Bacterial Filtration Abilities of Mycelium as a New Gauze Substitute: A Research Protocol
Bibliographic record
Abstract
Introduction: Climate change is escalating the demand for wound dressings which are produced from non-sustainable materials such as cotton and synthetic fibres. Mycelium presents a promising biodegradable and renewable substitute for gauze material, which could help reduce the negative impact of the medical textile industry on the environment. Mycelium extracted from Ganoderma tsugae exhibits healing properties for the skin, making it an appropriate gauze, but its ability to prevent bacterial infections is not well understood. This study seeks to experimentally investigate the ability of mycelium to filter bacteria from infecting third-degree burn wounds in comparison to a woven cotton gauze and a non-woven Acticoat gauze. No gauze, loosely woven gauze and impermeable gauze will be used as controls. It is hypothesized that a mycelium dressing would significantly affect the bacterial filtration efficiency because previous studies have shown mycelia to be effective at air filtration. Methods: The mycelium gauze will be made from the mycelium root network of the fungus Ganoderma tsugae. Third-degree burns will be introduced on mice followed by an assessment of the bacterial filtration efficiency of each dressing type. During a 28-day period, bacterial samples from the wound will be obtained and plated for cell counting analysis. Wound healing abilities and gauze porosities will also be measured. Results: It is anticipated that the mycelium gauze will result in the highest bacterial filtration efficiency due to the material’s filtration ability and tissue repair mechanisms. It is also expected that the mycelium gauze will result in improved wound healing as it promotes cell growth. Discussion: ANOVA will be used for a comparative assessment of bacterial filtration efficiencies among the different gauze types to indicate significant differences. Conclusion: The findings from this research could serve as the foundation for potential mycelium gauzes with enhanced functionality in hopes of contributing to the advancement of environmentally conscious medical textiles.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".