Formulation of sage essential oil (<i>Salvia officinalis</i>, L.) monoterpenes into chitosan hydrogels and permeation study with GC-MS analysis
Bibliographic record
Abstract
This study deals with the formulation of natural drugs into hydrogels. For the first time, compounds from the sage essential oil were formulated into chitosan hydrogels. A sample preparation procedure for hydrophobic volatile analytes present in a hydrophilic water matrix along with an analytical method based on the gas chromatography coupled with the mass spectrometry (GC-MS) was developed and applied for the evaluation of the identity and quantity of essential oil components in the hydrogels and saline samples. The experimental results revealed that the chitosan hydrogels are suitable for the formulation of sage essential oil. The monoterpene release can be effectively controlled by both chitosan and caffeine concentration in the hydrogels. Permeation experiment, based on a hydrogel with the optimized composition [3.5% (w/w) sage essential oil, 2.0% (w/w) caffeine, 2.5% (w/w) chitosan and 0.1% (w/w) Tween-80] in donor compartment, saline solution in acceptor compartment, and semi-permeable cellophane membrane, demonstrated the useful permeation selectivity. Here, (according to lipophilicity) an enhanced permeation of the bicyclic monoterpenes with antiflogistic and antiseptic properties (eucalyptol, camphor and borneol) and, at the same time, suppressed permeation of toxic thujone (not exceeding its permitted applicable concentration) was observed. These properties highlight the pharmaceutical importance of the developed chitosan hydrogel formulating sage essential oil in the dermal applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".