Accumulation mode particles and LPS exposure induce TLR-4 dependent and independent inflammatory responses in the lung
Bibliographic record
Abstract
BACKGROUND: Accumulation mode particles (AMP) are formed from engine combustion and make up the inhalable vapour cloud of ambient particulate matter pollution. Their small size facilitates dispersal and subsequent exposure far from their original source, as well as the ability to penetrate alveolar spaces and capillary walls of the lung when inhaled. A significant immuno-stimulatory component of AMP is lipopolysaccharide (LPS), a product of Gram negative bacteria breakdown. As LPS is implicated in the onset and exacerbation of asthma, the presence or absence of LPS in ambient particulate matter (PM) may explain the onset of asthmatic exacerbations to PM exposure. This study aimed to delineate the effects of LPS and AMP on airway inflammation, and potential contribution to airways disease by measuring airway inflammatory responses induced via activation of the LPS cellular receptor, Toll-like receptor 4 (TLR-4). METHODS: The effects of nebulized AMP, LPS and AMP administered with LPS on lung function, cellular inflammatory infiltrate and cytokine responses were compared between wildtype mice and mice not expressing TLR-4. RESULTS: The presence of LPS administered with AMP appeared to drive elevated airway resistance and sensitivity via TLR-4. Augmented TLR4 driven eosinophilia and greater TNF-α responses observed in AMP-LPS treated mice independent of TLR-4 expression, suggests activation of allergic responses by TLR4 and non-TLR4 pathways larger than those induced by LPS administered alone. Treatment with AMP induced macrophage recruitment independent of TLR-4 expression. CONCLUSIONS: These findings suggest AMP-LPS as a stronger stimulus for allergic inflammation in the airways then LPS alone.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".