Bibliographic record
Abstract
The ability to repair damaged DNA and to maintain genome stability is the utmost importance for the survival of any species. Hence, it is not surprising to find that DNA repair mechanisms are evolutionarily conserved and are expected to evolve to maintain the existence of species. In the last few years, there has been an exponential increase in the evidence linking RNA processing with DNA repair programs. For instance, the well-studied DNA base excision repair (BER) enzyme apurinic/apyrimidinic endonuclease 1 can cleave RNA molecules, regulate mRNA levels, and associate physically with proteins involved in RNA processing. It is now clear that not only the expression of noncoding RNAs are changed upon DNA damage, they can modulate the expression of genes involved in the genome stability programs. The five reviews in this Forum provide the up-to-date knowledge on DNA repair, with a focus on BER, and a perspective on how the two ancient biochemical pathways are linked. The contributions demonstrate the complexity of such interactions, but also pointed out the opportunities for new therapeutic interventions. Future in vivo studies on the link between DNA repair processes and RNA metabolism should contribute to our basic understanding of physiology, disease, and treatment strategies.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 0.012 |
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".