Screening for gastric cancer using exhaled breath samples.

BACKGROUND: The aim was to derive a breath-based classifier for gastric cancer using a nanomaterial-based sensor array, and to validate it in a large screening population. METHODS: A new training algorithm for the diagnosis of gastric cancer was derived from previous breath samples from patients with gastric cancer and healthy controls in a clinical setting, and validated in a blinded manner in a screening population. RESULTS: The training algorithm was derived using breath samples from 99 patients with gastric cancer and 342 healthy controls, and validated in a population of 726 people. The calculated training set algorithm had 82 per cent sensitivity, 78 per cent specificity and 79 per cent accuracy. The algorithm correctly classified all three patients with gastric cancer and 570 of the 723 cancer-free controls in the screening population, yielding 100 per cent sensitivity, 79 per cent specificity and 79 per cent accuracy. Further analyses of lifestyle and confounding factors were not associated with the classifier. CONCLUSION: This first validation of a nanomaterial sensor array-based algorithm for gastric cancer detection from breath samples in a large screening population supports the potential of this technology for the early detection of gastric cancer.

Time-space-resolved origami hierarchical electronics for ultrasensitive detection of physical and chemical stimuli.

Recent years have witnessed thriving progress of flexible and portable electronics, with very high demand for cost-effective and tailor-made multifunctional devices. Here, we report on an ingenious origami hierarchical sensor array (OHSA) written with a conductive ink. Thanks to origami as a controllable hierarchical framework for loading ink material, we have demonstrated that OHSA possesses unique time-space-resolved, high-discriminative pattern recognition (TSR-HDPR) features, qualifying it as a smart sensing device for simultaneous sensing and distinguishing of complex physical and chemical stimuli, including temperature, relative humidity, light and volatile organic compounds (VOCs). Of special importance, OSHA has shown very high sensitivity in differentiating between structural isomers and chiral enantiomers of VOCs - opening a door for wide variety of unique opportunities in several length scales.

Sensor arrays based on nanoparticles for early detection of kidney injury by breath samples.

The outcomes of acute kidney injury (AKI) could be severe and even lethal, if not diagnosed in its early stages and treated appropriately. Blood and urine biomarkers, currently in use as indicators for kidney function, are either inaccurate in various cases or not timely. We report on dramatic changes in exhaled breath composition, associated with kidney dysfunction after ischemic insult in rat models. Gas chromatography linked mass spectrometry examination of breath samples indicated significant elevations in the concentration of three exhaled volatile organic compounds, two to six hours after AKI was surgically induced. Relying on these findings, we introduce an array of sensors, based on organic-layer capped gold nanoparticles, sensitive to odor changes. The ability of the array to detect AKI via breath testing was examined and scored a sensitivity of 96%, only one hour after disease induction. FROM THE CLINICAL EDITOR: In this study, organic-layer capped gold nanoparticle-based biosensors are used to analyse breath samples in an acute kidney injury model, capitalizing on the observation that specific volatile organic compounds are present in breath samples in that condition. The authors report excellent sensitivity in as little as one hour after acute kidney injury. This method, if commercialized, may replace the current blood and urine sample analysis-based tests with a more convenient, rapid and accurate nanotechnology-based method.