Nondestructive, longitudinal measurement of collagen scaffold degradation using computed tomography and gold nanoparticles
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".