Fipronil induces lung inflammation in a mouse model (LB508)
Bibliographic record
Abstract
Fipronil is an insecticide present in anti‐flea and tick products used on household pets and on crops. Fipronil kills organisms by blocking the GABA‐gated chloride channels. Presently, very little data exists on the pulmonary impact of fipronil on mammals who may be exposed through inhalation, especially in situations where workers do not wear masks during sprays. We used a mouse model to investigate the pulmonary effects of oral or intranasal fipronil (8 mg/kg/bw for 7 days; n=15). Control mice (n=9) were given either ethanol (intranasal) or groundnut oil (oral) for 7 days. Hemotoxylin and eosin stained lung sections showed accumulation of inflammatory cells and changes in lung architecture in both the oral and intranasal groups compared to the controls. To determine whether exposure to fipronil may change susceptibility to bacterial infections through alterations of Toll‐like receptors (TLRs), TLR4 and 9 immunohistochemistry was performed. TLR4 was present in the nucleus of epithelium cells in the oral group and a robust staining observed in the alveolar septum, blood vessel and bronchial epithelium of the intranasal group. Increased TLR9 staining in the alveolar macrophages and septa was observed in the fipronil exposed groups compared to the control groups. We have also tested the effects of fipronil (5.72 um/1 ml) on U937 macrophage cell line, which reduced the viability of these cells. These data suggest that fipronil induces lung inflammation in vivo and cell death in vitro. Furthermore, the increased expression of TLR4 and TLR9 may increase susceptibility for bacterial lung inflammation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".