Peroxisome Proliferator–Activated Receptor γ Expression Correlates with the Differentiation Level of Normal, Premalignant, and Malignant Laryngeal Squamous Cells
Bibliographic record
Abstract
OBJECTIVE: Peroxisome proliferator-activated receptor gamma (PPAR gamma) has been implicated in the differentiation of several cell types, such as adipocytes, monocytes, and epidermal keratinocytes. This study concentrated on PPAR gamma's potential role in the maturation process of laryngeal squamous epithelium, both normal and premalignant, as well as in the differentiation grade of squamous cell carcinomas (SCCs) of the larynx. DESIGN: A retrospective study. SETTING: A basic research anatomy laboratory, operating within a tertiary care institution. MATERIALS: Clinical specimens from 89 subjects with normal laryngeal epithelium and hyperplastic, dysplastic, or malignant lesions. METHODS: Paraffin-section immunohistochemistry. MAIN OUTCOME MEASURES: Strength and extent of PPAR gamma presence, specified by stain intensity and ratio of stained cells, respectively. RESULTS: All stratified histologic categories, that is, normal mucosa, hyperplasia, and dysplasia, displayed a significant increase in PPAR gamma expression in suprabasal differentiated layers compared with basal undifferentiated ones (rho < .01). Hyperplasia and dysplasia manifested lower and higher levels of PPAR gamma, respectively, in comparison with normal epithelium. Whereas grade Pi and III SCCs were characterized by equal expression, well-differentiated tumours possessed considerably raised receptor content. Fluctuations of expression among various histologic categories lacked statistical significance, however. CONCLUSIONS: Our results suggest PPAR gamma induction throughout squamous cell differentiation in normal, premalignant, and malignant epithelia.
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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 | 0.001 |
| 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) | 0.002 | 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".