Molecular Weight Analyses and Enzymatic Degradation Profiles of the Soft-Tissue Fillers Belotero Balance, Restylane, and Juvéderm Ultra
Bibliographic record
Abstract
BACKGROUND: In this study, the authors sought to determine the molecular weight distribution of three hyaluronic acids-Belotero Balance, Restylane, and Juvéderm Ultra-and their rates of degradation following exposure to hyaluronidase. Lot consistency of Belotero Balance also was analyzed. METHODS: Three lots of Belotero Balance were analyzed using liquid chromatography techniques. The product was found to have high-molecular-weight and low-molecular-weight species. One lot of Belotero Balance was compared to one lot each of Juvéderm Ultra and Restylane. Molecular weights of the species were analyzed. The hyaluronic acids were exposed to ovine testicular hyaluronidase at six time points-baseline and 0.5, 1, 2, 6, and 24 hours-to determine degradation rates. RESULTS: Belotero Balance lots were remarkably consistent. Belotero Balance had the largest high-molecular-weight species, followed by Juvéderm Ultra and Restylane (p < 0.001). Low-molecular-weight differences among all three hyaluronic acids were not statistically significant. Percentages of high-molecular-weight polymer differ among the three materials, with Belotero Balance having the highest fraction of high-molecular-weight polymer. Degradation of the high-molecular-weight species over time showed different molecular weights of the high-molecular-weight fraction. Rates of degradation of the hyaluronic acids following exposure to ovine testicular hyaluronidase were similar. All hyaluronic acids were fully degraded at 24 hours. CONCLUSIONS: Fractions of high-molecular-weight polymer differ across the hyaluronic acids tested. The low-molecular-weight differences are not statistically significant. The high-molecular-weight products have different molecular weights at the 0.5- and 2-hour time points when exposed to ovine testicular hyaluronidase and are not statistically different at 24 hours.
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How this classification was reachedexpand
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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".