Intracorporeal vs extracorporeal anastomosis following neoplastic right hemicolectomy resection: a systematic review and meta-analysis of randomized control trials.

PURPOSE: While minimally invasive surgery is the preferred approach for right hemicolectomy, the choice of anastomotic technique is still debated. Both intracorporeal (ICA) and extracorporeal anastomosis (ECA) are described, with conflicting reports on safety and efficacy seen. This study aimed to examine impact of ICA and ECA on outcomes in right hemicolectomy. METHODS: A meta-analysis of randomized control trials (RCT) was performed. The primary outcome was overall morbidity. The secondary outcomes included both perioperative and post-operative outcomes. RESULTS: Four RCTs were included incorporating 399 patients (199 patients (49.9%) ICA Vs 200 (50.1%) ECA). There was no significant difference in overall morbidity (RR 0.79, 95% CI 0.43, 1.48, p = 0.47), anastomotic leak (RR 1.34, 95% CI 0.58, 3.13, p = 0.5) or surgical site infections (RR 0.53, 95% CI 0.17, 1.64, p = 0.27). ICA patients had a significantly less post-operative ileus (RR 0.53, 95% CI 0.3-0.94, p = 0.03) quicker return to first flatus (WMD - 0.71, 95% CI - 1.12, 0.31, p = 0.0005), first bowel motion (WMD - 0.53, 95% CI - 0.69, - 0.37, p < 0.00001) and first meal (WMD - 0.68, 95% CI - 1.33, - 0.03, p = 0.04). Pain scores were significantly better for ICA patients on POD 3 (WMD - 0.76, 95% CI - 1.23, - 0.28, p = 0.002), POD 4 (WMD - 0.90, 95% CI - 1.71, - 0.09, p = 0.03) and POD 5 (WMD - 0.67, 95% CI - 1.22, - 0.13, p = 0.01). Length of hospital stay was similar (WMD - 0.46, 95% CI - 1.14, 0.22, p = 0.19). CONCLUSION: ICA is associated with a quicker return to normal physiological function with equivalent post-operative morbidity. Both ECA and ICA are safe and feasible for restoring normal bowel continuity.

Fuel identification by neural network analysis of the response of vapor-sensitive sensor arrays.

Neural network analysis of the response of an array of vapor-sensitive detectors has been used to identify six different types of aviation fuel. The data set included 96 samples of JP-4, JP-5, JP-7, JP-8, JetA, and aviation gasoline (AvGas). A sample of each neat fuel was injected into a continuous stream of breathing air through an injection port from a gas chromatograph. The aspirated sample was then swept from the injection port to the chamber without separation. In the chamber, the sample was exposed to an array of eight vapor-sensitive detectors. The analog output of each detector was digitized and stored while the sample was swept into and through the chamber. The response of each detector was then averaged and stored as the final response or pattern of each sample. It was clear from a visual inspection of each of the radar plots that there was a characteristic pattern in the response of the array to five of the six different fuel types. This was confirmed using neural network analysis to study the entire data set. A two-step procedure was developed to separate the patterns of all six fuel tyes into their respective classes. In the first step, fuels were separated into one of five groups: JP-4, JP-5, JP-7, AvGas, or a combined JP-8/JetA group. In the second step, the fuels in the combined group were separated into either JP-8 or JetA groups.