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2000
Volume 22, Issue 7
  • ISSN: 1567-2050
  • E-ISSN: 1875-5828

Abstract

Introduction

Type 2 diabetes mellitus (T2D) is a known risk factor for developing Alzheimer’s disease (AD). Recent research shows that both diseases share complex and related pathophysiological processes. Network medicine approaches can help to elucidate common dysregulated processes among different diseases, such as AD and T2D. Thus, the aim of this work was to determine differentially expressed genes (DEGs) in AD and T2D and to apply a network medicine approach to identify the microRNAs (miRNAs) involved in the AD-T2D association.

Methods

Gene expression microarray data sets consisting of 384 control samples and 399 samples belonging to AD and T2D disease were analyzed to obtain DEGs shared by both diseases; the miRNAs associated with these DEGs were predicted using a network medicine approach. Finally, potential small molecules targeting these potentially deregulated miRNAs were identified.

Results

AD and T2D shared a subset of 82 downregulated DEGs. These genes were significantly associated ( 0.01) with the ontology terms of chemical synaptic deregulation. DEGs were associated with 12 miRNAs expressed in specific tissues for AD and T2D. Such miRNAs were also primarily associated with the ontology terms related to synaptic deregulation and cancer, and AKT signaling pathways. Steroid anti-inflammatory drugs, antineoplastics, and glucose metabolites were predicted to be potential regulators of the 12 shared miRNAs.

Discussion

The network medicine approach integrating DEGs and miRNAs enabled the identification of shared, potentially deregulated biological processes and pathways underlying the pathophysiology of AD and T2D. These common molecular mechanisms were also linked to drugs currently used in clinical practice, suggesting that this strategy may inform future drug repurposing efforts. Nonetheless, further in-depth biological validation is required to confirm these findings.

Conclusion

Network medicine allowed identifying 12 miRNAs involved in the AD-T2D association, and these could be drug targets for the design of new treatments; however, the identified miRNAs need further experimental confirmation.

© 2025 Bentham Science Publishers
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References

  1. Morselli GysiD. do ValleÍ. ZitnikM. AmeliA. GanX. VarolO. GhiassianS.D. PattenJ.J. DaveyR.A. LoscalzoJ. BarabásiA.L. Network medicine framework for identifying drug-repurposing opportunities for COVID-19.Proc. Natl. Acad. Sci. USA202111819202558111810.1073/pnas.202558111833906951
     [Google Scholar]
  2. ChenS. LiH. ZhengJ. HaoL. JingT. WuP. ZhangB. MaD. ZhangJ. MaJ. Expression profiles of exosomal micrornas derived from cerebrospinal fluid in patients with congenital hydrocephalus determined by microRNA sequencing.Dis. Markers2022202211610.1155/2022/534450835371347
     [Google Scholar]
  3. RajamakiB. HartikainenS. TolppanenA.M. The effect of comorbidities on survival in persons with Alzheimer’s disease: A matched cohort study.BMC Geriatr.202121117310.1186/s12877‑021‑02130‑z33750334
     [Google Scholar]
  4. ZhangJ. ChenC. HuaS. LiaoH. WangM. XiongY. CaoF. An updated meta-analysis of cohort studies: Diabetes and risk of Alzheimer’s disease.Diabetes Res. Clin. Pract.2017124414710.1016/j.diabres.2016.10.02428088029
     [Google Scholar]
  5. ChengG. HuangC. DengH. WangH. Diabetes as a risk factor for dementia and mild cognitive impairment: A meta-analysis of longitudinal studies.Intern. Med. J.201242548449110.1111/j.1445‑5994.2012.02758.x22372522
     [Google Scholar]
  6. Diniz PereiraJ. Gomes FragaV. Morais SantosA.L. CarvalhoM.G. CaramelliP. Braga GomesK. Alzheimer’s disease and type 2 diabetes mellitus: A systematic review of proteomic studies.J. Neurochem.2021156675377610.1111/jnc.1516632909269
     [Google Scholar]
  7. ChornenkyyY. WangW.X. WeiA. NelsonP.T. Alzheimer’s disease and type 2 diabetes mellitus are distinct diseases with potential overlapping metabolic dysfunction upstream of observed cognitive decline.Brain Pathol.201929131710.1111/bpa.1265530106209
     [Google Scholar]
  8. KamalM. PriyamvadaS. AnbazhaganA. JabirN. TabrezS. GreigN. Linking Alzheimer’s disease and type 2 diabetes mellitus via aberrant insulin signaling and inflammation.CNS Neurol. Disord. Drug Targets201413233834610.2174/18715273113126660137
     [Google Scholar]
  9. MichailidisM. MoraitouD. TataD.A. KalinderiK. PapamitsouT. PapaliagkasV. Alzheimer’s disease as type 3 diabetes: Common pathophysiological mechanisms between Alzheimer’s disease and type 2 diabetes.Int. J. Mol. Sci.2022235268710.3390/ijms2305268735269827
     [Google Scholar]
  10. HuangC. LuoJ. WenX. LiK. Linking diabetes mellitus with alzheimer’s disease: Bioinformatics analysis for the potential pathways and characteristic genes.Biochem. Genet.20226031049107510.1007/s10528‑021‑10154‑834779951
     [Google Scholar]
  11. CondratC.E. ThompsonD.C. BarbuM.G. BugnarO.L. BobocA. CretoiuD. SuciuN. CretoiuS.M. VoineaS.C. miRNAs as biomarkers in disease: Latest findings regarding their role in diagnosis and prognosis.Cells20209227610.3390/cells902027631979244
     [Google Scholar]
  12. SaliminejadK. KhorshidK.H.R. FardS.S. GhaffariS.H. An overview of microRNAs: Biology, functions, therapeutics, and analysis methods.J. Cell. Physiol.201923455451546510.1002/jcp.2748630471116
     [Google Scholar]
  13. MontanoM. MicroRNAs: MiRRORS of health and disease.Transl. Res.2011157415716210.1016/j.trsl.2011.02.00121420026
     [Google Scholar]
  14. HoP.T.B. ClarkI.M. LeL.T.T. MicroRNA-based diagnosis and therapy.Int. J. Mol. Sci.20222313716710.3390/ijms2313716735806173
     [Google Scholar]
  15. GhiamS. EslahchiC. ShahpasandK. Habibi-RezaeiM. GharaghaniS. Exploring the role of non-coding RNAs as potential candidate biomarkers in the cross-talk between diabetes mellitus and Alzheimer’s disease.Front. Aging Neurosci.20221495546110.3389/fnagi.2022.95546136092798
     [Google Scholar]
  16. AlamroH. BajicV. MacvaninM.T. IsenovicE.R. GojoboriT. EssackM. GaoX. Type 2 diabetes mellitus and its comorbidity, alzheimer’s disease: Identifying critical microRNA using machine learning.Front. Endocrinol.202313108465610.3389/fendo.2022.108465636743910
     [Google Scholar]
  17. BarrettT. WilhiteS.E. LedouxP. EvangelistaC. KimI.F. TomashevskyM. MarshallK.A. PhillippyK.H. ShermanP.M. HolkoM. YefanovA. LeeH. ZhangN. RobertsonC.L. SerovaN. DavisS. SobolevaA. NCBI GEO: Archive for functional genomics data sets--update.Nucleic Acids Res.201341Database issueD991D99523193258
     [Google Scholar]
  18. GautierL. CopeL. BolstadB.M. IrizarryR.A. affy—analysis of Affymetrix GeneChip data at the probe level.Bioinformatics200420330731510.1093/bioinformatics/btg40514960456
     [Google Scholar]
  19. McCallM.N. AlmudevarA. Affymetrix GeneChip microarray preprocessing for multivariate analyses.Brief. Bioinform.201213553654610.1093/bib/bbr07222210854
     [Google Scholar]
  20. RitchieM.E. PhipsonB. WuD. HuY. LawC.W. ShiW. SmythG.K. limma powers differential expression analyses for RNA-sequencing and microarray studies.Nucleic Acids Res.20154374710.1093/nar/gkv00725605792
     [Google Scholar]
  21. RipleyB.D. The R project in statistical computing.MSOR Connections200111232510.11120/msor.2001.01010023
     [Google Scholar]
  22. ChuT-M. BaoW. ThomasR.S. WolfingerR.D. Batch Profile Estimation, Correction, and Scoring.Batch Effects and Noise in Microarray Experiments.Hoboken, New JerseyJohn Wiley & Sons, Ltd200915516510.1002/9780470685983.ch13
     [Google Scholar]
  23. JohnsonW.E. LiC. RabinovicA. Adjusting batch effects in microarray expression data using empirical Bayes methods.Biostatistics20078111812710.1093/biostatistics/kxj03716632515
     [Google Scholar]
  24. McCarthyD.J. SmythG.K. Testing significance relative to a fold-change threshold is a TREAT.Bioinformatics200925676577110.1093/bioinformatics/btp05319176553
     [Google Scholar]
  25. SavageR.S. HellerK. XuY. GhahramaniZ. TrumanW.M. GrantM. DenbyK.J. WildD.L. R/BHC: Fast Bayesian hierarchical clustering for microarray data.BMC Bioinformatics200910124210.1186/1471‑2105‑10‑24219660130
     [Google Scholar]
  26. HulsenT. de VliegJ. AlkemaW. BioVenn – a web application for the comparison and visualization of biological lists using area-proportional Venn diagrams.BMC Genomics20089148810.1186/1471‑2164‑9‑48818925949
     [Google Scholar]
  27. BrufordE.A. AntonescuC.R. CarrollA.J. ChinnaiyanA. CreeI.A. CrossN.C.P. DalgleishR. GaleR.P. HarrisonC.J. HastingsR.J. HuretJ.L. JohanssonB. Le BeauM. MecucciC. MertensF. VerhaakR. MitelmanF. HUGO gene nomenclature committee (HGNC) recommendations for the designation of gene fusions.Leukemia202135113040304310.1038/s41375‑021‑01436‑634615987
     [Google Scholar]
  28. TimalsinaP. CharlesK. MondalA.M. STRING PPI score to characterize protein subnetwork biomarkers for human diseases and pathways.Proceedings of the 2014 IEEE International Conference on Bioinformatics and Bioengineering 2014, pp. 251-256.10.1109/BIBE.2014.46
     [Google Scholar]
  29. ChinC.H. ChenS.H. WuH.H. HoC.W. KoM.T. LinC.Y. cytoHubba: Identifying hub objects and sub-networks from complex interactome.BMC Syst. Biol.20148S4S1110.1186/1752‑0509‑8‑S4‑S1125521941
     [Google Scholar]
  30. ChenE.Y. TanC.M. KouY. DuanQ. WangZ. MeirellesG.V. ClarkN.R. Ma’ayanA. Enrichr: Interactive and collaborative HTML5 gene list enrichment analysis tool.BMC Bioinformatics201314112810.1186/1471‑2105‑14‑12823586463
     [Google Scholar]
  31. ChangL. ZhouG. SoufanO. XiaJ. miRNet 2.0: Network-based visual analytics for miRNA functional analysis and systems biology.Nucleic Acids Res.202048W1W244W25110.1093/nar/gkaa46732484539
     [Google Scholar]
  32. HsuS.D. LinF.M. WuW.Y. LiangC. HuangW.C. ChanW.L. TsaiW.T. ChenG.Z. LeeC.J. ChiuC.M. ChienC.H. WuM.C. HuangC.Y. TsouA.P. HuangH.D. miRTarBase: A database curates experimentally validated microRNA–target interactions.Nucleic Acids Res.201139D163D16910.1093/nar/gkq110721071411
     [Google Scholar]
  33. KaragkouniD. ParaskevopoulouM.D. ChatzopoulosS. VlachosI.S. TastsoglouS. KanellosI. PapadimitriouD. KavakiotisI. ManiouS. SkoufosG. VergoulisT. DalamagasT. HatzigeorgiouA.G. DIANA-TarBase v8: A decade-long collection of experimentally supported miRNA–gene interactions.Nucleic Acids Res.201846D1D239D24510.1093/nar/gkx114129156006
     [Google Scholar]
  34. XiaoF. ZuoZ. CaiG. KangS. GaoX. LiT. miRecords: An integrated resource for microRNA-target interactions.Nucleic Acids Res.200937DatabaseD105D11010.1093/nar/gkn85118996891
     [Google Scholar]
  35. KhanM.S.H. HegdeV. Obesity and diabetes mediated chronic inflammation: A potential biomarker in Alzheimer’s disease.J. Pers. Med.20201024210.3390/jpm1002004232455946
     [Google Scholar]
  36. AljanabiN.M. MamtaniS. Al-GhuraibawiM.M.H. YadavS. NasrL. Alzheimer’s and hyperglycemia: Role of the insulin signaling pathway and GSK-3 inhibition in paving a path to dementia.Cureus2020122688510.7759/cureus.688532190448
     [Google Scholar]
  37. FanY.C. HsuJ.L. TungH.Y. ChouC.C. BaiC.H. Increased dementia risk predominantly in diabetes mellitus rather than in hypertension or hyperlipidemia: A population-based cohort study.Alzheimers Res. Ther.201791710.1186/s13195‑017‑0236‑z28162091
     [Google Scholar]
  38. De FeliceF.G. FerreiraS.T. Inflammation, defective insulin signaling, and mitochondrial dysfunction as common molecular denominators connecting type 2 diabetes to Alzheimer disease.Diabetes20146372262227210.2337/db13‑195424931033
     [Google Scholar]
  39. ChenJ. LinJ. HuY. YeM. YaoL. WuL. ZhangW. WangM. DengT. GuoF. HuangY. ZhuB. WangD. RNADisease v4.0: An updated resource of RNA-associated diseases, providing RNA-disease analysis, enrichment and prediction.Nucleic Acids Res.202351D1D1397D140410.1093/nar/gkac81436134718
     [Google Scholar]
  40. KehlT. KernF. BackesC. FehlmannT. StöckelD. MeeseE. LenhofH.P. KellerA. miRPathDB 2.0: A novel release of the miRNA Pathway Dictionary Database.Nucleic Acids Res.202048D1D142D14710.1093/nar/gkz102231691816
     [Google Scholar]
  41. YuF. LiB. SunJ. QiJ. De WildeR.L. Torres-de la RocheL.A. LiC. AhmadS. ShiW. LiX. ChenZ. PSRR: A web server for predicting the regulation of miRNAS expression by small molecules.Front. Mol. Biosci.2022981729410.3389/fmolb.2022.81729435386297
     [Google Scholar]
  42. AfzalM. AlharbiK.S. AlzareaS.I. AlyamaniN.M. KazmiI. GüvenE. Revealing genetic links of Type 2 diabetes that lead to the development of Alzheimer’s disease.Heliyon2023911220210.1016/j.heliyon.2022.e1220236711310
     [Google Scholar]
  43. YeX. LiuM. WangX. ChengS. LiC. BaiY. YangL. WangX. WenJ. XuW. ZhangS. XuX. LiX. Exploring the common pathogenesis of Alzheimer’s disease and type 2 diabetes mellitus via microarray data analysis.Front. Aging Neurosci.202315107139110.3389/fnagi.2023.107139136923118
     [Google Scholar]
  44. PadmanabhanP. KneynsbergA. GötzJ. Super-resolution microscopy: A closer look at synaptic dysfunction in Alzheimer disease.Nat. Rev. Neurosci.2021221272374010.1038/s41583‑021‑00531‑y34725519
     [Google Scholar]
  45. WuD. LiY. ZhengL. XiaoH. OuyangL. WangG. SunQ. Small molecules targeting protein–protein interactions for cancer therapy.Acta Pharm. Sin. B202313104060408810.1016/j.apsb.2023.05.03537799384
     [Google Scholar]
  46. GuoL. TianJ. DuH. Mitochondrial dysfunction and synaptic transmission failure in Alzheimer’s disease.J. Alzheimers Dis.20175741071108610.3233/JAD‑16070227662318
     [Google Scholar]
  47. Garcia-SerranoA.M. DuarteJ.M.N. Brain metabolism alterations in type 2 diabetes: What did we learn from diet-induced diabetes models?Front. Neurosci.20201422910.3389/fnins.2020.0022932265637
     [Google Scholar]
  48. PolidoriN. MainieriF. ChiarelliF. MohnA. GianniniC. Early insulin resistance, type 2 diabetes, and treatment options in childhood.Horm. Res. Paediatr.202295214916610.1159/00052151534915489
     [Google Scholar]
  49. SędzikowskaA. SzablewskiL. Insulin and insulin resistance in alzheimer’s disease.Int. J. Mol. Sci.20212218998710.3390/ijms2218998734576151
     [Google Scholar]
  50. KshirsagarV. ThingoreC. JuvekarA. Insulin resistance: A connecting link between Alzheimer’s disease and metabolic disorder.Metab. Brain Dis.2021361678310.1007/s11011‑020‑00622‑232986168
     [Google Scholar]
  51. ErginK. ÇetinkayaR. Regulation of microRNAs.Methods Mol. Biol.2022225713210.1007/978‑1‑0716‑1170‑8_1
     [Google Scholar]
  52. AmbrosV. The functions of animal microRNAs.Nature2004431700635035510.1038/nature0287115372042
     [Google Scholar]
  53. ZhengM. WangP. Role of insulin receptor substance-1 modulating PI3K/Akt insulin signaling pathway in Alzheimer's disease.3 Biotech202111417910.1007/s13205‑021‑02738‑3
     [Google Scholar]
  54. DengX. LiuX. WeiY. WangK. ZhuJ. ZhongJ. ZhengJ. GuoR. ZhuY. YeQ. WangM. ChenY. HeJ. ChenZ. HuangS. LvC. ZhengG. LiuS. WenL. ErbB4 deficiency exacerbates olfactory dysfunction in an early-stage Alzheimer’s disease mouse model.Acta Pharmacol. Sin.202445122497251210.1038/s41401‑024‑01332‑638982150
     [Google Scholar]
  55. Mouton-LigerF. DumurgierJ. CognatE. HourregueC. ZetterbergH. VandersticheleH. VanmechelenE. Bouaziz-AmarE. BlennowK. HugonJ. PaquetC. CSF levels of the BACE1 substrate NRG1 correlate with cognition in Alzheimer’s disease.Alzheimers Res. Ther.20201218810.1186/s13195‑020‑00655‑w32690068
     [Google Scholar]
  56. MuhammadI.F. BornéY. BaoX. MelanderO. Orho-MelanderM. NilssonP.M. NilssonJ. EngströmG. Circulating HER2/ErbB2 levels are associated with increased incidence of diabetes: A population-based cohort study.Diabetes Care20194281582158810.2337/dc18‑255631201260
     [Google Scholar]
  57. RogersC. MoukdarF. McGeeM.A. DavisB. BuehrerB.M. DanielK.W. CollinsS. BarakatH. RobidouxJ. EGF receptor (ERBB1) abundance in adipose tissue is reduced in insulin-resistant and type 2 diabetic women.J. Clin. Endocrinol. Metab.2012973E329E34010.1210/jc.2011‑103322238402
     [Google Scholar]
  58. ZabłockaA. KazanaW. SochockaM. StańczykiewiczB. JanuszM. LeszekJ. OrzechowskaB. Inverse correlation between alzheimer’s disease and cancer: Short overview.Mol. Neurobiol.202158126335634910.1007/s12035‑021‑02544‑134523079
     [Google Scholar]
  59. XiaS. YuX. ChenG. Pain as a protective factor for Alzheimer disease in patients with cancer.Cancers202215124810.3390/cancers1501024836612244
     [Google Scholar]
  60. MaL.L. YuJ.T. WangH.F. MengX.F. TanC.C. WangC. TanL. Association between cancer and Alzheimer’s disease: Systematic review and meta-analysis.J. Alzheimers Dis.201442256557310.3233/JAD‑14016824906231
     [Google Scholar]
  61. FanR. XiaoC. WanX. ChaW. MiaoY. ZhouY. QinC. CuiT. SuF. ShanX. Small molecules with big roles in microRNA chemical biology and microRNA-targeted therapeutics.RNA Biol.201916670771810.1080/15476286.2019.159309430900502
     [Google Scholar]
  62. SunJ. XuM. RuJ. James-BottA. XiongD. WangX. CribbsA.P. Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications.Eur. J. Med. Chem.202325711550010.1016/j.ejmech.2023.11550037262996
     [Google Scholar]
  63. IsmailR. ParboP. MadsenL.S. HansenA.K. HansenK.V. SchaldemoseJ.L. KjeldsenP.L. StokholmM.G. GottrupH. EskildsenS.F. BrooksD.J. The relationships between neuroinflammation, beta-amyloid and tau deposition in Alzheimer’s disease: A longitudinal PET study.J. Neuroinflammation202017115110.1186/s12974‑020‑01820‑632375809
     [Google Scholar]
  64. KacířováM. ZmeškalováA. KořínkováL. ŽeleznáB. KunešJ. MaletínskáL. Inflammation: Major denominator of obesity, Type 2 diabetes and Alzheimer’s disease-like pathology?Clin. Sci.2020134554757010.1042/CS2019131332167154
     [Google Scholar]
  65. LinM.H. WuW.T. ChenY.C. LuC.H. SuS.C. KuoF.C. ChouY.C. SunC.A. Association between non-steroidal anti-inflammatory drugs use and the risk of type 2 diabetes mellitus: A nationwide retrospective cohort study.J. Clin. Med.20221111318610.3390/jcm1111318635683572
     [Google Scholar]
  66. Rivers-AutyJ. MatherA.E. PetersR. LawrenceC.B. BroughD. Anti-inflammatories in Alzheimer’s disease—potential therapy or spurious correlate?Brain Commun.202022fcaa10910.1093/braincomms/fcaa10933134914
     [Google Scholar]
  67. KuryłowiczA. KoźniewskiK. Anti-inflammatory strategies targeting metaflammation in type 2 diabetes.Molecules2020259222410.3390/molecules2509222432397353
     [Google Scholar]
  68. JainS. SinghR. PaliwalS. SharmaS. Targeting neuroinflammation as disease modifying approach to Alzheimer’s disease: Potential and challenges.Mini Rev. Med. Chem.202323222097211610.2174/138955752366623051112243537170998
     [Google Scholar]
  69. GuptaA. Effectiveness of ways to treat Alzheimer’s via studying ways to treat inflammatory processes in Alzheimer’s disease.J. Posit. School Psychol.20226415
     [Google Scholar]
  70. BeeriM.S. SchmeidlerJ. LesserG.T. MaroukianM. WestR. LeungS. WysockiM. PerlD.P. PurohitD.P. HaroutunianV. Corticosteroids, but not NSAIDs, are associated with less Alzheimer neuropathology.Neurobiol. Aging20123371258126410.1016/j.neurobiolaging.2011.02.01121458888
     [Google Scholar]
  71. SuhS. ParkM.K. Glucocorticoid-induced diabetes mellitus: An important but overlooked problem.Endocrinol. Metab.201732218018910.3803/EnM.2017.32.2.18028555464
     [Google Scholar]
  72. JolleyJ.A. RajanP.V. PetersenR. FongA. WingD.A. Effect of antenatal betamethasone on blood glucose levels in women with and without diabetes.Diabetes Res. Clin. Pract.20161189810410.1016/j.diabres.2016.06.00527351800
     [Google Scholar]
  73. HwangJ.L. WeissR.E. Steroid-induced diabetes: A clinical and molecular approach to understanding and treatment.Diabetes Metab. Res. Rev.20143029610210.1002/dmrr.248624123849
     [Google Scholar]
  74. RussellS.J. SalaR. ConaghanP.G. HabibG. VoQ. ManningR. KivitzA. DavisY. LufkinJ. JohnsonJ.R. KelleyS. BodickN. Triamcinolone acetonide extended-release in patients with osteoarthritis and type 2 diabetes: A randomized, phase 2 study.Rheumatology201857122235224110.1093/rheumatology/key26530203101
     [Google Scholar]
  75. ZhangM. LuoH. XiZ. RogaevaE. Drug repositioning for diabetes based on ‘omics’ data mining.PLoS One2015105012608210.1371/journal.pone.012608225946000
     [Google Scholar]
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Identification of MicroRNA Drug Targets for Alzheimer's and Diabetes Mellitus Using Network Medicine
Curr Alzheimer Res 22, 522 (2025); https://doi.org/10.2174/0115672050393875250626065205
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  • Article Type:     Research Article
Keyword(s): Alzheimer's disease; diabetes mellitus; drug design; metabolic disorder; microRNAs; Network medicine

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