Melatonin inhibits matrix metalloproteinase‐9 (MMP‐9) activation in the lipopolysaccharide (LPS)‐stimulated RAW 264.7 and BV2 cells and a mouse model of meningitis
Bibliographic record
Abstract
We explored anti-inflammatory potential of melatonin against the lipopolysaccharide (LPS)-induced inflammation in vivo and in vitro. RAW 264.7 and BV2 cells were stimulated by LPS, followed by the treatment with melatonin or vehicle at various time intervals. In a mouse model of meningitis induced by LPS, melatonin (5mg/kg) or vehicle was intravenously injected at 30min postinsult. The activity of matrix metalloproteinase-2 (MMP-2) and metalloproteinase-9 (MMP-9) was determined by gelatin zymography. Nuclear factor-kappa B (NFκB) translocation and binding activity were determined by immunocytochemistry and electrophoretic mobility shift assay (EMSA). Our results showed that either pretreatment or cotreatment with melatonin at 50-500 μm effectively inhibited the LPS-induced proMMP-9 activation in the RAW 264.7 and BV2 cells, respectively (P<0.05). This melatonin-induced proMMP-9 inhibition remained effective when treatment was delayed up to 2 and 6hr postinsult for RAW 264.7 and BV2 cells, respectively (P<0.05 for both groups). Additionally, melatonin significantly attenuated the rises of circulatory and cerebral MMP-9 activity, respectively (P<0.05) and reduced the loss of body weight (P<0.05) in mice with meningitis. Moreover, melatonin (50μm) effectively inhibited nuclear factor-kappa B (NFκB) translocation and binding activity in the LPS-treated RAW 264.7 and BV2 cells, respectively (P<0.05). These results demonstrate direct inhibitory actions of melatonin against postinflammatory NFκB translocation and MMP-9 activation and highlight its ability to inhibit systemic and cerebral MMP-9 activation following brain inflammation.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".