Low-dose lipopolysaccharide protects nerve cells against spinal cord injury via regulating the PI3K–AKT–Nrf2 signaling pathway
Bibliographic record
Abstract
This study explored the molecular mechanism behind the protective effects from low-dose lipopolysaccharide (LPS) on an in-vitro model of spinal cord injury (SCI). For this, PC12 cells were treated with different concentrations of LPS and the cell counting kit-8 assay was used to measure the toxicity of LPS to the cells. Next, we used immunofluorescence to measure nuclear translocation of Nrf2 in PC12 cells. PC12 cells were then treated with IGF-1 (PI3K agonist) and LY294002 (PI3K inhibitor). An in-vitro model of SCI was then established via oxygen-glucose deprivation/reoxygenation. Rates of apoptosis were measured using flow cytometry and the TUNEL assay. Low-dose LPS increased the expression levels of Nrf2, p-PI3K/PI3K, and p-AKT/AKT, and facilitated nuclear translocation of Nrf2. The activation of PI3K-AKT signaling by IGF-1 significantly increased the expression of Nrf2, whereas inhibition of PI3K-AKT signaling significantly decreased the expression of Nrf2. Low-dose LPS reduced the apoptotic ratio of PC12 cells, decreased the expression levels of caspase 3 and caspase 9, and increased the expression levels of HO-1, NQO1, and γ-GCS. Low-dose LPS also reduced the rate of apoptosis and oxidative stress by activating the PI3K-AKT-Nrf2 signaling pathway. Collectively, the results indicate that PI3K-AKT-Nrf2 signaling participates in the protective effects from low-dose LPS in an in-vitro PC12 cell model of SCI.
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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) | 0.000 | 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".