D115. A Role for Tannic Acid in Restoring Elastogenesis Impaired by Common Aesthetic Agents
Bibliographic record
Abstract
PURPOSE: Aesthetic management of aging skin relies on precise extracellular matrix (ECM) homeostasis to maximize youthful resilience, fullness, and elasticity while mitigating risk of dermal fibroses. Elastogenesis is critical to this. Currently, several ‘natural agents’ - growth factors, vitamins C and D3, hyaluronic acid (HA), and ferulic acid (FA) - are marketed to augment aesthetic outcomes in plastic surgery, however, these agents carry pleiotropic effects on ECM homeostasis and with some hindering elastic fiber formation while promoting fibrosis. Our team has demonstrated the benefit of tannic acids (TAs) to protect new elastic fibers from premature degradation. Here we sought to assay these agents with or without TAs to evaluate for ECM modifications. METHODS: In vitro evaluation of a) human dermal fibroblast culture and b) whole dermal explants were treated with Vitamins C and D3, hyaluronic acid (HA), and ferulic acid (FA). Each parallel culture was maintained in the presence or absence of tannic acid (TA) at 2.0 and 20.0 micro-molar concentrations. RESULTS: Vitamins D3, C, and HA boosted collagen and fibronectin synthesis but suppressed elastic fiber formation, while FA suppressed all ECM formation. Introducing 2 micro-molar TA to cultures reinstated regular elastic fiber synthesis, as well as improving collagen type 1 and fibronectin synthesis in FA-treated samples. 20 micro-molar TA in parallel cultures led to irregular elastin aggregates, causing widespread elastosis. CONCLUSION: TA in micro-molar levels can counteract the elastin inhibitory effects of Vitamin D3, HA, and FA, potentially elucidating the poor survival of fat grafts in HA-rich regions.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".