Environmental Particulate Matter Induces Murine Intestinal Inflammatory Responses and Alters the Gut Microbiome
Bibliographic record
Abstract
BACKGROUND: Particulate matter (PM) is a key pollutant in ambient air that has been associated with negative health conditions in urban environments. The aim of this study was to examine the effects of orally administered PM on the gut microbiome and immune function under normal and inflammatory conditions. METHODS: Wild-type 129/SvEv mice were gavaged with Ottawa urban PM10 (EHC-93) for 7-14 days and mucosal gene expression analyzed using Ingenuity Pathways software. Intestinal permeability was measured by lactulose/mannitol excretion in urine. At sacrifice, segments of small and large intestine were cultured and cytokine secretion measured. Splenocytes were isolated and incubated with PM10 for measurement of proliferation. Long-term effects of exposure (35 days) on intestinal cytokine expression were measured in wild-type and IL-10 deficient (IL-10(-/-)) mice. Microbial composition of stool samples was assessed using terminal restriction fragment length polymorphism. Short chain fatty acids were measured in caecum. RESULTS: Short-term treatment of wild-type mice with PM10 altered immune gene expression, enhanced pro-inflammatory cytokine secretion in the small intestine, increased gut permeability, and induced hyporesponsiveness in splenocytes. Long-term treatment of wild-type and IL-10(-/-) mice increased pro-inflammatory cytokine expression in the colon and altered short chain fatty acid concentrations and microbial composition. IL-10(-/-) mice had increased disease as evidenced by enhanced histological damage. CONCLUSIONS: Ingestion of airborne particulate matter alters the gut microbiome and induces acute and chronic inflammatory responses in the intestine.
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 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".