Strong Anion Determination in Biological Fluids by Capillary Electrophoresis for Clinical Diagnostics
Bibliographic record
Abstract
New methods for quantitative analysis of strong anions are required for diagnostic testing of human diseases. Current techniques suffer from poor selectivity and/or long analysis times that are not amenable for labile anions in high-saline or volume-restricted samples. We introduce a rapid assay (<5 min) based on capillary electrophoresis (CE) with indirect UV detection for simultaneous analysis of sulfate, sulfite, and chloride in human urine, plasma, and sweat specimens. Remarkable selectivity for strong anions is achieved by using an acidic background electrolyte under reversed polarity that results in electrokinetic rejection of matrix interferences at the capillary inlet. A dual co-ion probe system consisting of 5 mM naphthalene disulfonate (NDS) and 5 mM naphthalene trisulfonate (NTS) in 0.4 M formic acid, pH 2.0 is developed for detection of UV transparent anions (S/N ≈ 3, 60 μM with a 25 μm inner diameter fused-silica capillary) with good peak symmetry and baseline stability. Due to the chemical reactivity of sulfite, dilute formaldehyde is used as a reagent to form an acid-stable hydroxymethylsulfonate adduct. Method validation confirmed excellent linearity (R(2) > 0.999), good accuracy (mean bias ≈7%), and acceptable long-term reproducibility (CV < 10%) over 20 days. The assay allows for artifact-free determination of sulfate and sulfite with consistent results for chloride when compared to standard electrochemical methods (R(2) > 0.975). Preliminary data suggest that kidney-stone formers have lower urinary sulfate excretion relative to non-kidney-stone patient controls (p = 0.0261). CE offers a selective yet robust platform for routine analysis of strong anions that is needed for confirmatory testing of cystic fibrosis, sulfite oxidase deficiency, urolithiasis, and other disorders of sulfur metabolism and/or anion transport.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".