An In Vivo Study of Composite Microgels Based on Hyaluronic Acid and Gelatin for the Reconstruction of Surgically Injured Rat Vocal Folds
Bibliographic record
Abstract
PURPOSE The objective of this study was to investigate local injection with a hierarchically microstructured hyaluronic acid-gelatin (HA-Ge) hydrogel for the treatment of acute vocal fold injury using a rat model. METHOD Vocal fold stripping was performed unilaterally in 108 Sprague-Dawley rats. A volume of 25 μl saline (placebo controls), HA-bulk, or HA-Ge hydrogel was injected into the lamina propria (LP) 5 days after surgery. The vocal folds were harvested at 3, 14, and 28 days after injection and analyzed using hematoxylin and eosin staining and immunohistochemistry staining for macrophages, myofibroblasts, elastin, collagen type I, and collagen type III. RESULTS The macrophage count was statistically significantly lower in the HA-Ge group than in the saline group (p < .05) at Day 28. Results suggested that the HA-Ge injection did not induce inflammatory or rejection response. Myofibroblast counts and elastin were statistically insignificant across treatment groups at all time points. Increased elastin deposition was qualitatively observed in both HA groups from Day 3 to Day 28, and not in the saline group. Significantly more elastin was observed in the HA-bulk group than in the uninjured group at Day 28. Significantly more collagen type I was observed in the HA-bulk and HA-Ge groups than in the saline group (p < .05) at Day 28. The collagen type I concentration in the HA-Ge and saline groups was found to be comparable to that in the uninjured controls at Day 28. The concentration of collagen type III in all treatment groups was similar to that in uninjured controls at Day 28. CONCLUSION Local HA-Ge and HA-bulk injections for acute injured vocal folds were biocompatible and did not induce adverse response.
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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 | 0.000 | 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".