Skip to main content
SpringerLink
Log in

From Bedside to Bench: Methods in Precision Medicine

  • Chapter
  • First Online:
Precision Medicine in Stroke

  • 478 Accesses

Abstract

Precision medicine is a novel approach to clinical practice aimed at integrating traditional clinical data with biomarkers, which are able to further characterize patients, thus maximizing health outcomes in particular cases. Biomarker research is based on robust and rigorous laboratorial methodologies aimed at studying specific tissues, cells, or molecules. In this chapter, we provide a succinct overview of the various techniques available in histology and cell and molecular biology with a ubiquitous use in clinical medicine. We also give a few examples of known biomarkers in stroke and the techniques used for their discovery and identification/quantification.

Illustrations by Helena Pinheiro.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 159.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. National Research Council. Toward precision medicine: building a knowledge network for biomedical research and a new taxonomy of disease. Washington, DC: The National Academies Press; 2011.

    Google Scholar 

  2. Haendel MA, Chute CG, Robinson PN. Classification, ontology, and precision medicine. N Engl J Med. 2018;379:1452–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Biomarkers Definitions Working Group. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001;69:89–95.

    Article  Google Scholar 

  4. Califf RM. Biomarker definitions and their applications. Exp Biol Med (Maywood). 2018;243:213–21.

    Article  CAS  Google Scholar 

  5. Funkhouser WK. Clinical practice: anatomic pathology. In: Reisner HM, editor. Pathology: a modern case study. 2nd ed. New York: McGraw-Hill Education; 2020.

    Google Scholar 

  6. Mescher AL. Histology & its methods of study. In: Junqueira’s basic histology: text and atlas. 15th ed. McGraw-Hill Education: New York; 2018.

    Google Scholar 

  7. Gulley ML. Clinical practice: molecular pathology. In: Reisner HM, editor. Pathology: a modern case study. 2nd ed. New York: McGraw-Hill Education; 2020.

    Google Scholar 

  8. Mahanty S, Prigent A, Garraud O. Immunogenicity of infectious pathogens and vaccine antigens. BMC Immunol. 2015;16:31.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  9. Sanchez-Trincado JL, Gomez-Perosanz M, Reche PA. Fundamentals and methods for T- and B-cell epitope prediction. J Immunol Res. 2017;2017:2680160.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  10. Levinson W, Chin-Hong P, Joyce EA, Nussbaum J, Schwartz B. Overview of immunity. In: Review of medical microbiology & immunology: a guide to clinical infectious diseases. 16th ed. New York: McGraw-Hill; 2020.

    Google Scholar 

  11. Cossarizza A, Chang H-D, Radbruch A, Acs A, Adam D, Adam-Klages S, et al. Guidelines for the use of flow cytometry and cell sorting in immunological studies (second edition). Eur J Immunol. 2019;49:1457–973.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Spitzer MH, Nolan GP. Mass cytometry: single cells, many features. Cell. 2016;165:780–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Gadalla R, Noamani B, MacLeod BL, Dickson RJ, Guo M, Xu W, et al. Validation of CyTOF against flow cytometry for immunological studies and monitoring of human cancer clinical trials. Front Oncol. 2019;9:415.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Sahl SJ, Hell SW, Jakobs S. Fluorescence nanoscopy in cell biology. Nat Rev Mol Cell Biol. 2017;18:685–701.

    Article  CAS  PubMed  Google Scholar 

  15. Balu M, Mazhar A, Hayakawa CK, Mittal R, Krasieva TB, König K, et al. In vivo multiphoton NADH fluorescence reveals depth-dependent keratinocyte metabolism in human skin. Biophys J. 2013;104:258–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zuba-Surma EK, Ratajczak MZ. Analytical capabilities of the ImageStream cytometer. Methods Cell Biol. 2011;102:207–30.

    Article  PubMed  Google Scholar 

  17. Haynes BF, Soderberg KA, Fauci AS. Introduction to the immune system. In: Jameson JL, Fauci AS, Kasper DL, Hauser SL, Longo DL, Loscalzo J, editors. Harrison’s principles of internal Medicine. 20th ed. New York: McGraw-Hill Education; 2018.

    Google Scholar 

  18. Angkananard T, Anothaisintawee T, McEvoy M, Attia J, Thakkinstian A. Neutrophil lymphocyte ratio and cardiovascular disease risk: a systematic review and meta-analysis. Biomed Res Int. 2018;2018:2703518.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Zhang R, Wu X, Hu W, Zhao L, Zhao S, Zhang J, et al. Neutrophil-to-lymphocyte ratio predicts hemorrhagic transformation in ischemic stroke: a meta-analysis. Brain Behav. 2019;9:e01382.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Chabot-Richards DS, George TI. White blood cell counts: reference methodology. Clin Lab Med. 2015;35:11–24.

    Article  PubMed  Google Scholar 

  21. Kim J, Song T-J, Park JH, Lee HS, Nam CM, Nam HS, et al. Different prognostic value of white blood cell subtypes in patients with acute cerebral infarction. Atherosclerosis. 2012;222:464–7.

    Article  CAS  PubMed  Google Scholar 

  22. Saliba W, Barnett-Griness O, Elias M, Rennert G. Neutrophil to lymphocyte ratio and risk of a first episode of stroke in patients with atrial fibrillation: a cohort study. J Thromb Haemost. 2015;13:1971–9.

    Article  CAS  PubMed  Google Scholar 

  23. Guo Z, Yu S, Xiao L, Chen X, Ye R, Zheng P, et al. Dynamic change of neutrophil to lymphocyte ratio and hemorrhagic transformation after thrombolysis in stroke. J Neuroinflammation. 2016;13:199.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  24. Suh B, Shin DW, Kwon H-M, Yun JM, Yang H-K, Ahn E, et al. Elevated neutrophil to lymphocyte ratio and ischemic stroke risk in generally healthy adults. PLoS One. 2017;12:e0183706.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Feijó Delgado F, Cermak N, Hecht VC, Son S, Li Y, Knudsen SM, et al. Intracellular water exchange for measuring the dry mass, water mass and changes in chemical composition of living cells. PLoS One. 2013;8:e67590.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Alberts B, Johnson A, Lewis J, Morgan D, Raff M, Roberts K, et al. Molecular Biology of the Cell. 6th ed. Garland Science, Taylor and Francis Group: New York; 2015.

    Google Scholar 

  27. Kennelly PJ, Rodwell VW. Proteins: determination of primary structure. In: Rodwell VW, Bender DA, Botham KM, Kennelly PJ, Weil PA, editors. Harper’s Illustrated Biochemistry. 31st ed. New York: McGraw-Hill Education; 2018.

    Google Scholar 

  28. Kennelly PJ, Rodwell VW. Proteins: higher orders of structure. In: Rodwell VW, Bender DA, Botham KM, Kennelly PJ, Weil PA, editors. Harper’s Illustrated Biochemistry. 31st ed. New York: McGraw-Hill Education; 2018.

    Google Scholar 

  29. Weil PA. Protein synthesis & the genetic code. In: Rodwell VW, Bender DA, Botham KM, Kennelly PJ, Weil PA, editors. Harper’s Illustrated Biochemistry. 31st ed. New York: McGraw-Hill Education; 2018.

    Google Scholar 

  30. Brakke MK. Density gradient centrifugation: a new separation technique. J Am Chem Soc. 1951;73:1847–8.

    Article  CAS  Google Scholar 

  31. Pertoft H. Fractionation of cells and subcellular particles with Percoll. J Biochem Biophys Methods. 2000;44:1–30.

    Article  CAS  PubMed  Google Scholar 

  32. Matulis D. Selective precipitation of proteins. Curr Protoc Protein Sci. 2016;83:4.5.1–4.5.37.

    Article  Google Scholar 

  33. Bonifacino JS, Dell’Angelica EC, Springer TA. Immunoprecipitation. Curr Protoc Protein Sci. 2001;Chapter 9:Unit 9.8.

    Google Scholar 

  34. Büyükköroğlu G, Dora DD, Özdemir F, Hızel C. Chapter 15—Techniques for protein analysis. In: Barh D, Azevedo V, editors. Omics technologies and bio-engineering. London: Academic Press; 2018. p. 317–51.

    Chapter  Google Scholar 

  35. Hage DS. Analysis of biological interactions by affinity chromatography: clinical and pharmaceutical applications. Clin Chem. 2017;63:1083–93.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  36. Zhang C, Rodriguez E, Bi C, Zheng X, Suresh D, Suh K, et al. High performance affinity chromatography and related separation methods for the analysis of biological and pharmaceutical agents. Analyst. 2018;143:374–91.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Choudhary C, Mann M. Decoding signalling networks by mass spectrometry-based proteomics. Nat Rev Mol Cell Biol. 2010;11:427–39.

    Article  CAS  PubMed  Google Scholar 

  38. Züllig T, Trötzmüller M, Köfeler HC. Lipidomics from sample preparation to data analysis: a primer. Anal Bioanal Chem. 2020;412:2191–209.

    Article  PubMed  CAS  Google Scholar 

  39. Bujak R, Struck-Lewicka W, Markuszewski MJ, Kaliszan R. Metabolomics for laboratory diagnostics. J Pharm Biomed Anal. 2015;113:108–20.

    Article  CAS  PubMed  Google Scholar 

  40. Alsaleh M, Barbera TA, Andrews RH, Sithithaworn P, Khuntikeo N, Loilome W, et al. Mass spectrometry: a guide for the clinician. J Clin Exp Hepatol. 2019;9:597–606.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Pitt JJ. Principles and applications of liquid chromatography-mass spectrometry in clinical biochemistry. Clin Biochem Rev. 2009;30:19–34.

    PubMed  PubMed Central  Google Scholar 

  42. Levinson W, Chin-Hong P, Joyce EA, Nussbaum J, Schwartz B. Antigen–antibody reactions in the laboratory. In: Review of medical microbiology & immunology: a guide to clinical infectious diseases. 16th ed. New York: McGraw Hill; 2020.

    Google Scholar 

  43. Aydin S. A short history, principles, and types of ELISA, and our laboratory experience with peptide/protein analyses using ELISA. Peptides. 2015;72:4–15.

    Article  CAS  PubMed  Google Scholar 

  44. Mahmood T, Yang P-C. Western blot: technique, theory, and trouble shooting. N Am J Med Sci. 2012;4:429–34.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Spurrier B, Ramalingam S, Nishizuka S. Reverse-phase protein lysate microarrays for cell signaling analysis. Nat Protoc. 2008;3:1796–808.

    Article  PubMed  Google Scholar 

  46. Ciaccio MF, Wagner JP, Chuu C-P, Lauffenburger DA, Jones RB. Systems analysis of EGF receptor signaling dynamics with microwestern arrays. Nat Methods. 2010;7:148–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Maisel AS, Duran JM, Wettersten N. Natriuretic peptides in heart failure: atrial and B-type natriuretic peptides. Heart Fail Clin. 2018;14:13–25.

    Article  PubMed  Google Scholar 

  48. Rodríguez-Yáñez M, Sobrino T, Blanco M, de la Ossa NP, Brea D, Rodríguez-González R, et al. High serum levels of pro-brain natriuretic peptide (pro BNP) identify cardioembolic origin in undetermined stroke. Dis Markers. 2009;26:189–95.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Rost NS, Biffi A, Cloonan L, Chorba J, Kelly P, Greer D, et al. Brain natriuretic peptide predicts functional outcome in ischemic stroke. Stroke. 2012;43:441–5.

    Article  CAS  PubMed  Google Scholar 

  50. García-Berrocoso T, Giralt D, Bustamante A, Etgen T, Jensen JK, Sharma JC, et al. B-type natriuretic peptides and mortality after stroke: a systematic review and meta-analysis. Neurology. 2013;81:1976–85.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Nigro N, Wildi K, Mueller C, Schuetz P, Mueller B, Fluri F, et al. BNP but not s-cTnln is associated with cardioembolic aetiology and predicts short and long term prognosis after cerebrovascular events. PLoS One. 2014;9:e102704.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Bai J, Sun H, Xie L, Zhu Y, Feng Y. Detection of cardioembolic stroke with B-type natriuretic peptide or N-terminal pro-BNP: a comparative diagnostic meta-analysis. Int J Neurosci. 2018;128:1100–8.

    Article  CAS  PubMed  Google Scholar 

  53. Sudoh T, Kangawa K, Minamino N, Matsuo H. A new natriuretic peptide in porcine brain. Nature. 1988;332:78–81.

    Article  CAS  PubMed  Google Scholar 

  54. Jameson JL, Kopp P. Principles of human genetics. In: Jameson JL, Fauci AS, Kasper DL, Hauser SL, Longo DL, Loscalzo J, editors. Harrison’s principles of internal medicine. 20th ed. New York: McGraw-Hill Education; 2018.

    Google Scholar 

  55. Peirson SN, Butler JN. RNA extraction from mammalian tissues. Methods Mol Biol. 2007;362:315–27.

    Article  CAS  PubMed  Google Scholar 

  56. Talebi R, Seighalani R, Qanbari S. A handmade DNA extraction kit using laundry powder; insights on simplicity, cost-efficiency, rapidity, safety and the quality of purified DNA. Anim Biotechnol. 2019:1–7. https://doi.org/10.1080/10495398.2019.1684933.

  57. Weil PA. Molecular genetics, recombinant DNA, & genomic technology. In: Rodwell VW, Bender DA, Botham KM, Kennelly PJ, Weil PA, editors. Harper’s Illustrated Biochemistry. 31st ed. New York: McGraw-Hill Education; 2018.

    Google Scholar 

  58. He SL, Green R. Northern blotting. Method Enzymol. 2013;530:75–87.

    Article  CAS  Google Scholar 

  59. Tajadini M, Panjehpour M, Javanmard SH. Comparison of SYBR Green and TaqMan methods in quantitative real-time polymerase chain reaction analysis of four adenosine receptor subtypes. Adv Biomed Res. 2014;3:85.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  60. Bumgarner R. Overview of DNA microarrays: types, applications, and their future. Curr Protoc Mol Biol. 2013;Chapter 22:Unit 22.1.

    Google Scholar 

  61. Eichler EE. Genetic variation, comparative genomics, and the diagnosis of disease. N Engl J Med. 2019;381:64–74.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC, Baldwin J, et al. Initial sequencing and analysis of the human genome. Nature. 2001;409:860–921.

    Article  CAS  PubMed  Google Scholar 

  63. International HapMap Consortium. The International HapMap Project. Nature. 2003;426:789–96.

    Article  CAS  Google Scholar 

  64. Ozaki K, Ohnishi Y, Iida A, Sekine A, Yamada R, Tsunoda T, et al. Functional SNPs in the lymphotoxin-alpha gene that are associated with susceptibility to myocardial infarction. Nat Genet. 2002;32:650–4.

    Article  CAS  PubMed  Google Scholar 

  65. Buniello A, MacArthur JAL, Cerezo M, Harris LW, Hayhurst J, Malangone C, et al. The NHGRI-EBI GWAS catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 2019;47:D1005–12.

    Article  CAS  PubMed  Google Scholar 

  66. International Human Genome Sequencing Consortium. Finishing the euchromatic sequence of the human genome. Nature. 2004;431:931–45.

    Article  CAS  Google Scholar 

  67. Schneider VA, Graves-Lindsay T, Howe K, Bouk N, Chen H-C, Kitts PA, et al. Evaluation of GRCh38 and de novo haploid genome assemblies demonstrates the enduring quality of the reference assembly. Genome Res. 2017;27:849–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Quail MA, Smith M, Coupland P, Otto TD, Harris SR, Connor TR, et al. A tale of three next generation sequencing platforms: comparison of Ion Torrent, Pacific Biosciences and Illumina MiSeq Sequencers. BMC Genomics. 2012;13:341.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Adams DR, Eng CM. Next-generation sequencing to diagnose suspected genetic disorders. N Engl J Med. 2018;379:1353–62.

    Article  CAS  PubMed  Google Scholar 

  70. Metzker ML. Sequencing technologies—the next generation. Nat Rev Genet. 2010;11:31–46.

    Article  CAS  PubMed  Google Scholar 

  71. Lu H, Giordano F, Ning Z. Oxford nanopore MinION sequencing and genome assembly. Genomics Proteomics Bioinformatics. 2016;14:265–79.

    Article  PubMed  PubMed Central  Google Scholar 

  72. Lowe R, Shirley N, Bleackley M, Dolan S, Shafee T. Transcriptomics technologies. PLoS Comput Biol. 2017;13:e1005457.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Barros-Silva D, Marques CJ, Henrique R, Jerónimo C. Profiling DNA methylation based on next-generation sequencing approaches: new insights and clinical applications. Genes (Basel). 2018;9:429.

    Article  CAS  Google Scholar 

  74. 1000 Genomes Project Consortium, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, et al. A global reference for human genetic variation. Nature. 2015;526:68–74.

    Article  CAS  Google Scholar 

  75. Wetterstrand KA. DNA sequencing costs: data from the NHGRI Genome Sequencing Program (GSP). https://www.genome.gov/sequencingcostsdata. Accessed 15 Aug 2020.

  76. Yates AD, Achuthan P, Akanni W, Allen J, Allen J, Alvarez-Jarreta J, et al. Ensembl 2020. Nucleic Acids Res. 2020;48:D682–8.

    CAS  PubMed  Google Scholar 

  77. Phan L, Jin Y, Zhang H, Qiang W, Shekhtman E, Shao D, et al. ALFA: allele frequency aggregator. Bethesda, MD: National Center for Biotechnology Information, US National Library of Medicine; 2020. https://www.ncbi.nlm.nih.gov/snp/docs/gsr/alfa/. Accessed 15 Aug 2020.

    Google Scholar 

  78. Traylor M, Farrall M, Holliday EG, Sudlow C, Hopewell JC, Cheng Y-C, et al. Genetic risk factors for ischaemic stroke and its subtypes (the METASTROKE collaboration): a meta-analysis of genome-wide association studies. Lancet Neurol. 2012;11:951–62.

    Article  PubMed  PubMed Central  Google Scholar 

  79. NINDS Stroke Genetics Network (SiGN), International Stroke Genetics Consortium (ISGC). Loci associated with ischaemic stroke and its subtypes (SiGN): a genome-wide association study. Lancet Neurol. 2016;15:174–84.

    Article  CAS  Google Scholar 

  80. Malik R, Traylor M, Pulit SL, Bevan S, Hopewell JC, Holliday EG, et al. Low-frequency and common genetic variation in ischemic stroke: the METASTROKE collaboration. Neurology. 2016;86:1217–26.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Malik R, Chauhan G, Traylor M, Sargurupremraj M, Okada Y, Mishra A, et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat Genet. 2018;50:524–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Markus HS, Mäkelä K-M, Bevan S, Raitoharju E, Oksala N, Bis JC, et al. Evidence HDAC9 genetic variant associated with ischemic stroke increases risk via promoting carotid atherosclerosis. Stroke. 2013;44:1220–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Shroff N, Ander BP, Zhan X, Stamova B, Liu D, Hull H, et al. HDAC9 polymorphism alters blood gene expression in patients with large vessel atherosclerotic stroke. Transl Stroke Res. 2019;10:19–25.

    Article  CAS  PubMed  Google Scholar 

  84. Prestel M, Prell-Schicker C, Webb T, Malik R, Lindner B, Ziesch N, et al. The atherosclerosis risk variant rs2107595 mediates allele-specific transcriptional regulation of HDAC9 via E2F3 and Rb1. Stroke. 2019;50:2651–60.

    Article  CAS  PubMed  Google Scholar 

  85. Wang W, Sun G, Zhang L, Shi L, Zeng Y. Circulating microRNAs as novel potential biomarkers for early diagnosis of acute stroke in humans. J Stroke Cerebrovasc Dis. 2014;23:2607–13.

    Article  PubMed  Google Scholar 

  86. Li P, Teng F, Gao F, Zhang M, Wu J, Zhang C. Identification of circulating microRNAs as potential biomarkers for detecting acute ischemic stroke. Cell Mol Neurobiol. 2015;35:433–47.

    Article  PubMed  CAS  Google Scholar 

  87. Xie Q, Zhang X, Peng S, Sun J, Chen X, Deng Y, et al. Identification of novel biomarkers in ischemic stroke: a genome-wide integrated analysis. BMC Med Genet. 2020;21:66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Li P, Shen M, Gao F, Wu J, Zhang J, Teng F, et al. An antagomir to microRNA-106b-5p ameliorates cerebral ischemia and reperfusion injury in rats via inhibiting apoptosis and oxidative stress. Mol Neurobiol. 2017;54:2901–21.

    Article  CAS  PubMed  Google Scholar 

  89. Steiner T, Juvela S, Unterberg A, Jung C, Forsting M, Rinkel G, et al. European Stroke Organization guidelines for the management of intracranial aneurysms and subarachnoid haemorrhage. Cerebrovasc Dis. 2013;35:93–112.

    Article  PubMed  Google Scholar 

  90. Steiner T, Al-Shahi Salman R, Beer R, Christensen H, Cordonnier C, Csiba L, et al. European Stroke Organisation (ESO) guidelines for the management of spontaneous intracerebral hemorrhage. Int J Stroke. 2014;9:840–55.

    Article  PubMed  Google Scholar 

  91. Kobayashi A, Czlonkowska A, Ford GA, Fonseca AC, Luijckx GJ, Korv J, et al. European Academy of Neurology and European Stroke Organization consensus statement and practical guidance for pre-hospital management of stroke. Eur J Neurol. 2018;25:425–33.

    Article  CAS  PubMed  Google Scholar 

  92. Turc G, Bhogal P, Fischer U, Khatri P, Lobotesis K, Mazighi M, et al. European Stroke Organisation (ESO)–European Society for Minimally Invasive Neurological Therapy (ESMINT) guidelines on mechanical thrombectomy in acute ischemic stroke. J Neurointerv Surg. 2019;11:535–8.

    Article  PubMed  Google Scholar 

  93. Powers WJ, Rabinstein AA, Ackerson T, Adeoye OM, Bambakidis NC, Becker K, et al. Guidelines for the early management of patients with acute ischemic stroke: 2019 update to the 2018 guidelines for the early management of acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association. Stroke. 2019;50:e344–418.

    Article  PubMed  Google Scholar 

  94. Casolla B, Caparros F, Cordonnier C, Bombois S, Hénon H, Bordet R, et al. Biological and imaging predictors of cognitive impairment after stroke: a systematic review. J Neurol. 2019;266:2593–604.

    Article  PubMed  Google Scholar 

  95. Martin AJ, Price CI. A systematic review and meta-analysis of molecular biomarkers associated with early neurological deterioration following acute stroke. Cerebrovasc Dis. 2018;46:230–41.

    Article  CAS  PubMed  Google Scholar 

  96. Donkel SJ, Benaddi B, Dippel DWJ, Ten Cate H, de Maat MPM. Prognostic hemostasis biomarkers in acute ischemic stroke. Arterioscler Thromb Vasc Biol. 2019;39:360–72.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Dolmans LS, Rutten FH, Koenen NCT, Bartelink M-LEL, Reitsma JB, Kappelle LJ, et al. Candidate biomarkers for the diagnosis of transient ischemic attack: a systematic review. Cerebrovasc Dis. 2019;47:207–16.

    Article  CAS  PubMed  Google Scholar 

  98. Jickling GC, Sharp FR. Biomarker panels in ischemic stroke. Stroke. 2015;46:915–20.

    Article  PubMed  PubMed Central  Google Scholar 

  99. Misra S, Kumar A, Kumar P, Yadav AK, Mohania D, Pandit AK, et al. Blood-based protein biomarkers for stroke differentiation: a systematic review. Proteomics Clin Appl. 2017;11 https://doi.org/10.1002/prca.201700007.

  100. Bustamante A, López-Cancio E, Pich S, Penalba A, Giralt D, García-Berrocoso T, et al. Blood biomarkers for the early diagnosis of stroke: the stroke-chip study. Stroke. 2017;48:2419–25.

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. VMorais Lab—Mitochondria Biology & Neurodegeneration, Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal

    Filipe Cortes-Figueiredo & Vanessa A. Morais

  2. NeuroCure Clinical Research Center, Charité—Universitätsmedizin Berlin, Berlin, Germany

    Filipe Cortes-Figueiredo

  3. EGomes Lab—Cell Architecture, Instituto de Medicina Molecular João Lobo Antunes, Faculdade de Medicina, Universidade de Lisboa, Lisbon, Portugal

    Helena Pinheiro

Authors
  1. Filipe Cortes-Figueiredo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vanessa A. Morais
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Helena Pinheiro
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vanessa A. Morais .

Editor information

Editors and Affiliations

  1. Neurology, University of Lisbon, Lisbon, Portugal

    Ana Catarina Fonseca

  2. Department of Neurosciences, University of Lisbon, Lisbon, Portugal

    José M. Ferro

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Cortes-Figueiredo, F., Morais, V.A., Pinheiro, H. (2021). From Bedside to Bench: Methods in Precision Medicine. In: Fonseca, A.C., Ferro, J.M. (eds) Precision Medicine in Stroke. Springer, Cham. https://doi.org/10.1007/978-3-030-70761-3_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-70761-3_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-70760-6

  • Online ISBN: 978-3-030-70761-3

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics

Search

Navigation