Analysis of HO-PCBs and PCP in blood plasma from individuals with high PCB exposure living on the Chukotka Peninsula in the Russian Arctic
Bibliographic record
Abstract
A trace analytical method is presented for the analysis of hydroxylated polychlorinated biphenyl metabolites (HO-PCBs) and pentachlorophenol (PCP) in human plasma. The described methodology is a modification of a previously validated method used for PCB and organochlorine pesticide analysis. The modified method enables the combined analysis of phenolic and neutral halogenated compounds. A tandem Florisil column is used for separating the HO-PCBs and PCP from the neutral fraction, instead of the more common chemical partitioning. In the same step the neutral fraction is purified for GC analysis. The extraction of the HO-PCBs and PCP was found to be highly dependent on sufficient acidification of the sample and the polarity of the extracting solvent. Analysis of plasma samples gave recovery rates for (13)C(6)-PCP and (13)C(12)-4-HO-CB 187 of 64 and 72%, respectively. The limit of detection ranged between 2-20 pg g(-1) plasma for the HO-PCBs and 5 pg g(-1) plasma for PCP. No matrix interferences were observed in the chromatograms. In plasma samples (n = 15) from the native Chukchi people in Uelen (Russian Arctic), a population with high PCB exposure, the median ratio of sum HO-PCBs to sum PCBs was as high as 0.4 and the sum HO-PCBs and PCBs were significantly correlated (r(2) > 0.7, p < 0.01). The median sum HO-PCBs (10 congeners) was 5920 pg g(-1) plasma with 4-HO-CB 107 as the dominating congener (median: 1670 pg g(-1) plasma). The median PCP level was measured at 642 pg g(-1) plasma.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".