Scoring Central Nervous System Inflammation, Demyelination, and Axon Injury in Experimental Autoimmune Encephalomyelitis
Bibliographic record
Abstract
Experimental autoimmune encephalomyelitis (EAE) is a common immune-based model of multiple sclerosis (MS). This disease can be induced in rodents by active immunization with protein components of the myelin sheath and Complete Freund's adjuvant (CFA) or by the transfer of myelin-specific T effector cells from rodents primed with myelin protein/CFA into naïve rodents. The severity of EAE is typically scored on a 5-point clinical scale that measures the degree of ascending paralysis, but this scale is not optimal for assessing the extent of recovery from EAE. For example, clinical scores remain high in some EAE models (e.g., myelin oligodendrocyte glycoprotein [MOG] peptide-induced model of EAE) despite the resolution of inflammation. Thus, it is important to complement clinical scoring with histological scoring of EAE, which also provides a means to study the underlying mechanisms of cellular injury in the central nervous system (CNS). Here, a simple protocol is presented to prepare and stain spinal cord and brain sections from mice and to score inflammation, demyelination, and axonal injury in the spinal cord. The method for scoring leukocyte infiltration in the spinal cord can also be applied to score brain inflammation in EAE. A protocol for measuring soluble neurofilament light (sNF-L) in the serum of mice using a Small Molecule Assay (SIMOA) assay is also described, which provides feedback on the extent of overall CNS injury in live mice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".