Bibliographic record
Abstract
Abstract The importance of telomere length to human health, aging, and cancer continues to be underappreciated. This review examines some basics of telomere biology and relates how telomere function, telomerase activity, and mutations in TERC or TERT are involved in bone marrow failure, leukemias, and other cancers. Given the challenge to obtain accurate data on telomerase activity and telomere length in specific cell types, the situation in acute myeloid leukemia (AML) remains puzzling. In most cancers, telomerase levels are increased after cells have encountered a “telomere crisis,” which is typically associated with poor prognosis. Cells emerging from “telomere crisis” have defective DNA damage responses, resulting, for example, from loss of p53. Such cells often express elevated telomerase levels as a result of point mutations in the TERT promoter or amplification of the TERT gene. While telomeres in AML blasts are typically shorter than expected for normal leukocytes, most AML cells do not show evidence of having gone through a “telomere crisis.” In chronic myeloid leukemia (CML), the difference between the telomere length in nonmalignant T cells and malignant blasts from the same patient was found to correlate with the remaining duration of the chronic phase. This observation supports that a mitotic clock is ticking in CML stem cells and that disease progression in CML heralds the onset of a “telomere crisis.” The presence of very short telomeres in tumor cells was found to predict disease progression in chronic lymphocytic leukemia, myeloma, and various solid tumors. In view of these findings longitudinal studies of telomere length in AML appear worthwhile.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".