scholarly journals Enzymatic Debridement of Porcine Burn Wounds via a Novel Protease, SN514

2020 ◽  
Vol 41 (5) ◽  
pp. 1015-1028
Author(s):  
Randolph Stone ◽  
Angela R Jockheck-Clark ◽  
Shanmugasundaram Natesan ◽  
Julie A Rizzo ◽  
Nathan A Wienandt ◽  
...  

Abstract Necrotic tissue generated by a thermal injury is typically removed via surgical debridement. However, this procedure is commonly associated with blood loss and the removal of viable healthy tissue. For some patients and contexts such as extended care on the battlefield, it would be preferable to remove devitalized tissue with a nonsurgical debridement agent. In this paper, a proprietary debridement gel (SN514) was evaluated for the ability to debride both deep-partial thickness (DPT) and full-thickness burn wounds using an established porcine thermal injury model. Burn wounds were treated daily for 4 days and visualized with both digital imaging and laser speckle imaging. Strip biopsies were taken at the end of the procedure. Histological analyses confirmed a greater debridement of the porcine burn wounds by SN514 than the vehicle-treated controls. Laser speckle imaging detected significant increases in the perfusion status after 4 days of SN514 treatment on DPT wounds. Importantly, histological analyses and clinical observations suggest that SN514 gel treatment did not damage uninjured tissue as no edema, erythema, or inflammation was observed on intact skin surrounding the treated wounds. A blinded evaluation of the digital images by a burn surgeon indicated that SN514 debrided more necrotic tissue than the control groups after 1, 2, and 3 days of treatment. Additionally, SN514 gel was evaluated using an in vitro burn model that used human discarded skin. Treatment of human burned tissue with SN514 gel resulted in greater than 80% weight reduction compared with untreated samples. Together, these data demonstrate that SN514 gel is capable of debriding necrotic tissue and suggest that SN514 gel could be a useful option for austere conditions, such as military multi-domain operations and prolonged field care scenarios.

2020 ◽  
Vol 41 (Supplement_1) ◽  
pp. S77-S77
Author(s):  
Nehemiah T Liu ◽  
Randolph Stone ◽  
José Salinas ◽  
Robert J Christy

Abstract Introduction Early excision and grafting (E&G) remains a mainstay in the treatment of burns. Procedures to remove necrotic tissue from severe burn wounds continue to be challenging and may affect rates of successful grafting. The use of laser speckle imaging (LSI) may help detect necrotic tissue remaining but requires human interpretation. Additional decision-making support is needed, especially in prolonged field care settings. The purpose of this study was to evaluate whether graft success or failure can be predicted from LSI using machine learning (ML) and deep learning (DL) techniques in a porcine burn model of various burn depths. Methods Anesthetized Yorkshire pigs (n=12) were burned with a 5x5 cm square brass block (0.4 kg/cm2) @ 100°C to produce partial, deep partial, or full thickness burns (PT, DPT, FT) at 10 square sections on the back of each pig. Debridement was randomized from 1 (0.030”) to 4 (0.120”) passes performed using a dermatome, and then meshed split thickness skin grafts taken from 4 caudal donor sites were applied to wounds. Post-debridement was denoted as Day 0. Graft success/failure (>70% graft take) was determined at Day 7. Laser speckle images were captured at Days 0, 3, 7, 10, and 14. ML and DL were used to develop models in order to predict graft failure and burn/debridement depth. Model performance was measured using loss, accuracy, and confusion matrices. Results Of 120 sections corresponding to 12 pigs, 7.5% (9/120) were not burned, 41.7% (50/120) were PT, 25.8% (31/120) were DPT, and 25.0% (30/120) were FT burns. Graft failure was 19.2% (23/120), with a 50.0% (10/20) rate for DPT burns involving 1- or 2-pass debridement. Both ML and DL used 600 images for algorithm development; 540 images, for training; and 60 images, for testing. DL was superior over ML in test accuracy (93.3% vs 75.0%, p< 0.05). A three-stage architecture plus image resizing was employed in DL to predict graft failure and burn/debridement depth. A best convolutional neural network for graft failure prediction was obtained, yielding a minimum cross entropy loss of 11.7% and accuracies of 96.5% (training), 93.3% (testing 1), 100.0% (testing 2), and 96.2% (overall). Promising results were also obtained for predicting burn/debridement depth: 97.6% (training), 93.3% (testing). Conclusions This study showed that graft success or failure can be predicted from LSI and DL in a porcine burn model of various debridement depths. Use of this technology may provide a potential approach for accurately assessing early E&G in severely burned patients and may aid providers during prolonged field care scenarios. Applicability of Research to Practice Use of LSI in conjunction with DL may provide a potential approach for accurately assessing early E&G in severely burned patients and may aid providers during prolonged field care scenarios.


2019 ◽  
Vol 40 (Supplement_1) ◽  
pp. S234-S234
Author(s):  
R Stone ◽  
D Larson ◽  
J Wall ◽  
H Dillon ◽  
C Kowalczewski ◽  
...  

2019 ◽  
Vol 10 (4) ◽  
pp. 2020
Author(s):  
Jose Angel Arias-Cruz ◽  
Roger Chiu ◽  
Hayde Peregrina-Barreto ◽  
Ruben Ramos-Garcia ◽  
Teresita Spezzia-Mazzocco ◽  
...  

2014 ◽  
Vol 39 (3) ◽  
pp. 678 ◽  
Author(s):  
J. C. Ramirez-San-Juan ◽  
R. Ramos-Garcia ◽  
G. Martinez-Niconoff ◽  
B. Choi

2021 ◽  
Author(s):  
Ilya Balmages ◽  
Janis Liepins ◽  
Dmitrijs Bliznuks ◽  
Stivens Zolins ◽  
Ilze Lihacova ◽  
...  

2019 ◽  
Vol 122 ◽  
pp. 52-59 ◽  
Author(s):  
AmirHessam Aminfar ◽  
Nami Davoodzadeh ◽  
Guillermo Aguilar ◽  
Marko Princevac

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