Central nervous system Toll-like receptor expression in response to Theiler's murine encephalomyelitis virus-induced demyelination disease in resistant and susceptible mouse strains
Bibliographic record
Abstract
BACKGROUND: In immunopathological diseases, such as multiple sclerosis (MS), genetic and environmental factors that contribute to the initiation and progression of the disease are often discussed. The Theiler murine encephalomyelitis virus-induced demyelination disease (TMEV-IDD) model used to study MS reflects this: genetically susceptible mice infected intra-cerebrally with TMEV develop a chronic demyelination disease. TMEV-IDD can be induced in resistant mouse strains by inducing innate immunity with lipopolysaccharide (LPS). Interestingly, Toll-like receptor 4 (TLR4) is the cognate receptor for LPS and its activation can induces up-regulation of other TLRs, such as TLR7 (the receptor for TMEV) and 9, known to be involved in autoimmunity. Up-regulation of TLRs could be involved in precipitating an autoimmune susceptible state. Consequently, we looked at TLR expression in the susceptible (SJL/J) and resistant (C57BL/6) strains of mice infected with TMEV. The resistant mice were induced to develop TMEV-IDD by two LPS injections following TMEV infection. RESULTS: Both strains were found to up-regulate multiple TLRs (TLR2, 7 and 9) following the TMEV infection. Expression of these TLRs and of viral mRNA was significantly greater in infected SJL/J mice. The susceptible SJL/J mice showed up-regulation of TLR3, 6 and 8, which was not seen in C57BL/6 mice. CONCLUSION: Expression of TLRs by susceptible mice and the up-regulation of the TLRs in resistant mice could participate in priming the mice toward an autoimmune state and develop TMEV-IDD. This could have implications on therapies that target TLRs to prevent the emergence of conditions such as MS in patients at risk for the 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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".