Human-Induced Pluripotent Stem Cells in Plastic and Reconstructive Surgery
Bibliographic record
Abstract
A hallmark of plastic and reconstructive surgery is restoring form and function. Historically, tissue procured from healthy portions of a patient's body has been used to fill defects, but this is limited by tissue availability. Human-induced pluripotent stem cells (hiPSCs) are stem cells derived from the de-differentiation of mature somatic cells. hiPSCs are of particular interest in plastic surgery as they have the capacity to be re-differentiated into more mature cells, and cultured to grow tissues. This review aims to evaluate the applications of hiPSCs in the plastic surgery context, with a focus on recent advances and limitations. The use of hiPSCs and non-human iPSCs has been researched in the context of skin, nerve, vasculature, skeletal muscle, cartilage, and bone regeneration. hiPSCs offer a future for regenerated autologous skin grafts, flaps comprised of various tissue types, and whole functional units such as the face and limbs. Also, they can be used to model diseases affecting tissues of interest in plastic surgery, such as skin cancers, epidermolysis bullosa, and scleroderma. Tumorigenicity, immunogenicity and pragmatism still pose significant limitations. Further research is required to identify appropriate somatic origin and induction techniques to harness the epigenetic memory of hiPSCs or identify methods to manipulate epigenetic memory.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".