Development of a Simple Cost Effective Oxygenation System for In Vivo Solution State NMR in 10 mm NMR Tubes
Bibliographic record
Abstract
In vivo NMR is evolving into an important tool to understand biological processes and environmental responses. Current approaches use flow systems to sustain the organisms with oxygenated water and food (e.g., algae) inside the NMR. However, such systems have the potential to leak and clog (potentially damaging costly hardware), require large volumes of media, and multiple expensive HPLC pumps. The proposed “oxygenation system”, uses a simple “double slit” adapter and a single air/oxygen flow line into the NMR. The design is especially suited to larger diameter probes given that standard flow systems would require higher flow rates thus amplifying the potential and impact of leaks/clogs. Traditionally, in vivo NMR of small organisms (e.g., Daphnia ) have required 2D NMR in combination with 13 C enrichment to overcome susceptibility distortions and provide information rich metabolic profiles. Here Daphnia magna, Eisenia fetida and Artemia franciscana are used to demonstrate the potential of the oxygenation system. Survivability tests and 1 H time-resolved monitoring were first performed on D. magna, while E. fetida contained enough biomass to permit 1 H– 13 C HSQC, 13 C– 1 H HETCOR and 31 P NMR without isotopic enrichment. Finally, STOCSY of 1D 13 C NMR was used to follow the growth of A. franciscana (without 13 C enrichment) for 48 h after birth, which helps visualize trends across a series of 1D in vivo data. In summary, application of the oxygenation system toward larger diameter probes allows the collection of NMR data without enrichment, offering a promising solution to better understand processes in vivo.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".