Stem Cell Therapy: A New Treatment for Burns?
Bibliographic record
Abstract
Stem cell therapy has emerged as a promising new approach in almost every medicine specialty. This vast, heterogeneous family of cells are now both naturally (embryonic and adult stem cells) or artificially obtained (induced pluripotent stem cells or iPSCs) and their fates have become increasingly controllable, thanks to ongoing research in this passionate new field. We are at the beginning of a new era in medicine, with multiple applications for stem cell therapy, not only as a monotherapy, but also as an adjunct to other strategies, such as organ transplantation or standard drug treatment. Regrettably, serious preclinical concerns remain and differentiation, cell fusion, senescence and signalling crosstalk with growth factors and biomaterials are still challenges for this promising multidisciplinary therapeutic modality. Severe burns have several indications for stem cell therapy, including enhancement of wound healing, replacement of damaged skin and perfect skin regeneration - incorporating skin appendages and reduced fibrosis -, as well as systemic effects, such as inflammation, hypermetabolism and immunosuppression. The aim of this review is to describe well established characteristics of stem cells and to delineate new advances in the stem cell field, in the context of burn injury and wound healing.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".