The effect of glutathione and N-acetylcysteine on lipoperoxidative damage in patients with early septic shock.

Both the hyperproduction of oxygen free radicals (OFR) and the weakening of natural scavenging mechanisms have been implicated as contributors to multiple organ failure in septic shock. This study examined whether the antioxidants glutathione (GSH) and N-acetyl-L-cysteine (NAC) play a protective role against damage by OFR in early septic shock. We randomly entered 30 patients with septic shock into one of three groups within 24 h of diagnosis. All of the patients received septic shock therapy, including parenteral nutrition, antibiotics, and volume-expanding and inotropic agents. One group (Group B) also received 70 mg/kg/d of intravenous GSH, and a second group (Group C), 70 mg/kg/d of intravenous GSH and 75 mg/kg/d of intravenous NAC. The protection against OFR damage was evaluated by measuring expired ethane, plasma malondialdehyde, erythrocyte deformability, complement activation, and clinical scores at admission and on Days 3 and 5 of treatment. A significant decrease in peroxidative indexes was observed at Day 5 in Group B as compared with both the control group and basal values. The decrease in peroxidative indexes was even more marked in Group C. Clinical scores in this group were also significantly improved. In conclusion, the administration of high doses of NAC added to GSH significantly decreased the peroxidative stress of patients with septic shock.

EEG signal processing in anaesthesia. Use of a neural network technique for monitoring depth of anaesthesia.

BACKGROUND: The Bispectral Index (BIS) is a proprietary index of anaesthesia depth, which is correlated with the level of consciousness and probability of intraoperative recall. The present study investigates the use of a neural network technique to obtain a non-proprietary index of the depth of anaesthesia from the processed EEG data. METHODS: Two hundred patients, who underwent general abdominal surgery, were recruited for our trial. For anaesthesia we used a total i.v. technique, tracheal intubation, and artificial ventilation. Fourteen EEG variables, including the BIS, were extracted from the EEG, monitored with an EEG computerized monitor, and then stored on a computer. Data from 150 patients were used to train the neural network. All the variables, excluding the BIS, were used as input data in the neural network. The output targets of the network were provided by anaesthesia scores ranging from 10 to 100 assigned by the anaesthesiologist according to the observer's assessment of alertness and sedation (OAA/S) and other clinical means of assessing depth of anaesthesia. Data from the other 50 patients were used to test the model and for statistical analysis. RESULTS: The artificial neural network was successfully trained to predict an anaesthesia depth index, the NED (neural network evaluated depth), ranging from 0 to 100. The correlation coefficient between the NED and the BIS over the test set was 0.94 (P<0.0001). CONCLUSION: We have developed a neural network model, which evaluates 13 processed EEG parameters to produce an index of anaesthesia depth, which correlates very well with the BIS during total i.v. anaesthesia with propofol.