CD6 modulation ameliorates immune complex-mediated glomerulonephritis
Bibliographic record
Abstract
Abstract Lupus nephritis (LN) is a serious end organ complication of systemic lupus erythematosus (SLE) in which T cells are thought to play an essential role. CD6 is a co-stimulatory receptor on T cells, that binds to activated leukocyte cell adhesion molecule (ALCAM), a ligand expressed on antigen presentation cells and epithelial and endothelial tissues. The CD6-ALCAM pathway plays an integral role in modulating T cell activation and trafficking, and increased levels of CD6 are associated with pathogenic T cell responses. To assess the role of the CD6-ALCAM pathway in LN pathogenesis, we tested a monoclonal antibody against CD6 in a short-term, validated, inducible murine model of lupus nephritis known as nephrotoxic serum nephritis (NTN). NTN mice were treated 3× per week with an anti-CD6 mAb (10D12, 60ug/dose, n=23) or with vehicle control (n=23). Healthy mice were also included as a control (n=12). Mice treated with the anti-CD6 mAb displayed decreased levels of proteinuria (p<0.001) and significantly improved BUN levels (p<0.01) compared to vehicle control mice. Histology also significantly improved with anti-CD6 treatment (p<0.05). RT-PCR revealed significantly decreased levels of VCAM and RANTES in the kidneys of treated mice, while anti-inflammatory IL-10 was increased, compared to vehicle control mice. Flow cytometry analysis indicated decreased accumulation of both renal-infiltrating activated T cells (CD4+CD25+CD69+, p <0.01) inflammatory macrophages (p<0.05). Overall, these results indicate that the CD6-ALCAM pathway is an important driver of inflammation and pathology in LN and, thus, a promising therapeutic option that is more selective than the immunosuppressive therapies currently offered.
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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) | 0.000 | 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) | 0.002 | 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".