1D <sup>1</sup>H NMR as a Tool for Fecal Metabolomics
Bibliographic record
Abstract
Abstract Metabolomic studies allow a deeper understanding of the processes of a given ecological community than nucleic acid–based surveys alone. In the case of the gut microbiota, a metabolic profile of, for example, a fecal sample provides details about the function and interactions within the distal region of the gastrointestinal tract, and such a profile can be generated in a number of different ways. This unit elaborates on the use of 1D 1 H NMR spectroscopy as a commonly used method to characterize small‐molecule metabolites of the fecal metabonome (meta‐metabolome). We describe a set of protocols for the preparation of fecal water extraction, storage, scanning, measurement of pH, and spectral processing and analysis. We also compare the effects of various sample storage conditions for processed and unprocessed samples to provide a framework for comprehensive analysis of small molecules from stool‐derived samples. © 2020 Wiley Periodicals LLC Basic Protocol 1 : Extracting fecal water from crude fecal samples Alternate Protocol 1 : Extracting fecal water from small crude fecal samples Basic Protocol 2 : Acquiring NMR spectra of metabolite samples Alternate Protocol 2 : Acquiring NMR spectra of metabolite samples using Bruker spectrometer running TopSpin 3.x Alternate Protocol 3 : Acquiring NMR spectra of metabolite samples by semiautomated process Basic Protocol 3 : Measuring sample pH Support Protocol 1 : Cleaning NMR tubes Basic Protocol 4 : Processing raw spectra data Basic Protocol 5 : Profiling spectra Support Protocol 2 : Spectral profiling of sugars and other complex metabolites
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".