The Development and Miniaturization of In-Vivo Nuclear Magnetic Resonance Spectroscopy Towards Mass-Limited Environmental Samples
Bibliographic record
Abstract
The continual release of environmental contaminants is a global concern that impacts human and environmental health. To monitor and characterize the risk of contaminants, it is critical to determine their toxic mode of action. Unfortunately, our understanding of toxicity pathways is limited, in large part due to a lack of analytical methods capable of monitoring living organisms at the molecular level. Current methods often rely on apical endpoints; however, metabolomics focuses on characterizing biochemical changes in response to external stimuli. Recently, in-vivo Nuclear Magnetic Resonance (NMR) spectroscopy has demonstrated a distinguished ability to monitor living organisms in real-time with the potential to decipher complex interconnected response pathways. Unfortunately, environmentally crucial specimens, such as aquatic eggs, are challenging to study using in-vivo NMR due to their small size and the low sensitivity of NMR. This dissertation will address this challenge through miniaturization of in-vivo NMR. After an introduction to environmental metabolomics and NMR in Chapter 1, Chapter 2 focuses on optimizing in-vivo NMR using existing technology. This chapter examined NMR probes and experiments to establish that the optimal protocol to monitor 13C enriched living organisms was an inverse 2D experiment and an inverse cryoprobe. In the next chapter, NMR was first miniaturized here using microcoils. Chapter 3 compares two main microcoil designs (surface and 3D volume coils) for their ability to perform complex experiments on intact biological samples, concluding a 3D coil is most beneficial for in-vivo studies. In Chapter 4, a microlitre probe is modified with a separate lock and used to significantly reduce sample size requirements for the analysis of D. magna hemolymph and eggs. Additionally, a L volume flow system is introduced for an in-vivo stress study of neonates at the microcoil level. Lastly, Chapter 5 miniaturizes NMR using a novel Lenz lens design while integrating sensitivity enhancements from cryoprobes. The detection system created here leads to massive reductions in sample requirements and substantial increases in sensitivity for mass-limited samples. This dissertation miniaturizes in-vivo NMR techniques and positions it as an important approach to help improve the fundamental understanding of biochemical processes and for future environmental risk assessments.
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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) | 0.000 | 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 | 0.002 |
| 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".