Organic dust, causing both oxidative stress and Nrf2 activation, is phagocytized by bronchial epithelial cells
Bibliographic record
Abstract
Inhalation of organic dust (OD) from swine confinement facilities leads to pulmonary inflammation, airway hyperresponsiveness, and oxidative stress. In mice, pretreatment with a hydroxyl radical scavenger prevents airway inflammation and airway hyperresponsiveness (AHR) induced by OD exposure. We sought to determine a mechanism by which OD could induce oxidative stress in bronchial epithelial cells. Human bronchial epithelial cells (BEAS-2B or NHBE) were treated with various concentrations of OD, followed by evaluation of intracellular oxidative stress using 2',7'-dichlorofluorescein diacetate (DCFDA). After stimulation with OD, gene expression of antioxidant genes was assessed by real-time quantitative PCR followed by quantification of Nrf2 nuclear translocation using a luciferase reporter assay. Phagocytic markers (CD36 and CD68) were analyzed by FACS. Cells were treated with an actin inhibitor, cytochalasin D, before OD exposure and evaluated for Nrf2 nuclear translocation and DCFDA. Mice were pretreated with sulforaphane, the Nrf2 activator, before OD exposure and evaluated for pulmonary inflammation and airway reactivity. OD induced a time- and concentration-dependent increase in DCFDA. mRNA expression levels of Nrf2-dependent genes and Nrf2 nuclear translocation were increased after OD exposure. OD exposure increased the expression of CD68 and CD36. Cytochalasin D prevented oxidative stress and Nrf2 nuclear translocation after OD. Pretreatment with sulforaphane prevented OD-induced inflammation and AHR while increasing the uptake of OD in bronchial epithelial cells. Bronchial epithelial cells can phagocytose OD, resulting in an increase in endogenous oxidative stress. Nrf2-dependent mechanisms mediate the antioxidant response to OD.
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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) | 0.000 | 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 | 0.000 |
| 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 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".