EFFECTS OF ELECTRICAL STIMULATION ON THE HISTOLOGICAL PROPERTIES OF WOUNDS IN DIABETIC MICE
Bibliographic record
Abstract
The purpose of this study was to identify mechanisms underlying electrically stimulated wound closure in diabetic mice. Adult male mice (n = 58) with full-thickness excisional wounds were treated five times using negative polarity over the wound site for 15 minutes each over a 16-day period with sham (0 Volts) or 5.0, 10.0, 12.5 Volts. In addition, animals (diabetic (n = 33) and nondiabetic (n = 22)) received treatments of electrical stimulation (12.5 V), or sham treatment (0 V) at wound sites which were then harvested and prepared for histological analysis at 2, 8, and 16 days postwounding. Using computerized image analysis of sections stained with a picro sirus red-fast green staining technique, we found that increasing doses of electrical stimulation reduced collagen/noncollagenous protein ratios measured in the superficial scar of nondiabetic animals, with no effect in diabetic animals. In the deep scar, lower doses of electrical stimulation (5.0 V) produced significantly (p < 0.01) increased collagen deposition in wounds of nondiabetic animals compared with sham controls. Higher doses of electrical stimulation (12.5 V) were required to produce changes in diabetic animals than were observed in nondiabetic animals. These results suggest that electrical stimulation altered collagen deposition in excisional wounds of diabetic and nondiabetic animals. Electrical stimulation had a differential effect on wound healing in diabetic compared with nondiabetic animals. These data speak to the need to study the effects of electrical stimulation on healing in disease-specific models.
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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 | 0.000 | 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) | 0.001 | 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".