MétaCan
Menu
Back to cohort
Record W2070703548 · doi:10.1517/17530059.2.5.577

Biomarker identification for diagnosis of Alzheimer's disease

2008· article· en· W2070703548 on OpenAlexaff
Steven Pelech

Bibliographic record

VenueExpert Opinion on Medical Diagnostics · 2008
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsUniversity of British ColumbiaKinexus Bioinformatics Corporation (Canada)
Fundersnot available
KeywordsBiomarkerProteomicsMedicinePathologyDiseaseAlzheimer's diseaseTau proteinPeripheral blood mononuclear cellDifferential diagnosisBioinformaticsBiologyGene

Abstract

fetched live from OpenAlex

BACKGROUND: Alzheimer's disease (AD) is one of the most pressing and difficult to diagnose unmet diseases confronting industrialized countries. It is characterized by the appearance in the post mortem autopsied AD brain of amyloid plaques containing Aβ42 and paired helical filaments in neurofibrillary tangles with hyperphosphorylated tau. OBJECTIVE: To investigate the potential of proteomics approaches for AD diagnosis. METHODS: This reviews focuses on studies of the altered phosphorylation of tau and other proteins as detected in brain biopsy, cerebral spinal fluid (CSF) and blood samples. RESULTS/CONCLUSION: Detection of decreased Aβ42, and increased total and hyperphosphorylated tau in CSF from AD patients can provide a fairly reliable diagnosis. Furthermore, very recent studies have demonstrated altered levels of cytokines in plasma and differential gene expression and protein phosphorylation in peripheral blood mononuclear cells from AD patients. Identification of the roles of these proteins may provide valuable insights into the underlying molecular pathology of AD and possible sites for therapeutic intervention.

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)0.000
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.561
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.090
GPT teacher head0.393
Teacher spread0.303 · 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

Citations6
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueExpert Opinion on Medical DiagnosticsSame topicAlzheimer's disease research and treatmentsFrench-language works237,207