Improved Sensitive High Performance Liquid Chromatography Assay for Glucosamine in Human and Rat Biological Samples with Fluorescence Detection
Bibliographic record
Abstract
PURPOSE: An improved HPLC method with fluorescence detection was developed and validated for determination of glucosamine in human and rat biological samples. METHOD: Aliquot of 0.1 mL plasma was spiked with mannosamine HCl as the internal standard (IS); proteins were precipitated with acetonitrile; the clear layer was derivatized with 9-fluorenylmethyl chloroformate (8 mM/acetonitrile) in presence of borate 0.2 M buffer at 30 degrees C for 30 min. The excess derivatizing agent was removed with 1-aminoadamantane HCl (300 mM in acetonitrile-water 1:1). Chromatographic separation was achieved on a C18 (100 mm X 4.6 mm, id 3 microm) reversed phase column using 0.1% acetic acid/acetoniltrile gradient mobile phase at 1 mL/min flow rate. Glucosamine was determined in the plasma of a human and rats and also in rat urine. RESULTS: The analytes were detected at excitation and emission wavelengths of 263 and 315 nm, respectively. The assay was linear over the range of 0.05-20 microg/mL with a typical correlation coefficient of 0.999 and intra-day and inter-day coefficient of variation of <15%. The lowest limit of quantification was set at 50 ng/mL. The recovery for glucosamine and mannosamine was 98 and 96%, respectively. CONCLUSION: We were able to improve glucosamine assay suitable to quantify glucosamine in both human and rat plasma and rat 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".