Application of 96-well plate SPE method for analysis of persistent organic pollutants in low volume blood serum samples
Bibliographic record
Abstract
Though many persistent organic pollutants (POPs) are closely regulated the human population is still exposed to these ubiquitous chemicals from the environment and diet. Safe management and human biomonitoring of POPs is necessary to understand the risk of exposure. Within human biomonitoring the mass of sample is often limited, therefore robust methods using smaller sample amounts are necessary. This study developed a 96-well plate solid phase extraction (SPE) method for determination of selected POPs: polychlorinated biphenyls (PCBs), organochlorine pesticides (OCPs), polybrominated diphenyl ethers (PBDEs), hexabromocyclododecane (HBCD) and non-persistent novel flame retardants (NFRs) in low volume blood serum. Non-destructive clean-up coupling Oasis HLB extraction plate with Phree phospholipid removal plate was employed. Extraction efficiency was determined at low and high concentrations in certified reference materials NIST SRM 1957 and 1958, respectively. Target compounds deviated from certified values on average by 15% and 21% for SRM 1957 and SRM 1958, respectively. Observed limit of detections (LODs) ranged from 0.36 pg/mL (PCB 180) to 66.07 pg/mL (δ-HCH). The applicability for real samples is demonstrated on 48 samples from pregnant women enrolled in the pilot phase of the CELSPAC: TNG study. In total, 30 target compounds were detected in at least one sample. The method developed here provides a fast and reliable analysis of human blood serum with possibility to introduce automation for the sample preparation procedure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".