Introduction: Penetrating abdominal injury is a major cause of death in trauma. It may cause hypovolemia leading to tissue hypoperfusion, direct organ damage and cytokine activation that cause inflammatory damage, all of which lead to death. Alginate is a natural anionic polysaccharide typically derived from brown algae. Sodium alginate hydrogel, a hemostatic agent, offers a platform for targeting both mechanical and biological injuries. The current study assessed the effect of a sodium alginate denoted VLVG (Very Low Viscosity (high) G alginate) following abdominal trauma in a swine model of penetrating abdominal injury. Methods: Seven anesthetized pigs were instrumented with catheters and abdominal trauma was introduced by laparoscopic hepatectomy. Ten minutes after the induction of hypovolemic shock, three animals were intra-abdominally administered with VLVG and four animals with saline (controls). During 8h of continuous monitoring, various hemodynamic and biochemical variables were measured and liver biopsies for histological evaluation were taken. In order to compare the study group to the control in a specific time a-parametric Mann-Whitney test was used, assessment of tendency during time Friedman's test for a-parametric variables was used. In order to compare the effect of the treatment (i.e. normal saline VS VLVG alginate) repeated measures ANOVA model was used, and the p value was calculated based on the Greenhouse-Geiser test. This research was approved by the Hebrew University of Jerusalem ethics Committee number: MD16148533. Results: VLVG-treated animals were more hemodynamically stable vs controls as reflected by their lower heart rate and higher blood pressure. They also had lower levels of liver enzymes and lactate and tissue damage. Conclusions: Our results in this pilot abdominal injury model show that VLVG might be a promising new agent. The superior hemostatic and biocompatibility efficiency along with its tissue preserving properties may turn VLVG in the future to a device that could be used in the pre-hospital setting to improve survival of abdominal trauma injuries.

Penetrating abdominal injury is a major cause of death in trauma. Sodium alginate hydrogel, a hemostatic agent, offers a platform for targeting both mechanical and biological injuries. The current study assessed the effect of Very Low Viscosity (high) G (VLVG) alginate following abdominal trauma in a swine model of penetrating abdominal injury. Seven anesthetized pigs were instrumented with invasive monitoring catheters and abdominal trauma was introduced by laparoscopic hepatectomy. Ten minutes after the induction of hypovolemic shock, three animals were intra-abdominally administered with VLVG alginate (study group) and four animals with saline (control group). During 8 h of continuous monitoring, various hemodynamic and biochemical variables were measured and liver biopsies for histological evaluation were taken. Hemodynamically, VLVG alginate-treated animals were more stable than controls, as reflected by their lower heart rate and higher blood pressure (p < 0.05 for both). They also had lower levels of liver enzymes and lactate, and less histopathological damage. We show that VLVG alginate might be a promising new agent for reducing penetrating intra-abdominal injury, with hemostatic and biocompatibility efficiency, and tissue preserving properties. Future effort of integrating it with a dispersal device may turn it into a valuable pre-hospital emergency tool to improve survival of trauma casualties.

Background Tension pneumothorax is one of the leading causes of preventable death on the battlefield. Current prehospital diagnosis relies on a subjective clinical impression complemented by a manual thoracic and respiratory examination. These techniques are not fully applicable in field conditions and on the battlefield, where situational and environmental factors may impair clinical capabilities. We aimed to assemble a device able to sample, analyze, and classify the unique acoustic signatures of pneumothorax and hemothorax. Methods Acoustic data was obtained with simultaneous use of two sensitive digital stethoscopes from the chest wall of an ex-vivo porcine model. Twelve second samples of acoustic data were obtained from the in-house assembled digital stethoscope system during mechanical ventilation. The thoracic cavity was injected with increasing volumes of 200, 400, 600, 800, and 1000 ml of air or saline to simulate pneumothorax and hemothorax, respectively. The data was analyzed using a multi-objective genetic algorithm that was used to develop an optimal mathematical detector through the process of artificial evolution, a cutting-edge approach in the artificial intelligence discipline. Results The in-house assembled dual digital stethoscope system and developed genetic algorithm achieved an accuracy, sensitivity and specificity ranging from 64 to 100%, 63 to 100%, and 63 to 100%, respectively, in classifying acoustic signal as associated with pneumothorax or hemothorax at fluid injection levels of 400 ml or more, and regardless of background noise. Conclusions We present a novel, objective device for rapid diagnosis of potentially lethal thoracic injuries. With further optimization, such a device could provide real-time detection and monitoring of pneumothorax and hemothorax in battlefield conditions.

Background : Tension pneumothorax is one of the leading causes of preventable death on the battlefield. Current prehospital diagnosis relies on a subjective clinical impression complemented by a manual thoracic and respiratory examination. These techniques are not fully applicable in field conditions and on the battlefield, where situational and environmental factors may impair clinical capabilities. We aimed to assemble a device able to sample, analyze, and classify the unique acoustic signatures of pneumothorax and hemothorax. Methods : Tested on an ex-vivo porcine model, we have assembled a device consisting of two sensitive digital stethoscopes and sampled 12 seconds of mechanical ventilation breathing sounds over the animals’ thorax. The thoracic cavity was injected with increasing volumes of 200, 400, 600, 800, and 1000 ml of air and saline to simulate pneumothorax and hemothorax, respectively. The data was analyzed using a multi-objective genetic algorithm that was used to develop an optimal mathematical detector through the process of artificial evolution, a cutting-edge approach in the artificial intelligence discipline. Results : The algorithm was able to classify the signals according to their distinctive characteristics and to accurately predict, in up to 80% of cases, the presence of pneumothorax and hemothorax, starting from 400 ml, and regardless of background noise. Conclusions : We present a potential objective and rapid diagnosis modality that can overcome independent and subjective factors that may delay diagnosis and treatment of potentially lethal thoracic injuries, with emphasis on field conditions. A future diagnostic device could be embedded with the algorithm and provide real-time detection and monitoring of pneumothorax and hemothorax.

Background: Tension pneumothorax is a leading cause of preventable death on the battlefield. Current prehospital diagnosis relies on a subjective clinical impression complemented by a manual thoracic and respiratory examination. These techniques are not fully applicable in field conditions and on the battlefield, where situational and environmental factors may impair clinical capabilities. We aimed to assemble a device able to sample, analyze, and classify the unique acoustic signatures of pneumothorax and hemothorax. Methods: Tested on an ex-vivo porcine model, we have assembled a device consisting of two sensitive digital stethoscopes and sampled 12 seconds of mechanical ventilation breathing sounds over the animals’ thorax. The thoracic cavity was injected with increasing volumes of 200, 400, 600, 800, and 1000 ml of air and saline to simulate pneumothorax and hemothorax, respectively. The data was analyzed using a multi-objective genetic algorithm that was used to develop an optimal mathematical detector through the process of artificial evolution, a cutting-edge approach in the artificial intelligence discipline. Results: The algorithm was able to classify the signals according to their distinctive characteristics and to accurately predict, in up to 80% of cases, the presence of pneumothorax and hemothorax, starting from 400 ml, and regardless of background noise.Conclusions: We present a potential objective and rapid diagnosis modality that can overcome independent and subjective factors that may delay diagnosis and treatment of potentially lethal thoracic injuries, with emphasis on field conditions. A future diagnostic device could be embedded with the algorithm and provide real-time detection and monitoring of pneumothorax and hemothorax.