Lung Inflammation from Single and Repetitive Exposure to Glyphosate
Bibliographic record
Abstract
Abstract Background: Glyphosate is an active ingredient in herbicides used in agriculture worldwide. Exposure to glyphosate has been associated with respiratory dysfunctions in agricultural workers. However, the ability of glyphosate to induce inflammation in the lung is not well studied. Therefore, we evaluated lung inflammatory response to glyphosate at agricultural relevant dose for single and repetitive exposures. Methods: Male C57BL/6 mice were intranasally exposed to glyphosate (1 μg/40 μl) for 1-day or once daily for 5-days, and 10-days. After the exposure periods, mice were euthanized to collect the bronchoalveolar lavage (BAL) fluid and lung tissue. Results: Repetitive exposure to glyphosate for 5-days and 10-days showed an increase of neutrophils in BAL fluid and eosinophil peroxidase levels in lungs, a marker for eosinophils. Leukocyte infiltration in lungs was further confirmed through lung histology. Th2 cytokines including IL-5 and IL-13 were increased in BAL fluid after 10-days of glyphosate exposure whereas IL-4 was not increased. Lung sections from all glyphosate groups showed higher expression for ICAM-1, VCAM-1, and vWF adhesion molecules. TLR-4 and TLR-2 expression was increased in lungs after repetitive exposure to glyphosate. Conclusions: We conclude that repetitive exposure to glyphosate induces migration of neutrophils and eosinophils and release of Th2 cytokines. This study, for the first time, provides evidence for the role of ICAM-1, VCAM-1 and vWF in lungs of glyphosate-treated animals.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".