Hyperspectral Tracking of In Situ Tissue Regeneration and Remodeling in a Rationally Designed Biological Scaffold in a Rat Subcutaneous Model
Bibliographic record
Abstract
A remote and noncontact tissue regeneration tracking technique is of great significance in regenerative medicine for monitoring cell ingrowth, revascularization and remodeling of the implanted biological scaffold. This study has explored the bimodal hyperspectral imaging system to study quantitatively the action of a scaffold-induced tissue regeneration in the rat subcutaneous model, and verified the findings by the traditional histopathological analysis. The study reveals a pattern of rapid host cell ingrowth and revascularization within 2–3 weeks which is then followed by a slower scaffold remodeling process in a rationally designed biological scaffold. The hyperspectral tracking of tissue regeneration and remodeling process overcomes the limitations of the traditional destructive and time-consuming histopathology and provides a novel analytical tool for investigating in situ tissue formation in regenerative medicine. Data of this work are publicly available at:https://github.com/Zhiao1111/Hyperspectral-Tissue-Regeneration-and-Remodeling-Reference-Dataset.
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 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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".