Telomere Metabolism and Diagnostic Demonstration of Telomere Measurement in the Human Esophagus for Distinguishing Benign from Malignant Tissue by Tissue Quantitative Fluorescence in situ Hybridization
Bibliographic record
Abstract
OBJECTIVE: We have developed a novel method for evaluating telomere length in four different cell types in non-cancerous and cancerous mucosal tissue from 15 cases of squamous cell carcinoma of the esophagus using tissue quantitative fluorescence in situ hybridization (Q-FISH). We hypothesized that the very rapid cell proliferation observed in esophageal squamous cell carcinomas might accelerate the telomere shortening and chromosomal instability associated with carcinogenesis. METHODS: Tissue Q-FISH and the telomere to centromere intensity ratio (TCR) were used to compare telomere shortening in tissue sections taken from esophageal squamous cell carcinomas and adjacent non-cancerous esophageal tissues. RESULTS: The peak percentage of TCR was <1 for esophageal squamous carcinoma cells and >1 for the non-cancerous esophageal cell types. Basal layer cells had the longest telomeres in comparison with prickle, cancer, and stromal cells, and strongly expressed hTERT, cytokeratin 14 and CD49f, but not MIB-1. CONCLUSION: These results suggest the presence of stem cells in the basal layer of the esophagus. Esophageal squamous cell carcinomas also display anaphase bridges, evidencing chromosomal instability. In conclusion, our TCR method can be used to distinguish between benign and malignant tissue in esophageal lesions. In order to apply this approach clinically to individual cases, further studies are in progress.
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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 | 0.001 | 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 | 0.001 |
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".