Chlorpyriphos induces lung inflammation and alters response to <i>E. coli</i> lipopolysaccharide challenge
Bibliographic record
Abstract
Pesticided chlorpyriphos (CP), recently found in fodder and food in Punjab (India), has been linked to respiratory dysfunction. Because the effects of CP on lung immunity are not fully known, we studied that by exposing the mice to CP (3mg/kg/d orally for 60 d; N=20) followed by intranasal E. coli LPS (80μg) or normal saline (n=10 each). The mice were euthanized 12 hours post‐LPS treatment. Control mice (n=10 each) were given saline or the LPS alone. Mice treated both with CP and LPS had increase in total cell counts and leukocyte counts in broncho‐alveolar lavage (BAL) compared to other groups groups (P<0.05). Control mice showed normal lung histology compared to peribronchial and perivascular cell infiltration, loss of cilia, and congestion in 60d CP mice. While LPS induced acute inflammation in the lungs, combination of CP and LPS exacerbated histological signs of the inflammation. CP challenge increased lung staining of TNFα compared to control mice but not LPS or CP+LPS mice. TLR4, which ligates LPS, expression was increased in the airway epithelium, macrophages and alveolar septa of mice treated with CP or the LPS compared to the control. There were increased numbers of TLR9‐positive cells in lungs of CP‐treated mice compared to the control and the LPS groups but not CP+LPS mice (P<0.05). The data show CP caused lung inflammation and altered TLR expression which may have led to altered response to LPS challenge. Funding: GADVASU and UofSask
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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 | 0.000 |
| 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 | 0.001 | 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".