Bibliographic record
Abstract
One of the mechanisms used by epigenetic therapy is the elevation of host cell-derived double stranded RNA (dsRNA) baseline levels through overexpression of genomic repetitive elements especially Alu retroelements. The dsRNAs trigger immunogenic responses since immune system cannot distinguish between endogenous and exogenous dsRNAs derived from viral infections; hence called “Viral mimicry response”. These dsRNAs are recognized by pattern recognition receptors (PRRs) such as MDA-5 which further induce inflammatory responses through interferon secretion. However, the response is limited through the function of some editing enzymes such as ADAR1 which destabilizes the formation of dsRNAs and renders the therapy less efficient through attenuating interferon secretion by immune cells. Since, some cancer cells can survive even after ADAR1 inhibition, it is speculated that there might be other mechanism which contribute to dsRNA destabilization. Since dsRNA formation derived from retroelement transcripts mimics viral infections, we tried to review the mechanistic approaches applied during host-pathogen interaction to highlight a possible candidate which might be cogitable for further investigations in epigenetic therapy. dsRNAs produced by RNA viruses are sensed by PRRs and activate nuclear factor erythroid 2 p45-related factor 2 (NRF2) which further downregulates STING protein and attenuates IFN release. RNA viruses such as SARS-CoV-2 have the potential to impair NRF2 signaling and eliminate its inhibitory effect from STING, leading to excessive release of IFNs and destroy pulmonary cells through cytokine release storm (CRS). Here, we briefly explain that NRF2, in a very downstream side of anti-viral response, might be a potential candidate target in combination with epigenetic therapy to circumvent the limitations in cancer epigenetic therapy.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".