Preparation of Centella asiatica (L). and Hypericum perforatum (St. John's Wort) Plant Extracts and Development of Anti-Aging Herbal Cream Formulations
Bibliographic record
Abstract
Objective: The aim of the study is to use two different plant extracts such as St. John's Wort (Hypericum perforatum) and Centella asiatica (L.) in the cream formulation and to determine the anti-aging effect of the new formulation. Material-Method: The plants used in the study were obtained commercially and plant extracts were obtained using the classical extraction techniques in the literature. The active ingredients in the obtained extracts were determined by HPLC method. Physical, protective efficacy, microbial analysis and anti-aging tests were carried out for cream formulations obtained with extracts. Results: Plant extraction studies were carried out in the study. Centella asiatica (L.) was extracted with 20.8% yield at 20 mL of methanol and 60 ℃. As a result of HPLC analysis, it was determined that there were 1740 mg/kg asiatic acid and 4380 mg/kg madecasic acid in the extract. In the extraction studies performed on Hypericum perforatum, the expected active ingredient, hypericin, was not found. For this reason, commercial extract containing 2.5% hyperforin was obtained and the studies were continued on these extracts and final cream formulations were obtained. Conclusion: pH, viscosity, density, protective effectiveness tests and microbial analysis tests of the final formulations were performed. In addition, in vitro anti-aging studies have been carried out in accredited laboratories. With in-vitro anti-aging tests, it was determined that the level of collagen 1A increased more in the formulation where both extracts were used together.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".