Quantification of mast cells in oral reactive lesions - an immunohistochemical study.
Bibliographic record
Abstract
BACKGROUND: Reactive lesions (RLs) are the most common oral mucosal lesions that are benign in nature and are more likely to reoccur if the lesion or local irritants at the site are not completely removed. The histopathology is usually determined by the stage of the lesion, which includes neovascularization, inflammation, and fibrosis etc. Aim: To evaluate and compare mast cell counts in different reactive lesions with normal gingiva (NG) and to determine the correlation between mast cell count and inflammation, fibrosis, and angiogenesis using immunohistochemistry. MATERIALS & METHODS: 10 pyogenic granulomas (early and late), 10 irritational fibromas, 5 inflammatory fibrous hyperplasia, and 5 peripheral cemento-ossifying fibromas 5 normal gingiva were evaluated. Mast cell counts were compared. ANOVA and t-tests were used to analyze the data. Spearman correlation was used to compare the mast cell count to the inflammation, fibrosis, and vascular components. A p-value of 0.05 was considered statistically significant. RESULTS: The mean number of mast cells were increased in oral reactive lesions when compared to NG. Although mast cells were significantly higher in IFH and IF, there was no correlation found among mast cells and fibrosis/inflammation/vascularity. CONCLUSION: Reactive process involves multiple interactions among mast cells, endothelial cells, fibroblasts, and other immune cells, among which the role of mast cells has been evaluated. Mast cell count increases in these reactive lesions, possibly reflecting an important role in microenvironment modification, but it is not the sole cause of these lesions' pathogenesis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".