Growth factors expression in hyaluronic acid fat graft myringoplasty
Bibliographic record
Abstract
OBJECTIVES/HYPOTHESIS: To investigate the effect of hyaluronic acid (HA) associated to fat graft on growth factors expression during the healing process of tympanic membrane (TM) perforations in guinea pigs using the hyaluronic acid fat graft myringoplasty (HAFGM) technique. STUDY DESIGN: Prospective randomized animal study. METHODS: Thirty guinea pigs were divided equally into three groups: group I (control group), group II (fat graft myringoplasty technique), and group III (HAFGM technique). TMs were perforated on day 1 and then sampled on days 0, 3, 8, and 21 and tested for the expression of: epidermal growth factor (EGF), insulin-like growth factor (IGF), tumor necrosis factor α (TNF α), vascular endothelial growth factor (VEGF), and keratinocyte growth factor (KGF). Five perforated TMs were taken at day 0 from group I to serve as a reference level. RESULTS: Group III showed an increased expression of all tested growth factors, except for KGF. EGF was highest in the early healing process; then IGF peaked at day 8 with statistically significant increase compared to groups I and II. TNF α in group III was significantly higher than group I throughout the study, with a peak level at day 21. VEGF was significantly higher in group III compared to group I at days 3 and 21. Neovascularization and scarless TM closure was obtained in group III, while spontaneous closure was associated with thin-layered and scarred TM in group I. CONCLUSIONS: HA association to fat graft in perforated TM increases the expression of the endogenous growth factors, suggesting that such an association is advantageous for healing. LEVEL OF EVIDENCE: N/A.
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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".