Adipose stem cell-laden injectable thermosensitive hydrogel reconstructing depressed defects in rats: filler and scaffold
Bibliographic record
Abstract
H NMR showed the successful synthesis of the crosslinker. In vitro tests, CCK-8 assay and live/dead viability test showed that the hydrogel was non-toxic to adipose-derived stem cells (ASCs). SEM images also confirmed that ASCs could adhere to the hydrogel. Then we constructed a novel depressed defect model in rats and injected four different fillers in the depressed defects: (1) the hydrogel with ASCs, (2) the hydrogel only, (3) hyaluronic acid, and (4) PBS. After 4 weeks, gross and histological analyses showed the defects in hydrogel, hydrogel + ASCs, and HA groups improved significantly and there were no significant differences among them. Significant differences in thickness from skin to muscle in the defect was found between the hydrogel + ASCs group and the other groups after 6 months. The hydrogels degraded completely in defects in both the hydrogel group and the hydrogel + ASCs group, and were filled with adipocytes and multilocular immature adipocytes. Immunohistochemical study using s-100 and perilipin staining revealed adipocyte differentiation in the defect sites. We also used green fluorescent protein (GFP)-ASCs for tracing and found that exogenous added ASCs were involved in adipogenesis. In conclusion, such a cell attachable thermosensitive hydrogel has definite potential not only as a filler but also as a scaffold, and has a persistent effect for small depressed defects. It might ultimately become a new material in plastic and reconstructive surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".