In this international multi-centre study, the addition of AFI to HRE increased the detection of both the number of patients and the number of lesions with early neoplasia in patients with Barrett's oesophagus. The false positive rate of AFI was reduced after detailed inspection with NBI.

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This study has validated a simplified classification of the various morphologic patterns visualized in Barrett's esophagus and confirmed its reproducibility when used by NBI-expert and non-NBI-expert endoscopists.

Interobserver agreement for the classification of mucosal morphology was moderate. Although NBI was rated more highly than HR-WLE for imaging quality, this did not result in improved interobserver agreement or increased yield for identifying early neoplasia in Barrett's esophagus. This applied to non-expert as well as expert endoscopists.

BackgroundIn many countries, gastric cancer is not diagnosed until an advanced stage. An Internet-based e-learning system to improve the ability of endoscopists to diagnose gastric cancer at an early stage was developed and was evaluated for its effectiveness.MethodsThe study was designed as a randomized controlled trial. After receiving a pre-test, participants were randomly allocated to either an e-learning or non-e-learning group. Only those in the e-learning group gained access to the e-learning system. Two months after the pre-test, both groups received a post-test. The primary endpoint was the difference between the two groups regarding the rate of improvement of their test results.Findings515 endoscopists from 35 countries were assessed for eligibility, and 332 were enrolled in the study, with 166 allocated to each group. Of these, 151 participants in the e-learning group and 144 in the non-e-learning group were included in the analysis. The mean improvement rate (standard deviation) in the e-learning and non-e-learning groups was 1·24 (0·26) and 1·00 (0·16), respectively (P < 0·001).InterpretationThis global study clearly demonstrated the efficacy of an e-learning system to expand knowledge and provide invaluable experience regarding the endoscopic detection of early gastric cancer (R000012039).

A thermostable lipase was partially purified from the culture supernatant of a thermophilic Bacillus sp. The enzyme is optimally active at 60ºC and pH 8.0. The enzyme showed enhancement in activity in presence of benzene or hexane (30% v/v each). The activity (assayed by determining the release of pNP from pNP laurate) was stimulated up to 60% of these solvents in enzyme reaction mixture. The catalytic properties of this thermostable enzyme can be further improved via the use of different immobilization techniques and reaction conditions. Enzyme was immobilized on different solid supports and their enzyme activity and stability was compared. The enzyme was adsorbed on silica and HP-20 beads followed by cross-linking with gluteraldehyde on HP-20, which improved the thermostability of enzyme. The optimum pH (pH 8.5) was nearly same for aqueous and immobilized enzyme while optimum temperature was nearly 5ºC higher in case of immobilized enzyme. The immobilized/cross linked enzyme was more thermostable at 70 and 80ºC in comparison to aqueous and surface adsorbed lipase on silica and HP-20. The optimum temperature for esterification reactions was determined to be 60-65ºC. Half-life of immobilized lipase was nearly 2.5 x higher than the aqueous enzyme at 70ºC. Esterification of methanol and oleic acid to methyl oleate by immobilized enzyme was studied in detail.

The automatic detection of frames containing polyps from a colonoscopy video sequence is an important first step for a fully automated colonoscopy analysis tool. Typically, such detection system is built using a large annotated data set of frames with and without polyps, which is expensive to be obtained. In this paper, we introduce a new system that detects frames containing polyps as anomalies from a distribution of frames from exams that do not contain any polyps. The system is trained using a one-class training set consisting of colonoscopy frames without polyps -such training set is considerably less expensive to obtain, compared to the 2-class data set mentioned above. During inference, the system is only able to reconstruct frames without polyps, and when it tries to reconstruct a frame with polyp, it automatically removes (i.e., photoshop) it from the frame -the difference between the input and reconstructed frames is used to detect frames with polyps. We name our proposed model as anomaly detection generative adversarial network (ADGAN), comprising a dual GAN with two generators and two discriminators. To test our framework, we use a new colonoscopy data set with 14317 images, split as a training set with 13350 images without polyps, and a testing set with 290 abnormal images containing polyps and 677 normal images without polyps. We show that our proposed approach achieves the state-of-the-art result on this data set, compared with recently proposed anomaly detection systems.

Using a simplified classification, we demonstrated the feasibility of using NBI-Z to detect villous atrophy in patients presenting with suspected celiac disease.