# Physiology-driven reversible dynamic synthetic tissue model for responsive moulage in infected wound training

**Authors:** Alex T. Gong, Aleah M. DeSchmidt, Austin J. Baird, Jessica G. Gonzaga, Shi-Wen Olivia Yau, Rudolph J. Toepfer, Jessica Zhang, Conner J. Parsey, Rainer Leuschke, Jack E. Norfleet, Robert M. Sweet

PMC · DOI: 10.1371/journal.pone.0333565 · PLOS One · 2026-02-25

## TL;DR

A dynamic synthetic tissue model simulates infected wound progression and improves medical training for infection identification.

## Contribution

A physiology-driven, reversible synthetic tissue model that dynamically simulates infected wound progression for medical training.

## Key findings

- The model received a 4.1 average rating for realism on a 5-point scale.
- Physicians rated the model's training value at 4.3 on a 5-point scale.
- The dynamic aspect of the model was highly rated for improving simulation (4.7 average).

## Abstract

Misidentification of dermatologic manifestations of systemic infection can lead to life-threatening developments including sepsis. Training medical providers to accurately identify and respond to infections using simulation could reduce these rates. Moulage of infections are static and require a manual “scene change” during simulation scenarios. A high-fidelity, automated physiology-driven dynamic infection model was developed using the Modular Healthcare Simulation and Education platform that physically changes to simulate the progression of an infected wound. The silicone model was designed using resistive wire, wax actuators, and thermochromic powder. The resistive wire heats the wax, resulting in focal edema, skin warmth, and erythema that reverses with proper recognition and treatment. Without external treatment, purulence is released, and the patient will develop septic physiology. To evaluate the physiological realism and training value of the model, a survey of physicians was conducted using a five-point Likert scale of agreement with 5 being the highest rating. When asked to rate the realism of the model, the mean response was 4.1 ± 0.7. When asked if they thought the model had value for training infection identification, the mean response was 4.3 ± 0.9. Finally, when asked if the dynamic aspect would improve the simulation, the mean response was 4.7 ± 0.6. The dynamic infection model is functional and appealing for practitioners to assist in the early detection of infection. The use of a dynamic training model could potentially be used to replicate other dermatologic manifestations of systemic disease processes and improve medical training.

## Full-text entities

- **Genes:** IL6 (interleukin 6) [NCBI Gene 3569] {aka BSF-2, BSF2, CDF, HGF, HSF, IFN-beta-2}
- **Diseases:** fever (MESH:D005334), burn (MESH:D002056), Inflammation (MESH:D007249), Dynamic Wounds (MESH:D014947), Edema (MESH:D004487), bacterial (MESH:D001424), erythema (MESH:D004890), Purulence (MESH:D003234), infectious disease (MESH:D003141), Bloodstream Infection (MESH:D018805), HCAIs (MESH:D003428), death (MESH:D003643), pneumothorax (MESH:D011030), COVID-19 (MESH:D000086382), dermal infection (MESH:D007239), hypovolemia (MESH:D020896), infected wound (MESH:D014946), laceration (MESH:D022125)
- **Chemicals:** water (MESH:D014867), silicone (MESH:D012828), nitric oxide (MESH:D009569), paraffin wax (MESH:D010232), polyamide (MESH:D009757), Ecoflex (MESH:C472388), wax (MESH:D014885), TiO2 (MESH:C009495), PLA (MESH:C033616), NiCr (MESH:C066018), NO (MESH:D009614), LV Deadener (-), silicone rubber (MESH:D012826)
- **Species:** Aeromonas hydrophila (species) [taxon 644], Streptococcus pneumoniae (species) [taxon 1313], Pseudomonas aeruginosa (species) [taxon 287], Bacteria Latreille et al. 1825 (Bacteria stick insect, genus) [taxon 629395], Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

6 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12935211/full.md

## References

37 references — full list in the complete paper: https://tomesphere.com/paper/PMC12935211/full.md

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Source: https://tomesphere.com/paper/PMC12935211