Microneedling with a Novel, n-3-PUFA-Rich Formulation Accelerates Inflammation Resolution to Improve Skin Recovery Outcomes in Adults with Healthy Skin
Bibliographic record
Abstract
INTRODUCTION: Microneedling is a cosmetic procedure that leverages the skin's natural ability to heal in order to promote collagen formation and skin rejuvenation. To provide improved results, the technique can be combined with topical formulations. A new formulation of multiple actives, including omega-3 (n-3) polyunsaturated fatty acids (PUFAs), was designed to accelerate the resolution of inflammation and wound healing following micro-injury treatments, while enhancing the visible appearance of procedure results, including erythema, luminosity and skin texture. METHODS: In this randomised, controlled, split-face study, we examined 32 healthy female participants aged 30-70 years for 4 weeks following microneedling treatment with a novel multiple-active-ingredient formulation or conventional microneedling protocol with a hyaluronic acid control serum. Changes in skin condition were assessed by blinded clinical photography and expert evaluation. Measurements were collected at baseline, 1 h, 1 day, 7 days and 28 days post treatment. RESULTS: Significantly greater improvements in expert-assessed erythema, luminosity and skin texture were reported following application of the novel multiple-active-ingredient formulation than the hyaluronic acid control serum. This was confirmed by representative VISIA®-CR imaging. CONCLUSION: These data provide new evidence for the role of a novel multiple-active-ingredient formulation for improving skin outcomes up to 28 days following microneedling in adults with healthy skin when compared with a hyaluronic acid serum. The n-3 PUFA content of this formulation may drive accelerated inflammation resolution and wound healing alongside the complementary action of the other active ingredients, leading to the observed improvements in erythema, luminosity and skin texture.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".