Dendritic Cells Contribute to Autoimmune Kidney Injury in MRL-<i>Fas</i><i><sup>lpr</sup></i> Mice
Bibliographic record
Abstract
OBJECTIVE: Dendritic cells (DC) contribute to autoimmune disease progression and pathogenesis. Mature DC have been reported to secrete high mobility group box protein (HMGB-1), a novel inflammatory cytokine, via p38 mitogen-activated protein kinase (MAPK) activation. We investigated whether DC are involved in progression of autoimmune diseases followed by secretion of HMGB-1 via p38 MAPK activation in a lupus-prone mouse model. METHODS: FR167653, a specific inhibitor of p38 MAPK, was given orally from 3 months of age in MRL-Fas(lpr) mice. Cultured DC, treated with or without FR167653, were stimulated with tumor necrosis factor-alpha. RESULTS: Inhibition of p38 MAPK led to a reduction in the number of CD11c-positive cells, including those with the mature phenotype, in the diseased kidney and spleen, which resulted in improvement of kidney pathology in MRL-Fas(lpr) mice. The number of CD11c-positive cells in circulation was also reduced. HMGB-1 protein and transcripts detected in the diseased kidney, and the number of cells dual-positive for HMGB-1 and CD11c, were reduced by inhibition of p38 MAPK. Maturation of cultured DC and increased cytokines, including HMGB-1, in the supernatant were inhibited by FR167653 treatment. These results suggest that DC are involved in the progression of autoimmune kidney diseases in MRL-Fas(lpr) mice followed by HMGB-1 secretion via p38 MAPK activation. CONCLUSION: Our results indicated that DC secrete HMGB-1 via p38 MAPK activation to participate in autoimmunity in MRL-Fas(lpr) mice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".