Detection of thyroid hormones in urine by liquid chromatography coupled to tandem mass spectrometry
Bibliographic record
Abstract
Recently, the trend of thyroid hormones (TH) consumption in the sports community has been published. It is known the capacity of the exogenously administered TH to enhance metabolism, being an attractive feature for athletes, who search for weight control and increased caloric expenditure. This paper aimed the validation of a method to measure TH and related compounds in urine by liquid chromatography-tandem mass spectrometry (LC-MS/MS). The method was applied to urine samples collected before and after the administration of a diiodothyronine (3,5-T2) supplement. A method to detect nine TH included an enzymatic hydrolysis, liquid-liquid extraction, and solid-phase extraction. The extracts were analyzed by LC-MS/MS. Validated parameters showed good results for accuracy (85%-104%), precision (3%-16%), LOD (10-40 pg/mL, except for thyronacetic acids that was 200 pg/mL), and the combined uncertainty (2.2%-22%). Maximum concentration of 3,5-T2 in pre-administration samples was 0.71 ng/mL, and after 30 h of the last administration, concentrations returned to pre-administration values. Maximum values of ratios between the analyte and thyronine, T3, and T4 were 0.09, 0.19, and 0.12, respectively, and after 30 h of the last administration, the ratios reached back the basal values. Acidic or basic metabolites were not found in urine at least at the method LOD. A proposed method to assess TH in urine was validated, and as a proof of concept, its efficacy was demonstrated with an excretion study of 3,5-diiodothyronine. The consumption of 3,5-T2 was detected in urine measuring the analyte concentration and ratios between the analyte and thyronine, T3, and T4.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".