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Record W2164509255 · doi:10.1093/bfgp/elm010

Quantitative analysis of amyloid-  peptides in cerebrospinal fluid using immunoprecipitation and MALDI-Tof mass spectrometry

2007· article· en· W2164509255 on OpenAlexaff
Valentina Gelfanova, Richard E. Higgs, Ralph A. Dean, David M. Holtzman, Martin R. Farlow, Eric Siemers, Alvin Boodhoo, Y.-W. Qian, X. He, Z. Jin, Deanna Fisher, Kay L. Cox, John E. Hale

Bibliographic record

VenueBriefings in Functional Genomics and Proteomics · 2007
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsGreenfield Research (Canada)
Fundersnot available
KeywordsCerebrospinal fluidImmunoprecipitationMass spectrometryChemistryMatrix-assisted laser desorption/ionizationCoefficient of variationPeptideChromatographyAlzheimer's diseaseGene isoformMolecular biologyPathologyBiochemistryMedicineBiologyDesorptionDisease

Abstract

fetched live from OpenAlex

Immunoprecipitation (IP) combined with matrix-assisted laser desorption ionization (MALDI) time of flight (Tof) mass spectrometry has been used to develop quantitative assays for amyloid-beta (Abeta) peptides in cerebrospinal fluid (CSF). Inclusion of (15)N labelled standard peptides allows for absolute quantification of multiple Abeta isoforms in individual samples. Characterization of variability associated with all steps of the assay indicated that the IP step is the single largest contributor to overall variability. Optimization of the assay resulted in overall coefficient of variation <or=8% with high agreement to an Abeta(1-40) and Abeta(1-42) ELISA assay. Application of the MALDI-Tof assay to CSF obtained from healthy volunteers and Alzheimer's disease patients indicated statistically significant 43% lower levels of Abeta(1-42) in the AD group (P = 0.0025).

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.582

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.310
Teacher spread0.274 · 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.

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

Citations42
Published2007
Admission routes1
Has abstractyes

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