Bacterial cellulose/glycolic acid/glycerol composite membrane as a system to deliver glycolic acid for anti-aging treatment
Bibliographic record
Abstract
Glycolic acid (GA), as an anti-aging skincare ingredient, plays a pivotal role in anti-aging treatment. However, its benefits could be overshadowed due to its side effects including skin burning and irritation when overused. Bacterial cellulose (BC) is a highly pure form of cellulose, biosynthesized in the form of a swollen membrane by several kinds of bacteria that was demonstrated to modulate the release of model drugs owing to its porous and 3D fibrous network structure, and glycerol (GL), as a plasticizer, could enhance the controlled drug delivery. Herein, we report a topical controlled drug delivery system based on BC membrane, GA and GL for controlling sustainable release of GA to reduce its side effects on the skin, while maintaining its prolonged and maximum therapeutic effect. The results showed that the incorporation of GL increased the malleability and flexibility of BC/GA/GL membrane, as compared with BC/GA membrane. In addition, the GL enhanced the control of the GA delivery, as evidenced by a higher swelling capacity and thereby a slower release of the GA from BC/GA/GL membrane. More importantly, in vitro study indicated that both BC/GA and BC/GA/GL membranes could effectively stimulate endogenous collagen synthesis in NIH3T3 cells owing to the release of GA, and that BC/GA/GL membrane is more conducive to a long-term cell adhesion, spreading, and proliferation of NIH3T3 and HaCaT cells due to its lower and sustainable release of GA than BC/GA membrane. This study suggests the BC/GL/GA composite membrane holds great promise as an appealing platform to control the release of GA to greatly promote renewal of skin cells for effective anti-aging treatment.
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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 | 0.001 | 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 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".