Safe eye surgery: non-technical aspects

The traditional training of surgeons focused exclusively on developing knowledge, clinical expertise, and technical (surgical) skills. However, analyses of the reasons for adverse events in surgery have revealed that many underlying causes originate from behavioural or non-technical aspects of performance (eg, poor communication among members of the surgical team) rather than from a lack of surgical (ie, technical) skills. Therefore, technical skills appear to be necessary but not sufficient to ensure patient safety. Paying attention to non-technical skills, such as team working, leadership, situation awareness, decision making, and communication, will increase the likelihood of maintaining high levels of error-free performance. Identification and training of non-technical skills has been developed for high-risk careers, such as civil aviation and nuclear power. Only recently, training in non-technical skills has been adopted by the surgical world and anaesthetists. Non-technical skills need to be tailored to the environment where they are used, and eye surgery has some substantial differences compared with other surgical areas, for example, high volume of surgery, use of local anaesthetics, and very sophisticated equipment. This review highlights the need for identification of the non-technical skills relevant to eye surgeons and promotion of their use in the training of eye surgeons.

Carboxymethylethanolamine, a biomarker of phospholipid modification during the maillard reaction in vivo

Nepsilon-(Carboxymethyl)lysine (CML) is a stable chemical modification of proteins formed from both carbohydrates and lipids during autoxidation reactions. We hypothesized that carboxymethyl lipids such as (carboxymethyl)phosphatidylethanolamine (carboxymethyl-PE) would also be formed in these reactions, and we therefore developed a gas chromatography-mass spectrometry assay for quantification of carboxymethylethanolamine (CME) following hydrolysis of phospholipids. In vitro, CME was formed during glycation of dioleoyl-PE under air and from linoleoylpalmitoyl-PE, but not from dioleoyl-PE, in the absence of glucose. In vivo, CME was detected in lipid extracts of red blood cell membranes, approximately 0.14 mmol of CME/mol of ethanolamine, from control and diabetic subjects, (n = 22, p > 0.5). Levels of CML in erythrocyte membrane proteins were approximately 0.2 mmol/mol of lysine for both control and diabetic subjects (p > 0.5). For this group of diabetic subjects there was no indication of increased oxidative modification of either lipid or protein components of red cell membranes. CME was also detected in fasting urine at 2-3 nmol/mg of creatinine in control and diabetic subjects (p = 0.085). CME inhibited detection of advanced glycation end product (AGE)-modified protein in a competitive enzyme-linked immunosorbent assay using an anti-AGE antibody previously shown to recognize CML, suggesting that carboxymethyl-PE may be a component of AGE lipids detected in AGE low density lipoprotein. Measurement of levels of CME in blood, tissues, and urine should be useful for assessing oxidative damage to membrane lipids during aging and in disease.

Measurement of proton-driven antiport in Escherichia coli

Secondary active transport of substrates across the inner membrane is vital to the bacterial cell. Of the secondary active transporter families, the ubiquitous major facilitator superfamily (MFS) is the largest and most functionally diverse (Reddy et al., 2012). Recently, it was reported that the MFS multidrug efflux protein MdtM from Escherichia coli (E. coli) functions physiologically in protection of bacterial cells against bile salts (Paul et al., 2014). The MdtM transporter imparts bile salt resistance to the bacterial cell by coupling the exchange of external protons (H+) to the efflux of bile salts from the cell interior via an antiport reaction. This protocol describes, using fluorometry, how to detect the bile salt/H+ antiport activity of MdtM in inverted membrane vesicles of an antiporter-deficient strain of E. coli TO114 cells by measuring transmembrane ∆pH. This method exploits the changes that occur in the intensity of the fluorescence signal (quenching and dequenching) of the pH-sensitive dye acridine orange in response to changes in [H+] in the vesicular lumen. Due to low levels of endogenous transporter expression that would normally make the contribution of individual transporters such as MdtM to proton-driven antiport difficult to detect, the method typically necessitates that the transporter of interest be overexpressed from a multicopy plasmid. Although the first section of the protocol described here is very specific to the overexpression of MdtM from the pBAD/Myc-His A expression vector, the protocol describing the subsequent measurement of bile salt efflux by MdtM can be readily adapted for measurement of antiport of other substrates by any other antiporter that exchanges protons for countersubstrate.

Modeling of titanium alloys by using artificial neural networks

Titanium alloy exhibits an excellent combination of bio-compatibility, corrosion resistance, strength and toughness. The microstructure of an alloy influences the properties. The microstructures depend mainly on alloying elements, method of production, mechanical, and thermal treatments. The relationships between these variables and final properties of the alloy are complex, non-linear in nature, which is the biggest hurdle in developing proper correlations between them by conventional methods. So, we developed artificial neural networks (ANN) models for solving these complex phenomena in titanium alloys.

In the present work, ANN models were used for the analysis and prediction of the correlation between the process parameters, the alloying elements, microstructural features, beta transus temperature and mechanical properties in titanium alloys. Sensitivity analysis of trained neural network models were studied which resulted a better understanding of relationships between inputs and outputs. The model predictions and the analysis are well in agreement with the experimental results. The simulation results show that the average output-prediction error by models are less than 5% of the prediction range in more than 95% of the cases, which is quite acceptable for all metallurgical purposes.