Laquinimod Reduces Neuronal Injury Through Inhibiting Microglial Activation (P1.193)
Bibliographic record
Abstract
OBJECTIVE: To investigate the mechanism by which laquinimod reduces brain atrophy and progression of disability observed in two Phase III trials in multiple sclerosis (MS). BACKGROUND: Persistent activation of microglia occurs in MS and contributes to the neurodegeneration processes. Thus, we investigated whether laquinimod alters properties of microglia in culture and in experimental autoimmune encephalomyelitis (EAE), and whether this reduced bystander neuronal injury. DESIGN/METHODS: Microglia was cultured from human surgical brain resections. EAE was induced in mice. RESULTS: The activation of human microglia in culture increased levels of several inflammatory molecules, and these elevations were attenuated by pre-treatment with laquinimod. Laquinimod prevented the decline in activated microglia of miR124a, a microRNA implicated in maintaining microglia quiescence, and it reduced the activity of several signaling pathways in microglia. In EAE, axonal injury correlated with accumulation of microglia/macrophages in the spinal cord. EAE mice treated with laquinimod before onset of clinical signs subsequently displayed reduced microglia/macrophage density and axonal injury. Remarkably, when laquinimod treatment was initiated well into the disease course when axons were degenerating, the progressive axonal loss was halted. Besides inflammatory molecules associated with microglia, the level of inducible nitric oxide (NO) synthase capable of producing free radical toxicity was attenuated by laquinimod in EAE mice. In co-culture of microglia and neurons where microglia activation caused neuronal death, laquinimod decreased NO levels and prevented neurotoxicity. CONCLUSION: Laquinimod is a novel inhibitor of microglial activation that lowers microglia-induced pro-inflammatory cytokine secretion and neuronal cell death in culture and axonal injury/loss in EAE.
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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 | 0.000 | 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) | 0.003 | 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".