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Record W4225727164 · doi:10.1002/trc2.12289

Characterization of cerebrospinal fluid biomarkers associated with neurodegenerative diseases in healthy cynomolgus and rhesus macaque monkeys

2022· article· en· W4225727164 on OpenAlexafffund
Emma Robertson, Susan E. Boehnke, Natalia de M. Lyra e Silva, Brittney Armitage‐Brown, Andrew Winterborn, Douglas J. Cook, Fernanda G. De Felice, Douglas P. Munoz

Bibliographic record

VenueAlzheimer s & Dementia Translational Research & Clinical Interventions · 2022
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsProvidence Health CareKingston Health Sciences CentreQueen's University
FundersFondation Brain Canada
KeywordsCerebrospinal fluidRhesus macaqueCisterna magnaMacaqueLumbar punctureBiomarkerMedicinePathologyLumbarPhysiologyBiologyNeuroscienceImmunologyAnatomy

Abstract

fetched live from OpenAlex

Monkeys are becoming important translational models of neurodegenerative disease. To facilitate model development, we measured cerebrospinal fluid (CSF) concentrations of key biomarkers in healthy male and female cynomolgus and rhesus macaques. Amyloid beta (Aβ40, Aβ42), tau (total tau [t-tau], phosphorylated tau [pThr181]), and neurofilament light (NfL) concentrations were measured in CSF of 82 laboratory-housed, experimentally naïve cynomolgus (n = 33) and rhesus (n = 49) macaques. Aβ40 and Aβ42 were significantly higher in rhesus, and female rhesus were higher than males. NfL and t-tau were higher in males, and NfL was higher in rhesus macaques. p-tau was not affected by species or sex. We also examined whether sample location (lumbar or cisterna puncture) affected concentrations. Sample acquisition site only affected NfL, which was higher in CSF from lumbar puncture compared to cisterna magna puncture. Establishing normative biomarker values for laboratory-housed macaque monkeys provides an important resource by which to compare to monkey models of neurodegenerative diseases.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.183
GPT teacher head0.456
Teacher spread0.273 · 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 teacher head, not a consensus.

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

Citations11
Published2022
Admission routes2
Has abstractyes

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