Development and Application of a Low-Volume Flow System for Solution-State <i>in Vivo</i> NMR
Bibliographic record
Abstract
In vivo nuclear magnetic resonance (NMR) spectroscopy is a particularly powerful technique, since it allows samples to be analyzed in their natural, unaltered state, criteria paramount for living organisms. In this study, a novel continuous low-volume flow system, suitable for in vivo NMR metabolomics studies, is demonstrated. The system allows improved locking, shimming, and water suppression, as well as allowing the use of trace amounts of expensive toxic contaminants or low volumes of precious natural environmental samples as stressors. The use of a double pump design with a sump slurry pump return allows algal food suspensions to be continually supplied without the need for filters, eliminating the possibility of clogging and leaks. Using the flow system, the living organism can be kept alive without stress indefinitely. To evaluate the feasibility and applicability of the flow system, changes in the metabolite profile of 13 C enriched Daphnia magna over a 24-h period are compared when feeding laboratory food vs exposing them to a natural algal bloom sample. Clear metabolic changes are observed over a range of metabolites including carbohydrates, lipids, amino acids, and a nucleotide demonstrating in vivo NMR as a powerful tool to monitor environmental stress. The particular bloom used here was low in microcystins, and the metabolic stress impacts are consistent with the bloom being a poor food source forcing the Daphnia to utilize their own energy reserves.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".