A high-throughput platform for the rapid screening of vitamin D status by direct infusion-MS/MS
Bibliographic record
Abstract
Vitamin D is an important fat-soluble prohormone with pleiotropic effects on human health, such as immunomodulation of the innate and adaptive immune system. There is an unmet clinical need for a rapid screening platform for 25-hydroxyvitamin D (25OH-D) determination without chromatographic separation that offers better precision and accuracy than immunoassays. Here, we introduce a high-throughput method for assessing vitamin D status from blood specimens based on direct infusion-MS/MS (DI-MS/MS) following click derivatization using 2-nitrosopyridine. We developed an optimized liquid-phase extraction protocol to minimize ion suppression when directly infusing serum or plasma extracts via a capillary electrophoresis system for quantitative determination of 25OH-D. Acceptable reproducibility (mean coefficient of variation = 10.9%, n = 412), recovery (mean = 102% at 15, 30, and 45 nmol/l), and linearity ( R 2 > 0.998) were achieved for 25OH-D with lower detection limits (limit of detection ∼1.2 nmol/l, S/N ∼ 3), greater throughput (∼3 min/sample), and less bias than a commercial chemiluminescence immunoassay prone to batch effects. There was mutual agreement in 25OH-D concentrations from reference blood samples measured by DI-MS/MS as compared with LC-MS/MS (mean bias = 7.8%, n = 18). We also demonstrate that this method could reduce immunoassay misclassification of vitamin D deficiency in a cohort of critically ill children ( n = 30). In conclusion, DI-MS/MS offers a viable alternative to LC-MS/MS for assessment of vitamin D status in support of large-scale studies in nutritional epidemiology as well as clinical trials to rapidly screen individual patients who may benefit from vitamin D supplementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".