Soluble Wood Smoke Extract Promotes Barrier Dysfunction in Alveolar Epithelial Cells through a MAPK Signaling Pathway
Bibliographic record
Abstract
Wildfire smoke induces acute pulmonary distress and is of particular concern to risk groups such as the sick and elderly. Wood smoke (WS) contains many of the same toxic compounds as those found in cigarette smoke (CS) including polycyclic aromatic hydrocarbons, carbon monoxide, and free radicals. CS is a well-established risk factor for respiratory diseases such as asthma and COPD. Limited studies investigating the biological effects of WS on the airway epithelium have been performed. Using a cell culture-based model, we assessed the effects of a WS-infused solution on alveolar epithelial barrier function, cell migration, and survival. The average geometric mean of particles in the WS was 178 nm. GC/MS analysis of the WS solution identified phenolic and cellulosic compounds. WS exposure resulted in a significant reduction in barrier function, which peaked after 24 hours of continuous exposure. The junctional protein E-cadherin showed a prominent reduction in response to increasing concentrations of WS. Furthermore, WS significantly repressed cell migration following injury to the cell monolayer. There was no difference in cell viability following WS exposure. Mechanistically, WS exposure induced activation of the p44/42, but not p38, MAPK signaling pathway, and inhibition of p44/42 phosphorylation prevented the disruption of barrier function and loss of E-cadherin staining. Thus, WS may contribute to the breakdown of alveolar structure and function through a p44/42 MAPK-dependent pathway and may lead to the development and/or exacerbation of respiratory pathologies with chronic exposure.
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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 | 0.001 |
| 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 | 0.000 | 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".