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Record W2410507446 · doi:10.1021/acsami.5b02404

In Situ Electrochemical ELISA for Specific Identification of Captured Cancer Cells

2015· article· en· W2410507446 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Applied Materials & Interfaces · 2015
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Bio-sensing Technologies
Canadian institutionsCanada Research ChairsUniversity of Toronto
FundersCanadian Cancer Society Research InstituteCanadian Institutes of Health Research
KeywordsCirculating tumor cellCancer cellCancerMaterials scienceIn situCancer researchMolecular biologyBiologyMetastasisMedicineInternal medicineChemistry

Abstract

fetched live from OpenAlex

Circulating tumor cells (CTCs) are cancer cells disseminated from a tumor into the bloodstream. Their presence in patient blood samples has been associated with metastatic disease. Here, we report a simple system that enables the isolation and detection of these rare cancer cells. By developing a sensitive electrochemical ELISA method integrated within a microfluidic cell capture system, were we able to reliably detect very low levels of cancer cells in whole blood. Our results indicate that the new system provides the clinically relevant specificity and sensitivity needed for a convenient, point-of-need assay for cancer cell counting.

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 score0.014
Threshold uncertainty score0.483

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.018
GPT teacher head0.237
Teacher spread0.219 · 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