The role of dorsal root ganglia activation and brain‐derived neurotrophic factor in multiple sclerosis
Bibliographic record
Abstract
Multiple sclerosis (MS) is characterized by focal destruction of the white matter of the brain and spinal cord. The exact mechanisms underlying the pathophysiology of the disease are unknown. Many studies have shown that MS is predominantly an autoimmune disease with an inflammatory phase followed by a demyelinating phase. Recent studies alongside current treatment strategies, including glatiramer acetate, have revealed a potential role for brain-derived neurotrophic factor (BDNF) in MS. However, the exact role of BDNF is not fully understood. We used the experimental autoimmune encephalomyelitis (EAE) model of MS in adolescent female Lewis rats to identify the role of BDNF in disease progression. Dorsal root ganglia (DRG) and spinal cords were harvested for protein and gene expression analysis every 3 days post-disease induction (pdi) up to 15 days. We show significant increases in BDNF protein and gene expression in the DRG of EAE animals at 12 dpi, which correlates with peak neurological disability. BDNF protein expression in the spinal cord was significantly increased at 12 dpi, and maintained at 15 dpi. However, there was no significant change in mRNA levels. We show evidence for the anterograde transport of BDNF protein from the DRG to the dorsal horn of the spinal cord via the dorsal roots. Increased levels of BDNF within the DRG and spinal cord in EAE may facilitate myelin repair and neuroprotection in the CNS. The anterograde transport of DRG-derived BDNF to the spinal cord may have potential implications in facilitating central myelin repair and neuroprotection.
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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 | 0.001 | 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".