Development of a chromatographic method to study oxidative potential of airborne particulate matter
Bibliographic record
Abstract
Abstract. Oxidative potential (OP) is a measure of inhalation toxicity of airborne particulate matter (PM). The redox-active constituents of PM react with lung antioxidants (AOs) in the epithelial lining fluid (ELF), resulting in oxidation of AOs. The excessive loss of AOs leads to oxidative stress, inflammation of the epithelial tissue, and chronic diseases. In this work, we developed a novel rapid chromatographic method, employing an ultra-high performance liquid chromatograph coupled to a triple-quadruple mass spectrometer (UHPLC-MS/MS), to determine the OP of ambient PM, and investigated the application of electrochemical oxidation-reduction potential (ORP) as an alternative approach for estimating OP. We measured the direct oxidation of AOs, ascorbic acid, glutathione, and cysteine, and formation of glutathione disulfide and cystine, following PM addition to various simulated ELF (SELF) formulations which, in addition to AOs, contained inorganic salts, a phospholipid, and proteins. The assay performance was evaluated using standard reference PM, and we investigated the links between OP dose-response, time-dependence, and the ORP. The new assay showed a high precision, and when applied to PM, OP and ORP increased with both reaction time and PM concentrations in SELF. The presence of SELF inorganic species and surfactant lipid increased the OP determined through oxidation of glutathione and cysteine, but showed an opposite effect with ascorbic acid. The presence of proteins did not affect the OP. The findings suggest that ORP measurement could be used as an alternative and simple approach for estimating the OP of ambient PM.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".