Production of biomass and bioactive compounds from cell and organ cultures of ginseng, He-shou-wu, purple coneflower, and St. John's wort for the use in cosmetic industry
Bibliographic record
Abstract
Plants and their products have been utilized as raw materials in the preparation of cosmetics for millennia. Currently, research is being done to find new plant materials that may be used as ingredients in cosmetic preparations, including body sprays, shampoos, conditioners, hair dyes/sprays, and other cosmetics. The plants that are used in the preparation of cosmetic products are usually procured from nature, however, the quality and quantity of bioactive ingredients present in the biomass vary based on the species, environment, and geographical locations from where the material has been procured. In addition, procurement of plant material from natural resources may lead to a shortage of material and even endanger the status of rare plants in the natural environment. Therefore, there is increased interest in the use of plant cell, tissue, and organ cultures (PCTOC) for the production of raw materials and bioactive specialized metabolites. There is also scope for increasing the accumulation of biomass and bioactive compounds in PCTOC by adopting various strategies such as optimization of culture medium, culture environment, elicitation, and other bioprocess methods. Furthermore, PCTOC-produced raw materials are free from contaminants, pesticides, and heavy metals and an important benefit of producing biomass in vitro is that it is easily accepted by regulatory authorities and consumers. In the current review, we describe the bioactive compounds of ginseng, purple coneflower, He-shou-wu, and St. John's wort which have cosmetological importance. Additionally, we elucidate the PCTOC method adopted for the production of biomass and bioactive compounds in these plants.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".