A matrix‐assisted laser desorption ionization–time‐of‐flight–time‐of‐flight–mass spectrometry‐based toxicoproteomic screening method to assess in vitro particle potencies
Bibliographic record
Abstract
Abstract Knowledge of biological reactivity and underlying toxicity mechanisms of airborne particulate matter (PM) is central to the characterization of the risk associated with these pollutants. An integrated screening platform consisting of protein profiling of cellular responses and cytotoxic analysis was developed in this study for the estimation of PM potencies. Mouse macrophage (J774A.1) and human lung epithelial cells (A549) were exposed in vitro to Ottawa urban particles (EHC6802) and two reference mineral particles (TiO2and SiO2). Samples from the in vitro exposure experiment were tested following an integrated classical cytotoxicity/toxicoproteomic assessment approach for cellular viability (CellTiter Blue®, lactate dehydrogenase) and proteomic analyses. Cellular proteins were pre‐fractionated by molecular weight cut‐off filtration, digested enzymatically and were analyzed by matrix‐assisted laser desorption ionization–time‐of‐flight–time‐of‐flight–mass spectrometry for protein profiling and identification. Optimization of detergent removal, pre‐fractionation strategies and enzymatic digestion procedures led to increased tryptic peptide (m/z) signals with reduced sample processing times, for small total protein contents. Proteomic analyses using this optimized procedure identified statistically significant (P < 0.05) PM dose‐dependent changes at the molecular level. Ranking of PM potencies based on toxicoproteomic analysis were in line with classical cytotoxicity potency‐based ranking. The high content toxicoproteomic approach exhibited the potential to add value to risk characterization of environmental PM exposures by complementing and validating existing cytotoxicity testing strategies.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".