Total X-Ray Fluorescence for the Analysis of Multiple Elements in Dried Blood Spots
Bibliographic record
Abstract
Dried blood spots (DBS) have been hailed as a simple way to collect, transport and store biological (blood) samples for several analytical purposes. The technique is minimally invasive which makes it a cost-effective alternative to traditional venipuncture procedures. Despite this promise, there have been challenges in measuring analyte concentrations in DBS owing to small sample volumes, concentrations, and other factors. Total Reflection X-Ray Fluorescence (TXRF) may help overcome some these barriers, and thus the aim of this research was to develop and validate a novel method to perform a multi-element analysis (copper, zinc, selenium, iron, and lead) in DBS using TXRF. A TXRF-based method for multi-element analysis was validated by analyzing DBS from different human blood standard reference materials (SRM, Institut National de Santé Publique du Québec, INSPQ; n=7) with varying and known concentrations of elements. Percent recoveries, calculated by comparing DBS to known values, and coefficients of variation were analyzed for accuracy and precision. Stability of analyses was assessed by comparing results over 16 batch runs. The method was validated by quantifying the elements of interest in capillary whole blood and DBS collected from 49 healthy individuals. The limits of quantification of Cu, Fe, Pb, Se, and Zn were 5.08, 10.7, 3.45, 3.48, and 4.50 µg/L, respectively. The precision of the method was between 3.0-15.2% for the elements of interest. Percent recoveries for Cu, Zn, Se, and Pb using an entire 25 µL DBS of SRM were 106.7± 10%, 97.9± 11.9%, 105.5± 9.2%, and 80.8± 16.40%, respectively. These results show the potential of TXRF as an alternative analytical technique to quantify multiple elements in DBS samples. The results will be further explored with ICP-MS data.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".