New Spectrofluorimetric Method for Determining Serotonin: Application to Human Urine
Bibliographic record
Abstract
In this present paper, we investigate the development of simple, rapid, accurate, reproducible and sensitive methods for the determination of serotonin (5-HT) in urine. For this purpose stationary fluorescence was used as method. With regard to the analysis of serotonin, the liquid-liquid extraction (LLE) solid phase extraction (SPE) and standard addition procedures were used. Several physicochemical factors affecting the sensitivity of the fluorescence intensity of serotonin were optimized, including the system of solvent (organic, micellar), the pH and the salts. The study of the analytical performances of the method led to very low limits of detection (LOD) varying between 0.1 and 3 ng/mL and to limits of quantification (LOQ) ranging between 0.4 and 10 ng/mL. This confirms the sensitivity of the method. Thus the low values of standard deviations (DRS) (between 0.3 and 6.6%) testify the good reproducibility of the measurements with satisfactory covering rate (89 to 111%). Accordingly, our results show that the spectrofluorimetric method is simple, fast and sensitive and can be applied to the routine analysis and does not require expensive equipment nor tiresome chemical pretreatments.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".