Antioxidant enzyme levels in oral squamous cell carcinoma and normal human oral epithelium
Bibliographic record
Abstract
BACKGROUND: The antioxidant enzymes (manganese- and copper-zinc-containing superoxide dismutases, catalase and glutathione peroxidase) limit cell injury induced by reactive oxygen species. The purpose of the study was to determine whether human oral squamous cell carcinomas have altered antioxidant enzyme levels. This study is the first to undertake this task in human oral mucosa and squamous cell carcinoma. METHODS: Semiquantitative immunohistochemistry was used to examine 26 archived oral squamous cell carcinoma biopsies. Fourteen well-differentiated and 12 poorly differentiated tumors were examined, as were 12 specimens of oral mucosa. All sections were reviewed by two oral and maxillofacial pathologists, and image analysis of the immunostained sections was performed using NIH Image. Antioxidant enzyme staining intensities were compared in the different groups by Duncan's multiple range test. RESULTS: In general, mucosal basal cells displayed lower antioxidant enzyme levels than spinous cells, and primary tumor cells displayed lower antioxidant enzyme staining intensities than did their normal cell counterparts. Moreover, poorly differentiated tumor cells showed lower antioxidant enzyme staining intensities than well-differentiated tumor cells. Manganese-containing superoxide dismutase staining intensities were, however, higher in well-differentiated oral squamous cell carcinomas than their normal cells of origin. CONCLUSIONS: Detection of antioxidant enzymes may be a useful future marker in the molecular diagnosis of the oral cancer. Moreover, it may be possible to not only monitor the effectiveness of chemopreventive and therapeutic strategies in oral cancer using these enzymes, but to monitor tumor recurrence.
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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".