Automated identification and quantification of metabolites in human fecal extracts by nuclear magnetic resonance spectroscopy
Bibliographic record
Abstract
Abstract We report the development of a software program, called MagMet‐F, that automates the processing and quantification of 1D 1H NMR of human fecal extracts. To optimize the program, we identified 82 potential fecal metabolites using 1D 1H NMR of six human fecal extracts using manual profiling and a literature review of known fecal metabolites. We acquired pure versions of those metabolites and then acquired their 1D 1H NMR spectra at 700 MHz to generate a fecal metabolite spectral library for MagMet‐F. The fitting of these metabolites by MagMet‐F was iteratively optimized to replicate manual profiling. We validated MagMet‐F's automated profiling using a test set of six fecal extracts. It correctly identified 80% of the compounds and quantified those within <20% of the values determined by manual profiling using Chenomx. We also compared MagMet‐F's profiling performance to two other open‐access NMR profiling tools, Bayesil and Batman. MagMet‐F outperformed both. Bayesil repeatedly overestimated metabolite concentrations by 10% to 40% while Batman was unable to properly quantify any compounds and took 10–20× longer. We have implemented MagMet‐F as a freely accessible web server to enable automated, fast and convenient 1D 1H NMR spectral profiling of fecal samples. MagMet‐F is available at https://www.magmet.ca .
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".