Evaluation of Ki-67 as a Histological Index of Burn Damage in a Swine Model
Bibliographic record
Abstract
Histological diagnosis of burn depth lacks consensus. The purpose of this study was to determine whether Ki-67, a cell proliferation marker, provides an index of integument viability after burn injury. Induction of thermal burn injuries (3, 12, 20, 30, 75, 90, and 120 seconds) were made with a brass rod heated to 100°C on the dorsal trunk of the swine. Controls were created with a brass rod heated to 37.5°C. Four 6-mm biopsies were obtained from each site for histological analysis of Ki-67. Biopsies were taken at the following times postinjury: 1, 12, 24, 36, 48, 72, and 96 hours. The results illustrate a characteristic Ki-67 nuclear staining in the basal layer of the epidermis and in the hair follicle. With increasing thermal injury, the nuclei of the cells changed morphology: condensing, fragmenting, and elongating. The uniqueness of the labeling index was to include only morphologically intact nuclei as having capacity to proliferation. Quantitative analysis showed a reduction in the mean number of Ki-67-positive cells, suggesting a reduced regenerative capacity. This study supports using this index as a means of performing histology for burn depth analysis. In future studies, determining viability of partial-thickness burns will require multiple histological markers such as Ki-67 in addition to hematoxylin and eosin staining.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".