Monocyte-derived cytokines in multiple sclerosis
Bibliographic record
Abstract
MS is an inflammatory, presumably autoimmune, disease mediated by the activation of T cells, B cells and monocytes (MO). Inflammation is thought to occur early during the relapsing-remitting phase of MS (RRMS), whereas in the later phases of MS such as secondary progressive MS (SPMS), inflammation tends to diminish. Our objective was to compare the types and amounts of proinflammatory and regulatory cytokines produced by MO from relapsing-remitting patients with or without treatment with IFN-beta (RRMS+ therapy, RRMS- therapy), respectively, from secondary progressive patients (SPMS) and from healthy controls (HC). MO were isolated by a density-gradient technique and three different techniques (RNase protection assay, ELISA and intracellular cytokine staining) were used to assess cytokine levels. An increase in IL6, IL12 and TNF-alpha was observed by all three methods for RRMS- therapy and for SPMS patients compared to HC and RRMS+ therapy patients. We conclude that proinflammatory and regulatory monokines can be derived from MO of MS patients and that these levels are modulated by IFN-beta therapy. Although it is believed that inflammation tends to diminish in SPMS patients, our data show that inflammatory cytokines continue to be released at high levels, suggesting that IFN-beta or IL10 treatment may be beneficial for this group.
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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) | 0.000 | 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 | 0.001 |
| 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".