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Record W2915437259 · doi:10.1039/c9nr00313d

Nondestructive, longitudinal measurement of collagen scaffold degradation using computed tomography and gold nanoparticles

2019· article· en· W2915437259 on OpenAlexaff
Tyler A. Finamore, Tyler E. Curtis, James V. Tedesco, Kathryn Grandfield, Ryan K. Roeder

Bibliographic record

VenueNanoscale · 2019
Typearticle
Languageen
FieldEngineering
TopicBone Tissue Engineering Materials
Canadian institutionsMcMaster University
Fundersnot available
KeywordsScaffoldMaterials scienceNondestructive testingColloidal goldComputed tomographyNanoparticleDegradation (telecommunications)Biomedical engineeringTomographyNanotechnologyRadiologyMedicineComputer science

Abstract

fetched live from OpenAlex

Biodegradable materials, such as collagen scaffolds, are used extensively in clinical medicine for tissue regeneration and/or as an implantable drug delivery vehicle. However, available methods to study biomaterial degradation are typically invasive, destructive, and/or non-volumetric. Therefore, the objective of this study was to investigate a new method for nondestructive, longitudinal, and volumetric measurement of collagen scaffold degradation. Gold nanoparticles (Au NPs) were covalently conjugated to collagen fibrils during scaffold preparation to enable contrast-enhanced imaging of collagen scaffolds. The X-ray attenuation of as-prepared scaffolds increased linearly with increased Au NP concentration such that ≥60 mM Au NPs provided sufficient contrast to measure scaffold degradation. Collagen scaffold degradation kinetics were measured to increase during in vitro enzymatic degradation in media with an increased concentration of collagenase. The scaffold degradation kinetics measured by micro-CT exhibited lower variability compared with gravimetric measurement and were validated by measurement of the release of Au NPs from the same samples by optical spectroscopy. Thus, Au NPs and CT synergistically enabled nondestructive, longitudinal, and volumetric measurement of collagen scaffold degradation.

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 score0.073
Threshold uncertainty score0.588

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 head0.019
GPT teacher head0.211
Teacher spread0.191 · 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

Citations28
Published2019
Admission routes1
Has abstractyes

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