promising rodent models in Parkinson's disease. Parkinsonism & related disorders. 2018;46 Suppl 1:S10–S4. Epub 2017/08/02. doi: 10.1016/j.parkreldis.2017.07.027 28760592.

70. Morissette M, Di Paolo T. Non-human primate models of PD to test novel therapies. J Neural Transm (Vienna). 2018;125(3):291–324. Epub 2017/04/10. doi: 10.1007/s00702-017-1722-y 28391443.

71. Breger LS, Fuzzati Armentero MT. Genetically engineered animal models of Parkinson's disease: From worm to rodent. The European journal of neuroscience. 2019;49(4):533–60. Epub 2018/12/16. doi: 10.1111/ejn.14300 30552719.

72. Connolly BS, Lang AE. Pharmacological treatment of Parkinson disease: a review. JAMA: the journal of the American Medical Association. 2014;311(16):1670–83. Epub 2014/04/24. doi: 10.1001/jama.2014.3654 24756517.

73. Volkmann J. Deep brain stimulation for the treatment of Parkinson's disease. J Clin Neurophysiol. 2004;21(1):6–17. Epub 2004/04/21. doi: 10.1097/00004691-200401000-00003 15097290.

74. Scheperjans F. Gut microbiota, 1013 new pieces in the Parkinson's disease puzzle. Curr Opin Neurol. 2016;29(6):773–80. Epub 2016/11/03. doi: 10.1097/WCO.0000000000000389 27653288.

75. Lafuente JV, Requejo C, Carrasco A, Bengoetxea H. Nanoformulation: A Useful Therapeutic Strategy for Improving Neuroprotection and the Neurorestorative Potential in Experimental Models of Parkinson's Disease. International review of neurobiology. 2017;137:99–122. Epub 2017/11/15. doi: 10.1016/bs.irn.2017.09.003 29132545.

76. Nussbaum RL. The Identification of Alpha-Synuclein as the First Parkinson Disease Gene. Journal of Parkinson's disease. 2017;7(s1):S43–S9. Epub 2017/03/12. doi: 10.3233/JPD-179003 28282812.

77. Savitt D, Jankovic J. Targeting alpha-Synuclein in Parkinson's Disease: Progress Towards the Development of Disease-Modifying Therapeutics. Drugs. 2019. Epub 2019/04/15. doi: 10.1007/s40265-019-01104-1 30982161.

78. Picconi B, Hernandez LF, Obeso JA, Calabresi P. Motor complications in Parkinson's disease: Striatal molecular and electrophysiological mechanisms of dyskinesias. Movement disorders: official journal of the Movement Disorder Society. 2018;33(6):867–76. Epub 2017/12/09. doi: 10.1002/mds.27261 29219207.

79. Iderberg H, Francardo V, Pioli EY. Animal models of L-DOPA-induced dyskinesia: an update on the current options. Neuroscience. 2012;211:13–27. Epub 2012/04/03. doi: 10.1016/j.neuroscience.2012.03.023 22465440.

80. Fabregat A, Jupe S, Matthews L, Sidiropoulos K, Gillespie M, Garapati P, et al. The Reactome Pathway Knowledgebase. Nucleic acids research. 2018;46(D1):D649–D55. Epub 2017/11/18. doi: 10.1093/nar/gkx1132 29145629.

81. Rascol O, Perez-Lloret S, Ferreira JJ. New treatments for levodopa-induced motor complications. Movement disorders: official journal of the Movement Disorder Society. 2015;30(11):1451–60. Epub 2015/08/22. doi: 10.1002/mds.26362 26293004.

82. Burre J, Sharma M, Sudhof TC. Cell Biology and Pathophysiology of alpha-Synuclein. Cold Spring Harb Perspect Med. 2018;8(3). Epub 2017/01/22. doi: 10.1101/cshperspect.a024091 28108534.

83. Abbruzzese G, Marchese R, Avanzino L, Pelosin E. Rehabilitation for Parkinson's disease: Current outlook and future challenges. Parkinsonism & related disorders. 2016;22 Suppl 1:S60–4. Epub 2015/09/12. doi: 10.1016/j.parkreldis.2015.09.005 26360239.

84. Tuffery P. Accessing external innovation in drug discovery and development. Expert opinion on drug discovery. 2015;10(6):579–89. Epub 2015/04/26. doi: 10.1517/17460441.2015.1040759 25910932.

85. Novack GD. Translating Drugs From Animals to Humans: Do We Need to Prove Efficacy? Translational vision science & technology. 2013;2(6):1. Epub 2013/10/01. doi: 10.1167/tvst.2.6.1 24078898.

86. Zwierzyna M, Overington JP. Classification and analysis of a large collection of in vivo bioassay descriptions. PLoS computational biology. 2017;13(7):e1005641. Epub 2017/07/06. doi: 10.1371/journal.pcbi.1005641 28678787.

87. Kilkenny C, Parsons N, Kadyszewski E, Festing MF, Cuthill IC, Fry D, et al. Survey of the quality of experimental design, statistical analysis and reporting of research using animals. PloS one. 2009;4(11):e7824. Epub 2009/12/04. doi: 10.1371/journal.pone.0007824 19956596.


Článek vyšel v časopise

PLOS One


2019 Číslo 12