Factors affecting skin keratinocyte and fibroblast extraction yields for the production of living skin substitutes to treat severely burned patients
Bibliographic record
Abstract
Backgrounds As autografting is limited for severely burned patients due to a lack of healthy donor sites, tissue-engineered autologous skin substitutes have emerged as a promising alternative. Yet, challenges persist, particularly regarding production time. Since cell culture is influenced by multiple factors, identifying them is crucial for improving culture yields. This retrospective study aimed to identify factors affecting skin cell extraction yields. Methods Culture data (method used, etc.) and clinical data (medical history, etc.) from all available patient records over a 35-year-period were collected. 18 variables were assessed using XGBoost as a variable selection tool, before fitting mixed-effects multivariate linear modeling. Results As expected, age inversely correlated with keratinocyte and fibroblast extraction yields, decreasing by 0.048×10 6 cells/cm² per year (CI 95% = [-0.065;-0.031]) and 0.035×10 6 cells/cm² per year (CI 95% = [-0.050;-0.019]), respectively. Keratinocyte yield also rose by 0.936×10 6 cells/cm² (CI 95% = [0.175;1.697]) when hairs could be grasped during the epidermis-dermis separation. Conversely, fibroblast yield increased by 0.042×10 6 cells/cm² per day post-burn (CI 95% = [0.007;0.076]) and by 0.019×10 6 cells/cm² per percentage of TBSA burned (CI 95% = [0.002;0.036]). Conclusions This findings provides valuable insights into factors influencing skin cell extraction yields, which may help optimize skin biopsy parameters, ultimately improving production efficiency.
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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 | 0.001 |
| 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".