Exposure to Ingested Airborne Pollutant Particulate Matter Increases Mucosal Exposure to Bacteria and Induces Early Onset of Inflammation in Neonatal IL-10–Deficient Mice
Bibliographic record
Abstract
BACKGROUND: Epidemiological associations between early-life air pollution exposure and increased risk of inflammatory bowel diseases have been shown. Our aim was to determine if exposure to airborne particulate matter (PM(10)) during the neonatal period would alter colitis in the interleukin (IL)-10(-/-) mouse model. METHODS: IL-10(-/-) pregnant dams and pups were fed chow ± PM(10) (9 μg/g) and pups were studied at 10, 14, and 20 weeks. Twenty-week-old mice were given 2% dextran sodium sulfate. Metagenomic analysis of stool was performed. Bacterial translocation was assessed by serum lipopolysaccharide and culturing bacteria from mesenteric lymph nodes and spleen. Cytokine expression was measured in gut homogenates using the MesoScale discovery platform. PM(10) was applied to CMT93 cells ± J744 macrophages, and resistance and cytokine secretion were assessed. THP-1 macrophages were incubated with Escherichia coli HB101 ± PM(10) for assessment of uptake and killing. RESULTS: PM(10) exposure increased colonic proinflammatory cytokines and bacterial translocation into mesenteric lymph nodes, whereas IL-17A levels were reduced in PM(10)-fed 10-week-old mice. Bifidobacterium was decreased in mice fed PM(10), whereas serum lipopolysaccharide was increased. PM(10) interfered with phagocytosis and killing in THP-1 cells. In coculture, PM(10) increased tumor necrosis factor α and fluorescein isothiocyanate-dextran flux. After dextran sodium sulfate treatment, PM10-fed mice responded with increased colonic tumor necrosis factor α and IL-1β and a larger percentage of PM(10)-fed mice had live bacteria in the mesenteric lymph nodes. CONCLUSIONS: Our data suggest that early exposure to pollution particulates can result in an earlier onset of intestinal disease in genetically susceptible hosts and can alter responses to gut injury in later life.
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 | 0.001 | 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 | 0.001 |
| 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 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".