CSF SERPINA3 Levels Are Elevated in Patients With Progressive MS
Bibliographic record
Abstract
Objective To identify biomarkers associated with progressive phases of MS and with neuroprotective potential. Methods Combined analysis of the transcriptional and proteomic profiles obtained in CNS tissue during chronic progressive phases of experimental autoimmune encephalomyelitis (EAE) with the transcriptional profile obtained during the differentiation of murine neural stem cells into neurons. Candidate biomarkers were measured by ELISA in the CSF of 65 patients with MS (29 with relapsing-remitting MS [RRMS], 20 with secondary progressive MS, and 16 with primary progressive MS [PPMS]) and 30 noninflammatory neurologic controls (NINCs). Results Integrative analysis of gene and protein expression data identified 2 biomarkers, the serine protease inhibitor Serpina3n and the calcium-binding protein S100A4, which were upregulated in chronic progressive EAE and whose expression was induced during neuronal differentiation. Immunofluorescence studies revealed a primarily neuronal expression of S100A4 and Serpina3n during EAE. CSF levels of SERPINA3, the human ortholog of murine Serpina3n, and S100A4 were increased in patients with MS compared with NINCs (SERPINA3: 1,320 vs 838.6 ng/mL, p = 0.0001; S100A4: 1.6 vs 0.8 ng/mL, p = 0.02). Within the MS group, CSF SERPINA3 levels were significantly elevated in patients with progressive forms, mainly patients with PPMS compared with patients with RRMS (1,617 vs 1,129 ng/mL, p = 0.02) and NINCs (1,617 vs 838.6 ng/mL, p = 0.0001). Of interest, CSF SERPINA3 levels significantly correlated with CSF neurofilament light chain levels only in the PPMS group (r = 0.62, p = 0.01). Conclusion These results point to a role of SERPINA3 as a biomarker associated with the progressive forms of MS, particularly PPMS.
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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".