Research evidence — not a medical device — not personalized medicine as a clinical product

Thesis #3 · working method manuscript · 20 September 2026

Disease Profiles for Complex Pathologies: A Gated Method for Systemic Personalized-Medicine Research Objects

Kelechi Emeka Ogbonna · Project Confluence · Vancouver citations · shipped schema 1.0.0 at 64ba76b

Scope. This page is a scholarly landing record for a method paper. It is not a patient chart, not clinical decision support, and not personalized medicine as a clinical product. Qualitative boards are not simulated patient benefit. See DISCLAIMER.md.

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Abstract

Personalized medicine is often described as if a molecular chart of a person would yield a dose. That leap is a product claim. Complex pathologies — triple-negative breast cancer with coupled metabolic and immune exclusion, glioblastoma with hypoxic and invasive niches, pancreatic ductal adenocarcinoma with a desmoplastic stromal barrier, and dormant or occult residual disease — do not become usable research objects by pasting a knowledge-graph page into an ordinary differential equation.

This thesis defines the Disease Profile as a versioned research object for systemic personalized-medicine research. Completing a four-question thinking laboratory (who is asking; which disease, not “cancer”; regime; where the system is stuck) exports a profile with observables, candidate mechanisms, an explicit non-parameter list, admitted hypotheses, Vancouver citations, and a fixed research-only disclaimer. Admission is gated:

Knowledge ≠ Evidence ≠ Mechanism ≠ Parameter ≠ Prediction

OnCo knowledge, OnCo confidence.probability, Idea maturity, and the legacy gene-to-parameter map must not enter Θ. Profiles feed confluence.profiles.HypothesisObject records aimed at named public datasets (example: data/hypotheses/tnbc_lactate_immune_exclusion.yaml → TCGA-BRCA). They do not write coefficients. The method cites CONFLUENCE adapter P0 (pull request #9), the merged Disease Profile pack (pull request #11), and Complexity Science CaseCards. Worked examples are qualitative boards, not outcomes.

Keywords

Disease Profile; research object; personalized-medicine research; thinking laboratory; knowledge gates; HypothesisObject; OnCo; CONFLUENCE; CaseCard; TNBC; glioblastoma; PDAC; dormancy; FAIR; identifiability; not CDS

1. Problem Statement

How can a laboratory encode a complex pathology as a versioned, reusable research object without treating a patient chart, a knowledge-graph page, or a qualitative board as an identified parameter or as clinical decision support [1–5,27,31,34,35,50,51]? That is a computational and medical-methods problem. It is not a claim to treat patients.

2. Justification of the Study

Existing tools fail by overclaiming personalized medicine as a product [1,2], by smuggling OnCo knowledge or legacy gene-to-parameter maps into Θ [27,31–33], and by missing the five-layer gate [31,50,51]. FAIR research objects and identifiability literature justify a versioned profile that can refuse those leaps [34,35,50,51].

3. Significance of the Study

Scientific and methodological significance is for researchers: a replayable Disease Profile, HypothesisObjects aimed at named public datasets, and an explicit non-parameter list. This is not clinical CDS, not a patient chart, and not personalized medicine as a clinical product [3–5].

Contents of the PDF

  1. Problem Statement — one computational / medical-methods research problem (not a treatment claim)
  2. Justification of the Study — overclaiming, parameter smuggling, missing gates
  3. Significance of the Study — methodological value for researchers; not CDS / not a cure
  4. Introduction — two meanings of personalized medicine; research objects versus charts
  5. Aims — shipped contract, four questions, gates, case pack, HypothesisObjects, P0 / CaseCard sit
  6. Methods — confluence/profiles/ + data/profiles/cases/; named public datasets
  7. Worked examples — qualitative boards for the four shipped pack JSON files
  8. Discussion — efficiency of adding a new complex disease without an ODE edit
  9. Limitations and future work
  10. 77 Vancouver references (Crossref-verified DOIs; Highwire citation_reference metas) and the product-refusal disclaimer

Shipped contract (no parallel schema)

Pull request #11 is merged on main at 64ba76b. Thesis #3 cites confluence/profiles/, schemas/disease_profile.schema.json, and data/profiles/cases/. It does not propose a second contract and does not edit CancerODE.

Related artefacts (cited, not over-claimed)

References

Numbered Vancouver list matching the PDF (n = 77). In-text numbers and this bibliography are bijective. Journal DOIs were checked on Crossref. No DOI is invented. Not clinical CDS.

  1. Hamburg MA, Collins FS. The path to personalized medicine. N Engl J Med. 2010;363(4):301-304. doi:10.1056/NEJMp1006304. PMID: 20551152.
  2. Jameson JL, Longo DL. Precision medicine — personalized, problematic, and promising. N Engl J Med. 2015;372(23):2229-2234. doi:10.1056/NEJMsb1503104. PMID: 26014593.
  3. Ogbonna KE. DISCLAIMER.md [Internet]. Project Confluence; 2026 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/blob/main/DISCLAIMER.md
  4. Ogbonna KE. Awaiting external clinical validation [Internet]. Project Confluence; 2026 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/blob/main/docs/AWAITING_CLINICAL_VALIDATION.md
  5. Ogbonna KE. Merge readiness [Internet]. Project Confluence; 2026 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/blob/main/docs/MERGE_READINESS.md
  6. Altrock PM, Liu LL, Michor F. The mathematics of cancer: integrating quantitative models. Nat Rev Cancer. 2015;15(12):730-745. doi:10.1038/nrc4029. PMID: 26597528. PMCID: PMC5663316.
  7. Gatenby RA, Silva AS, Gillies RJ, Frieden BR. Adaptive therapy. Cancer Res. 2009;69(11):4894-4903. doi:10.1158/0008-5472.CAN-08-3658. PMID: 19487300. PMCID: PMC2676449.
  8. Bianchini G, Balko JM, Mayer IA, Sanders ME, Gianni L. Triple-negative breast cancer: challenges and opportunities of a heterogeneous disease. Nat Rev Clin Oncol. 2016;13(11):674-690. doi:10.1038/nrclinonc.2016.66. PMID: 27184417.
  9. Li X, Wenes M, Romero P, Huang SC, Fendt SM, Ho PC. Navigating metabolic pathways to enhance antitumour immunity and immunotherapy. Nat Rev Clin Oncol. 2019;16(7):425-441. doi:10.1038/s41571-019-0203-7. PMID: 30914826.
  10. Joyce JA, Fearon DT. T cell exclusion, immune privilege, and the tumor microenvironment. Science. 2015;348(6230):74-80. doi:10.1126/science.aaa6204. PMID: 25838376.
  11. Schmid P, Adams S, Rugo HS, Schneeweiss A, Barrios CH, Iwata H, et al. Atezolizumab and nab-paclitaxel in advanced triple-negative breast cancer. N Engl J Med. 2018;379(22):2108-2121. doi:10.1056/NEJMoa1809615. PMID: 30345906.
  12. National Cancer Institute. Triple-negative breast cancer [Internet]. Bethesda (MD): NCI; [cited 2026 Sep 20]. Available from: https://www.cancer.gov/types/breast/patient/triple-negative-brochure
  13. Hambardzumyan D, Bergers G. Glioblastoma: defining tumor niches. Trends Cancer. 2015;1(4):252-265. doi:10.1016/j.trecan.2015.10.009. PMID: 27088132.
  14. Brat DJ, Castellano-Sanchez AA, Hunter SB, Pecot M, Cohen C, Hammond EH, et al. Pseudopalisades in glioblastoma are hypoxic, express extracellular matrix proteases, and are formed by an actively migrating cell population. Cancer Res. 2004;64(3):920-927. doi:10.1158/0008-5472.CAN-03-2073. PMID: 14871821.
  15. Giese A, Bjerkvig R, Berens ME, Westphal M. Cost of migration: invasion of malignant gliomas and implications for treatment. J Clin Oncol. 2003;21(8):1624-1636. doi:10.1200/JCO.2003.05.063. PMID: 12697889.
  16. Semenza GL. Hypoxia-inducible factors in physiology and medicine. Cell. 2012;148(3):399-408. doi:10.1016/j.cell.2012.01.021. PMID: 22304911.
  17. Feig C, Gopinathan A, Neesse A, Chan DS, Cook N, Tuveson DA. The pancreas cancer microenvironment. Clin Cancer Res. 2012;18(16):4266-4276. doi:10.1158/1078-0432.CCR-11-3114. PMID: 22896693.
  18. Provenzano PP, Cuevas C, Chang AE, Goel VK, Von Hoff DD, Hingorani SR. Enzymatic targeting of the stroma ablates physical barriers to treatment of pancreatic ductal adenocarcinoma. Cancer Cell. 2012;21(3):418-429. doi:10.1016/j.ccr.2012.01.007. PMID: 22439937.
  19. Neesse A, Michl P, Frese KK, Feig C, Cook N, Jacobetz MA, et al. Stromal biology and therapy in pancreatic cancer. Gut. 2011;60(6):861-868. doi:10.1136/gut.2010.226092. PMID: 20966025.
  20. Olive KP, Jacobetz MA, Davidson CJ, Gopinathan A, McIntyre D, Honess D, et al. Inhibition of Hedgehog signaling enhances delivery of chemotherapy in a mouse model of pancreatic cancer. Science. 2009;324(5933):1457-1461. doi:10.1126/science.1171362. PMID: 19460966.
  21. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-249. doi:10.3322/caac.21660. PMID: 33538338.
  22. Sung H, Filho AM, Laversanne M, Ferlay J, Siegel RL, Soerjomataram I, et al. Global cancer statistics 2024: GLOBOCAN estimates of incidence and mortality worldwide for 34 cancers in 186 countries. CA Cancer J Clin. 2026;76(4):e70090. doi:10.3322/caac.70090. PMID: 42417444.
  23. Aguirre-Ghiso JA. Models, mechanisms and clinical evidence for cancer dormancy. Nat Rev Cancer. 2007;7(11):834-846. doi:10.1038/nrc2256. PMID: 17957189.
  24. Sosa MS, Bragado P, Aguirre-Ghiso JA. Mechanisms of disseminated cancer cell dormancy: an awakening field. Nat Rev Cancer. 2014;14(9):611-622. doi:10.1038/nrc3793. PMID: 25118602.
  25. Massagué J, Obenauf AC. Metastatic colonization by circulating tumour cells. Nature. 2016;529(7586):298-306. doi:10.1038/nature17038. PMID: 26791720.
  26. Giancotti FG. Mechanisms governing metastatic dormancy and reactivation. Cell. 2013;155(4):750-764. doi:10.1016/j.cell.2013.10.029. PMID: 24209616.
  27. Gomila J, OnCo contributors. OnCo: a public, cited knowledge graph of oncology [Internet]. 2026 [cited 2026 Sep 20]. Available from: https://onco.cc
  28. Gomila J, OnCo contributors. OnCo Ideas [Internet]. 2026 [cited 2026 Sep 20]. Available from: https://onco.cc/ideas/
  29. Gomila J, OnCo contributors. OnCo source repository [Internet]. GitHub; 2026 [cited 2026 Sep 20]. Code MIT; data CC BY-NC 4.0. Available from: https://github.com/judegomila/OnCo
  30. Ogbonna KE. Project Confluence [Internet]. GitHub; 2026 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence
  31. Ogbonna KE. OnCo → CONFLUENCE ontology and evidence-ingestion spec (v0.3) [Internet]. Project Confluence; 2026 Sep 18 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/blob/main/docs/ONCO_CONFLUENCE_ONTOLOGY_SPEC.md
  32. Ogbonna KE. OnCo adapter (P0) [Internet]. Project Confluence; 2026 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/blob/main/docs/ONCO_ADAPTER.md
  33. Ogbonna KE. Knowledge gates for dynamical oncology models: findings from an OnCo × CONFLUENCE integration [Internet]. Project Confluence; 2026 Sep 18 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/blob/main/docs/manuscript/ONCO_CONFLUENCE_THESIS_FINDINGS.md
  34. Wilkinson MD, Dumontier M, Aalbersberg IJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3(1):160018. doi:10.1038/sdata.2016.18. PMID: 26978244.
  35. Bechhofer S, Buchan I, De Roure D, Missier P, Ainsworth J, Bhagat J, et al. Why linked data is not enough for scientists. Future Gener Comput Syst. 2013;29(2):599-611. doi:10.1016/j.future.2011.08.004.
  36. Ogbonna KE. feat/onco-adapter-p0 (pull request #9) [Internet]. Project Confluence; 2026 Sep 18 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/pull/9
  37. Ogbonna KE. confluence/onco/schemas.py [Internet]. Project Confluence; 2026 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/blob/main/confluence/onco/schemas.py
  38. Ogbonna KE. CaseCard schema and NSTG-gated in-silico pathway explorer (pull request #2) [Internet]. Complexity Science; 2026 Sep 20 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/complexity-science/pull/2
  39. Ogbonna KE. Complexity Science README [Internet]. GitHub; 2026 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/complexity-science
  40. Ogbonna KE. pathology_cases/SCHEMA.md [Internet]. Complexity Science (draft pull request #2, commit d2e881b); 2026 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/complexity-science/blob/d2e881bd13c2fc067b1b33e2498f9eab09b79a3b/pathology_cases/SCHEMA.md
  41. Federal Ministry of Health, Nigeria. Nigeria Standard Treatment Guidelines. 3rd ed. Abuja: Federal Ministry of Health; 2022.
  42. Ogbonna KE. Thinking lab — disease-specific cancer systems [Internet]. Project Confluence; 2026 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/blob/main/evidence/thinking.html
  43. Ogbonna KE. Disease Profile exporter + offline auditor + citation policy (pull request #11, merged as 64ba76b) [Internet]. Project Confluence; 2026 Sep 20 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/pull/11
  44. Gomila J, OnCo contributors. CONTRIBUTING.md and IdeaSchema (src/lib/schema.ts) [Internet]. OnCo; 2026 [cited 2026 Sep 20]. Available from: https://github.com/judegomila/OnCo
  45. Ogbonna KE. validation/pdac_data_sources.md [Internet]. Project Confluence; 2026 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/blob/main/validation/pdac_data_sources.md
  46. Ogbonna KE. PDAC rogue closure [Internet]. Project Confluence; 2026 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/blob/main/docs/pdac_rogue_closure.md
  47. World Health Organization. Cancer [Internet]. Geneva: WHO; 2026 [cited 2026 Sep 20]. Available from: https://www.who.int/news-room/fact-sheets/detail/cancer
  48. Federal Ministry of Health, Nigeria. Nigeria National Cancer Control Plan 2018–2022 [Internet]. Abuja: Federal Ministry of Health; 2018 [cited 2026 Sep 20]. Available from: https://www.iccp-portal.org/sites/default/files/plans/NCCP_Final%20%5B1%5D.pdf
  49. Federal Ministry of Health, Nigeria. Nigeria Essential Medicines List. 7th ed [Internet]. Abuja: Federal Ministry of Health; 2020 [cited 2026 Sep 20]. Available from: https://www.who.int/publications/m/item/nigeria--essential-medicines-list-2020-(english)
  50. Bellman R, Åström KJ. On structural identifiability. Math Biosci. 1970;7(3-4):329-339. doi:10.1016/0025-5564(70)90132-X.
  51. Raue A, Kreutz C, Maiwald T, Bachmann J, Schilling M, Klingmüller U, et al. Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood. Bioinformatics. 2009;25(15):1923-1929. doi:10.1093/bioinformatics/btp358. PMID: 19505944.
  52. Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483(7391):603-607. doi:10.1038/nature11003. PMID: 22460905.
  53. Li H, Ning S, Ghandi M, Kryukov GV, Gopal S, Deik A, et al. The landscape of cancer cell line metabolism. Nat Med. 2019;25(5):850-860. doi:10.1038/s41591-019-0404-8. PMID: 31068703.
  54. Ogbonna KE. Structural identifiability of a real-CCLE-calibrated metabolic ODE model (manuscript draft) [Internet]. Project Confluence; 2026 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/blob/main/docs/manuscript/structural_identifiability_ccle_manuscript.md
  55. Ogbonna KE. Citation policy [Internet]. Project Confluence; 2026 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/blob/main/docs/CITATION_POLICY.md
  56. Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013;45(10):1113-1120. doi:10.1038/ng.2764. PMID: 24071849.
  57. Cancer Genome Atlas Network. Comprehensive molecular portraits of human breast tumours. Nature. 2012;490(7418):61-70. doi:10.1038/nature11412. PMID: 23000897.
  58. Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008;455(7216):1061-1068. doi:10.1038/nature07385. PMID: 18772890.
  59. National Cancer Institute. Genomic Data Commons: TCGA-GBM [Internet]. Bethesda (MD): NCI; [cited 2026 Sep 20]. Available from: https://portal.gdc.cancer.gov/projects/TCGA-GBM
  60. Puchalski RB, Shah N, Miller J, Dalley R, Nomura SR, Yoon JG, et al. An anatomic transcriptional atlas of human glioblastoma. Science. 2018;360(6389):660-663. doi:10.1126/science.aaf2666. PMID: 29748285. PMCID: PMC6414061.
  61. Cancer Genome Atlas Research Network. Integrated genomic characterization of pancreatic ductal adenocarcinoma. Cancer Cell. 2017;32(2):185-203.e13. doi:10.1016/j.ccell.2017.07.007. PMID: 28810144.
  62. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov. 2012;2(5):401-404. doi:10.1158/2159-8290.CD-12-0095. PMID: 22588877.
  63. Moffitt RA, Marayati R, Flate EL, Volmar KE, Loeza SG, Hoadley KA, et al. Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nat Genet. 2015;47(10):1168-1178. doi:10.1038/ng.3398. PMID: 26343385. GEO: GSE71729.
  64. Yang S, He P, Wang J, Schetter A, Tang W, Funamizu N, et al. A novel MIF signaling pathway drives the malignant character of pancreatic cancer by targeting NR3C2. Cancer Res. 2016;76(13):3838-3850. doi:10.1158/0008-5472.CAN-15-2841. PMID: 27197190. GEO: GSE62452.
  65. Zhang G, Schetter A, He P, Funamizu N, Gaedcke J, Ghadimi BM, et al. DPEP1 inhibits tumor cell invasiveness, enhances chemosensitivity and predicts clinical outcome in pancreatic ductal adenocarcinoma. PLoS One. 2012;7(2):e31507. doi:10.1371/journal.pone.0031507. PMID: 22363658. GEO: GSE28735.
  66. Ogbonna KE. Call for longitudinal pathology and omics data [Internet]. Project Confluence; 2026 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/blob/main/CALL_FOR_DATA.md
  67. International Agency for Research on Cancer. Global cancer statistics 2024: GLOBOCAN estimates of incidence and mortality worldwide for 34 cancers in 186 countries [Internet]. Lyon: IARC; 2026 Jul 8 [cited 2026 Sep 20]. Available from: https://www.iarc.who.int/news-events/global-cancer-statistics-2024-globocan-estimates-of-incidence-and-mortality-worldwide-for-34-cancers-in-186-countries/
  68. Ghandi M, Huang FW, Jané-Valbuena J, Kryukov GV, Lo CC, McDonald ER 3rd, et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature. 2019;569(7757):503-508. doi:10.1038/s41586-019-1186-3. PMID: 31068700.
  69. Foulkes WD, Smith IE, Reis-Filho JS. Triple-negative breast cancer. N Engl J Med. 2010;363(20):1938-1948. doi:10.1056/NEJMra1001389. PMID: 21067385.
  70. Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science. 2009;324(5930):1029-1033. doi:10.1126/science.1160809. PMID: 19460998.
  71. Ogbonna KE. confluence/profiles/disease_profile.py [Internet]. Project Confluence; 2026 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/blob/main/confluence/profiles/disease_profile.py
  72. Ogbonna KE. schemas/disease_profile.schema.json [Internet]. Project Confluence; 2026 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/blob/main/schemas/disease_profile.schema.json
  73. Ogbonna KE. Disease Profile case pack (data/profiles/cases/) [Internet]. Project Confluence; 2026 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/tree/main/data/profiles/cases
  74. Ogbonna KE. confluence/profiles/hypothesis_object.py [Internet]. Project Confluence; 2026 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/blob/main/confluence/profiles/hypothesis_object.py
  75. Ogbonna KE. H-TNBC-LAC-EXCL-001 (data/hypotheses/tnbc_lactate_immune_exclusion.yaml) [Internet]. Project Confluence; 2026 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/blob/main/data/hypotheses/tnbc_lactate_immune_exclusion.yaml
  76. Ogbonna KE. docs/CITATION_POLICY.md [Internet]. Project Confluence; 2026 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/blob/main/docs/CITATION_POLICY.md
  77. Ogbonna KE. scripts/build_disease_profile_pack.py [Internet]. Project Confluence; 2026 [cited 2026 Sep 20]. Available from: https://github.com/cloudynirvana/project-confluence/blob/main/scripts/build_disease_profile_pack.py

Disclaimer

This document is not personalized medicine as a clinical product. Disease Profiles are computational research artefacts. They are not a medical device, not CDS, not a diagnosis, not a dose, and not a cure. Data from OnCo (onco.cc), CC BY-NC 4.0; commercial use needs a licence.

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