Low-protein diet accelerates wound healing in mice post-acute injury
Bibliographic record
Abstract
BACKGROUND: Wound healing processes are influenced by macronutrient intake (protein, carbohydrate and fat). The most favourable diet for cutaneous wound healing is not known, although high-protein diets are currently favoured clinically. This experimental study investigates the optimal macronutrient balance for cutaneous wound healing using a mouse model and the Geometric Framework, a nutrient modelling method, capable of analyzing the individual and interactive effects of a wide spectrum of macronutrient intake. METHODS: ) were surgically created on the dorsal area of male C57BL/6 mice. Mice were then allocated to one of 12 high-energy diets that varied in protein, carbohydrate and fat content. In select diets, wound healing processes, cytokine expression, energy expenditure, body composition, muscle and fat reserves were assessed. RESULTS: Using the Geometric Framework, we show that a low-protein intake, coupled with a balanced intake of carbohydrate and fat is optimal for wound healing. Mice fed a low-protein diet progressed quickly through wound healing stages with favourable wound inflammatory cytokine expression and significantly accelerated collagen production. These local processes were associated with an increased early systemic inflammatory response and a higher overall energy expenditure, related to metabolic changes occurring in key macronutrient reserves in lean body mass and fat depots. CONCLUSIONS: The results suggest that a low-protein diet may have a greater potential to accelerate wound healing than the current clinically used high-protein diets.
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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 | 0.000 |
| 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) | 0.000 | 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".