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Record W2281011226 · doi:10.1002/jbio.201500263

Quantitative phase measurements of tendon collagen fibres

2016· article· en· W2281011226 on OpenAlexafffund
Dylan Maciel, Samuel P. Veres, Hans Juergen Kreuzer, Laurent Kreplak

Bibliographic record

VenueJournal of Biophotonics · 2016
Typearticle
Languageen
FieldMedicine
TopicTendon Structure and Treatment
Canadian institutionsSaint Mary's UniversityDalhousie University
FundersOffice of Naval ResearchNatural Sciences and Engineering Research Council of Canada
KeywordsTendonBiomedical engineeringCollagen fiberPhase (matter)Materials scienceChemistryAnatomyBiologyMedicine

Abstract

fetched live from OpenAlex

Collagen is the main component of structural mammalian tissues. In tendons, collagen is arranged into fibrils with diameters ranging from 30 nm to 500 nm. These fibrils are further assembled into fibres several micrometers in diameter. Upon excessive thermal or mechanical stress, damage may occur in tendons at all levels of the structural hierarchy. At the fibril level, reported damage includes swelling and the appearance of discrete sites of plastic deformation that are best observed at the nanometer-scale using, for example, scanning electron microscopy. In this paper, digital in-line holographic microscopy is used for quantitative phase imaging to measure both the refractive index and diameter of collagen fibres in a water suspension in the native state, after thermal treatments, and after mechanical overload. Fibres extracted from tendons and subsequently exposed to 70 °C for 5, 15, or 30 minutes show a significant decrease in refractive index and an increase in diameter. A significant increase in refractive index is also observed for fibres extracted from tendons that were subjected to five tensile overload cycles.

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)0.000
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 score0.009
Threshold uncertainty score0.217

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 head0.068
GPT teacher head0.360
Teacher spread0.292 · 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

Citations9
Published2016
Admission routes2
Has abstractyes

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