NLR-Dependent Regulation of Inflammation in Multiple Sclerosis
Bibliographic record
Abstract
Multiple sclerosis (MS) is an autoimmune disease of the central nervous system (CNS) associated with inappropriate activation of lymphocytes, hyperinflammatory responses, demyelination, and neuronal damage. In the past decade, a number of biological immunomodulators have been developed that suppress the peripheral immune responses and slow down the progression of the disease. However, once the inflammation of the CNS has commenced, it can cause serious permanent neuronal damage. Therefore, there is a need for developing novel therapeutic approaches that control and regulate inflammatory responses within the CNS. Nucleotide-binding oligomerization domain (NOD)-like receptors (NLRs) are intracellular regulators of inflammation expressed by many cell types within the CNS. They redirect multiple signaling pathways initiated by pathogens and molecules released by injured tissues. NLR family members include positive regulators of inflammation, such as NLRP3 and NLRC4 and anti-inflammatory NLRs, such as NLRX1 and NLRP12. They exert immunomodulatory effect at the level of peripheral immune responses, including antigen recognition and lymphocyte activation and differentiation. Also, NLRs regulate tissue inflammatory responses. Understanding the molecular mechanisms that are placed at the crossroad of innate and adaptive immune responses, such as NLR-dependent pathways, could lead to the discovery of new therapeutic targets. In this review, we provide a summary of the role of NLRs in the pathogenesis of MS. We also summarize how anti-inflammatory NLRs regulate the immune response within the CNS. Finally, we speculate the therapeutic potential of targeting NLRs in MS.
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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) | 0.001 | 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 | 0.000 |
| 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".