Differential Immunoreactivity of p16 in Leiomyosarcomas and Leiomyoma Variants
Bibliographic record
Abstract
Several studies have now examined the cDNA expression profiles of healthy myometrium, leiomyomas (LM), and leiomyosarcomas (LMS). This has produced a list of candidate genes that might be useful tools for distinguishing these entities from each other. The potential candidates identified from this body of research include insulinlike growth factor 1, h-caldesmon, cytokeratin 18, and the cyclin-dependent kinase 4 inhibitor, p16. To determine whether the immunohistochemical expression of these proteins could aid in the diagnosis of LMS and LM variants, we constructed a tissue microarray consisting of cases of healthy myometrium (n = 10), LM (not otherwise specified and variants; n = 47), and LMS (n = 8), and then measured the immunoreactivity of each of these proteins. The cases were scored on the basis of staining intensity (weak, moderate, or strong) and extent (focal or diffuse) and were assigned a final score from 0 to +3. Immunostaining for p16 was statistically stronger in LMS than in LM and its subtypes (P < 0.001). Specifically, the p16 immunostaining score in LMS cases (n = 8) was at least +2, whereas the p16 immunostaining scores in all LM cases (n = 47) were either 0 (n = 35) or +1 (n = 12). The expression of the remaining antibodies did not show a statistically significant difference between the 2 groups. Furthermore, none of the markers studied showed any differences among the LM variants. The results of this study confirm the overexpression of p16 in LMS and suggest that p16 can serve as a reliable immunohistochemical marker in distinguishing uterine LMS from LM and its benign variants.
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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) | 0.001 | 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".