The crosstalk between DNA-damage responses and innate immunity
Bibliographic record
Abstract
DNA damage is typically caused during cell growth by DNA replication stress or exposure to endogenous or external toxins. The accumulation of damaged DNA causes genomic instability, which is the root cause of many serious disorders. Multiple cellular organisms utilize sophisticated signaling pathways against DNA damage, collectively known as DNA damage response (DDR) networks. Innate immune responses are activated following cellular abnormalities, including DNA damage. Interestingly, recent studies have indicated that there is an intimate relationship between the DDR network and innate immune responses. Diverse kinds of cytosolic DNA sensors, such as cGAS and STING, recognize damaged DNA and induce signals related to innate immune responses, which link defective DDR to innate immunity. Moreover, DDR components operate in immune signaling pathways to induce IFNs and/or a cascade of inflammatory cytokines via direct interactions with innate immune modulators. Consistently, defective DDR factors exacerbate the innate immune imbalance, resulting in severe diseases, including autoimmune disorders and tumorigenesis. Here, the latest progress in understanding crosstalk between the DDR network and innate immune responses is reviewed. Notably, the dual function of innate immune modulators in the DDR network may provide novel insights into understanding and developing targeted immunotherapies for DNA damage-related diseases, even carcinomas.
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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 | 0.002 | 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) | 0.003 | 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".