Localization of near-infrared labeled antibodies to the central nervous system in experimental autoimmune encephalomyelitis
Bibliographic record
Abstract
Antibodies, including antibodies to the RNA binding protein heterogeneous nuclear ribonucleoprotein A1, have been shown to contribute to the pathogenesis of multiple sclerosis, thus it is important to assess their biological activity using animal models of disease. Near-infrared optical imaging of fluorescently labeled antibodies and matrix metalloproteinase activity were measured and quantified in an animal model of multiple sclerosis, experimental autoimmune encephalomyelitis. We successfully labeled, imaged and quantified the fluorescence signal of antibodies that localized to the central nervous system of mice with experimental autoimmune encephalomyelitis. Fluorescently labeled anti-heterogeneous nuclear ribonucleoprotein A1 antibodies persisted in the central nervous system of mice with experimental autoimmune encephalomyelitis, colocalized with matrix metalloproteinase activity, correlated with clinical disease and shifted rostrally within the spinal cord, consistent with experimental autoimmune encephalomyelitis being an ascending paralysis. The fluorescent antibody signal also colocalized with matrix metalloproteinase activity in brain. Previous imaging studies in experimental autoimmune encephalomyelitis analyzed inflammatory markers such as cellular immune responses, dendritic cell activity, blood brain barrier integrity and myelination, but none assessed fluorescently labeled antibodies within the central nervous system. This data suggests a strong association between autoantibody localization and disease. This system can be used to detect other antibodies that might contribute to the pathogenesis of autoimmune diseases of the central nervous system including multiple sclerosis.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".