A personalized nutrition plan based on genetic profile improves outcomes of facial regeneration with Platelet-Rich Fibrin liquid matrices
Bibliographic record
Abstract
Aim: The importance of nutrition in the prevention of skin aging has been shown by large observational studies.However, there are no studies assessing dietary changes as adjunct procedures to aesthetic interventions.The objective of this study was to assess whether a personalized nutritional plan conveys additional benefits to plateletrich fibrin (PRF) facial regeneration.Methods: Forty-seven healthy women (mean age 52.5 years old, SD = 7.7) were offered minimally invasive facial regeneration with the use of PRF liquid matrices, as well as a personalized nutritional plan.The nutritional plan was informed by a nutrigenetic test based on 128 polymorphisms.Horizontal forehead lines, zygomatic wrinkles or midcheek furrows, nasolabial folds, perioral expression wrinkles, and marionette line were assessed separately with the use of the Facial Wrinkles Assessment Scale (FWAS).Results: The total FWAS score change was statistically significantly better in women who reported an at least partial adaptation of nutritional recommendations for at least three months (Z = 2.4, P = 0.008). Conclusion: Personalized nutritional recommendations based on individual needs as well as generally accepted dietary guidelines can improve treatment outcomes of minimally invasive facial skin aesthetics interventions.
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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".