Effect of the Use of a Cream with Leucine and Lactic Acid Associated with Electrostimulation in Contouring and Facial Tonus: A Randomized Clinical Controlled Trial
Bibliographic record
Abstract
Evidence has supported the use of leucine as a promising agent for the maintenance of muscle tone. This study aimed to assess the combined effect of leucine and cream-based lactic acid (novel cosmetic product), associated with the use of surface electrical stimulation to improve contour and facial tonus in women. A total of 23 women were randomly allocated into two groups: Experimental (EG)—use of the leucine-based cream and lactic acid + electrostimulation for facial toning (mean intensity 13 Hz and protocol in progression); and placebo (GP)—use of the placebo cream (without the addition of leucine and lactic acid) + stimulation with the same protocol as the EG. Each group used their cream daily and underwent the intervention protocol three x/week with stimulation for 40 min, for a total of 8 weeks. Three main outcomes were reported: angular variation of facial contour by means of photogrammetry, muscle tone through the electromyographic activity of the masseter and zygomatic muscles during rest and functional tasks of biting and smiling. A significant effect of the intervention and between the groups was obtained for the experimental group against the placebo group for facial contour and muscle tone. An increased muscular activity of the masseter (average 28%) when smiling, and a reduction of zygomatic activity (in average 41%) when biting were found. The use of cream containing leucine and lactic acid combined with electrostimulation contributes to the improvement of facial contour and muscle tone when biting and smiling.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".