Recent Patents in Cell-Based Strategies for Soft Tissue Engineering in Plastic and Reconstructive Surgery
Bibliographic record
Abstract
Reconstructive surgery is performed on abnormal or damaged soft tissues, caused by trauma, burns, congenital defects, tumours or disease. Damage to the underlying fatty tissues results in scar tissue formation and deformity, as well as the potential for reduced mobility if the injury occurs near a joint. The general aim in the clinic is to rebuild the affected tissues, and by doing so, to recover or improve function. Relevant clinical procedures include the revision of scar tissue from burns or trauma, laceration repair, the reconstruction of the breast after mastectomy or lumpectomy, and the restoration of the normal body structure following the removal of sarcomas or skin cancers. Since adipose tissue comprises the bulk of the adult tissues treated in these reconstructive approaches, the importance of vascularized fat in the field of plastic and reconstructive surgery is well established. Soft tissue engineering holds great promise for the improvement of standard reconstructive methodologies. In this context, two main approaches have arisen, which can be employed individually or in combination: (i) cell-based therapies and (ii) biologically compatible tissue scaffolds. This review provides a brief description of recent patents and findings in soft tissue regeneration for plastic and reconstructive surgery. Keywords: Adipose tissue, cell therapy, plastic and reconstructive surgery, scaffolds, stem cells, tissue engineering, volume augmentation, wound healing, Reconstructive surgery, trauma, congenital defects, mastectom, lumpectomy, sarcomas, mesenchymal stem cells, embryonic stem cells, pluripotent stem cells, adipose-derived stem cells, cytometry, osteogenic, chondrogenic, neurogenic, adipogenic lineages, retinoic acid, isobutylmethylxanthine, dexamethasone, glucocorticoid, Keratinocytes, leukemia inhibitory factor, mammoplasty, abdominoplasty, polydactylism, hepatocyte growth factor, adipogenesis, panniculectomy, lipoaspirates, polyethylene glycol diacrylate, hydrogel, glycosaminoglycans, Integra, Alloderm, Strattice
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.009 |
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".