Impact of oxidative stress on epithelial repair and alveolar oedema, within <i>in vitro</i> model of lung injury
Bibliographic record
Abstract
Repair of lung injury is a complex process including epithelial growth, alveolar clearance and down‐regulation of inflammatory response. The impacts of oxidative stress in this process are still unknown. To study this question, we developed an in vitro model of lung injury to characterise the molecular and cellular mechanisms of distal lung epithelial repair. Alveolar epithelial cells in primary culture were submitted to oxidative stress with bleomycin (B : 12,5–150mU/ml) or DMNQ (D : 2,5–15μM). Bleomycin and DMNQ inhibit the epithelial repair (24–48h) following a mechanical injury in a time and concentration dependent manner (p<0,0001). These agents also decrease by 60% and 80% respectively the cell migration in Boyden's chamber (p<0,0001) and decrease cell proliferation measured as thymidine 3 H incorporation by 95% and 70% (p<0,0001). These results show that oxidative stress has an impact on alveolar epithelial cell repair by inhibiting the cell migration and proliferation processes. The impact of bleomycin on the ionic transport was studied in Ussing chamber. The treatment decreases by 37% the total as well as amiloride‐sensitive transepithelial current (p<0,05). Basolateral permeabilisation in presence of a Na gradient shows that the epithelial Na channel (ENaC) is not involved in this decrease. The oxidative stress therefore in addition to inhibiting epithelial repair also decreases the Na transport involved in lung liquid clearance. Supported by RSR‐FRSQ, CCFF, CIHR.
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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 | 0.000 | 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".