Towards Automated Toxicity Testing Using Novel Technologies to Reduce Spectral Overlap and Address Sensitivity Limitations in Environmental In-Vivo NMR
Bibliographic record
Abstract
Toxicity testing of living organisms has been undergoing a paradigm shift over the last decade. Focus has moved from examining apical endpoints such as death or reproduction, to measuring sub-lethal impacts from a biochemical perspective, with future aspirations to automate the approach. One of the ways this is examined is through metabolomics, the study of the impacts to an organism’s metabolic system as a result of an external stressor, such as a potential toxin. Recently, the field of in-vivo nuclear magnetic resonance (NMR) spectroscopy to study metabolomics has provided breakthroughs in toxicity testing information. Much of this work has been done with the aquatic organism Daphnia magna, a model organism for aquatic toxicity studies. While robust, NMR suffers from a lack of sensitivity and heavy spectral overlap in complex systems, such as living organisms. Thus, this thesis introduces novel technologies which help address sensitivity limitations and remove overlap in in-vivo NMR while working towards creating an automated dosing platform in line with the paradigm shift of toxicity testing. First, a new holistic approach to testing is examined which uses three NMR techniques in tandem, providing complementary information of toxin interactions including binding, physical partitioning, and biochemical response inside living organisms. Binding with the organism’s outer shell and metabolic oxidative stress responses were measured, which could not have been examined independently. Additionally, two new NMR pulse sequences are introduced. The first examines new bond formation between different nuclear isotopes (13C-12C), and food incorporation into living biomass of D. magna was examined. In the second sequence, suites of molecules chosen by the user can be isolated from an unchanged matrix, while focusing increases sensitivity over traditional 1H NMR. Four metabolites indicative of oxidative stress were monitored in D. magna simultaneously, while the unselected signals were filtered out. Finally, an automated dosing platform was created by the combination of digital microfluidics (DMF) and NMR to keep D. magna alive for prolonged periods using automated movement of food and water. Moving forward, these four new approaches could be used in tandem to increase sensitivity, reduce overlap, and automate the toxicity testing process.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".