Conserved Telomere Length in Human Ectopic Thyroids: An Argument Against Premature Differentiation Causing Arrested Migration
Bibliographic record
Abstract
BACKGROUND: In humans, the cause of arrested migration of the median thyroid anlage resulting in an ectopic sublingual gland is unknown. These ectopic glands have a normal follicular architecture but their thyrotropin-induced growth is insufficient, leading to congenital hypothyroidism in the vast majority of affected subjects. We hypothesized that arrested migration is due to premature differentiation [reflected by decreased telomere length (TL)], as observed in neural tube defects in mice. METHODS: Absolute TL and telomerase reverse transcriptase (hTERT) expression was measured in four ectopic and six orthotopic thyroids. TL was measured by quantitative polymerase chain reaction of genomic DNA, whereas hTERT expression was measured by quantitative polymerase chain reaction of total RNA. RESULTS: The mean±standard deviation TL (in kilobases per diploid genome) was 140.45±40.07 in ectopic and 97.50±30.48 in orthotopic thyroids (p=0.12). Expression of hTERT was quiescent in both ectopic and orthotopic thyroids. CONCLUSIONS: Compared with orthotopic thyroids, TL shortening is not observed in ectopic thyroid tissues and, consequently, no compensatory hTERT expression was measured. This makes premature differentiation an unlikely cause of arrested migration and it suggests, indirectly, that ectopic thyroids are not at higher risk of cancer than orthotopic thyroids.
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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 | 0.002 |
| 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 | 0.001 |
| 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".