Dried Blood Spot Analysis by Digital Microfluidics Coupled to Nanoelectrospray Ionization Mass Spectrometry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it