Bibliographic record
Abstract
Telomerase is a specialized RNA template-containing reverse transcriptase that mediates telomere repeat synthesis at chromosome ends. The maintenance of telomere length and integrity is essential for cell survival. Telomerase is active in most immortal and tumor cells, whereas the majority of normal human cells demonstrate no detectable activity and undergo telomere shortening. The identification of a possible role for telomerase in cellular aging and cancer has led to numerous studies designed to characterize this ribonucleoprotein enzyme. Inhibiting telomerase activity in immortal human cells reduces cellular proliferative capacity and can lead to cell death. Identifying mechanisms to specifically inhibit telomerase activity in malignant cells could thus be of great therapeutic value in the treatment of cancer. In this review, we summarize the current understanding of the mechanism of action of human telomerase. The biochemical characterization of telomerase is necessary for the design and evaluation of antitelomerase therapies. Different strategies are currently under investigation to design inhibitors that target the reverse transcriptase and RNA components of the telomerase complex. Recent advances in the design of these inhibitors and their properties are discussed.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".