Dj1 deficiency protects against atherosclerosis with anti-inflammatory response in macrophages
Bibliographic record
Abstract
Abstract Inflammation is a key contributor to atherosclerosis with macrophages playing a pivotal role through the induction of oxidative stress and cytokine/chemokine secretion. DJ1, an anti-oxidant protein, has shown to paradoxically protect against chronic and acute inflammation. However, the role of DJ1 in atherosclerosis remains elusive. To assess the role ofDj1in atherogenesis, we generated whole-bodyDj1-deficient atherosclerosis-proneApoenull mice (Dj1−/−Apoe−/−). After 21 weeks of atherogenic diet,Dj1−/−Apoe−/−mice were protected against atherosclerosis with significantly reduced plaque macrophage content. To assess whether haematopoietic or parenchymalDj1contributed to atheroprotection inDj1-deficient mice, we performed bone-marrow (BM) transplantation and show thatDj1-deficient BM contributed to their attenuation in atherosclerosis. To assess cell-autonomous role of macrophageDj1in atheroprotection, BM-derived macrophages fromDj1-deficient mice andDj1-silenced macrophages were assessed in response to oxidized low-density lipoprotein (oxLDL). In both cases, there was an enhanced anti-inflammatory response which may have contributed to atheroprotection inDj1-deficient mice. There was also an increased trend of plasma DJ-1 levels from individuals with ischemic heart disease compared to those without. Our findings indicate an atheropromoting role ofDj1and suggests that targetingDj1may provide a novel therapeutic avenue for atherosclerosis treatment or prevention.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".