Three-stage management of complex pancreatic trauma with gastroduodenopancreatectomy: A case report.

INTRODUCTION: Severe injuries of the pancreatic head and duodenum in haemodynamically unstable patients are complex management. The purpose of this study is to report a case of complex pancreatic trauma induced by gunshot and managed with surgical approaches at three different times. PRESENTATION OF CASE: Exploratory laparotomy was indicated after initial emergency room care, with findings of cloudy blood-tinged fluid and blood clots on the mesentery near the hepatic angle, on the region of the 2nd portion of the duodenum and at the pancreatic head. Gastroduodenopancreatectomy was performed with right hemicolectomy and the peritoneal cavity was temporarily closed by a vacuum peritoneostomy. Surgical reopening occurred on the fifth postoperative day, and the patient was subjected to single-loop reconstruction of the intestinal transit with telescoping pancreaticojejunal anastomosis, biliodigestive anastomosis with termino-lateral hepaticojejunal anastomosis with a Kehr drain and gastroenteroanastomosis in 2 planes. The terminal ileostomy was maintained. After 2 days, the patient was subjected to abdominal wall closure without complications, which required relaxing Gibson incisions and wound closure with polypropylene mesh placement in a pre-aponeurotic position closed with multiple stitches. RESULTS: The patient was discharged on the 40th post-trauma day without drains, with a functioning ileostomy and with a scheduled reconstruction of intestinal transit. CONCLUSION: In the presence of multiple associated injuries, hemodynamic instability and the need for an extensive surgical procedure such as duodenopancreatectomy, damage control surgery performed in stages as reported here enables the clinical stabilization of the patient for definitive treatment, achieving better survival results.

Discontinuity Detection in the Shield Metal Arc Welding Process.

This work proposes a new methodology for the detection of discontinuities in the weld bead applied in Shielded Metal Arc Welding (SMAW) processes. The detection system is based on two sensors-a microphone and piezoelectric-that acquire acoustic emissions generated during the welding. The feature vectors extracted from the sensor dataset are used to construct classifier models. The approaches based on Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers are able to identify with a high accuracy the three proposed weld bead classes: desirable weld bead, shrinkage cavity and burn through discontinuities. Experimental results illustrate the system's high accuracy, greater than 90% for each class. A novel Hierarchical Support Vector Machine (HSVM) structure is proposed to make feasible the use of this system in industrial environments. This approach presented 96.6% overall accuracy. Given the simplicity of the equipment involved, this system can be applied in the metal transformation industries.