Evaluation of solid-phase extraction approaches for LC-MS metabolomics
Bibliographic record
Abstract
C global LC-MS metabolomics methods for human plasma commonly rely on solvent protein precipitation using methanol or acetonitrile. Such rapid and unselective approaches are most suitable for the detection of medium to high abundance metabolites, whereas the coverage of low abundance metabolome remains poor. In this work, we explore the use of solid-phase extraction (carbon-based, pentafluorophenyl, core-shell nanoparticle and polystyrene-divinylbenzene) for the extraction and enrichment of low abundance metabolites in human plasma on global scale. We compare the optimized protocols in terms of metabolite coverage, precision and recovery of selected metabolites after LC-MS analysis of the extracts using both reversed-phase and HILIC chromatography coupled to high-resolution Oorbitrap mass spectrometer. Our results clearly demonstrate the advantages and potential of sequential solid-phase extraction in global LC-MS metabolomics approaches. Dajana Vuckovic et al., J Anal Bioanal Tech 2013, 4:5 http://dx.doi.org/10.4172/2155-9872.S1.013
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".