Short-term stabilities of 21 amino acids in dried blood spots
Bibliographic record
Abstract
ABSTRACT BACKGROUND Dried blood spots (DBSs) have potential use in remote health applications for individual and population diagnosis, and can enable epidemiological surveillance for known and unknown diseases. The preparation and transportation of DBSs from remote settings often exposes these cards to extreme environmental stress that may impact the quality of the diagnostic data. Given these risks, it is essential to investigate the individual stabilities of biomarkers in DBSs. This paper details the stability of routinely-analyzed amino acids (AAs) on DBSs under environmental conditions that simulate a global health workflow. METHODS The extractions of 21 AAs from three sets of DBSs prepared on cellulose and cotton filter paper were optimized for quantitation by dansylation-UPLC/MRM-MS. The effects of sunlight exposure, temperature, humidity, and storage time were studied. RESULTS The AAs were stable in DBSs after 4-hour sunlight exposure, and after storage at -20 and 4 °C for 30 days. At 25 and 40 °C, only 7 AAs showed significant concentration decreases over time, while 2 showed concentration increases. The changes were accelerated by high humidity. Histidine was the least stable AA under the conditions tested. CONCLUSIONS This study provides quantitative data on the short-term stabilities of 21 AAs in DBSs on cellulose and cotton-based filter paper, under environmental conditions that simulate a global-health workflow. These results highlight the importance of assessing the stability of clinically-relevant biomarkers in DBSs. Based on the measured stabilities, we recommend that higher-temperature and high-humidity storage of DBS samples be avoided for AA analysis in remote health applications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".