Integrated Digital Microfluidics NMR Spectroscopy: A Key Step toward Automated In Vivo Metabolomics
Bibliographic record
Abstract
Toxicity testing is currently undergoing a paradigm shift from examining apical end points such as death, to monitoring sub-lethal toxicity in vivo. In vivo nuclear magnetic resonance (NMR) spectroscopy is a key platform in this endeavor. A proof-of-principle study is presented which directly interfaces NMR with digital microfluidics (DMF). DMF is a “lab on a chip” method allowing for the movement, mixing, splitting, and dispensing of μL-sized droplets. The goal is for DMF to supply oxygenated water to keep the organisms alive while NMR detects metabolomic changes. Here, both vertical and horizontal NMR coil configurations are compared. While a horizontal configuration is ideal for DMF, NMR performance was found to be sub-par and instead, a vertical-optimized single-sided stripline showed most promise. In this configuration, three organisms were monitored in vivo using 1 H- 13 C 2D NMR. Without support from DMF droplet exchange, the organisms quickly showed signs of anoxic stress; however, with droplet exchange, this was completely suppressed. The results demonstrate that DMF can be used to maintain living organisms and holds potential for automated exposures in future. However, due to numerous limitations of vertically orientated DMF, along with space limitations in standard bore NMR spectrometers, we recommend future development be performed using a horizontal (MRI style) magnet which would eliminate practically all the drawbacks identified here.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".