Detection of mast cells in ameloblastomas and odontogenic keratocysts
Bibliographic record
Abstract
Background: MCs (MCs) have been ascribed to mediating several diseases, including malignant neoplasms.These cells can play a role in angiogenesis, tissue remodeling and immune modulation and favor neoplasm progression.Despite the studies analyzing the contribution of MCs in odontogenic lesions, its biological behavior in ameloblastomas (AMBs) and odontogenic keratocysts (OKCs) remains unclear.This study aims to detect MCs in OKCs and AMBs and clarify the role of MCs in these lesions.Material and Methods: A total of 40 odontogenic lesions were analyzed.This included 20 OKCs and 20 AMBs, 10 being the solid type and the other 10 being the unicystic type of AMB.All cases were histologically reviewed in hematoxylin-eosin.Clinical data, such as age, gender, location, size, radiographic presentation and, histologic patterns were collected from the clinical charts.The Mann-Whitney U test (MWU) was used verify the hypothesis, through inferential statistics.The level of significance used in the statistical test was 0.5%.Results: MCs were observed in 60% of OKCs, and 35% of AMBs.The ratio of MCs observed in OKCs was 0.37, 0.48 in solid AMBs and 0.01 in unicystic AMBs.There was no significant difference between number of MCs in AMBs and OKCs, however, a significant difference was observed between solid and unicystic AMBs (p ≤ 0.01).Conclusions: MCs may play an important role in the biological behavior of AMBs and OKCs.However, in this study it was not possible to confirm the contribution of MCs in the biological behavior of these lesions and more studies are needed to clarify this relation.
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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 | 0.002 | 0.001 |
| 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) | 0.002 | 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".