Experimental myositis: an optimised version of C-protein-induced myositis
Bibliographic record
Abstract
INTRODUCTION: Inflammatory myopathies (IM) are a group of severe autoimmune diseases, sharing some similarities, whose cause is unknown and treatment is empirical.While C-protein-induced myositis (CIM), the most currently used mouse model of IM, has removed some roadblocks to understand and improve the treatment of IM, it has only been partially characterised and its generation limited by poor reproducibility. This study aimed at optimising the generation and the characterisation of CIM. METHODS: In silico analysis was run to identify the top three specific and immunogenic regions of C-protein. The cognate polypeptides were synthesised and used to immunise C57BL/6N mice. Grip strength, walking ability, serum creatine kinase levels and muscle pathology (histological and electron microscopic features) were assessed. Immune cell proportions and interferon signature in muscles were also determined. RESULTS: Among the three C-protein polypeptides with the highest immunogenic score, immunisation with the amino acids 965-991 induced the most severe phenotype (experimental myositis (EM)) characterised by 37% decrease in strength, 36% increase in hind base width, 45% increase in serum creatine-kinase level and 80% increase in histological inflammatory score. Optical and electron microscopy revealed mononuclear cell infiltrate, myofibre necrosis, atrophy, major histocompatibility complex-I expression as well as sarcolemmal, sarcomeric and mitochondrial abnormalities. Autoantibodies targeting C-protein, proinflammatory T-lymphocytes, macrophages, and type I and II interferon-stimulated transcripts were detected within the muscle of EM mice. CONCLUSION: EM recapitulates the common hallmarks of IM. This costless, high throughput, reproducible and robust model, generated in the most commonly used background for genetically engineered mice, may foster preclinical research in IM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| 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 teacher head, 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".