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Improvements in narrow band imaging (NBI) may provide an improved view of colonic mucosa for detection of polyps and adenomas. In this issue, Leung et al. report findings to suggest that this next-generation NBI technology is superior to conventional high-definition white light endoscopy in polyp detection. These findings are based on brighter illumination, which has been a problem with older generations of NBI, which did not increase polyp detection but were useful for polyp characterization. Although these findings are very promising for this new role of second-generation NBI in polyp detection, the study must be viewed with consideration of the history of the older NBI system, the analysis of which through multiple positive and negative studies ultimately led to the conclusion that it was not beneficial for detection.

Introduction Electronic-noses (e-noses) have the ability of converting smells to digital signatures. By interacting with volatile organic compounds (VOC's) emitted by metabolic end-products of disease, e-noses create distinct patterns of aggregate VOC's to generate unique signatures that can be used to identify disease. The detection of Clostridium difficile (C. diff) by VOC's is prototypic as it has been successfully identified by scent using canines. We seek to pilot a novel portable e-nose device to profile fecal headspace to diagnose C. diff infection. Methods We are prospectively enrolling patients undergoing stool testing by C. diff PCR for infectious diarrhea for fecal headspace analysis using the Aetholab e-nose (eNose Company, Zutphen, NL). Stool specimens were obtained from the clinical microbiology lab of our institution within 7 days of ambient collection and aliquots of 10-ml of homogenized stool were transferred to disposable analysis bottles. The e-nose circulates fecal headspace gas within each analysis bottle to allow interaction with its respective sensor array. Each sensor array comprises of three metal-oxide sensors that undergo reversible redox reactions with VOC's at an electrochemical interface. Electrical resistance changes are measured by applying a 32-point thermal cycle to the sensor array generating a digital signature (Figure 1). This process has been shown to interact with a wide range of VOC's. Combined data from the 3 sensors in 7 combinations (A, B, C, AB, AC, AC, ABC) is permuted across 3 scaling factors generating a total of 21 data sets per analysis. These are then compressed through a multi-way analysis to reduce the large amount of data to avoid spurious associations. Finally each sample's compressed dataset is introduced into an artificial neural network (ANN) using a supervised approach. The 5 best models generated using a leave-one-out approach are used to obtain the reported results. Results Separation was observed using all 20 C. diff-PCR positive and 53 C. diff-PCR negative stools. The ROC curve for this model is shown in Figure 2 with an AUC = 0.85. This demonstrated 80% sensitivity, 85% specificity with 84% accuracy. The positive predictive value was 0.67 and the negative predictive value 0.92. Matthews correlation coefficient was 0.64. Conclusion Through the use of a pattern-recognition e-nose device it is possible to distinguish C. diffinfected stools. This is the first time that this type of device has been used for this diagnosis, and also the first time this e-nose has been applied for fecal headspace analysis of infectious disease. This indicates that C. diff testing can translate into a point-of-care evaluation.

Background: The overall risk of progression to esophageal adenocarcinoma (EAC) in BE subjects without dysplasia is lower than previously estimated. There is hence lack of consensus on the effectiveness of current surveillance programs. Stratification of risk of progression is appealing, as it may help identify BE patients at higher risk who can be monitored more intensively or offered endoscopic therapy. Though several studies have identified factors predicting progression, evidence on several clinically relevant risk factors influencing progression is not consistent. We aimed to identify the predictors of progression in BE subjects without high grade dysplasia by performing a systematic review and meta-analysis of relevant studies. Methods: A comprehensive search of several databases including PubMed, EMBASE, and Web of Science databases (earliest inception to July 28th, 2014) was performed and studies reporting outcomes of BE cohorts under surveillance were identified. We included studies that reported predictors of progression to HGD/ EAC as an outcome in patients with non-dysplastic BE (NDBE) and BE-low grade dysplasia (LGD). A pooled estimate (odds ratio) of the potential of age, sex, smoking, alcohol use, BMI, baseline LGD (vs NDBE), BE length, and medication use in predicting progression to HGD/EAC in NDBE/ LGD patients was calculated. Results: 23 studies reporting predictors of progression as an outcome were identified from the systematic review. 1231 events in 74943 patients in 204926.2 patient years of follow-up were analyzed. Table 1 shows the pooled odds ratio (OR) of predictors analyzed. Increasing age, male sex, ever smoking (current or past), and increasing BE segment length were predictive of increased risk of progression to HGD/EAC with low heterogeneity (9-13 studies). Patients with LGD had an almost four fold increased risk of progression compared to those with no dysplasia. NSAID and statin use were protective against progression to HGD/EAC without heterogeneity. Alcohol use and obesity as measured by BMI did not influence progression rates. Conclusion: Age, male sex, ever smoking (current or past), and BE length are predictive of increased risk of progression to HGD/ EAC. Subjects with baseline LGD are at 4 times increased risk of progression compared to those without dysplasia. These factors may be used to risk stratify BE subjects as part of a prediction score, to identify those who could potentially benefit from intensive surveillance or endoscopic therapy. Smoking is a modifiable risk factor for cancer prevention in BE subjects. Table 1: Pooled odds ratio (OR) of predictors of progression to HGD / EAC.