A novel method for source-specific hemoglobin adducts of nitro-polycyclic aromatic hydrocarbons
Bibliographic record
Abstract
Polycyclic aromatic hydrocarbons (PAH) are ubiquitous air pollutants associated with negative impacts on growth, development and behavior in children. Source-specific biological markers of PAH exposure are needed for targeting interventions to protect children. Nitro-derivatives of PAH can act as markers of exposure to diesel exhaust, gasoline exhaust, or general combustion sources. Using a novel HPLC-APCI-MS/MS detection method, we examined four hemoglobin (Hb) adducts of nitro-PAH metabolites and the Hb adduct of a benzo[a]pyrene (BaP) metabolite in 22 umbilical cord blood samples. The samples were collected from a birth cohort with comprehensive data on prenatal PAH exposure, including prenatal personal air monitoring and DNA adducts in maternal and umbilical cord blood. Using non-parametric analyses, heat maps, and principal component analysis (PCA), we analyzed the relationship between the five Hb adducts and previous PAH measurements, with each measurement representing a different duration of exposure. We found that Hb adducts derived from several diesel-related nitro-PAHs (2-nitrofluorene and 1-nitropyrene) were significantly correlated (r = 0.77, p ≤ 0.0001) and grouped together in PCA. Nitro-PAH derived Hb adducts were largely unrelated to previously collected measures of exposure to a number of PAH parent compounds. These measures need to be validated in a larger sample.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".