Automated High-Throughput Quantification of Phenyl-γ-valerolactones and Creatinine in Urine by Laser Diode Thermal Desorption
Bibliographic record
Abstract
Quantification of nutritional biomarkers is crucial to accurately assess the dietary intake of different classes of (poly)phenols in large epidemiological studies. High-throughput analysis is mandatory to apply this methodology in large cohorts. However, the current validated methods to quantify (poly)phenols metabolites in biological fluids use ultra performance liquid chromatography (UPLC), leading to analysis time of several minutes per sample. To significantly reduce the run time, we developed and validated a method to quantify in urine the flavan-3-ols biomarkers, phenyl-γ-valerolactones (PVLs), using laser diode thermal desorption (LDTD). This mass spectrometry source allows direct introduction of sample extracts, resulting in analysis time of less than 10 s per sample. Also, to encompass the problem associated with the cost and availability of sulfated and glucuronide analytical standards, urine samples were subjected to enzymatic hydrolysis. Creatinine was also quantified to normalize the results obtained from the urinary spot. Results obtained with LDTD-MS/MS were cross-validated by UPLC-MS/MS using 155 urine samples. Coefficient of correlation was above 0.975 for PVLs and creatinine. For all analytes, the accuracy was between 90% and 113% by LDTD-MS/MS. Altogether, sample preparation was fully automated to demonstrate the application potential of this method to large cohorts.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".