High‐performance liquid chromatography analysis of curcumin in rat plasma: application to pharmacokinetics of polymeric micellar formulation of curcumin
Bibliographic record
Abstract
A simple, rapid and reliable high-performance liquid chromatographic (HPLC) method was developed and validated for the determination of curcumin in rat plasma. Plasma was precipitated with acetonitrile after addition of the internal standard (IS), 4-hydroxybenzophenone. Separation was achieved on a Waters muBondapak C(18) column (3.9 x 300 mm, 5 microm) using acetonitrile (55%) and citric buffer, pH 3.0 (45%) as the mobile phase (flow rate = 1.0 mL/min). The UV detection wavelength was 300 and 428 nm for IS and curcumin, respectively. The extraction efficiencies were 97.08, 95.69 and 94.90% for 50, 200 and 1000 ng/mL of curcumin in rat plasma, respectively. The calibration curve was linear over the range 0.02-1 microg/mL with a correlation coefficient of r(2) > 0.999. The intra- and inter-day coefficients of variation were less than 13%, and mean intra- and inter-day errors were less than +/-6% at 50, 200 and 1000 ng/mL of curcumin. This assay was successfully applied to the pharmacokinetic studies of both solubilized curcumin and its polymeric micellar formulation in rats. It was found that polymeric micelles increased the half-life of curcumin 162-fold that of solubilized curcumin and increased the volume of distribution (Vd(ss)) by 70-fold.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".