Role of IL-17-producing lymphocytes in severity of multiple sclerosis upon natalizumab treatment
Bibliographic record
Abstract
OBJECTIVE: Natalizumab is known to prevent T-helper cells entering the central nervous system (CNS). We hypothesize that more pathogenic T-helper cells are present outside the CNS and a possible relationship to disease severity. METHODS: Characterization and enrichment of human CD4+IL-17+ cells were performed ex vivo using peripheral blood mononuclear cells from natalizumab-treated relapsing-remitting multiple sclerosis (RRMS) patients ( n = 33), untreated RRMS patients ( n = 13), and healthy controls ( n = 33). Magnetic resonance imaging (MRI) scans were performed routinely for patients. RESULTS: Lymphocytes were elevated in peripheral blood of natalizumab-treated patients compared to untreated patients and healthy controls. Whereas group comparison for CD4+IL-17+ numbers also differed, CD4+IFN-γ+ and CD4+IL-22+ counts were not increased. CD4+IL-17+ cells not only expressed but also secreted IL-17. In natalizumab-treated patients, IL-17+ cell frequency was found to correlate with T1-hypointense lesions, but was not an indicator for rebound activity after treatment discontinuation, except in one patient who experienced a fulminant rebound, and interestingly, in whom the highest IL-17+ cell levels were observed. CONCLUSION: Increased lymphocytes and CD4+IL-17+ cells in the blood of RRMS patients receiving natalizumab corroborate the drug's mechanism of action, that is, blocking transmigration to CNS. Correlation between IL-17-expressing lymphocytes and T1-hypointense lesions underlines the important role of these cells in the disease pathology.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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 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".