Differential angiogenic induction impacts nasal polyp tissue growth
Bibliographic record
Abstract
Abstract Background: In chronic rhinosinusitis with nasal polyps, inflammatory edema drives tissue remodeling favoring anomalous growth of the nasal mucosa, but a proangiogenic contribution of nasal polyp in support of tissue growth is still controversial. The chorioallantoic membrane of chicken embryo model was employed to address the potentiality of nasal tissue fragments to modulate angiogenesis.Methods: Fifty-seven fertilized eggs were implanted with polyp or healthy nasal mucosa tissue or were kept as non-implanted controls. The embryos' size, length, and development stage, and chorioallantoic membrane vasculature morphology were evaluated after 48h. Quantitative computer vision techniques applied to digital chorioallantoic membrane images automatically calculated the branching index as the ratio between the areas of the convex polygon surrounding the vascular tree and the vessels’ area. Results: Mucosal, but not polyp tissue implants, hampered embryo development and induced underdeveloped chorioallantoic membranes with anastomosed, interrupted, and regressive vessels. Vessels’ areas and branching indexes were higher among the chorioallantoic membranes with polyp implants and controls than among those with healthy mucosa implants. Discuss: This study demonstrated for the first time a differential angiogenic induction from nasal polyp tissue supporting tissue growth when compared with healthy nasal mucosa.Conclusion: Nasal polyp presents differential angiogenic induction that impacts tissue growth.
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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".