A Robust Method for Iodine Status Determination in Epidemiological Studies by Capillary Electrophoresis
Bibliographic record
Abstract
Iodine deficiency is the most common preventable cause of intellectual disabilities in children. Global health initiatives to ensure optimum nutrition thus require continuous monitoring of population-wide iodine intake as determined by urinary excretion of iodide. Current methods to analyze urinary iodide are limited by complicated sample pretreatment, costly infrastructure, and/or poor selectivity, posing restrictions to large-scale epidemiological studies. We describe a simple yet selective method to analyze iodide in volume-restricted human urine specimens stored in biorepositories by capillary electrophoresis (CE) with UV detection. Excellent selectivity is achieved when using an acidic background electrolyte in conjunction with dynamic complexation via α-cyclodextrin in an unmodified fused-silica capillary under reversed polarity. Sample self-stacking is developed as a novel online sample preconcentration method to boost sensitivity with submicromolar detection limits for iodide (S/N ≈ 3, 0.06 μM) directly in urine. This assay also allows for simultaneous analysis of environmental iodide uptake inhibitors, including thiocyanate and nitrate. Rigorous method validation confirmed good linearity (R(2) = 0.9998), dynamic range (0.20 to 4.0 μM), accuracy (average recovery of 93% at three concentration levels) and precision for reliable iodide determination in pooled urine specimens over 29 days of analysis (RSD = 11%, n = 87).
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".