Bioavailability Studies of BioTurmin-WD (Water Dispersible Curcuminoids) Using Caco-2 Cell Model
Bibliographic record
Abstract
<p>Curcuminoids is the mixture of curcumin, demethoxycurcumin and bisdemethoxycurcumin obtained from dried rhizomes of <em>Curcuma longa, </em>commonly used for its wide therapeutic value. However, the absorption efficacy of curcuminoids is too low to exhibit its proper therapeutic value. Thus, a new preparation named as BioTurmin-WD (water dispersible curcuminoids) was developed for improved bioavailability. By using human Caco-2 cell monolayer, the permeability efficacy of BioTurmin-WD was evaluated and compared with that of 95% pure curcuminoids. Caco-2 model predicts the<em> in vivo </em>absorption of drugs across the gut wall by measuring the rate of transport of a compound across the Caco-2 cell line. BioTurmin-WD was added to the apical layer and basolateral samples were collected over 120 min to examine the concentration diffusing across the cell monolayer. Permeable curcuminoids across the cell monolayer was analysed through reverse phase high pressure liquid chromatography (RP-HPLC). Apparent permeabilities (P<sub>app</sub>) of BioTurmin-WD and 95% curcuminoids were found to be 5.89 × 10<sup>-6</sup> and 2.65 × 10<sup>-6</sup> cm/s respectively. The apparent permeability coefficient of BioTurmin-WD was 7.03-fold higher than 95% pure curcuminoids. Percentage permeability of BioTurmin-WD (0.2945) was much higher than 95% curcuminoids (0.0859). Results indicated that BioTurmin-WD have a much higher absorption capacity (bioavailability) compared to 95% pure curcuminoids. Thus, BioTurmin-WD may be useful as a dietary supplement with greater bioavailability to exert clinical benefits in humans at a lower dosage.</p>
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".