Challenges Associated with Sample Preparation for the Analysis of PBDEs in Human Serum
Bibliographic record
Abstract
Polybrominated diphenyl ethers (PBDEs) are used as flame retardants in many applications; however, certain PBDE congeners are persistent, bioaccumulative, and toxic to both humans and the environment. PBDEs have been found in human specimens, and a variety of analytical techniques have been used for their determination in biological matrixes. Nevertheless, obtaining a relatively clean analytical blank sample during PBDE analysis is a big challenge because of the ubiquitous nature of these compounds. Thus, the present study was conducted to compare the PBDE background levels associated with the three most commonly used extraction techniques: liquid-liquid extraction (LLE), SPE, and accelerated solvent extraction (ASE). Conventionally used blank matrixes (HPLC grade water, Milli-Q water, and air) were spiked with internal standards and extracted using LLE, SPE, or ASE. The extracts were analyzed by GC/electron ionization-tandem MS. The ASE method achieved the lowest background levels for nearly all the PBDE congeners analyzed, which may be attributed to the stainless steel and closed-vessel nature of the ASE cells.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".