Optimized adipose tissue engineering strategy based on a neo‐mechanical processing method
Bibliographic record
Abstract
Decellularized adipose tissue (DAT) represents a promising scaffold for adipose tissue engineering. However, the unique and prolonged lipid removal process required for adipose tissue can damage extracellular matrix (ECM) constituents. Moreover, inadequate vascularization limits the recellularization of DAT in vivo. We proposed a neo-mechanical protocol for rapidly breaking adipocytes and removing lipid content from adipose tissue. The lipid-depleted adipose tissue was then subjected to a fast and mild decellularization to fabricate high-quality DAT (M-DAT). Adipose liquid extract (ALE) derived from this mechanical process was collected and incorporated into M-DAT to further optimize in vivo recellularization. Ordinary DAT was fabricated and served as a control. This developed strategy was evaluated based on decellularization efficiency, ECM quality, and recellularization efficiency. Angiogenic factor components and angiogenic potential of ALE were evaluated in vivo and in vitro. M-DAT achieved the same decellularization efficiency, but exhibited better retention of ECM components and recellularization, compared with those with ordinary DAT. Protein quantification revealed considerable levels of angiogenic factors (basic fibroblast growth factor, epidermal growth factor, transforming growth factor-β1, and vascular endothelial growth factor) in ALE. ALE promoted tube formation in vitro and induced intense angiogenesis in M-DAT in vivo; furthermore, higher expression of the adipogenic factor PPARγ and greater numbers of adipocytes were evident following ALE treatment, compared with those in the M-DAT group. Mechanical processing of adipose tissue led to the production of high-quality M-DAT and angiogenic factor-enriched ALE. The combination of ALE and M-DAT could be a promising strategy for engineered adipose tissue construction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".