Cost-Effective HPLC-UV Method for Quantification of Vitamin D<sub>2</sub> and D<sub>3</sub> in Dried Blood Spot: A Potential Adjunct to Newborn Screening for Prophylaxis of Intractable Paediatric Seizures
Bibliographic record
Abstract
25-Hydroxyvitamin D (25-(OH)D) deficiency is recently been described as one of the multiple factors responsible for pediatric seizures.25-Hydroxyvitamin D3 and 25-Hydroxyvitamin D2 are the well-known markers to determine Vitamin D status.In this work we report the development of a sensitive and cost effective HPLC technique for the quantification of the vitamin D metabolites from dried blood spot samples (DBS).The metabolites were extracted using acetonitrile-methanol-0.1% formic acid (60 : 20 : 20 (v/v)) and analyzed on an Acclaim C 18 column (150 4.6 mm i.d., 3 µm) at a flow rate of 1 mL/min.The method was linear in the range of 10-80 ng/mL.Limit of detection and limit of quantification (LOQ) of the method were 5 and 10 ng/mL respectively.Extensive stability studies demonstrated the analytes to be stable in stock and matrix with a percent change within the acceptable range of 15%.Comparison of the newly developed HPLC-DBS method with the reported LC-MS-DBS and electrochemiluminescence immunoassay (ECLIA) methods followed by Bland-Altman analysis demonstrated a bias of 0.08 and 0.14, respectively proving the methods are comparable.Application of the developed method to a pediatric seizure cohort depicted 46.6% of cases as deficient and 26.6% as insufficient for 25-(OH)D.Among deficient cases 8 samples were below 10 ng/mL and exact amount was not calculated since these were below the LOQ levels.The mean standard deviation (S.D.) in the remaining 6 deficient cases was 13.22 2.80 ng/mL.The levels in healthy infants were 33.9 6.11 ng/mL.The method can be used routinely for assessing 25-(OH)D deficiency in newborn.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
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".