Identification of micro-/nanoplastics in human placental blood using comprehensive multidimensional pyrolysis - gas chromatography x ion mobility mass spectrometry
Bibliographic record
Abstract
BACKGROUND: Micro-/nanoplastics (MNPs) are ubiquitous environmental contaminants and there has been a growing concern about their potential adverse effects on human health. The present study reports on the development of a novel pyrolysis-gas chromatography x ion mobility mass spectrometry method to identify MNPs in placental blood, while reducing false positive detections from matrix interferences. RESULTS: Base digestion and filtration yielded acceptable recoveries: 90 ± 11 % for polystyrene (PS), 93 ± 16 % for polyethylene (PE), and 53 ± 18 % for polypropylene (PP). Limit of Detections (LODs) ranged from 0.15 to 0.60 μg/mL, depending on the polymer. Placental blood samples were collected from 46 donors and analyzed in triplicate, resulting in measurements for 138 samples. Forty-three samples contained at least one polymer type above the limit of detection, and 10 samples contained at least one polymer type above the limit of quantification. Total plastic concentration in samples with detectable levels (>LOD) of MNPs averaged 0.9 μg/mL and ranged between 0.2 and 3.6 μg/mL. Orthogonal separation by ion mobility revealed that 10/22 PE detections were false positives. SIGNIFICANCE: This study is the first to integrate ion mobility separation to differentiate between genuine and false detection of PE in human blood. Without the aid of ion mobility separation, the concentration of PE in individual samples was overestimated by up to 233 % of the mean MNP concentration, underlining the importance of multidimensional separation for individual exposure analysis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.000 | 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".