Comparison of human telomerase reverse transcriptase messenger RNA and telomerase activity as urine markers for diagnosis of bladder carcinoma.
Bibliographic record
Abstract
BACKGROUND: Human telomerase reverse transcriptase (hTERT) has been identified as the catalytic subunit of telomerase ribonucleoprotein complex known to be required for cellular immortality and oncogenesis. Although human telomerase activity (hTA) is considered as a general marker for malignancy based on its presence in most malignant tumors including bladder cancer, its detection in urine is affected by many factors. The objective of this study was to compare the clinical utility of detecting urine hTERT messenger RNA (mRNA) by multiplex hTERT/GAPDH RT-PCR and urine hTA by telomerase repeat amplification protocol (TRAP) in the diagnosis of bladder cancer. METHODS AND RESULTS: Cystoscopy urine samples or bladder washes prospectively collected from 35 patients with confirmed (35) or clinically suspected (5) transitional cell carcinoma (TCC) of the bladder were examined by TRAP, hTERT/GAPDH RT-PCR, and urine cytology. The control group comprised 21 healthy volunteers and 3 patients without TCC. The hTERT/GAPDH RT-PCR test showed significantly higher diagnostic sensitivity than TRAP assay (94.3% vs 48.6%, P <.001) and urine cytology (95.2% vs 61.9%, P =.008) for confirmed TCCs. In particular, for superficial TCCs low grade (I-II), the hTERT/GAPDH RT-PCR test outperformed TRAP (90% vs 25%, P <.001) and urine cytology (91.7% vs 58.3%, P =.46). The overall specificity of the hTERT/GAPDH RT-PCR, TRAP and urine cytology was 92% (22/24), 100% (24/24), and 100% (3/3), respectively. A positive hTERT mRNA expression was also detected in urologic specimens from 3 patients with previous history of TCC, 3 to 6 months before cystoscopic evidence of cancer. CONCLUSION: In this pilot study, the hTERT mRNA expression in urine sediments is a more sensitive marker for diagnosis of TCC of the bladder than hTA and cytology. However, there is a higher false-positive rate.
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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 | 0.003 | 0.007 |
| 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 | 0.001 | 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".