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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it