Advancing the Analysis of Terbutaline in Urine Samples Using Novel Enzyme Hydrolysis
Bibliographic record
Abstract
Aim: To investigate the efficiency of two new fast-acting enzymes, recombinant arylsulfatase (IMCS-PSF) and mutant β-glucuronidase from Escherichia coli (IMCSzyme), in hydrolyzing specific terbutaline metabolites. Materials & methods: Two purified novel enzymes are used to precisely determine the amount of each metabolite in urine at different time points after oral administration. After systematically evaluating the hydrolysis efficiency of the novel enzymes compared with commercially available enzymes, these recently developed enzymes were applied to establish the separate urine concentration profiles of terbutaline and each metabolite. Results & discussion: The results highlight the highly efficient arylsulfatase enzyme expressed from E. coli for urine analysis of terbutaline while suggesting sulfoconjugates as the main terbutaline metabolites. Conclusion: This study demonstrated the high efficiency of the IMCS-PSF enzyme in hydrolyzing terbutaline conjugates in comparison with other enzyme reagents typically used for the analysis of terbutaline and sulfoconjugates are the main terbutaline metabolites in urine.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".