Synchrotron-Based in Situ Characterization of the Scaffold Mass Loss from Erosion Degradation
Bibliographic record
Abstract
The mass loss behavior of degradable tissue scaffolds is critical to their lifespan and other degradation-related properties including mechanical strength and mass transport characteristics. This paper presents a novel method based on synchrotron imaging to characterize the scaffold mass loss from erosion degradation in situ, or without the need of extracting scaffolds once implanted. Specifically, the surface-eroding degradation of scaffolds in a degrading medium was monitored in situ by synchrotron-based imaging; and the time-dependent geometry of scaffolds captured by images was then employed to estimate their mass loss with time, based on the mathematical model that was adopted from the literature of surface erosion with the experimentally-identified model parameters. Acceptable agreement between experimental results and model predictions was observed for scaffolds in a cylindrical shape, made from poly(lactic-co-glycolic) acid (PLGA) and polycaprolactone (PCL). This study illustrates that geometry evaluation by synchrotron-based imaging is an effective means to in situ characterize the scaffold mass loss as well as possibly other degradation-related properties.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".