Bibliographic record
Abstract
Abstract Abstract SCI-12 In order to distinguish a normal telomere from a double strand break, a minimum number of telomere repeats must “cap” each chromosome end. The length of each repeat array will reflect a unique history of addition and losses. Telomere losses are known to occur sporadic as well as with every replication cycle. Losses of telomeric DNA are countered by the telomerase enzyme containing telomerase RNA (encoded by the TERC gene) and a reverse transcriptase protein (encoded by TERT gene) as minimal components. Telomerase levels are high in cells of the germline and immortal cellines and the telomere length is typically maintained in such cells. In contrast, telomerase activity is limiting in most human somatic (stem) cells and as a result the average length of telomere repeats in most somatic cells shows a highly significant decline with age. The hypothesis that loss of telomere repeats acts as a “mitotic clock” and a tumor suppressor mechanism in stem cells is strongly supported by recent studies of patients with mild telomerase deficiency resulting from haplo-insufficiency for either the TERC or TERT gene. Such genetic defects can give rise to various disorders including autosomal dominant Dyskeratosis Congenita (DKC), aplastic anemia, liver fibrosis and pulmonary fibrosis. Other recent studies have revealed that amplification of the hTERT gene is one of the most common genetic abnormalities in various cancers. Paradoxically, it is becoming clear that SNPs within the TERT locus are among the most reproducible risk factors for the development of different types of cancer including lung cancer, acute myeloid leukemia and chronic lymphocytic leukemia. The links between hypo- and hyperproliferative consequences of inborn telomerase deficiencies and SNP's in the TERT gene are poorly understood. It seems plausible that the increased risk of leukemia development in aplastic anemia, myelodysplastic syndrome and Dyskeratosis Congenita, results from stem cell failure. Could reduced stem cell numbers by itself provide a risk factor for tumor development? More direct measures of stem cell numbers in vivo are needed to examine this possibility and the relationship between stem cell numbers and tumor development in patients with defective telomere maintenance as well as in normal individuals as a function of age. Measurements of the average telomere length as well as the length of telomere repeats at individual chromosome ends in specific cells and tissues will further calrify the involvement of telomeres in bone marrow failure, normal aging and tumor biology. Disclosures Lansdorp: Repeat Diagnostics Inc.: Equity Ownership.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".