<i>Hypericum ascyron L</i>. extract reduces particulate matter‐induced airway inflammation in mice
Bibliographic record
Abstract
The consequences of increased industrialization increased the risk of asthma and breathing difficulties due to increased particulate matter in the air. We aim to investigate the therapeutic properties of Hypericum ascyron L. extract (HAE) in airway inflammation and unravel its mechanism of action. We conducted nitric oxide and cell viability assay, real-time PCR and western blot analyses along with in vitro studies. in vivo studies include a model of coal fly ash and diesel exhaust particle (CFD)-induced airway inflammation in mice. HAE reduced coal fly ash (CFA)-induced nitric oxide secretion without exhibiting cytotoxicity in MH-S cells. HAE also reduced the mRNA expression of pro-inflammatory cytokines and reduced the expression of proteins in the NFκB and MAPK pathways. In a mice model of CFD-induced airway inflammation, HAE effectively reduced neutrophil infiltration in bronchoalveolar lavage fluid (BALF) and increased the amount of T cells in the BALF, lungs, and blood while reducing all other immune cell subtypes to reduce airway inflammatory response. CXCL-1, IL-17, MIP-2, and TNF-α expression in the BALF were also reduced. HAE effectively reduced MIP-2 and TNF-α mRNA expression in the lung tissue of mice. In a nutshell, HAE is effective in preventing airway inflammation induced by CFA in MH-S cells, as well as inflammation induced by CFD in mice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.004 | 0.004 |
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; both teacher heads agree on what is shown here.
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".