Role of Brd4 in the production of inflammatory cytokines in mouse macrophages treated with titanium particles
Bibliographic record
Abstract
Brd4 protein is an important epigenetic regulator involved in the process of inflammatory cytokine production in many diseases. However, whether and how Brd4 participates in the process of wear-particle-induced inflammation remain unclear. This study aimed to investigate the potential role of Brd4 in titanium (Ti) particle-induced inflammatory cytokine production in mouse macrophage RAW264.7 cells. Our experiment detected Brd4 expressed in both normal synovium and periprosthetic osteolysis interface membrane, but the expression increased in the interface membrane as compared with that in normal synovium. Treatment with Ti particles significantly increased TNF-α, IL-6, and IL-1β production in RAW264.7 cells, which was inhibited by JQ1 or Brd4-siRNA. Ti particles enhanced the expression of Brd4, which was abrogated by JQ1. Ti particles enhanced NF-κB p65 and IKK phosphorylation and attenuated IκBα protein expression, which were abrogated by JQ1. Co-immunoprecipitation analysis indicated that Ti particles promoted the binding of Brd4 to acetylated NF-κB p65 (lysine-310), which was also abrogated in JQ1-treated RAW264.7 cells. In conclusion, Brd4 expression increases in interface membrane and Brd4 participates in the production of pro-inflammatory cytokines induced by Ti particles via promoting the activation of NF-κB signaling and binding to acetylated NF-κB p65 (lysine-310) in mouse macrophages.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".