Exploring the Applications of Carbon-Detected NMR in Living and Dead Organisms Using a <sup>13</sup>C-Optimized Comprehensive Multiphase NMR Probe
Bibliographic record
Abstract
Comprehensive multiphase–nuclear magnetic resonance (CMP–NMR) is a non-invasive approach designed to observe all phases (solutions, gels, and solids) in intact samples using a single NMR probe. Studies of dead and living organisms are important to understand processes ranging from biological growth to environmental stress. Historically, such studies have utilized 1 H-based phase editing for the detection of soluble/swollen components and 1 H-detected 2D NMR for metabolite assignments/screening. However, living organisms require slow spinning rates (∼500 Hz) to increase survivability, but at such low speeds, complications from water sidebands and spectral overlap from the modest chemical shift window (∼0–10 ppm) make 1 H NMR challenging. Here, a novel 13 C-optimized E-Free magic angle spinning CMP probe is applied to study all phases in ex vivo and in vivo samples. This probe consists of a two-coil design, with an inner single-tuned 13 C coil providing a 113% increase in 13 C sensitivity relative to a traditional multichannel single-CMP coil design. For organisms with a large biomass (∼0.1 g) like the Ganges River sprat ( ex vivo ), 13 C-detected full spectral editing and 13 C-detected heteronuclear correlation (HETCOR) can be performed at natural abundance. Unfortunately, for a single living shrimp (∼2 mg), 13 C enrichment was still required, but 13 C-detected HETCOR shows superior data relative to heteronuclear single-quantum coherence at low spinning speeds (due to complications from water sidebands in the latter). The probe is equipped with automatic-tuning–matching and is compatible with automated gradient shimming─a key step toward conducting multiphase screening of dead and living organisms under automation in the near future.
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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 | 0.000 | 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) | 0.001 | 0.000 |
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".