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Record W1972210545 · doi:10.1063/1.2740481

Instrument and technique for the <i>in vitro</i> screening of platelet activation from whole blood samples

2007· article· en· W1972210545 on OpenAlexafffund
Yves Martin, Mariette Lépine, Abdelfettah Bannari, Patrick Vermette

Bibliographic record

VenueReview of Scientific Instruments · 2007
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsUniversité de Sherbrooke
FundersUniversité de Sherbrooke
KeywordsPlatelet activationPlateletEx vivoWhole bloodFlow cytometryBiomedical engineeringMaterials scienceAdhesionIn vivoIn vitroDetection limitFoulingPlatelet adhesionBiophysicsComputer scienceChemistryMembraneChromatographyImmunologyMedicineBiochemistryBiologyComposite materialBiotechnology

Abstract

fetched live from OpenAlex

The measurement of platelet activation is very difficult to accomplish clinically as platelets are readily activated by in vitro manipulations. Although techniques such as platelet aggregation and flow cytometry exist to estimate platelet function, important limitations prevent these techniques to be widely accepted. In this study, low-fouling surfaces used to limit ex vivo platelet activation were locally bioactivated to rapidly detect platelet activation from whole blood through the selective local adhesion and aggregation of artificially activated platelets. To achieve this result, a fabrication method was developed to create arrays of anti-CD62 and anti-CD61 proteins covalently immobilized on substrates covered by low-fouling graft layers. Moreover, to further limit ex vivo platelet activation and to obtain reproducible results, a custom-made flow chamber was designed and fabricated with the help of computer-assisted mathematical modeling to create defined shear environments. This diagnostic instrument has the potential to allow the rapid estimation of platelet activation levels in whole blood.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.576
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.029
GPT teacher head0.286
Teacher spread0.257 · 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 designBench or experimental
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

Citations3
Published2007
Admission routes2
Has abstractyes

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