Benchtop NMR Spectroscopy of <i>In Vivo</i> Multicellular Organisms
Bibliographic record
Abstract
NMR spectroscopy is a critical tool for environmental and biological research, but the physical and financial barriers of standard “high-field” NMR spectrometers can limit applications, especially in the environmental sciences. Low-field benchtop NMR ( 1 H resonance frequencies generally ≤100 MHz) is more accessible, but its lower sensitivity and increased spectral overlap have limited the study of complex samples. Living organisms are among the most heterogeneous samples, and it is unclear if useful information can be extracted in vivo using benchtop NMR. Here, the potential of low-field (80 MHz) in vivo NMR is first assessed by analyzing 13 C-labeling of unicellular green algae and then by monitoring a process within a multicellular organism ( T. californicus ). This is followed by studying live brine shrimp ( A. franciscana ) at 13 C natural abundance. Adults are compared to brine shrimp cysts, with a number of spectral assignments possible and differences between the life stages clearly evident. High-field NMR is used to confirm peak assignments and provide a more comprehensive characterization of biomolecules present, ultimately making the low-field NMR data more useful. Standard experiments such as 1D 1 H, 1D 13 C and 2D HSQC are conducted, as well as more advanced experiments such as 13 C-SSFP, which greatly enhances 13 C sensitivity, and reverse HSQC, which decreases spectral overlap. Ultimately, this work demonstrates that low-field NMR can effectively analyze live organisms with or without isotopic enrichment and that it holds great potential for future work, such as in vivo analysis of organisms directly in the field if/when portable NMR spectrometers become available.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".