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Record W2552693195

Contact Imager for Integrated Functional Assessment of Organs-on-a-chip

2014· dissertation· en· W2552693195 on OpenAlexfundno aff
Paige Elyse Dickie

Bibliographic record

VenueTSpace (University of Toronto) · 2014
Typedissertation
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaEli Lilly and Company
KeywordsOrgan-on-a-chipChipComputer scienceSystems engineeringNeuroscienceEngineeringMaterials sciencePsychologyNanotechnologyTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

We describe a microfluidic platform for the assessment of small blood vessel function. The new, compact modular platform allows to reversibly host mouse mesenteric arteries on a microfluidic chip, without the need for any manual on-chip manipulation. The luminal and abluminal sides of chip-hosted vessels are exposed to well-defined chemical microenvironments, and physiological levels of transmural pressure and temperature are established. Vasoconstriction was induced by abluminal presentation of phenylephyrene and dose-response experiments were conducted. Vessel constriction was directly determined by direct projection of the chip-hosted artery via a fiber-optic faceplate onto a CMOS sensor, therefore removing the need for a microscope and an external camera. We believe the demonstrated approach will be broadly applicable for the routine functional assessment of different microtissues and organs-on-a-chip. In the context of intact arteries, the approach may ultimately enable the clinical assessment of patient microvascular status.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.028
Threshold uncertainty score0.093

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0280.008

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 head0.017
GPT teacher head0.286
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueTSpace (University of Toronto)Same topic3D Printing in Biomedical ResearchFrench-language works237,207