SpringerLink
Log in

Proteomics Principles and Clinical Applications

  • Chapter
  • First Online:
Principles of Genetics and Molecular Epidemiology

Abstract

Proteomics refers to the study of the proteome and the set of proteins present in a cell or organism in health or disease; this comprises a wide variety of techniques, from the 2D-electrophoresis gels to the most advanced technologies such as NMR spectroscopy. Hence, in this chapter we performed a deep review regarding the available proteomic technologies, and the outstanding clinical studies that have used such tools in the quest for useful biomarkers for diagnostic, and for the identification of potential therapeutic targets, including the proteomic profiles displayed in neurodegenerative and mitochondrial-derived diseases, aging and aged-related syndromes, and infectious diseases caused by viruses, bacteria, fungi, and parasites. Finally, this chapter gives a complete overview of the scope of proteomics, since this has proven to be highly sensitive, and specific in a wide range of samples and several diseases, suggesting their potential for being considered in the daily clinical practices.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 139.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Abbreviations

BLAST:

Basic local alignment search tool

Ca2+:

Calcium

CEA:

Carcinoembryonic antigen

CSF:

Cerebrospinal fluid

CATH:

Class Architecture Topology and Homologous superfamily

DNA:

Deoxyribonucleic acid

EGF:

Epidermal growth factor

GST-ORF:

Glutathione-S-transferase open reading frame

HIV:

Human immunodeficiency virus

HD:

Huntington’s disease

PARK7:

Parkin 7

PD:

Parkinson’s disease

PDB:

Protein Data Bank

QMEAN:

Qualitative Model Energy Analysis

RNA:

Ribonucleic acid

SARS-CoV-2:

Severe acute respiratory syndrome coronavirus 2

SCOP:

Structural Classification of Protein

SOD1:

Superoxide dismutase 1

VEGF:

Vascular endothelial growth factor

References

  1. Ahmad Y, Lamond AI. A perspective on proteomics in cell biology. Trends Cell Biol. 2014;24:257–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Strachan T, Read A. Human Molecular Genetics. Garland Science; 2018.

    Book  Google Scholar 

  3. Analytical tools to assess aging in humans: The rise of geri-omics. Trends Analyt Chem. 2016;80:204–12.

    Google Scholar 

  4. Reed TT, Sultana R, Butterfield DA. Redox proteomics of Oxidatively modified brain proteins in mild cognitive impairment. Neuroproteomics. 2011;

    Google Scholar 

  5. Cheung TK, Lee C-Y, Bayer FP, McCoy A, Kuster B, Rose CM. Defining the carrier proteome limit for single-cell proteomics. Nat Methods. 2021;18:76–83.

    Article  CAS  PubMed  Google Scholar 

  6. Wu CH, Chen C. Bioinformatics for comparative proteomics. Humana Press; 2010.

    Google Scholar 

  7. Aslam B, Basit M, Nisar MA, Khurshid M, Rasool MH. Proteomics: technologies and their applications. J Chromatogr Sci. 2017;55:182–96.

    Article  CAS  PubMed  Google Scholar 

  8. ND L, Lehninger AL, Nelson DL, Cox MM, University Michael M Cox. Lehninger principles of biochemistry. Macmillan; 2005.

    Google Scholar 

  9. Waterhouse A, Bertoni M, Bienert S, et al. SWISS-MODEL: homology modelling of protein structures and complexes. Nucleic Acids Res. 2018;46:W296–303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. O’Farrell PH. High resolution two-dimensional electrophoresis of proteins. J Biol Chem. 1975;250:4007–21.

    Article  PubMed  Google Scholar 

  11. Martzen MR, McCraith SM, Spinelli SL, Torres FM, Fields S, Grayhack EJ, Phizicky EM. A biochemical genomics approach for identifying genes by the activity of their products. Science. 1999;286:1153–5.

    Article  CAS  PubMed  Google Scholar 

  12. Petersen RC. How early can we diagnose Alzheimer disease (and is it sufficient)? The 2017 Wartenberg lecture. Neurology. 2018;91:395–402.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Márquez F, Yassa MA. Neuroimaging biomarkers for Alzheimer’s disease. Mol Neurodegener. 2019;14:21.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Di Resta C, Ferrari M. New molecular approaches to Alzheimer’s disease. Clin Biochem. 2019;72:81–6.

    Article  PubMed  Google Scholar 

  15. Guo L-H, Alexopoulos P, Wagenpfeil S, Kurz A, Perneczky R, Alzheimer’s Disease Neuroimaging Initiative. Plasma proteomics for the identification of Alzheimer disease. Alzheimer Dis Assoc Disord. 2013;27:337–42.

    Article  CAS  PubMed  Google Scholar 

  16. Johnson ECB, Dammer EB, Duong DM, et al. Large-scale proteomic analysis of Alzheimer’s disease brain and cerebrospinal fluid reveals early changes in energy metabolism associated with microglia and astrocyte activation. Nat Med. 2020;26:769–80.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Rehiman SH, Lim SM, Neoh CF, Majeed ABA, Chin A-V, Tan MP, Kamaruzzaman SB, Ramasamy K. Proteomics as a reliable approach for discovery of blood-based Alzheimer’s disease biomarkers: a systematic review and meta-analysis. Ageing Res Rev. 2020;60:101066.

    Article  CAS  PubMed  Google Scholar 

  18. Rivero-Segura NA, Guerrero-Cruz AA, Barrera-Vázquez OS. Age-related neurodegenerative diseases: an update. Clinical genetics and genomics of. Aging. 2020:27–41.

    Google Scholar 

  19. Dixit A, Mehta R, Singh AK. Proteomics in human Parkinson’s disease: present scenario and future directions. Cell Mol Neurobiol. 2019;39:901–15.

    Article  PubMed  Google Scholar 

  20. van Dijk KD, Teunissen CE, Drukarch B, Jimenez CR, Groenewegen HJ, Berendse HW, van de Berg WDJ. Diagnostic cerebrospinal fluid biomarkers for Parkinson’s disease: a pathogenetically based approach. Neurobiol Dis. 2010;39:229–41.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Lachén-Montes M, González-Morales A, Iloro I, Elortza F, Ferrer I, Gveric D, Fernández-Irigoyen J, Santamaría E. Unveiling the olfactory proteostatic disarrangement in Parkinson’s disease by proteome-wide profiling. Neurobiol Aging. 2019;73:123–34.

    Article  PubMed  Google Scholar 

  22. Basso M, Giraudo S, Corpillo D, Bergamasco B, Lopiano L, Fasano M. Proteome analysis of human substantia nigra in Parkinson’s disease. Proteomics. 2004;4:3943–52.

    Article  CAS  PubMed  Google Scholar 

  23. Dutta D, Ali N, Banerjee E, Singh R, Naskar A, Paidi RK, Mohanakumar KP. Low levels of Prohibitin in substantia Nigra makes dopaminergic neurons vulnerable in Parkinson’s disease. Mol Neurobiol. 2018;55:804–21.

    Article  CAS  PubMed  Google Scholar 

  24. Licker V, Kövari E, Hochstrasser DF, Burkhard PR. Proteomics in human Parkinson’s disease research. J Proteome. 2009;73:10–29.

    Article  CAS  Google Scholar 

  25. Werner CJ, Heyny-von Haussen R, Mall G, Wolf S. Proteome analysis of human substantia nigra in Parkinson’s disease. Proteome Sci. 2008;6:8.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Choi J, Rees HD, Weintraub ST, Levey AI, Chin L-S, Li L. Oxidative modifications and aggregation of Cu,Zn-superoxide dismutase associated with Alzheimer and Parkinson diseases. J Biol Chem. 2005;280:11648–55.

    Article  CAS  PubMed  Google Scholar 

  27. Licker V, Côte M, Lobrinus JA, Rodrigo N, Kövari E, Hochstrasser DF, Turck N, Sanchez J-C, Burkhard PR. Proteomic profiling of the substantia nigra demonstrates CNDP2 overexpression in Parkinson’s disease. J Proteome. 2012;75:4656–67.

    Article  CAS  Google Scholar 

  28. Gómez A, Ferrer I. Increased oxidation of certain glycolysis and energy metabolism enzymes in the frontal cortex in Lewy body diseases. J Neurosci Res. 2009;87:1002–13.

    Article  PubMed  Google Scholar 

  29. Choi J, Levey AI, Weintraub ST, Rees HD, Gearing M, Chin L-S, Li L. Oxidative modifications and Down-regulation of ubiquitin carboxyl-terminal hydrolase L1 associated with idiopathic Parkinson’s and Alzheimer's diseases. J Biol Chem. 2004;279:13256–64.

    Article  CAS  PubMed  Google Scholar 

  30. Hong Z, Shi M, Chung KA, et al. DJ-1 and α-synuclein in human cerebrospinal fluid as biomarkers of Parkinson’s disease. Brain. 2010;133:713–26.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Guo J, Sun Z, **ao S, Liu D, ** G, Wang E, Zhou J, Zhou J. Proteomic analysis of the cerebrospinal fluid of Parkinson’s disease patients. Cell Res. 2009;19:1401–3.

    Article  CAS  PubMed  Google Scholar 

  32. Abdi F, Quinn JF, Jankovic J, et al. Detection of biomarkers with a multiplex quantitative proteomic platform in cerebrospinal fluid of patients with neurodegenerative disorders. J Alzheimers Dis. 2006;9:293–348.

    Article  CAS  PubMed  Google Scholar 

  33. Sinha A, Patel S, Singh MP, Shukla R. Blood proteome profiling in case controls and Parkinson’s disease patients in Indian population. Clin Chim Acta. 2007;380:232–4.

    Article  CAS  PubMed  Google Scholar 

  34. Pan C, Zhou Y, Dator R, et al. Targeted discovery and validation of plasma biomarkers of Parkinson’s disease. J Proteome Res. 2014;13:4535–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Alberio T, Pippione AC, Comi C, Olgiati S, Cecconi D, Zibetti M, Lopiano L, Fasano M. Dopaminergic therapies modulate the T-CELL proteome of patients with Parkinson’s disease. IUBMB Life. 2012;64:846–52.

    Article  CAS  PubMed  Google Scholar 

  36. Mila S, Albo AG, Corpillo D, Giraudo S, Zibetti M, Bucci EM, Lopiano L, Fasano M. Lymphocyte proteomics of Parkinson’s disease patients reveals cytoskeletal protein dysregulation and oxidative stress. Biomark Med. 2009;3:117–28.

    Article  CAS  PubMed  Google Scholar 

  37. Boerger M, Funke S, Leha A, Roser A-E, Wuestemann A-K, Maass F, Bähr M, Grus F, Lingor P. Proteomic analysis of tear fluid reveals disease-specific patterns in patients with Parkinson’s disease - a pilot study. Parkinsonism Relat Disord. 2019;63:3–9.

    Article  PubMed  Google Scholar 

  38. Kumar S, Singh P, Sharma S, Ali J, Baboota S, Pottoo FH. Proteomic analysis of Huntington’s disease. Curr Protein Pept Sci. 2020;21:1218–22.

    Article  CAS  PubMed  Google Scholar 

  39. Shacham T, Sharma N, Lederkremer GZ. Protein Misfolding and ER stress in Huntington’s disease. Front Mol Biosci. 2019;6:20.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Hosp F, Gutiérrez-Ángel S, Schaefer MH, Cox J, Meissner F, Hipp MS, Hartl F-U, Klein R, Dudanova I, Mann M. Spatiotemporal proteomic profiling of Huntington’s disease inclusions reveals widespread loss of protein function. Cell Rep. 2017;21:2291–303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Sekijima Y, Wiseman RL, Matteson J, Hammarström P, Miller SR, Sawkar AR, Balch WE, Kelly JW. The biological and chemical basis for tissue-selective amyloid disease. Cell. 2005;121:73–85.

    Article  CAS  PubMed  Google Scholar 

  42. Chu CT, Plowey ED, Wang Y, Patel V, Jordan-Sciutto KL. Location, location, location. J Neuropathol Exp Neurol. 2007;66:873–83.

    Article  CAS  PubMed  Google Scholar 

  43. Altelaar AFM, Munoz J, Heck AJR. Next-generation proteomics: towards an integrative view of proteome dynamics. Nat Rev Genet. 2013;14:35–48.

    Article  CAS  PubMed  Google Scholar 

  44. Hao Y, Ye M, Chen X, Zhao H, Hasim A, Guo X. Discovery and validation of FBLN1 and ANT3 as potential biomarkers for early detection of cervical cancer. Cancer Cell Int. 2021;21:125.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Mardamshina M, Geiger T. Next-generation proteomics and its application to clinical breast cancer research. Am J Pathol. 2017;187:2175–84.

    Article  CAS  PubMed  Google Scholar 

  46. Cho J-Y, Sung H-J. Proteomic approaches in lung cancer biomarker development. Expert Rev Proteomics. 2009;6:27–42.

    Article  CAS  PubMed  Google Scholar 

  47. Reymond MA, Schlegel W. Proteomics in cancer. Adv Clin Chem. 2007;44:103–42.

    Article  CAS  PubMed  Google Scholar 

  48. Macklin A, Khan S, Kislinger T. Recent advances in mass spectrometry based clinical proteomics: applications to cancer research. Clin Proteomics. 2020;17:17.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Palmfeldt J, Bross P. Proteomics of human mitochondria. Mitochondrion. 2017;33:2–14.

    Article  CAS  PubMed  Google Scholar 

  50. Calvo SE, Clauser KR, Mootha VK. MitoCarta2.0: an updated inventory of mammalian mitochondrial proteins. Nucleic Acids Res. 2016;44:D1251–7.

    Article  CAS  PubMed  Google Scholar 

  51. Smith AC, Robinson AJ. MitoMiner v3.1, an update on the mitochondrial proteomics database. Nucleic Acids Res. 2016;44:D1258–61.

    Article  CAS  PubMed  Google Scholar 

  52. Arnaud M-C, Gazarian T, Palacios Rodriguez Y, Gazarian K, Sakanyan V. Array assessment of phage-displayed peptide mimics of human immunodeficiency virus type 1 gp41 immunodominant epitope: binding to antibodies of infected individuals. Proteomics. 2004;4:1959–64.

    Article  CAS  PubMed  Google Scholar 

  53. List EO, Berryman DE, Bower B, Sackmann-Sala L, Gosney E, Ding J, Okada S, Kopchick JJ. The use of proteomics to study infectious diseases. Infect Disord Drug Targets. 2008;8:31–45.

    Article  CAS  PubMed  Google Scholar 

  54. Steel LF, Shumpert D, Trotter M, Seeholzer SH, Evans AA, London WT, Dwek R, Block TM. A strategy for the comparative analysis of serum proteomes for the discovery of biomarkers for hepatocellular carcinoma. Proteomics. 2003;3:601–9.

    Article  CAS  PubMed  Google Scholar 

  55. Tan X-F, Wu S-S, Li S-P, Chen Z, Chen F. Alpha-1 antitrypsin is a potential biomarker for hepatitis B. Virol J. 2011;8:274.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Sheraz M, Cheng J, Tang L, Chang J, Guo J-T. Cellular DNA topoisomerases are required for the synthesis of hepatitis B virus covalently closed circular DNA. J Virol. 2019; https://doi.org/10.1128/JVI.02230-18.

  57. Tian W, Zhang N, ** R, et al. Immune suppression in the early stage of COVID-19 disease. Nat Commun. 2020;11:1–8.

    Article  CAS  Google Scholar 

  58. Saleh S, Staes A, Deborggraeve S, Gevaert K. Targeted proteomics for studying pathogenic bacteria. Proteomics. 2019;19:e1800435.

    Article  PubMed  Google Scholar 

  59. Peng B, Li H, Peng X. Proteomics approach to understand bacterial antibiotic resistance strategies. Expert Rev Proteomics. 2019;16:829–39.

    Article  CAS  PubMed  Google Scholar 

  60. Flores-Villalva S, Rogríguez-Hernández E, Rubio-Venegas Y, Cantó-Alarcón JG, Milián-Suazo F. What can proteomics tell us about tuberculosis? J Microbiol Biotechnol. 2015;25:1181–94.

    Article  CAS  PubMed  Google Scholar 

  61. Bisht D, Sharma D, Sharma D, Singh R, Gupta VK. Recent insights into mycobacterium tuberculosis through proteomics and implications for the clinic. Expert Rev Proteomics. 2019;16:443–56.

    Article  CAS  PubMed  Google Scholar 

  62. Zhang B, Liu B, Zhou Y, Zhang X, Zou Q, Liu X. Contributions of mass spectrometry-based proteomics to understanding -host interactions. Pathogens. 2020; https://doi.org/10.3390/pathogens9070581.

  63. Hotez PJ, Brindley PJ, Bethony JM, King CH, Pearce EJ, Jacobson J. Helminth infections: the great neglected tropical diseases. J Clin Invest. 2008;118:1311–21.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Heukelbach J, Feldmeier H. Ectoparasites--the underestimated realm. Lancet. 2004;363:889–91.

    Article  PubMed  Google Scholar 

  65. Ndao M. Diagnosis of parasitic diseases: old and new approaches. Interdiscip Perspect Infect Dis. 2009;2009:278246.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Sánchez-Ovejero C, Benito-Lopez F, Díez P, Casulli A, Siles-Lucas M, Fuentes M, Manzano-Román R. Sensing parasites: proteomic and advanced bio-detection alternatives. J Proteome. 2016;136:145–56.

    Article  Google Scholar 

  67. Barrett J, Jefferies JR, Brophy PM. Parasite proteomics. Parasitol Today. 2000;16:400–3.

    Article  CAS  PubMed  Google Scholar 

  68. Capelli-Peixoto J, Mule SN, Tano FT, Palmisano G, Stolf BS. Proteomics and Leishmaniasis: potential clinical applications. Proteomics Clin Appl. 2019;13:e1800136.

    Article  PubMed  Google Scholar 

  69. Klaips CL, Jayaraj GG, Hartl FU. Pathways of cellular proteostasis in aging and disease. J Cell Biol. 2018;217:51–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Anisimova AS, Alexandrov AI, Makarova NE, Gladyshev VN, Dmitriev SE. Protein synthesis and quality control in aging. Aging. 2018;10:4269–88.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Hartl FU. Protein Misfolding diseases. Annu Rev Biochem. 2017;86:21–6.

    Article  CAS  PubMed  Google Scholar 

  72. Harman D. Aging: a theory based on free radical and radiation chemistry. J Gerontol. 1956;11:298–300.

    Article  CAS  PubMed  Google Scholar 

  73. Reeg S, Grune T. Protein oxidation in aging: does it play a role in aging progression? Antioxid Redox Signal. 2015;23:239–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Korovila I, Hugo M, Castro JP, Weber D, Höhn A, Grune T, Jung T. Proteostasis, oxidative stress and aging. Redox Biol. 2017;13:550–67.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Systematic review and analysis of human proteomics aging studies unveils a novel proteomic aging clock and identifies key processes that change with age. Ageing Res Rev. 2020;60:101070.

    Google Scholar 

  76. Cruz-Jentoft AJ, Bahat G, Bauer J, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48:16–31.

    Article  PubMed  Google Scholar 

  77. Murphy S, Zweyer M, Mundegar RR, Swandulla D, Ohlendieck K. Proteomic serum biomarkers for neuromuscular diseases. Expert Rev Proteomics. 2018;15:277–91.

    Article  CAS  PubMed  Google Scholar 

  78. Ubaida-Mohien C, Lyashkov A, Gonzalez-Freire M, et al. Discovery proteomics in aging human skeletal muscle finds change in spliceosome, immunity, proteostasis and mitochondria. 2019; https://doi.org/10.7554/eLife.49874.

  79. Whittaker K, Burgess R, Jones V, Yang Y, Zhou W, Luo S, Wilson J, Huang R-P. Quantitative proteomic analyses in blood: a window to human health and disease. J Leukoc Biol. 2019;106:759–75.

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Dirección de Investigación, Instituto Nacional de Geriatría, Ciudad de México, Mexico

    Ixchel Ramírez-Camacho

  2. Posgrado en Biología Experimental, Departamento de Ciencias de la Salud, Universidad Autónoma Metropolitana Unidad Iztapalapa, Mexico City, Mexico

    Gibrán Pedraza-Vázquez

  3. Laboratorio de Estudios sobre Tripanosomiasis, Departamento de Inmunología, Instituto de Investigaciones Biomédicas, Universidad Nacional Autónoma de México, México City, Mexico

    Karla Daniela Rodríguez-Hernández

  4. Licenciatura en Ciencias Genómicas UNAM, México, Mexico

    Elizabeth Sulvaran-Guel

  5. Dirección de Investigación, Instituto Nacional de Geriatría (INGER), Instituto Nacional de Geriatría, Ciudad de México, Mexico

    Nadia Alejandra Rivero-Segura

Authors
  1. Ixchel Ramírez-Camacho
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gibrán Pedraza-Vázquez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Karla Daniela Rodríguez-Hernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Elizabeth Sulvaran-Guel
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nadia Alejandra Rivero-Segura
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nadia Alejandra Rivero-Segura .

Editor information

Editors and Affiliations

  1. Dirección de Investigación Instituto Nacional de Geriatría (INGER), Ciudad de México, Mexico

    Juan Carlos Gomez-Verjan

  2. Dirección de Investigación Instituto Nacional de Geriatría (INGER) Instituto Nacional de Geriatría, Ciudad de México, Mexico

    Nadia Alejandra Rivero-Segura

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ramírez-Camacho, I., Pedraza-Vázquez, G., Rodríguez-Hernández, K.D., Sulvaran-Guel, E., Rivero-Segura, N.A. (2022). Proteomics Principles and Clinical Applications. In: Gomez-Verjan, J.C., Rivero-Segura, N.A. (eds) Principles of Genetics and Molecular Epidemiology. Springer, Cham. https://doi.org/10.1007/978-3-030-89601-0_6

Download citation

Publish with us

Policies and ethics

Search

Navigation