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BIOMARCATORI NEUROCHIMICI NELLE FASI INIZIALI DI DEMENZA

5 -7 Aprile 2019, Viareggio. III Meeting delle Neuroscienze Toscane. BIOMARCATORI NEUROCHIMICI NELLE FASI INIZIALI DI DEMENZA. Dr. Filippo Baldacci. OVERLAPPING AMONG DEMENTIAS AND NEURODEGENERATIVE DISEASES (NDD).

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BIOMARCATORI NEUROCHIMICI NELLE FASI INIZIALI DI DEMENZA

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  1. 5 -7 Aprile 2019, Viareggio III Meeting delle Neuroscienze Toscane BIOMARCATORI NEUROCHIMICI NELLE FASI INIZIALI DI DEMENZA Dr. Filippo Baldacci

  2. OVERLAPPING AMONG DEMENTIAS AND NEURODEGENERATIVE DISEASES (NDD) Abbreviations: Aβ42, amyloid‑β42; ALS, amyotrophic lateral sclerosis; APOE, apolipoprotein E; APP, amyloid precursor protein; bvFTD, behavioural variant frontotemporal dementia; DJ1, parkinson protein 7; DLB, dementia with Lewy bodies; EOAD, early-onset Alzheimer disease; FUS, fused in sarcoma; GBA, glucocerebrosidase; GRN, granulin; HP‑tau, hyperphosphorylated-tau; LOAD, late-onset Alzheimer disease; LRRK2, leucine-rich repeat kinase 2; MAPT, microtubule-associated protein tau; PARK2, parkin RBR E3 ubiquitin protein ligase; PCA, posterior cortical atrophy; PD, Parkinson disease; PINK1, PTEN induced putative kinase 1; PPA, primary progressive aphasia; PS, presenilin; PSP, progressive supranuclear palsy; SNCA, α‑synuclein; SOD1, superoxide dismutase 1; TARDBP/TDP‑43, TAR DNA-binding protein 43; TOMM40, translocase of outer mitochondrial membrane 40 homologue; TREM2, triggering receptor expressed on myeloid cells 2; UPS, ubiquitin proteasome system; VCP, valosin containing protein. Pievani M, NatRewNeurol 2014, modified

  3. AD HETEROGENEITY Pathologicalspectrum of AD (Rabinovici, Alzheimers Dement (NY), 2017) AD: Alzheimer's disease; VaD: vascular dementia; LBD: Lewy body disease; HS: hippocampal sclerosis; (McCann, Parkinsonism and Related Disorders, 2014)

  4. AD: DEFINITION OF A PATHOPHYSIOLOGICAL ENTITY IWG: 2007 IWG: 2010 NIA-AA: 2011 IWG: 2014 NIA-AA: 2018

  5. DIAGNOSTIC CRITERIA AD, IWG-2 2014 “pathophysiological-diagnosticbiomarkers(PET amyloidetracer, CSF, geneticanalysis) and topographicbiomarkers of progression(FDG -PET, RM volumetryhippocampal)” Dubois, Lancet Neurol 2014

  6. CSF AND BLOOD BIOMARKERS FOR THE DIAGNOSIS OF AD The upper and lower values delimiting the gray zone indicate the 95% confidence interval (95% CI) values calculated using the ADNI cohort (n=116 for controls and n=100 for AD) Coart E, Alzh&Dem 2015 • CSF core biomarkers and NFL differentiated AD and prodromal AD from controls with good performance. CSF NSE, VLP-1, HFABP, and YKL-40 were moderate. • Plasma T-tau had large effect sizes when differentiating between controls and AD T-tau, P-tau, Aβ42, and NFL in CSF should be used in clinical practice and clinical research NFL=neurofilament light chain, marker of large myelinated axonal fibers Olsson B, Lancet Neurol 2016

  7. CSF NEUROGRANIN (Ng) CONCENTRATIONS ARE ASSOCIATED WITH AD PATHOPHYSIOLOGY IN AUTOPSY CONFIRMED CASES Higher CSF Ng levels were positively associated with greater Aβ neuritic plaque and tau tangle pathology scores. In the hippocampus and amygdala CSF Ng was associated with plaque but not with tangle, α-synuclein, or TAR DNA-binding protein 43 loads. It correlates with longitudinal decline of MMSE 116 pts Ng = marker of dendriticsynpatic damage Portelius E, Acta Neuropathologica 2018

  8. NOVEL BIOMARKERS NG, NFL,YKL-40 ALONE AND IN COMBINATION DID NOT IMPROVE DIAGNOSTIC ACCURACY IN NDD COMPARING WITH CORE BIOMARKERS The first 10 combinations of CSF biomarkers with the best diagnostic accuracy in discriminating HC versus ADD and ADD versus FTD (the results were displayed by the highest AUROC value to the lowest). Abbreviations: Aβ1-42, 42-amino acid-long amyloid beta peptide; ADD, Alzheimer’s disease dementia; AUROC, area under the receiver operating characteristic curve; C.I., confidence intervals; CSF, cerebrospinal fluid; FTD, frontotemporal dementia; HC, cognitively healthy controls; NFL, neurofilaments light chain protein; p-tau, hyperphosphorylated tau; t-tau, total tau.NOTE. The AUROC curves result from fitting a logistic regression model within a 10-fold cross validation scheme to biomarkers data adjusted for age, sex, and site. Hampel H , Alzheimer and Dementia 2018

  9. CSF α-SYN AND OTHER CSF BIOMARKERS IN PD AND PARKINSONISMS • CSF total α-synislower in PD than in controls(α-synsequestration in Lewybodies) • CSF α-syniscorrelated to t-tau and p-tau in PD; CSF t-tau and p-tau are lower in PD than in controls • CSF oligomers of α-synuclein and α-synucleinphophorylated are increased in PD • Increase of CSF total α-syn in AD patients when compared with controls, PD and atypical parkinsonisms • Conflicting results in prognosis and staging of PD: • Lower CSF total α-syn levels or higher oligomeric α-syncorrelated with higher HYahr scores. • Correlation between phosphorylated a-syn and UPDRS III scores. • Higher CSF total α-synlevels at the baseline predict cognitive decline in PD • The measurement of CSF total α-synhas not a diagnostic accuracy satisfactory (also in the early phase) • A high variability of the total α­syn in the different laboratories • Contamination of CSF with blood increases the levels of α-syn(CSF hemoglobin levels are therefore measured in order to exclude contaminated samples) • CSF Aβ42 isreduced in PDD in comparison to PD and ispredictive of dementiain PD • CSF NFL and YKL-40 are increased in atypical park. compared to PD Magdalinou N, JNNP 2014; Blennow K, MovDisorders 2016; Kim D, Journal of clinicalNeurology 2016; Parnetti L, BiomarkMed 2016

  10. THE FTLD SPECTRUM AND ITS POTENTIAL BIOMARKERS bvFTLD: Imaging results consistent with frontal and/or anterior temporal atrophy on CT or MRI or frontal hypoperfusion or hypometabolism on SPECT or PET Non fluent/Agrammaticvariant PPA: predominant left posterior fronto-insular atrophy on MRI or predominant left posterior fronto-insular hypoperfusion or hypometabolism on SPECT or PET Semanticvariant PPA: predominant anterior temporal lobe atrophy and/or predominant anterior temporal hypoperfusion or hypometabolism on SPECT or PET Logopenic variant PPA (??): predominant left posterior perisylvian or parietal atrophy on MRI and/or predominant left posterior perisylvian or parietal hypoperfusion or hypometabolism on SPECT or PET Structural changes on imaging reflect phenotypes rather than pathology • FTLD-TDP43 (types A, B, and C) [50% of FTD] • FTLD-tau 35-50% (Pick-disease 30%, CBD 35%, PSP 30%) • FTLD-FUS • A family history in up to 40% of cases, a clear autosomal dominant history accounts for 10% of cases. Mutations in C9orf72, MAPT, and GRN genes account for about 60% of all familiar cases • Biomarkers strongly indicative of AD (or other NDDs) are exclusionary CSF and plasma progranulin screening of genetic FTD with GRN mutation CSF t-tau, p-tau, t-TDP-43 and p-TDP-43, NFL, AgRP, YKL-40, somatostatin, reelin, enzyme activity of catalase may distinguish FTLD from other NDDs and/or different subtypes of FTLD Piguet, Lancet Neurol 2010; Gorno-Tempini, Neurology 2011; Bang, Lancet Neurol 2015; OeckeBiochimica et BiophysicaActa 2015; TeunissenAlz&Dem 2016; Kuiperij, JAD 2016

  11. BLOOD BIOMARKERS AS SCREENING TOOL IN A MULTI-STAGE NDD DIAGNOSTIC STEPS • Predictive Value (PV) of a screening test, besides being related to its intrinsic sensitivity and specificity, is critically dependent on disease prevalence. • As prevalence of the disease increases, the positive PV (PPV) of a test increases, while the negative PV (NPV) decreases. • PV of a screening test for NDD would be subject to variations, based on the prevalence of NDD • AD and dementia disorders are quite frequent in the general population, (5-6 % in western countries) • Amyotrophic lateral sclerosis (ALS) is a rare disease with a prevalence of 2/3 cases per 100 000 people. • NDD prevalence as a whole is high • Old age, familiar history of NDD, psychiatric disorder onset in midlife, and diabetes represent conditions identifying asymptomatic subpopulations with higher probability to develop NDD Hampel H, JAD 2018 (modified); Hampel H, Nat Rev Neurol 2018; Baldacci F, Exp Mol Diagn 2019

  12. DIFFICULTIES OF BLOOD BIOMARKERS DISCOVERING • The biomarker must be able to cross the blood–brain barrier • Low concentration of CNS-derived proteins in the blood (blood:CSF volume ratio causing a substantial dilution) • Biomarker expressed in peripheral tissues (e.g., Aβ is also expressed in blood platelets and several other tissues) • The high amount of other proteins in blood (e.g., albumin and immunoglobulins) may interfere in the assays • Blood may also contain endogenous antibodies directed against the non-human monoclonal antibodies of the assay, which may cause flawed results • The analyte of interest may undergo proteolytic degradation by various proteases in plasma (tau, which is stable in CSF but has a very short (∼10 h) halflife in blood) • The technical issues are mainly a question of sensitivity and antibody specificity • Standard immunochemical methods have often been insufficiently sensitive to allow the reliable quantification of CNS-derived molecules in the blood • Lack of standardization of pre-analytical protocols Zetterberg H, et al. Journal of Neuroscience Methods 2018; Hampel H, et al. Nature RevNeurol 2018; Andreassos U, et al. Alzheimer & Dementia 2016

  13. ULTRASENSITIVE TECHNOLOGIES TO FACILITATE RESEARCH ON BLOOD BIOMARKERS FOR NDD With an average of 1000x greater sensitivity than current immunoassays, analyte concentrations can be efficiently measured in blood Digital ELISA (Enzyme Linked Immunosorbent Assay) based on single molecule arrays (Simoa) has improved sensitivity of traditional ELISA from picomolar (10-12 M) to femtomolar (10-15 M) Digital ELISA counts signal generated from single immunocomplexes formed on superparamagnetic beads confined in arrays of femtoliter-sized wells in which fluorescent molecules are highly concentrated.

  14. POTENTIAL CLINICAL UTILITY OF PLASMA AMYLOID-β BIOMARKERS IN PREDICTING BRAIN AMYLOID-β BURDEN High-performance plasma amyloid-β biomarkers by immunoprecipitation coupled with mass spectrometry AUROC for each biomarker when predicting individual Aβ+/Aβ- status in CN,MCI,ADD • The composite biomarker showed very high areas under the AUROC in both data sets (discovery, 96.7%, n= 121 and validation, 94.1%, n= 111) with an accuracy near to 90% when using amyloid-β-PET as a gold standard. • Plasma biomarkers correlated with amyloid-β-PET burden and CSF Aβ1–42 levels. Biomarker performance was validated in a blinded manner using independent data sets (Japan and Australia) and involved an established large-scale multicentre cohort Nakamura A, Nature 2018

  15. PLASMA TAU IN AD Plasma tau partly reflects AD pathology, but the overlap between normal aging andAD is large, especially in patients without dementia. Despite group-level differences, these results do not support plasma tau as an AD biomarker in individual people Longitudinally followed patients with AD (n = 179), MCI (n = 195), and HC (n = 189) Plasma tau was analyzed with the Human Total Tau kit (research use only grade, Quanterix, Lexington, MA) on the Simoa HD-1 analyzer (CE marker) Mattsson N, Neurology 2016 Plasma tau was higher in AD and associated with significantly greater decline of MMSE over time, slightly greater decline of hippocampal volume over time, greater ventricular volume at baseline and greater increase in ventricular volume over time, and greater decline in FDG-PET over time

  16. PLASMA/SERUM NFL IS ASSOCIATED WITH NDD DIAGNOSIS PD and atypicalparkinsonisms CJ 8 studies on AD/MCI/SCD; 5 studies on ALS; 5 studies on FTD; 4 studies on PD/atypical parkinsonisms; 3 studies on sCJ; 2 studies on HD; 2 VD Steinacker P, et al. Scientific Report 2016 Hansson O, et al. Neurology 2017 FTD: bvFTD, PPA Steinacker P, et al. Neurology 2017 Steinacker P, et al. Neurology 2018 Wylke C et al. JNNP 2016

  17. PLASMA/SERUM NFL IS ASSOCIATED WITH NDD DIAGNOSIS ALS 8 studies on AD/MCI/SCD; 5 studies on ALS; 5 studies on FTD; 4 studies on PD/atypical parkinsonisms; 3 studies on sCJ; 2 studies on HD; 2 VD Feneberg E, et al. Neurology 2018 HD Vasculardementia Duering M, et al. Journal of Stroke 2018 Wylke C et al. JNNP 2016 Byrne LM, et al. Lancet Neurology 2017

  18. PLASMA NFL IS ASSOCIATED WITH AD DIAGNOSIS Plasma NFL correlated with poor cognition and AD-related atrophy (at baseline and longitudinally) and with brain hypometabolism(longitudinally) • Plasma NFL differentiated ADD vs controls, with an AUROC of 0.87. The AUROCs were 0.87 to 0.90 for CSF NFL, Aβ42, t-tau, p-tau • Plasma Tau differentiated ADD vs controls with AUROC of 0.78 Plasma NFL moderatelycorrelated with CSF NFL Ultrasensitive assay (Simoa) was used to measure plasma NFL concentration in 193 HC, 197 MCI, and 180 ADD Mattsson N, JAMA neurology 2017

  19. PLASMA/SERUM NFL IS ASSOCIATED WITH NDD PROGRESSION PSP survival ALS survival Premanifest HD Baseline plasma NfL is predictive of death Baseline plasma NfL is predictive of death Kaat LD et al. Park rel Dis 2018 Lu C et al. Neurology 2015 Geneticpre AD Genetic FTD survival Baseline plasma NfL at is predictive of disease onset after 3-year follow-up Byrne L et al. Lancet neurol 2017 Baseline plasma NfL is predictive of death Baseline plasma NfL increase overtime in carriers Meeter LH et al. Anna Cli Trans Neuro 2016 Preische O et al. Nature Med 2019

  20. CONCLUSIONS Pathophysiologicalbiomarkers(preclinical, prodromal, diagnostic, prognostic) BIOMARKERS of AD: • CSF Aβ42, t-tau, p-tau CANDIDATE BIOMARKERS of PD: • CSF Aβ1-42, t-tau, p-tau CANDIDATE BIOMARKERS of FTD: • CSF (plasma) progranulin CANDIDATE BIOMARKERS of NEURODEGENERATION: • CSF (plasma) NFL Predictive of cognitive decline (prodromal, diagnostic, prognostic) Screening of genetic FTD due to GRN mutations Screening of neurodegenerative diseases, and disease progression monitoring

  21. CONCLUSIONS • ClinicalPhenotype (cognitive profile, cognitive network disruption) • Staging (Asymptomatic at risk, SMC, MCI, Dementia) • Rate of disease progression (MCI stable, MCI converters) • Pathophysiologicalmechanisms (Liquidbiopsy) ???ClinicalDiagnosis??? COGNITIVE EVALUATION BIOMARKERS

  22. GRAZIE !!! Neurology Unit, Department of Clinical and Experimental Medicine University of Pisa Cellular Biochemistry and MolecularBiology Department of Pharmacy University of Pisa Linda Giampietri Paolo Libertini Alessandro Galgani Daniela Frosini Lucia Petrozzi Annalisa Lo Gerfo Lucia Chico Ubaldo Bonuccelli Gabriele Siciliano Gloria Tognoni Filippo Sean Giorgi Roberto Ceravolo Valentina Nicoletti Cristina Pagni Simona Cintoli Caludia Radicchi Luca Tommassini Claudia Martini Maria Letizia Trincavelli Simona Daniele Deborah Pietrobono Rebecca Piccarducci

  23. CSF AND BLOOD BIOMARKERS FOR AD CSF Aβ42/ Aβ40 and Aβ42/ Aβ38: - Goodcorrelation with amyloid-PET (betterpredictionthan Aβ42 alone) - Recognize HC with low Aβ production - Recognize AD with high Aβpeptides production Core CSF biomarkers CSF Aβ42/ Aβ40: - Goodcorrelation with amyloid-PET (betterpredictionthan Aβ42 alone) Aβoligomers CSF t-tau/ Aβ42; CSF p-tau/Aβ42: - Predictive of amyloid-PET depositionbetterthanany single biomarker Fagan AM, ArchNeurol 2011 Seeburger JL, JAD 2015 Lewckzeb P, JAD 2017 Janelidze S, AnnClinTranslNeurol 2016 Blennow K, NeurolTher 2017

  24. CSF AND BLOOD BIOMARKERS FOR AD While plasma and CSF t-tau levels correlate poorly, the correlation is very tight between plasma and CSF NFL Candidate biomarkers Synapticmarkers: - CSF Neurogranin(dendritic): specific, diagnostic, prognosticalso in HC - CSF SNAP-25, synaptotagmin (vesicle, presynaptic):: AD and MCI > HC) - CSF t-alpha-syn (presynaptic) :AD and MCI > PD and HC Axonalmarkers: - CSF NFL, plasma NFL: AD and MCI > HC, notspecific associated with cognitive impairment, prognosticvalue Inflammatorymarkers: - CSF YKL-40: AD>MCI>HC, predictivevalue MCI converters - CSF TREM2 (?) - IL-6, TNF-alpha, IL-1beta, TGF-beta, IL-12 > AD vs HC Neuronalcalciumsensorprotein: - CSF VILIP: AD>HC, cognitive declineprogression in MCI Ohrfelt A, Alz Res Ther 2016 Korff A, JAD 2013 Berge G, BMC neurol 2016 Brinkmalm A, Moldegen 2014 Blennow K, NeurolTher 2017 Baldacci F, ExpRevProteom 2017 Swardfager W, (metanalysis), BiolPsich 2010

  25. POTENTIAL OCULAR AD BIOMARKERS Beta-amyloid (Aβ) sequelae in the human eye in AD Shah TM, MolecularPsychiatry 2017

  26. TOWARDS A NOVEL CLASSIFICATION OF THE SPECTRUM OF NDs DATA-DRIVEN CATEGORIZATION «SMART-DATA» DENSITY-BASED CLUSTERING (no a priori N of clusters) INITIAL POPULATION «BIG-DATA» HOMOGENOUS AND SYSTEMATIC DATA COLLECTION Baldacci F, Methods in MolecularBiology 2018

  27. STATE OF COMPLETION OF BIOMARKERS DEVELOPMENT IN AD Proof of concept (preclinical) AD diagnosis (cross-sectional) MCI-AD progression (longitudinalretrospective) MCI-AD progression (longitudinalprospective) Sufficient evidence of analytical validity (phase 1 of a structured framework adapted from oncology: Pepe MS, Phases of Biomarker Development for Early Detection of Cancer, Journal of the National Cancer Institute, 2001) is available for all biomarkers, but their clinical validity (phases 2 and 3) and clinical utility (phases 4 and 5) are incomplete Frisoni GB, Lancet Neurol 2017

  28. OVERLAPPING AMONG DEMENTIAS AND NEURODEGENERATIVE DISEASES CEREBROVASCULAR DISEASE Abbreviations: Aβ42, amyloid‑β42; ALS, amyotrophic lateral sclerosis; APOE, apolipoprotein E; APP, amyloid precursor protein; bvFTD, behavioural variant frontotemporal dementia; DJ1, parkinson protein 7; DLB, dementia with Lewy bodies; EOAD, early-onset Alzheimer disease; FUS, fused in sarcoma; GBA, glucocerebrosidase; GRN, granulin; HP‑tau, hyperphosphorylated-tau; LOAD, late-onset Alzheimer disease; LRRK2, leucine-rich repeat kinase 2; MAPT, microtubule-associated protein tau; PARK2, parkin RBR E3 ubiquitin protein ligase; PCA, posterior cortical atrophy; PD, Parkinson disease; PINK1, PTEN induced putative kinase 1; PPA, primary progressive aphasia; PS, presenilin; PSP, progressive supranuclear palsy; SNCA, α‑synuclein; SOD1, superoxide dismutase 1; TARDBP/TDP‑43, TAR DNA-binding protein 43; TOMM40, translocase of outer mitochondrial membrane 40 homologue; TREM2, triggering receptor expressed on myeloid cells 2; UPS, ubiquitin proteasome system; VCP, valosin containing protein. Pievani M, NatRewNeurol 2014, modified

  29. AD PATHOPHYIOLOGY Patogenesi Neuroinflammation Aβamyloidcascadehypothesis Cerebrovascularriskfactors Oxidative stress Ironmetabolismalterations Aβamyloid peptide accumulation Age, genetic and environmentalfactors Tau proteinhyperphosphorilation Insuline-resistence Endothelialdamage BBB permeability Complexpathophysiologicalmechanisms -syn and otherproteindepositions

  30. A B POTENTIAL DIAGNOSTIC VALUE OF RED BLOOD CELLS A-SYNUCLEIN HETEROAGGREGATES IN AD N = 39 AD/MCI biomarker + N = 39 age and sexmatchedcontrols C D ROC curves to evaluate the utility of RBC biomarkers concentrations in discriminating AD/MCI patients from HC. α-syn-tau heterodimers AUROC = 0.76 (95% CI 0.65 – 0.87, P < 0.001); α-syn-Aβ1-42 heterodimers AUROC = 0.72 (95% CI 0.61 – 0.84, P = 0.001); AUROC α-syn = 0.64 (95% CI 0.51 – 0.77, P = 0.036) F E Baldacci F, Daniele S, et al. MolecularNeurobiology

  31. PLASMA P-TAU181 NOVEL BIOMARKER FOR AD HC=172 MCI=57 ADD=40 • Plasma t-tau and pTau181 > in ADD than HC. • Plasma pTau181 was more strongly associated with both Aβ and tau PET than t-tau. • Plasma pTau181 was a more sensitive and specific predictor of elevated brain Aβ than t-tau • Biomarker of AD pathophysiology and as a noninvasive screener for elevated brain Aβ Mielke MM, A&D 2018 in press

  32. TOWARD A BIOLOGICAL DEFINITION OF AD A/T/N SYSTEM A (amyloid) = CSF Aβ, Amyloid-PET T (tau) = CSF p-tau, tau-PET N (neurodegeneration)= CSF t-tau, hyppocampal volume, FDG-PET NIA-AA A&D 2018

  33. BIOMARKERS DEFINITION The National Institutes of Health (NIH) defines a biomarker as “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacological responses to a therapeutic intervention” (2001). In other words, is a tool to aid in the study of human diseases by confirminga diagnosis and tracking disease progression, and may help toidentify specific therapeutic targets. In general biomarkers involve measurements of biological samples (fluids and biopsy) or measurements using brain imaging techniques (structural, functional)

  34. ANATOMY OF AD BIOMARKERS – LIQUID BIOPSY (Pathological mechanisms implicated in AD and associated fluid biomarkers) Aβ38 amyloid beta 38, Aβ40 amyloid beta 40, Aβ42 amyloid beta 42, AD Alzheimer’s disease, BACE1 β-site amyloid precursor protein cleaving enzyme 1, hFABPheart-type fatty acid-binding protein, IP-10 interferon-γ-induced protein 10, NF-L neurofilament light, P-tau phosphorylated tau, SNAP-25 synaptosome-associated protein 25, TDP-43 transactive response DNA-binding protein 43, TREM2 triggering receptor expressed on myeloid cells 2, T-tau total tau, VILIP-1 visinin-like protein 1 Molinuevo JL, Actha Neuropathologica, 2019

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