IN VIVO CHARACTERIZATION OF LESS PAINFUL PROPOFOL NANOEMULSION USING PALM OIL FOR INTRAVENOUS DRUG DELIVERY
Bibliographic record
Abstract
Objective: The objective of present work was to evaluate the effectiveness of propofol in nanoemulsion based palm oil that called as NEMS™, which was a choice of anesthetic drug to induce and maintenance general anesthesia to reduce pain on injection activity and also to evaluate the in vivo characterization of propofol in NEMS™. Methods: Preparation of propofol nanoemulsion using NEMS™ technology has been performed for propofol 1% in NEMS™ (P1%), and propofol 2% in NEMS™ (P2%). Determination of free propofol concentration in aqueous phase was conducted using HPLC and rat paw lick test was evaluated as in vivo test to assay the intensity of pain on injection site. The sleep recovery test was conducted to evaluate the pharmacological effect and erythrocyte hemolysis test also conducted to ensure the safety of propofol in NEMS™. All of the test results were compared with Diprivan®1% as a positive standard. Results: The contents of free propofol in formulation P1% and Diprivan®1% in aqueous-phase were 6.20±0.03 µg/ml and 15.02±0.33 µg/ml, respectively (*P<0.05). The rat paw lick test showed that the formulation P1% was significantly (*P<0.05) less painful when compared to Diprivan®1%. There were no significant differences in pharmacological effect for all of the formulations (*P>0.05). The erythrocyte haemolysis test show that all formulation still safe for our blood. Conclusion: Palm oil can be used as a carrier for propofol and it was successfully reduced the free propofol contents and the intensity of pain on injection site in rats.
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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".