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Record W2625465761

An Investigation into the Feasibility of Utilizing Fluidic Shear Stresses to Functionally Organize Conducting Airway Epithelium

2014· dissertation· en· W2625465761 on OpenAlexfundno aff
Dennis Trieu

Bibliographic record

VenueTSpace (University of Toronto) · 2014
Typedissertation
Languageen
FieldMedicine
TopicTissue Engineering and Regenerative Medicine
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsFluidicsAirwayShear (geology)EpitheliumEngineeringMechanical engineeringCell biologyGeotechnical engineeringMaterials scienceBiologyMedicineAerospace engineeringPathologyComposite materialSurgery
DOInot available

Abstract

fetched live from OpenAlex

There are currently no reliable clinical treatments for reconstructing large segments of injured trachea. Recent implants have suboptimal mucociliary clearance in the trachea due to improper organization of the airway epithelium. Organization of the airway epithelium determines ciliary beat direction and coordination for proper mucociliary clearance. Fluidic shear stresses have been shown to influence ciliary organization and short-term airflow shear stresses have been shown to affect ciliary function. An in vitro fluidic flow system is developed for inducing long-term airflow shear stresses on airway epithelium to influence epithelial organization. Validation of the system is determined by measuring cell viability and mature epithelial cell markers. The system is utilized to investigate the organizational effects of long-term airflow shear stresses on maturing cells. From the experiments, airflow shear stresses are unable to override existing organizing cues. The goal of this study is to provide valuable information on strategies to generate functional epithelium.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0020.000

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 head0.037
GPT teacher head0.291
Teacher spread0.254 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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