Adipose tissue micrograft in a scaffold of plasma-gel combined with platelet-derived growth factors in dermal wrinkle regeneration
Bibliographic record
Abstract
Background: The dermal aging process and the formation of deep wrinkles are a biological involution that also involves the regeneration system of cells immersed in the extracellular matrix and the papillary dermis. The progressive loss of niches of adult stem cells (MSCs) is more evident after the first third of life; it increases the phenotypic expression and the characteristics of the tissue senescence process. The purpose of this study was to clinically demonstrate that in viable micrograft there may be an improvement of deep wrinkles and surrounding tissues. Methods: This study involved 11 female patients who underwent the correction of deep dermal wrinkles through a suspension containing 0.8 mL of viable micrografts in a 5 mL plasma gel scaffold, obtained from the centrifugation of a 20 cc venous sample peripheral blood, gelled by heat in a dry steriliser and the buffy coat coming from the same venous sample, in order verify overtime the improvement of the interested anatomical area. Individual signs of wrinkles and the degree of correction obtained for each treatment and each area were objectively evaluated by using a 10-0 visual analog scale (VAS), Modified Vancouver scale and Berardesca's scale. Results: With this technique excellent results were obtained. In fact, wrinkles were improved, as well as surrounding tissues, even after 60 days, as shown by the Berardesca's, VAS and Modified Vancouver scales. Conclusion: This retrospective clinical evaluation allowed us to consider the excellent clinical results obtained with this method for the treatment of deep wrinkles and surrounding tissues, through a suspension of progenitors with MSCs derived from adipose tissue (ADSCa) in a not inflammatory plasma gel scaffold combined with buffy coat.
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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) | 0.001 | 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".