Amelioration of skin and kidney disease in a spontaneous murine lupus model via CD6 modulation
Bibliographic record
Abstract
Abstract Systemic lupus erythematosus (SLE) is a prototypic autoimmune disease that can affect multiple organ systems, including the kidneys, skin, and brain. T cells are an important mediator in this end organ damage. CD6 is a co-stimulatory receptor, predominantly expressed on T cells, which binds with activated leukocyte cell adhesion molecule (ALCAM), a ligand expressed on antigen presentation cells and various epithelial and endothelial tissues. This signaling pathway is vital for T cell activation, proliferation, differentiation and trafficking. We found increased expression of both CD6 and ALCAM in the kidneys of MRL/lpr mice (a spontaneous model of SLE) versus healthy control B6 mice. In a separate experiment, female MRL/lpr mice were aged to 9–10 weeks of age, at which point we began treating with either anti-CD6 antibody (60 ug/dose, intraperitoneally twice per week), isotype control (60 ug/dose, twice per week), or cyclophosphamide (25 mg/kg, once per week). We also included a no treatment group and a group of MRL/MpJ mice, a congenic healthy control strain. Mice treated with anti-CD6 show lower levels of proteinuria and BUN (p<0.05), improved survival rates, and decreased renal pathology compared to isotype control mice. Flow cytometry revealed decreased numbers of activated and effector T cells within the kidneys of anti-CD6 treated mice compared to isotype control mice. While there was no difference in anti-DNA levels, anti-CD6 treatment significant improved the spontaneous skin lesions associated with disease progression. Overall, these results indicate that targeting CD6-ALCAM interactions may have promising therapeutic potential within the context of different end organ pathologies within lupus.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".