Telomere Shortening in the Esophagus of Japanese Alcoholics: Relationships with Chromoendoscopic Findings, ALDH2 and ADH1B Genotypes and Smoking History
Bibliographic record
Abstract
Chromoendoscopy with Lugol iodine staining provides important information on the development of squamous cell carcinoma (SCC). In particular, distinct iodine-unstained lesions (DIULs) larger than 10 mm show a high prevalence in high-grade intraepithelial neoplasia. It has also been reported that inactive ALDH2*1/*2 and less-active ADH1B*1/*1, and smoking, are risk factors for esophageal SCC. We previously examined telomere shortening in the esophageal epithelium of alcoholics, and suggested a high prevalence of chromosomal instability in such individuals. In the present study, we attempted to analyze telomere lengths in 52 DIULs with reference to both their size and multiplicity, ALDH2 and ADH1B genotypes, and smoking history. Patients with DIULs <10 mm (n = 42) had significantly longer telomeres than those with DIULs ≥10 mm (n = 10, p = 0.008). No significant differences in telomere length were recognized between the ALDH2 and ADH1B genotypes (ALDH2 active/inactive = 35/17, ADH1B active/inactive = 32/20; p = 0.563, 0.784, respectively) or among four groups of patients divided according to smoking history (never-, ex-, light, and heavy smokers = 3, 6, 21, and 22 patients, respectively; p = 0.956). Patients without multiple DIULs (n = 17) had significantly longer telomeres than patients with multiple DIULs (n = 35, p = 0.040). It is suggested that alcoholism reduces telomere length in the esophagus, irrespective of genotype or smoking habit. Telomere shortening may not generate cancer directly, but may create conditions under which SCC can develop more easily, depending on subsequent exposure to carcinogens.
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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".