Burns in the Elderly: Potential Role of Stem Cells
Bibliographic record
Abstract
Burns in the elderly continue to be a challenge despite advances in burn wound care management. Elderly burn patients continue to have poor outcomes compared to the younger population. This is secondary to changes in the quality of the aged skin, leading to impaired wound healing, aggravated immunologic and inflammatory responses, and age-related comorbidities. Considering the fast-growing elderly population, it is imperative to understand the anatomic, physiologic, and molecular changes of the aging skin and the mechanisms involved in their wound healing process to prevent complications associated with burn wounds. Various studies have shown that stem cell-based therapies improve the rate and quality of wound healing and skin regeneration; however, the focus is on the younger population. In this paper, we start with an anatomical, physiological and molecular dissection of the elderly skin to understand why wound healing is delayed. We then review the potential use of stem cells in elderly burn wounds, as well as the mechanisms by which mesenchymal stem cell (MSCs)-based therapies may impact burn wound healing in the elderly. MSCs improve burn wound healing by stimulating and augmenting growth factor secretion and cell proliferation, and by modulating the impaired elderly immune response. MSCs can be used to expedite healing in superficial partial thickness burns and donor site wounds, improve graft take and prevent graft breakdown.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".