Data relating to the transcriptomes of human lung epithelial cells exposed to radon-emitting rock, tobacco smoke or cannabis smoke
Bibliographic record
Abstract
Presented herein are RNA expression data linked to the exposure of human lung epithelial cells to either low dose radon-emitting rock, tobacco smoke or cannabis smoke. Two cell lines were used, one representing a ‘normal’ lung epithelial cell (BEAS-2B, derived from immortilized bronchial epithelial cells from a cadaver) and one representing a ‘cancerous’ lung epithelial cell (NCI-H1975, derived from a primary lung adenocarcinoma from a non-smoker). Control cells were cultured under standard conditions. Test cells were either (a) continuously cultured in the presence of pulverized uranium-containing rock emitting 38 Bq/m3 radon, or (b) exposed five days a week, to a 1:10,000 dilution of either tobacco or cannabis smoke from one cigarette. RNA was extracted from the cells at various time-points over a period of 1–17 weeks (7–140 days). cDNA libraries were prepared from the RNA, and the libraries were sequenced. Raw, aligned sequencing data, from 38 biosamples, are available through a public repository. Differential gene expression data, relating to control and test samples from various time-points, are linked to this article. Detailed analyses relating to these data can be found in the article “Human lung epithelial cells cultured in the presence of radon-emitting rock experience gene expression changes similar to those associated with tobacco smoke exposure” [1].
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".