Blockade of anti-dsDNA ameliorates systemic lupus erythematosus in MRL/Faslpr mice through ameliorating inflammation via the PKCδ–NLRC4 axis
Bibliographic record
Abstract
Anti-double-stranded DNA (anti-dsDNA) is closely associated with the inflammatory burden in the brain after ischemic stroke. Here, we studied the inflammatory cascade and investigated the mechanisms behind the pro-inflammatory role of dsDNA in systemic lupus erythematosus (SLE). The serum levels of interleukin-1beta (IL-1β) and IL-6 in SLE patients and the corresponding controls were evaluated using ELISA, and the expression level of caspase-1 was evaluated using quantitative real-time polymerase chain reaction (qRT-PCR). We found that the serum levels of IL-1β and IL-6 were increased in the SLE patients. The expression of caspase-1 was upregulated and positively correlated with the levels of pro-inflammatory factors. The level of anti-dsDNA was also elevated and positively correlated with the results for the mean fluorescence intensity (MFI) of caspase-1. Additionally, we evaluated the functions of PRKCD encoding protein kinase c delta (PKCδ) and NLRC4, in vivo, in MRL/Faslpr mice. We found that renal injury was aggravated, and the levels of pro-inflammatory factors were increased in the MRL/Faslpr mice. We also found that increased levels of NLRC4 in the mice exacerbated renal injury and increased the levels of pro-inflammatory factors, whereas inhibition of PKCδ had the opposite results. These findings provide unique perspectives on pathogenesis of SLE and indicate that inhibition of anti-dsDNA could attenuate renal inflammatory burden, representing a promising therapeutic opportunity for SLE.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".