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Record W2893741908 · doi:10.1186/s40035-018-0127-7

Cerebrospinal fluid phosphorylated tau, visinin-like protein-1, and chitinase-3-like protein 1 in mild cognitive impairment and Alzheimer’s disease

2018· article· en· W2893741908 on OpenAlexafffund
Hua Zhang, Kok Pin Ng, Joseph Therriault, Min Su Kang, Tharick A. Pascoal, Pedro Rosa‐Neto, Serge Gauthier

Bibliographic record

VenueTranslational Neurodegeneration · 2018
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsMcGill University Health CentreMcGill University
FundersNational Institute on AgingFonds de Recherche du Québec - SantéNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchNational Institutes of HealthGenentechIXICOH. Lundbeck A/SServierMeso Scale DiagnosticsChongqing Medical UniversityEisaiWeston Brain InstituteNorthern California Institute for Research and EducationPfizerBiogenBioClinicaF. Hoffmann-La RocheUniversity of Southern CaliforniaEli Lilly and CompanyU.S. Department of DefenseAlzheimer's Disease Neuroimaging InitiativeNovartis Pharmaceuticals CorporationBristol-Myers SquibbConsortium canadien en neurodégénérescence associée au vieillissementAlzheimer's AssociationFoundation for the National Institutes of Health
KeywordsCerebrospinal fluidNeuroimagingDementiaAlzheimer's Disease Neuroimaging InitiativeMedicineInternal medicineBiomarkerPathologyNeuropathologyTau proteinNeurologyHyperintensityLogistic regressionCognitive declineCorrelationDiseaseMagnetic resonance imagingPsychologyAlzheimer's diseaseOncologyNeuroscienceBiologyRadiology

Abstract

fetched live from OpenAlex

BACKGROUND: Visinin-like protein-1 (VILIP-1) and chitinase-3-like protein 1 (CHI3L1 or YKL-40) in cerebrospinal fluid (CSF) are newly discovered markers indicating neuronal damage and microglial activation, respectively. Phosphorylated tau (p-tau) reflects the neuropathology of Alzheimer's disease (AD) and is useful as diagnostic markers for AD. However, it is unknown whether these biomarkers have similar or complementary information in AD. METHODS: We stratified 121 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database into cognitively normal (CN), stable mild cognitive impairment (sMCI), progressive MCI (pMCI), and dementia due to AD. Analysis of covariance (ANOVA) and chi-square analyses, Spearman correlation, and logistic regression models were performed to test the demographic, associations between biomarkers, and diagnostic accuracies, respectively. Linear mixed-effects models were used to evaluate the effects of CSF amyloid-β (Aβ) on above biomarkers within diagnostic groups, the combination of diagnostic group and Aβ status as predictor, and CSF biomarkers as predictors of AD features, including cognition measured by Mini-Mental State Examination (MMSE) and brain structure and white matter hyperintensity (WMH) measured by magnetic resonance imaging (MRI). RESULTS: < 0.001) and strongly associated with Aβ pathology across clinical stages of AD, while YKL-40 was correlated with Aβ pathology in CN and AD groups. VILIP-1 was associated with acceleration of cognitive decline, hippocampal atrophy, and expansion of ventricles in longitudinal analyses. YKL-40 was associated with hippocampal atrophy at baseline and follow-up, while p-tau was only associated with worsening WMH at baseline. CONCLUSIONS: CSF levels of p-tau, VILIP-1, and YKL-40 may have utility for discriminating between cognitively normal subjects and patients with AD. Increased levels of both VILIP-1 and YKL-40 may be associated with disease degeneration. These CSF biomarkers should be considered for future assessment in the characterization of the natural history of AD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.296
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations55
Published2018
Admission routes2
Has abstractyes

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