In vitro toxicity assessment of atmospheric particulate matter on human lung and hepatic cells with agar membrane-based sampling and exposure strategy
Bibliographic record
Abstract
In vitro toxicology research into the health effects of atmospheric particulate matter (PM) depends on the extraction of PM from filters. Previously, we proposed an efficient PM sampling and extraction method based on low-cost agar membrane and performed an exposure study with RAW264.7 macrophages. Here, we extended the application of this strategy, employing lung and hepatic cells, to validate its reliability and reproducibility. One traditional strategy using polytetrafluoroethylene (PTFE) filter was adopted for comparison. Cytotoxicity and proteomics results showed that the PM extracted by two methods induced comparable toxicity to A549 and BEAS-2B cells, while the PM extracted from the agar membranes induced higher toxicity to HepG2 and HL7702 cells than that from the PTFE filters. The differences in the investigations might be associated with the cell's sensitivity to the extracted suspended particles. This work indicated the importance of assessing PM cytotoxicity from the perspective of extraction methods and cell lines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.001 | 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".