<i>Cryptoporus volvatus</i>polysaccharides attenuate LPS-induced expression of pro-inflammatory factors via the TLR2 signaling pathway in human alveolar epithelial cells
Bibliographic record
Abstract
CONTEXT: Cryptoporus volvatus (Peck) Hubb grows wild in China, and its fruiting bodies have been used traditionally to treat asthma and bronchitis. OBJECTIVES: This study evaluates the anti-inflammatory effect of Cryptoporus polysaccharides (CP) extracted from fruiting bodies of C. volvatus on lipopolysaccharide (LPS)-induced pro-inflammatory factors and the signaling pathways involved in human alveolar epithelial cells. MATERIALS AND METHODS: To evaluate the effects of CP on LPS-induced pro-inflammatory factors, A549 cells were pre-incubated with CP 1, 10, and 100 μg/ml for 1 h and then stimulated with LPS 10 μg/ml for 24 h. The expression of pro-inflammatory factors monocyte chemoattractant protein-1 (MCP-1), tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), Toll-like receptor 2 (TLR2), and phosphorylation of ERK1/2, p38, and NF-κB p65 were measured by q-PCR, ELISA, and western blotting. RESULTS: CP decreased LPS-induced mRNA expression of MCP-1, TNF-α, and IL-1β (IC50 = 83.3, 85.2, and 91.6 μg/ml, respectively) and their correspondent protein expression (IC50 = 88.6, 76.4, and 81.6 μg/ml, respectively). Investigation of potential mechanisms indicated that CP 100 μg/ml reduced LPS-induced expression of TLR2 mRNA (66.9%, p < 0.01) and protein (63.2%, p < 0.01) that was a result of the decreased pro-inflammatory factors. LPS induction increased the expression of TLR2 and the phosphorylation of p38 and ERK1/2, NF-kB p65 concomitantly. CP 100 μg/ml inhibited the LPS-induced phosphorylation of the signaling proteins (p < 0.05). CONCLUSIONS: This suggests that CP pretreatment down-regulates LPS-mediated inflammation in lung epithelial cells. This study further confirmed that CP is a potential anti-inflammatory drug for the treatment of airway inflammatory diseases.
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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) | 0.000 | 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 | 0.000 |
| 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".