Dried Blood Spot Analysis by Digital Microfluidics Coupled to Nanoelectrospray Ionization Mass Spectrometry
Bibliographic record
Abstract
Dried blood spot (DBS) samples on filter paper are surging in popularity as a sampling and storage vehicle for a wide range of clinical and pharmaceutical applications. For example, a DBS sample is collected from every baby born in the province of Ontario, Canada, for quantification of approximately one hundred analytes that are used to screen for 28 conditions, including succinylacetone (SA), a marker for hepatorenal tyrosinemia. Unfortunately, the conventional methods used to evaluate DBS samples for newborn screening and other applications are tedious and slow, with limited options for automated analysis. In response to this challenge, we have developed a method to couple digital microfluidics (DMF) to nanoelectrospray ionization mass spectrometry (nESI-MS) for SA quantification in DBS samples. The new system is formed by sandwiching a pulled glass capillary emitter between the two DMF substrates such that the capillary emitter is immobilized without external seals or gaskets. Moreover, we introduce a new feedback control system that enables high-fidelity droplet manipulation across DBS samples without manual intervention. The system was validated by application to on-chip extraction, derivatization, and analysis of SA and other analytes from DBS samples, with comparable performance to gold-standard methods. We propose that the new methods described here can potentially contribute to a new generation of analytical techniques for quantifying analytes in DBS samples for a wide range of 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".