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Record W2075826965 · doi:10.1021/ja801951p

Aptamer-Facilitated Biomarker Discovery (AptaBiD)

2008· article· en· W2075826965 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of the American Chemical Society · 2008
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsPrincess Margaret Cancer CentreYork UniversityUniversity of Toronto
Fundersnot available
KeywordsAptamerChemistryBiomarker discoveryBiomarkerIn vitroDNAMolecular biologyComputational biologyCell biologyProteomicsBiochemistryBiologyGene

Abstract

fetched live from OpenAlex

Here we introduce a technology for biomarker discovery in which (i) DNA aptamers to biomarkers differentially expressed on the surfaces of cells being in different states are selected; (ii) aptamers are used to isolate biomarkers from the cells; and (iii) the isolated biomarkers are identified by means of mass spectrometry. The technology is termed aptamer-facilitated biomarker discovery (AptaBiD). AptaBiD was used to discover surface biomarkers that distinguish live mature and immature dendritic cells. We selected in vitro two DNA aptamer pools that specifically bind to mature and immature dendritic cells with a difference in strength of approximately 100 times. The aptamer pools were proven to be highly efficient in flow- and magnetic-bead-assisted separation of mature cells from immature cells. The two aptamer pools were then used to isolate biomarkers from the cells. The subsequent mass spectrometry analysis of the isolated proteins revealed unknown biomarkers of immature and mature dendritic cells.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.012
GPT teacher head0.265
Teacher spread0.253 · 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