Investigation of the Involvement of Macrophages and T Cells in D-Penicillamine-Induced Autoimmunity in the Brown Norway Rat
Bibliographic record
Abstract
The initiating events in drug-induced autoimmunity are poorly understood and difficult to study. We examined the role of macrophages and T-cells in the Brown Norway rat model of D-penicillamine-induced autoimmunity. When activated, macrophages can act as both antigen presenting cells, initiating immune responses, and as phagocytic cells mediating systemic tissue damage. We found that B7(+) macrophages are the major antigen-presenting cell type infiltrating the spleen and caecum early in the response to Dpenicillamine. As well, the increase in splenic B7(+) macrophages correlates with the incidence of autoimmune disease. Treatments that increase the incidence of disease accentuate the increase in splenic B7(+) macrophages, and treatments that prevent disease also prevent the increase in B7(+) macrophages. In vivo depletion of macrophages appeared to decrease, but not totally prevent, autoimmune disease. The role of T-cells in D-penicillamine-induced autoimmunity was also examined using the T-cell inhibitor tacrolimus. Short-term treatment with tacrolimus not only prevented disease onset but also reversed ongoing disease and prevented disease relapse upon re-challenge with D-penicillamine. The results of this study indicate that both macrophages and T-cells could be important immune cell types involved in D-penicillamine-induced autoimmunity. Furthermore, the effects of tacrolimus in this model suggest that short-term tacrolimus treatment may be an effective way to prevent or treat IDRs in high-risk patients.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".