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Record W2741274828 · doi:10.1002/jbm.a.36161

Thermo‐physical properties of synthetic mucus for the study of airway clearance

2017· article· en· W2741274828 on OpenAlexafffund
Olivier Lafforgue, Nizar Bouguerra, Sébastien Poncet, Isabelle Seyssiecq, Julien Favier, Saïd Elkoun

Bibliographic record

VenueJournal of Biomedical Materials Research Part A · 2017
Typearticle
Languageen
FieldMedicine
TopicInhalation and Respiratory Drug Delivery
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMucusMaterials scienceViscositySurface tensionRheologyComposite materialCervical mucusThermal conductivityChemical engineeringBiomedical engineeringThermodynamicsChemistryMedicineBiochemistry

Abstract

fetched live from OpenAlex

In this article, dynamic viscosity, surface tension, density, heat capacity and thermal conductivity, of a bronchial mucus simulant proposed by Zahm et al., Eur Respir J 1991; 4: 311-315 were experiementally determined. This simulant is mainly composed of a galactomannan gum and a scleroglucan. It was shown that thermophysical properties of synthetic mucus are dependant of scleroglucan concentrations. More importantly and for some scleroglucan concentrations, the syntetic mucus, exhibits, somehow, comparable thermophysical properties to real bronchial mucus. An insight on the microstructure of this simulant is proposed and the different properties enounced previously have been measured for various scleroglucan concentrations and over a certain range of operating temperatures. This synthetic mucus is found to mimic well the rheological behavior and the surface tension of real mucus for different pathologies. Density and thermal properties have been measured for the first time. © 2017 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 105A: 3025-3033, 2017.

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)0.003
metaresearch head score (Gemma)0.002
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 score0.020
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.143
GPT teacher head0.413
Teacher spread0.270 · 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

Citations20
Published2017
Admission routes2
Has abstractyes

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