Aptamer-based screening of cerebrospinal fluid for protein biomarkers of multiple sclerosis
Bibliographic record
Abstract
Abstract Background Multiple sclerosis is a degenerative autoimmune disease marked by the presence of physical, mental, and psychiatric symptoms. Diagnosis is often made very late in the disease. Hence, there is a need for better diagnostic and prognostic biomarkers as well as new therapeutic targets. In this study, MS patients’ CSF was interrogated for potential diagnostic biomarkers using an aptamer based screen. Methods The CSF of 6 MS, 10 neurological disease controls (NC), and 8 healthy controls (HC) were subjected to an aptamer based screen of 1128 unique human proteins. Dysregulated proteins were selected using group wise fold change (FC) and Mann Whitney tests. A subset of the elevated proteins was chosen for validation by ELISA in a set of pilot CSF samples (11 MS and 6 HC). Results The aptamer-based screen showed 12 proteins to be elevated in MS vs HC CSF and 22 proteins to be elevated in MS vs NC CSF (FC > 1.3 P < 0.05). Among these, IP10, ITAC, BAFF, and LGMN were elevated in MS CSF but not NC, compared to healthy controls (FC > 1.25 P < 0.1). Of the ELISA-validated proteins, BCMA, IP-10, CD48, and MMP9 showed elevation in MS CSF in comparison to healthy CSF, while LGMN did not. I-TAC and MMP-7 could not be detected by ELISA. Conclusion An ultra-sensitive and comprehensive aptamer-based biomarker screening platform has uncovered several elevated proteins in MS CSF, including chemokines, cytokines, and B-cell drivers. Although validation is still in progress, the identified proteins show promise as diagnostic markers or therapeutic targets for MS.
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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 | 0.000 | 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 | 0.000 |
| 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".