T-cell immunity in the experimental autoimmune vasculitis rat model
Bibliographic record
Abstract
ANCA-vasculitis (AAV) is a small-vessel vasculitis characterized by the presence of autoantibodies against proteinase-3 (PR3) or myeloperoxidase (MPO). The dynamics of the T-cell response within tissues is studied best in animal models. It was the aim to analyze the lesional T-cell dynamics in the experimental autoimmune vasculitis model. Female Wistar Kyoto-rats were immunized with human MPO emulsified in complete Freund's adjuvant. Control animals received complete Freund's adjuvant without MPO. Selected groups received anti-IL17A treatment. Lesional T-cells from kidneys were assessed by flow cytometry (FACS), realtime polymerase chain reaction (PCR) and EliSpot. All animals immunized with MPO developed signs of vasculitis. At week six, lung damage expressed as petechial bleeding score and renal damage quantified by albuminuria were highest. As analyzed by FACS, the fraction of renal Th17 cells peaked at week six in MPO rats equaling the proportion of Th1 cells. MPO-specific renal Th1 and Th17 cells were detectable by EliSpot at weeks four and six post-immunization in MPO-immunized rats being absent in control rats. Neutralization of IL-17A did not affect the development of humoral and cellular anti-MPO immunity. Likewise, pulmonary and renal vasculitis were not ameliorated. In summary, the dynamics of the lesional T-cell response in the EAV model shows a major participation of MPO-specific Th17 and Th1 cells in renal vasculitis. Simple cytokine neutralization was not efficacious in this disease model so that combined neutralization approaches should be studied further.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".