Sensory Neurons of the Dorsal Root Ganglia Become Hyperexcitable in a T-Cell-Mediated MOG-EAE Model of Multiple Sclerosis
Bibliographic record
Abstract
Multiple sclerosis (MS) is an autoimmune, demyelinating disease of the central nervous system. Patients with MS typically present with visual, motor, and sensory deficits. However, an additional complication of MS in large subset of patients is neuropathic pain. To study the underlying immune-mediated pathophysiology of pain in MS we employed the myelin oligodendrocyte glycoprotein (MOG)-induced experimental autoimmune encephalitis (EAE) model in mice. Since sensory neurons are crucial for nociceptive transduction, we investigated the effect of this disease on sensory neurons of the lumbar dorsal root ganglia (DRG). Here, we report the disease was associated with activation of the complement system and the NLRP3 inflammasome in the DRG. We further observe a transient increase in the number of complement component 5a receptor 1-positive (C5aR1+) immune cells, CD4+ T-cells, and Iba1+ macrophages in the DRG. The absence of any significant change in the levels of mRNA for myelin proteins in the DRG and the sciatic nerve suggests that demyelination in the PNS is not a trigger for the immune response in the DRG. However, we did observe an induction of activating transcription factor 3 (ATF3) at disease onset and chronic disruption of cytoskeletal proteins in the DRG demonstrating neuronal injury in the PNS in response to the disease. Electrophysiological analysis revealed the emergence of hyperexcitability in medium-to-large (≥26 µm) diameter neurons, especially at the onset of MOG-EAE signs. These results provide conclusive evidence of immune activation, neuronal injury, and peripheral sensitization in MOG-EAE, a model classically considered to be centrally mediated.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".