InTERTpreting telomerase structure and function
Bibliographic record
Abstract
The Nobel Prize in Physiology or Medicine was recently awarded to Elizabeth Blackburn, Carol Greider and Jack Szostak for their pioneering studies on chromosome termini (telomeres) and their discovery of telomerase, the enzyme that synthesizes telomeres. Telomerase is a unique cellular reverse transcriptase that contains an integral RNA subunit, the telomerase RNA and a catalytic protein subunit, the telomerase reverse transcriptase (TERT), as well as several species-specific accessory proteins. Telomerase is essential for genome stability and is associated with a broad spectrum of human diseases including various forms of cancer, bone marrow failure and pulmonary fibrosis. A better understanding of telomerase structure and function will shed important insights into how this enzyme contributes to human disease. To this end, a series of high-resolution structural studies have provided critical information on TERT architecture and may ultimately elucidate novel targets for therapeutic intervention. In this review, we discuss the current knowledge of TERT structure and function, revealed through the detailed analysis of TERT from model organisms. To emphasize the physiological importance of telomeres and telomerase, we also present a general discussion of the human diseases associated with telomerase dysfunction.
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".