The Clinical Course of Experimental Autoimmune Encephalomyelitis is Associated with a Profound and Sustained Transcriptional Activation of the Genes Encoding Toll‐like Receptor 2 and CD14 in the Mouse CNS
Bibliographic record
Abstract
Experimental autoimmune encephalomyelitis (EAE) is an autoimmune demyelinating disease commonly used to model the pathogenetic mechanisms involved in multiple sclerosis (MS). In this study, we examined the effects of immunization with the myelin oligodendrocyte glycoprotein MOG(35-55) on the expression of molecules of the innate immune system, namely toll-like receptor 2 (TLR2) and CD14. Expression of the mRNA encoding TLR2 increased in the choroid plexus, the leptomeninges and within few isolated cells in the CNS parenchyma 4 to 8 days after immunization with MOG. At day 10, the signal spread across the meninges, few perivascular regions and over isolated groups of parenchymal cells. Three weeks after the MOG treatment, at which time animals showed severe clinical symptoms, a robust expression of both TLR2 and CD14 transcripts occurred in barrier-associated structures, as well as parenchymal elements of the spinal cord, and within numerous regions of the brain including, the medulla, cerebellum and the cortex. Dual labeling provided the anatomical evidence that microglia/macrophages were positive for TLR2 in the brain of EAE mice. The regions that exhibited chronic expression of TLR2 and CD14 were also associated with an increase in NF-kappaB activity and transcriptional activation of genes encoding numerous proinflammatory molecules. The present data provide evidence that receptors of the pathogen-associated molecular patterns are strongly induced in the CNS of EAE mice, further reinforcing the concept that the innate immune system plays a determinant role in this autoimmune demyelinating disease.
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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 | 0.001 |
| 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".