Organoid for Air Pollution Toxicity Assessment: Advances and Environmental Applicability
Bibliographic record
Abstract
Air pollution is the leading environmental risk to human health. Toxicological studies indicate the toxic effects of air pollution on human disease according to in vitro cell lines and in vivo models. However, these evaluation tools have large uncertainties owing to the discrepancies of the in vivo microenvironment and species between these employed models and human. Recently, organoids have emerged as powerful tools for studying the impact of air pollution on health, including the mechanisms of particulate matter, novel pollutants inducing toxic injuries, and infectious diseases. Compared with animal models and conventional cell culture systems, organoids offer higher human relevance. However, the systematic methodological immaturity and potential challenges of organoids in air pollution research remain unclear. This review explored the potential of organoids on studying the health effects of air pollution, highlighting their advantages over traditional toxicological models and addressing the challenges that need to be overcome. We propose the future effort for developing organoid systems for air pollution exposure applications, incorporating environmental exposure biobank, microfluidic technologies, and gene editing tools, to further enhance toxicological predictive capabilities. These innovative approaches can gain deeper insights into the mechanisms of air pollution-induced toxic effects and broaden the applicability of organoids in environmental health.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".