Biochemistry, Physiology, and Tissue Interactions of Contemporary Biodegradable Injectable Dermal Fillers
Bibliographic record
Abstract
BACKGROUND: Injectable dermal fillers are becoming increasingly popular for soft tissue augmentation and rejuvenation. Most contemporary biodegradable products are derived from hyaluronic acid, calcium hydroxylapatite, or poly-L-lactic acid. Achievement of desired cosmetic outcomes is largely dependent on selection of the optimal injectable product based on the chemical composition, the physiologic interactions with surrounding tissue, product longevity, and a thorough understanding of potential adverse reactions. OBJECTIVE: To review and describe the biochemistry, physiology, and tissue interactions of the most commonly used contemporary biodegradable dermal fillers. METHODS: A thorough review of the literature was performed with additional review of pertinent clinical cases and corresponding histopathology. RESULTS: This article provides a comprehensive review of the biochemistry, physiology, and potential tissue interactions of the most commonly used biodegradable dermal fillers. The underlying biochemical properties of each product and how they contribute to specific physiologic and adverse tissue reactions is described. CONCLUSION: Understanding of the innate differences in the physical properties, and physiologic responses to soft tissue fillers allows clinicians to achieve desired aesthetic outcomes with fewer adverse events.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".