Method Development and Validation of Dried Blood Spots as a Tool for Mercury Exposure Assessment
Bibliographic record
Abstract
Dried blood spots (DBS) provide a minimally invasive collection method with potential to be used as a field-based research tool for exposure to environmental contaminants such as mercury (Hg). Even though previous studies provided novel techniques to measure Total Mercury (THg) and Methylmercury (MeHg) in residual DBS from newborn screening programs, a range of limitations remains including challenges with detection limit, lack of mercury speciation, and validation with paired DBS-Blood samples collected under laboratory conditions. This study follows our previous work on the matter and specifically evaluates the suitability of DBS to assess methylmercury (MeHg) and THg exposure in human and fish blood samples. It focuses on paired DBS-blood samples from venous and capillary sources of 49 human volunteers, and 10 fish (Artic Char) samples. Paired DBS-blood, reference material and venous blood from one volunteer, was used to develop the analytical method. We used a GC-CVAFS to analyze MeHg and a Dual-Stage Gold Preconcentration for THg. Method development results based on EPA1630 guidelines, wet spikes and the use of blood reference materials showed that the measurement of MeHg in whole blood and DBS was both accurate (95-107%) and precise (2-11%). Initial results showed a high correlation for DBS-blood (r2=0.83) for MeHg (average 0.96 ± 0.73 µg/L) for human samples, as well as for DBS-blood fish samples MeHg (r2=0.94), (average 919.46 ± 492.97µg/L). When coupled with other recent work in this area, there is growing confidence in the use of DBS samples to measure MeHg exposure.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".