Bibliographic record
Abstract
Studies in hematopoietic stem cell (HSC) biology are often focused on "self-renewal" and differentiation. Implicit in the word self-renewal is that the two daughter cells generated by a self-renewal division are identical to the parental cell. Strictly speaking, this is not possible because DNA is continuously damaged and repaired by DNA-repair mechanisms that are not 100% efficient. It is important to note that the efficiency of DNA repair varies greatly among different stem cell types. For example, embryonic stem cells are quite resistant to DNA damage and maintain the length of telomere repeats on serial passage, whereas HSCs are quite sensitive to DNA damage and less able to maintain telomere length. Most likely, differences between stem cell types in DNA repair and telomere maintenance pathways coevolved with cell mass, turnover, reproductive strategy, and life span. This idea has given rise to the notion that many aspects of normal aging could primarily reflect limitations in DNA repair and telomere-maintenance pathways in the (stem) cells of the soma. In humans, levels of telomerase in HSCs are under extremely tight control, as is illustrated by the marrow failure in patients with (mild) telomerase deficiencies. Here, the role of telomerase in human HSC biology is reviewed, and it is proposed that telomerase has an important role in the repair of G-rich DNA.
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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".