High performance liquid chromatographic determination of glucosamine in rat plasma.
Bibliographic record
Abstract
PURPOSE: A high performance liquid chromatographic method was developed for the determination of glucosamine (GlcN) in rat plasma. METHOD: Internal standard, galactosamine, was added to 100 micro L of plasma containing GlcN followed by precipitation of plasma proteins with acetonitrile. Evaporation of the decanted supernatant solution was accelerated by the addition of methanol. GlcN was derivatized by addition of a solution containing 1-naphthyl isothiocyanate. Sample cleanup included passage through an anion exchange cartridge. Analysis was accomplished by injection of 0.1 mL of the sample solution into an isocratic HPLC system consisting of a C18 column, a mobile phase of acetonitrile: water: acetic acid: triethylamine (4.5: 95.5:0.1:0.05), a flow rate of 0.9 mL/min, and a UV detector set at 254 nm. RESULTS: Galactosamine and GlcN appeared 26 and 29 min post-injection, respectively. The assay was linear over the range of 1.25-400 micro g/mL (CV<10%) with a detection limit of 0.63 microg/mL and a limit of quantification of 1.25 microg/mL. The method was applied to the determination of GlcN in rat plasma after oral administration of 350 mg/kg of GlcN hydrochloride. CONCLUSION: The present assay is specific, sensitive, precise, and accurate and is suitable for pharmacokinetic studies.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".