In vitro and in vivo evaluation of a moisture treatment cream containing three critical elements of natural skin moisturization
Bibliographic record
Abstract
OBJECTIVES: To evaluate skin barrier and hydration effects of a new rebalancing moisture treatment (TRMT) and to assess efficacy and tolerability in subjects with photodamaged skin. METHODS: In an epidermal skin model, tissues (n = 5/group) were topically treated with 25 µL of TRMT, 25 µL of a market-leading moisturizer (MLM), or untreated for 60 minutes. Hydration was measured at 0, 15, and 30 minutes. Tissues were harvested for gene expression analysis of markers associated with skin barrier and hydration: Claudin (CLD), Aquaporin (AQP), Hyaluronic Acid Syntheses (HAS), and Hyaluronidase (HYAL). A clinical study evaluated twice-daily application of TRMT, assessing changes in fine lines/wrinkles, brightness, texture, erythema, and tolerability from baseline through week 8. Hydration was measured using electrical impedance. RESULTS: TRMT and MLM demonstrated significant increases in hydration vs untreated tissue at each timepoint (P < .005), with greater hydration effects observed for TRMT vs MLM. TRMT-treated tissues demonstrated greater expression of CLD, AQP, and HA, and reduced expression of HYAL vs untreated and MLM-treated tissues. Twice-daily application of TRMT demonstrated significant improvements at 2 weeks in fine lines/wrinkles (P < .001), brightness (P < .0001), texture (P < .0004), and hydration (P < .004). At 8 weeks, statistically significant improvements were achieved in all categories. CONCLUSION: In an epidermal skin model, TRMT demonstrated significant increases in hydration, greater hydration effects, and expression of key markers associated with skin barrier and hydration vs a MLM. Twice-daily application of TRMT was well tolerated and resulted in early, significant improvements in hydration and visible improvements in skin brightness, texture, fine lines/wrinkles, and erythema at 8 weeks.
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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 | 0.001 | 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".