Ex vivo Comprehensive Multiphase NMR of whole organisms: A complementary tool to in vivo NMR
Bibliographic record
Abstract
Nuclear Magnetic Resonance (NMR) spectroscopy is a non-invasive analytical technique which allows for the study of intact samples. Comprehensive Multiphase NMR (CMP-NMR) combines techniques and hardware from solution state and solid state NMR to allow for the holistic analysis of all phases (i.e. solutions, gels and solids) in unaltered samples. This study is the first to apply CMP-NMR to deceased, intact organisms and uses 13C enriched Daphnia magna (water fleas) as an example. D. magna are commonly used model organisms for environmental toxicology studies. As primary consumers, they are responsible for the transfer of nutrients across trophic levels, and a decline in their population can potentially impact the entire freshwater aquatic ecosystem. Though in vivo research is the ultimate tool to understand an organism’s most biologically relevant state, studies are limited by conditions (i.e. oxygen requirements, limited experiment time and reduced spinning speed) required to keep the organisms alive, which can negatively impact the quality of the data collected. In comparison, ex vivo CMP-NMR is beneficial in that; organisms do not need oxygen (eliminating air holes in rotor caps and subsequent evaporation); samples can be spun faster, leading to improved spectral resolution; more biomass per sample can be analyzed; and experiments can be run for longer. In turn, higher quality ex vivo NMR, can provide more comprehensive NMR assignments, which in many cases could be transferred to better understand less resolved in vivo signals. This manuscript is divided into three sections: 1) multiphase spectral editing techniques, 2) detailed metabolic assignments of 2D NMR of 13C enriched D. magna and 3) multiphase biological changes over different life stages, ages and generations of D. magna. In summary, ex vivo CMP-NMR proves to be a very powerful approach to study whole organisms in a comprehensive manner and should provide very complementary information to in vivo based research.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".