Short term exposure to respirable smoke extracts from indoor cooking in Nepal leads to altered human airway epithelial cell (AEC) gene expression, DNA methylation (mC) and hydroxymethylation (hmC)
Bibliographic record
Abstract
Background: Populations in low and middle-income countries are exposed to household air pollution from the combustion of solid biomass fuel for indoor cooking, causing respiratory symptoms/illness and premature death. The mechanisms mediating the effects are unclear. Objectives: To assess the comparative effects of traditional cookstove (TCS), improved cookstove (ICS), LPG stove smoke extract (SE) and ambient air (AA) on AEC gene expression, mC and hmC. Methods: Biomass combustion smoke from TCS and ICS; LPG combustion fume from LPG stoves and clean AA were channelled through media to generate combustion SE. AECs were treated with SEs for 24 hours. DNA and RNA were extracted, and gene expression, mC and hmC profiled using Illumina Novaseq6000 flowcell and HumanMethylationEPIC Beadchip respectively. Analysis was performed in R. Pathway analysis was carried out using IPA (QIAGEN). Results: 52 genes were statistically differentially expressed after TCS exposure, relative to AA but none with ICS or LPG exposure. These genes were enriched in pathways including Ferroptosis and NRF2-mediated Oxidative Stress Response. Of the 52 genes, expression quantitative trait methylation analysis identified fifteen and fourteen genes with significant association with mC (88) and hmC (197) CpGs respectively. Ferroptosis and downstream regulator NFE2L2 were linked to 14 mC and hmC associated genes. Conclusions: TCS smoke exposure induces AEC gene expression changes not seen with ICS and LPG smoke. mC and hmC may differentially mediate TCS exposure induced changes in gene expression.
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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) | 0.002 | 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".