Secretomes as an emerging class of bioactive ingredients for enhanced cosmeceutical applications
Bibliographic record
Abstract
Skin ageing is predominantly caused by either intrinsic or extrinsic factors, leading to undesirable skin features. Advancements in both molecular and cellular fields have created possibilities in developing novel stem cell-derived active ingredients for cosmeceutical applications and the beauty industry. Mesenchymal stromal cell (MSC)-derived secretomes or conditioned media hold great promise for advancing skin repair and regeneration due to the presence of varying cytokines. These cytokines signal our cells and trigger biological mechanisms associated with anti-inflammatory, antioxidant, anti-ageing, proliferative and immunomodulatory effects. In this review, we discuss the potential of MSC secretomes as novel biomaterials for skincare and rejuvenation by illustrating their mechanism of action related to wound healing, anti-ageing and whitening properties. The advantages and disadvantages of secretomes are compared with both plant-based and animal-derived extracts. In addition, this paper reviews the current safety standards, regulations, market products and research work related to the cosmeceutical applications of secretomes along with strategies to maintain and improve the therapeutic efficacy and production of secretomes. The future outlook of beauty industry is also presented. Lastly, we highlight significant challenges to be addressed for the clinical realization of MSC secretomes-based skin therapies as well as providing perspectives for the future direction of secretomes.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".