Effects of Teriflunomide on B Cell Subsets in MuSK-Induced Experimental Autoimmune Myasthenia Gravis and Multiple Sclerosis
Bibliographic record
Abstract
Antigen-specific immune responses are crucially involved in both multiple sclerosis (MS) and myasthenia gravis (MG). Teriflunomide is an immunomodulatory agent approved for treatment of MS through inhibition of lymphocyte proliferation. MG associated with muscle-specific tyrosine kinase (MuSK) antibodies often manifests with a severe disease course, prompting development of effective treatment methods. To evaluate whether teriflunomide treatment may ameliorate MuSK-autoimmunity, experimental autoimmune MG (EAMG) was induced by immunizing C57BL/6 (B6) mice three times with MuSK in complete Freund's adjuvant (CFA) (n = 17). MuSK-immunized mice were treated daily with teriflunomide (n = 8) or PBS (n = 9) starting from the third immunization (week 8) to termination (week 14). Clinical severity of EAMG was monitored. Immunological alterations were evaluated by measurement of anti-MuSK IgG, neuromuscular junction deposits, and flow cytometric analysis of lymph node cells. In MS patients under teriflunomide treatment, the peripheral blood B cell subset profile was analyzed. B6 mice treated with teriflunomide displayed relatively preserved body weight, lower EAMG prevalence, reduced average clinical grades, higher inverted screen scores, diminished anti-MuSK antibody and NMJ deposit levels. Amelioration of EAMG findings was associated with reduced memory B cell ratios in the lymph nodes. Similarly, MS patients under teriflunomide treatment showed reduced memory B cell, plasma cell, and plasmablast ratios. Teriflunomide treatment has effectively ameliorated MuSK-autoimmunity and thus may putatively be used in long-term management of MuSK-MG as an auxiliary treatment method. Teriflunomide appears to exert beneficial effects through inhibition of effector B cells.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".