Development and validation of a dried blood spot test for thiamine deficiency among infants by HPLC–fluorimetry
Bibliographic record
Abstract
Abstract Thiamine deficiency, if detected early in infancy, can be treated with thiamine supplementation and can prevent seizures, other disabilities and death. The dried blood spot (DBS) sampling technique is an attractive sample collection technique for infants. The present study reports the development and validation of a highly sensitive and precise method for quantification of thiamine diphosphate from DBS. The method utilizes full‐spot analysis of a volumetrically deposited 40 μl DBS. The analyte was extracted from the DBS using 50% methanol and then derivatized using potassium ferricyanide to thiochrome. Separation was achieved with the help of an Inertsil ODS C 18 column (5.0 μm, 250 × 4.6 mm) using 150 m m phosphate buffer pH 7–acetonitrile (90:10, % v/v) as the mobile phase. The use of a fluorimetric detector gave a good response to the thiochrome derivative offering good sensitivity for the method. The excitation and emission wavelengths were 367 and 435 nm, respectively. The limit of detection and lower limit of quantification were 5 and 10 ng/ml, respectively. Linearity was demonstrated from 10 to 1000 ng/ml, and precision (CV) was <12.08%, at all tested quality control levels. The method accuracy was 89.34–118.89% with recoveries >80%. Bland–Altman analysis of DBS sampling vs. whole blood demonstrated a mean bias of only 1.16 ng/ml, with a majority of the 60 investigated patient samples lying within 7.2% of the corresponding concentration measured in blood, thereby meeting the clinical desirable biological specification criterion and showing that the two methods are comparable.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".