Copper fabric improves the metabolic profile of obese mice: Potential role of the gut microbiota
Bibliographic record
Abstract
Copper and copper alloys have antimicrobial activity through the rapid contact killing of viruses, bacteria and yeasts on their surface. Dysregulation of host microbiota can contribute to the pathogenesis of inflammatory diseases such as obesity, diabetes and cardiovascular disease. Anecdotal evidence noted improved overall well-being in individuals sleeping on copper-containing fabric bedding. We hypothesized that the beneficial effect of copper-infused fabric bedding on cardiometabolic health is linked to changes in gut microbiota composition. This study utilized a mouse model of diet-induced obesity to assess the beneficial effects of copper-infused fabric bedding on metabolic health. Body composition, inflammatory markers, metabolic and cardiovascular status and changes in the faecal microbiota composition were evaluated for up to 2 months in mice fed with a normal chow diet or high fat high cholesterol diet in the presence of bedding made with and without copper-infused fabric. Results showed that mice subjected to diet-induced obesity and housed in cages with copper-infused fabric liner displayed less body weight gain than mice in cages with control fabric. Mice housed with copper-infused fabric also displayed improved glucose tolerance and reduced inflammation biomarker lipocalin-2. We also observed a beneficial shift in gut bacterial composition of obese mice housed with copper fabric bedding. Taken in conjunction, our study provides direct animal-based evidence supporting the beneficial effects of copper fabric on metabolic health.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".