CXCL10 Is Associated with Increased Cerebrospinal Fluid Immune Cell Infiltration and Disease Duration in Multiple Sclerosis
Bibliographic record
Abstract
Background: Cerebrospinal fluid (CSF) is an important sampling site for putative biomarkers and contains immune cells. CXCL10 is a multiple sclerosis (MS)-relevant chemokine that is present in the injured central nervous system and recruits CXCR3+ immune cells toward injured tissues. Objective: Perform a comprehensive evaluation to determine a potential relationship between CXCL10 and various immune cell subsets in the CNS of MS and control cases. Methods: In MS and control cases, CXCL10 was measured in the CSF and plasma by ELISA. Immune cells within both the CSF and peripheral blood were quantified by flow cytometry. Results: Compared to non-inflammatory neurological disease (NIND) cases, MS cases had significantly higher CXCL10 in CSF (p = 0.021); CXCL10 was also correlated with total cell numbers in CSF (p = 0.04) and T cell infiltrates (CD3+, p = 0.01; CD4+, p = 0.01; CD8+, p = 0.02); expression of CXCR3 on peripheral immune cell subsets was not associated with CSF CXCL10. Conclusions: Elevated levels of CXCL10 in the CSF of MS cases are associated with increased T cells but appear to be independent of peripheral CXCR3 expression. These results support the importance of elevated CXCL10 in MS and suggest the presence of an alternative mechanism of CXCL10 outside of solely influencing immune cell trafficking.
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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 | 0.001 |
| 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) | 0.003 | 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".