The consequences of viral infection on host DNA damage response: a focus on SARS-CoVs
Bibliographic record
Abstract
DNA damage and genome instability in host cells are introduced by many viruses during their life cycles. Severe acute respiratory syndrome coronaviruses (SARS-CoVs) manipulation of DNA damage response (DDR) is an important area of research that is still understudied. Elucidation of the direct and indirect interactions between SARS-CoVs and DDR not only provides important insights into how the viruses exploit DDR pathways in host cells but also contributes to our understanding of their pathogenicity. Here, we present the known interactions of both SARS-CoV and SARS-CoV-2 with DDR pathways of the host cells, to further understand the consequences of infection on genome integrity. Since this area of research is in its early stages, we try to connect the unlinked dots to speculate and propose different consequences on DDR mechanisms. This review provides new research scopes that can be further investigated in vitro and in vivo, opening new avenues for the development of anti-SARS-CoV-2 drugs.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".