Immunohistochemical detection of Hsp90 and Ki-67 in pterygium
Bibliographic record
Abstract
BACKGROUND: To examine the immunohistochemical expression of heat shock protein 90 (Hsp90) and Ki-67 protein in human pterygium. MATERIALS AND METHODS: Tissues obtained during pterygium surgery of 15 patients who underwent the bare-sclera procedure and 10 normal conjunctivae were studied. All of these pterygia were primary ones. Recurrent pterygia were excluded. Normal bulbar conjunctivas (2 x 2 mm) were obtained from the nasal region close to the limbus from patients during their cataract and retina surgeries. Immunohistochemical detection of Hsp90 and Ki67 was done using the streptavidin-biotin method in paraffin embedded tissue sections. RESULTS: The percentage of cells stained for Hsp90 was greater for pterygium epithelium (76 ± 10.8) than for normal conjunctiva (1.4 ± 0.8). In each pterygium sample more than 60% of cells were positive. The differences in positive cells between normal and pterygium epithelium were highly significant for Hsp90 (P < 0,001).Pterygium epithelium also showed a higher percentage of cells that stained for Ki67 (10.1 ± 9.5) than for normal conjunctiva (2.1 ± 1.9). The differences in positive cells were also statistically significant for Ki67 (P < 0.01). Although there were significant differences in the majority of samples observed. It was noted that in some samples there was no difference between normal and pterygium epithelium for Ki67. CONCLUSION: Our results indicate an abnormal expression of Hsp90 and ki-67 in pterygium samples when compared to normal conjunctiva.The finding of abnormal expression of levels of Hsp90 in pterygium samples can stimulate new research into pterygium and its recurrence. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/1128478792898812.
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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) | 0.002 | 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".