Kallikrein‐related peptidase expression in odontogenic cysts and tumors: An immunohistochemical comparative study
Bibliographic record
Abstract
AIM: The aim of the present study was to profile the expression of human kallikrein (KLK)-related peptidases (KLK) in odontogenic lesions. METHODS: Paraffin-embedded, formalin-fixed, non-odontogenic (control) and odontogenic lesions were stained for KLK using a standard immunohistochemical technique. The intensity and proportion of epithelial cells stained was scored. Reverse transcription-polymerase chain reaction was utilized to evaluate KLK 1-15 mRNA expression in ameloblastomas. RESULTS: KLK 3, 4, 9, 11, and 14 were present in all lesions. KLK 3 staining was increased in ameloblastomas and keratocystic odontogenic tumors. KLK 5 was present only in Keratocystic odontogenic tumor. KLK 6 was significantly higher in ameloblastomas than in other lesions. For KLK 7, keratocystic odontogenic tumors and nasopalatine duct cysts were significantly different. KLK 6, 8, 10, 11, and 13 were significantly higher in ameloblastomas than in other lesions. KLK 9 was increased in keratocystic odontogenic tumors and dentigerous cysts. The expression of KLK 1, 4, 7, 8, 10, and 12 mRNA was found in ameloblastomas. CONCLUSION: The results suggested that KLK 6, 8, 10, and 13 could be involved in the progression of ameloblastomas. KLK 10 could have a greater role in odontogenic lesions, rather than non-odontogenic lesions. Future studies aim to define the specific roles of KLK cascades in odontogenic lesions.
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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 | 0.001 | 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".