Injectable Mussel‐Inspired Immobilization of Platelet‐Rich Plasma on Microspheres Bridging Adipose Micro‐Tissues to Improve Autologous Fat Transplantation by Controlling Release of PDGF and VEGF, Angiogenesis, Stem Cell Migration
Bibliographic record
Abstract
Platelets-rich plasma (PRP) can produce growth factors (GFs) to improve angiogenesis. However, direct injection of PRP does not lead to highly localized GFs. The current study employs a mussel-inspired polydopamine to immobilize PRP on gelatin microspheres (GMs) with the purpose of bridging adipose micro-tissues to help implanted fat survive (GM-pDA-PRP). Enhanced PRP adhesion leads to a prolonged and localized production of GFs, which is verified by platelet counting and by ELISA of vascular endothelial growth factors (VEGFs) and of platelet derived growth factors (PDGFs). The GM-pDA-PRP "hatches" a microenvironment for the proliferation of adipose-derived stem cells. After the adipose micro-tissue has bridged with GM-pDA-PRP after 16 weeks, triple-fluorescence staining reveals that the mature adipocytes, blood vessels, and capillaries are arranged like in normal adipose tissue. The survival fat increases significantly compared to that in control, PRP, and GM-PRP groups (84.8 ± 11.4% versus 47.8 ± 8.9%, 56.9 ± 9.7%, and 60.2 ± 10.5%, respectively). Both histological assessments and CD31 immunofluorescence indicate that the improvement of angiogenesis in GM-pDA-PRP is higher than in the fat graft group (6.4-fold in quantitative CD31 positive cells). The CD34 positive cells in the GM-pDA-PRP group are around 3.5-fold the amount in the fat graft group, which suggests that more stem cells migrate to the implant area. Cell proliferation staining shows that the number of Ki67 positive cells is around five times as high as that in the fat graft group.
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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) | 0.000 | 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".