ES-62 prevents development of lupus-like pathology in the MRL/Lpr mouse (P5210)
Bibliographic record
Abstract
Abstract The prevalence of autoimmunity in developing countries is lower than that in the developed world and this inverse relationship correlates with the level of chronic parasitic infections. Co-evolution of parasites with the human immune system has resulted in evasion strategies that prevent the host clearing the parasite yet limit pathology. One mechanism involves the release of immunomodulators like ES-62, secreted by the filarial nematode Acanthocheilonema viteae: ES-62 exhibits broad anti-inflammatory activity and therapeutic potential in autoimmune diseases such as rheumatoid arthritis. Here we show that ES-62 protects against nephritis, as indicated by proteinuria levels, in the murine MRL/Lpr model of systemic lupus erythematosus (SLE). It has been proposed that the pro-inflammatory cytokine IL-17 is a primary driver of disease, in mouse models as well as in SLE patients, but our data do not support targeting of IL-17 as the major protective mechanism underlying ES-62 efficacy. Indeed, and consistent with this, we have shown that neutralising antibodies specific for IL-17 do not block development of proteinuria. ES-62 is a large immunogenic glycoprotein and therefore not suitable for therapeutic use. Thus, since the active moiety of ES-62 is phosphorylcholine (PC), we have recently developed small molecular analogues based around PC that mimic the immunomodulatory effects of ES-62 in autoimmune disorders, including SLE.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".