Propofol attenuates BV2 microglia inflammation via NMDA receptor inhibition
Bibliographic record
Abstract
Activated microglia, involved in the occurrence and improvement of sepsis-associated encephalopathy, can induce the expression of pro-inflammatory cytokines and pro-inflammatory enzymes, resulting in inflammation-mediated neuronal cell death. It was reported that propofol could inhibit lipopolysaccharide (LPS) induced pro-inflammatory cytokine and pro-inflammatory enzyme expression in BV2 and primary microglial cells. However, the underlying mechanism is not well known. In the present study, we investigated whether and how propofol inhibited LPS-induced the expression of pro-inflammatory cytokines and pro-inflammatory enzymes in BV2 cells. LPS induced pro-inflammatory cytokine and pro-inflammatory enzyme expression, NF-κB, extracellular regulated kinase 1/2 (ERK), calcium (Ca 2+ )/calmodulin-dependent protein kinase II (CaMK II) phosphorylation, and BV2 cell Ca 2+ accumulation. Propofol could reverse these effects induced by LPS. MK801, an inhibitor of the NMDA receptor, could attenuate LPS-induced Ca 2+ accumulation, the expression of pro-inflammatory cytokines and pro-inflammatory enzymes, and phosphorylation of NF-κB, ERK, and CaMK II, which was similar to propofol. Moreover, these effects of propofol could be counteracted by rapastinel, an activator of the NMDA receptor. The present study suggested that propofol, via inhibiting the NMDA receptor, attenuating Ca 2+ accumulation, and inhibiting CaMK II, ERK1/2, and NF-κB phosphorylation, down-regulated LPS-induced pro-inflammatory cytokine and pro-inflammatory enzyme expression.
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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 | 0.000 | 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".