Healing of excisional wound in alloxan induced diabetic sheep: A planimetric and histopathologic study.
Bibliographic record
Abstract
Healing of skin wound is a multi-factorial and complex process. Proper treatment of diabetic wounds is still a major clinical challenge. Although diabetes mellitus can occur in ruminants, healing of wounds in diabetic ruminants has not yet been investigated. The aim of this study was to evaluate healing of ovine excisional diabetic wound model. Eight 4-month-old Iranian Makoui wethers were equally divided to diabetic and nondiabetic groups. Alloxan monohydrate (60 mg kg(-1), IV) was used for diabetes induction. In each wether, an excisional wound was created on the dorsum of the animal. Photographs were taken in distinct times for planimetric evaluation. Wound samples were taken on day 21 post-wounding for histopathologic evaluations of epidermal thickness, number of fibroblasts and number of new blood vessels. The planimetric study showed slightly delay in wound closure of diabetic animals, however, it was not significantly different from nondiabetic wounds (p ≥ 0.05). Furthermore, epidermal thickness, number of fibroblasts and number of blood vessels were significantly lower in diabetic group (p < 0.05). We concluded that healing of excisional diabetic wounds in sheep may be compromised, as seen in other species. However, contraction rate of these wounds may not be delayed due to metabolic features of ruminants and these animals might go under surgeries without any serious concern. However, healing quality of these wounds may be lower than normal wounds.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".