Expression of Telomerase Reverse Transcriptase (TERT) in Malignant Mesotheliomas
Bibliographic record
Abstract
To evaluate the usefulness of determinations of telomerase activity for distinguishing malignant from benign mesothelial lesions, immunohistochemical (using a rabbit polyclonal antibody and the peroxidase method; n = 68) and in situ hybridization (using sense and antisense oligonucleotide probes; n = 46) studies were made on malignant mesotheliomas (epithelioid, 39; sarcomatoid, 18, including 2 of the desmoplastic type; and biphasic, 11) and 19 benign mesothelial lesions (benign mesothelial hyperplasia, 3; and reactive pleuritis, 16). In addition, biochemical studies of telomerase activity were made in 9 of the malignant mesotheliomas. Telomerase activity was detected histochemically in all but one of the malignant mesotheliomas, but only in one (pleuritis) of the benign lesions, in which it was present only in activated lymphocytes. Antisense hybridization signals indicated the presence of telomerase mRNA mainly in the cytoplasm of the malignant cells. Sense probes gave negative results. Biochemical determinations revealed a strong telomerase activity in the 9 malignant mesotheliomas examined. This study demonstrates the usefulness of immunohistochemical staining for the evaluation of mesotheliomas. The required immunostaining can be performed using paraffin sections of formalin-fixed tissues.
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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) | 0.000 | 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".