Upper digestive endoscopy with biopsy and histopathological evaluation of the biopsy material is the standard method for diagnosing gastric cancer (GC). However, this procedure may not be widely available for screening in the developing world, whereas in developed countries endoscopy is frequently used without major clinical gain. There is a high demand for a simple and non-invasive test for selecting the individuals at increased risk that should undergo the endoscopic examination. Here, we studied the feasibility of a nanomaterial-based breath test for identifying GC among patients with gastric complaints. Alveolar exhaled breath samples from 130 patients with gastric complaints (37 GC/32 ulcers / 61 less severe conditions) that underwent endoscopy/biopsy were analyzed using nanomaterial-based sensors. Predictive models were built employing discriminant factor analysis (DFA) pattern recognition, and their stability against possible confounding factors (alcohol/tobacco consumption; Helicobacter pylori) was tested. Classification success was determined (i) using leave-one-out cross-validation and (ii) by randomly blinding 25% of the samples as a validation set. Complementary chemical analysis of the breath samples was performed using gas chromatography coupled with mass spectrometry. Three DFA models were developed that achieved excellent discrimination between the subpopulations: (i) GC vs benign gastric conditions, among all the patients (89% sensitivity; 90% specificity); (ii) early stage GC (I and II) vs late stage (III and IV), among GC patients (89% sensitivity; 94% specificity); and (iii) ulcer vs less severe, among benign conditions (84% sensitivity; 87% specificity). The models were insensitive against the tested confounding factors. Chemical analysis found that five volatile organic compounds (2-propenenitrile, 2-butoxy-ethanol, furfural, 6-methyl-5-hepten-2-one and isoprene) were significantly elevated in patients with GC and/or peptic ulcer, as compared with less severe gastric conditions. The concentrations both in the room air and in the breath samples were in the single p.p.b._v range, except in the case of isoprene. The preliminary results of this pilot study could open a new and promising avenue to diagnose GC and distinguish it from other gastric diseases. It should be noted that the applied methods are complementary and the potential marker compounds identified by gas-chromatography/mass spectrometry are not necessarily responsible for the differences in the sensor responses. Although this pilot study does not allow drawing far-reaching conclusions, the encouraging preliminary results presented here have initiated a large multicentre clinical trial to confirm the observed patterns for GC and benign gastric conditions.

To study the feasibility of a novel method in nanomedicine that is based on breath testing for identifying Alzheimer's disease (AD) and Parkinson's disease (PD), as representative examples of neurodegenerative conditions. Alveolar breath was collected from 57 volunteers (AD patients, PD patients and healthy controls) and analyzed using combinations of nanomaterial-based sensors (organically functionalized carbon nanotubes and gold nanoparticles). Discriminant factor analysis was applied to detect statistically significant differences between study groups and classification success was estimated using cross-validation. The pattern identification was supported by chemical analysis of the breath samples using gas chromatography combined with mass spectrometry. The combinations of sensors could clearly distinguish AD from healthy states, PD from healthy states, and AD from PD states, with a classification accuracy of 85, 78 and 84%, respectively. Gas chromatography combined with mass spectrometry analysis showed statistically significant differences in the average abundance of several volatile organic compounds in the breath of AD, PD and healthy subjects, thus supporting the breath prints observed with the sensors. The breath prints that were identified with combinations of nanomaterial-based sensors have future potential as cost-effective, fast and reliable biomarkers for AD and PD.

We present a pilot study that aims to examine the possibility to easily and noninvasively detect and discriminate females with ovarian cancer (OC) from females that have no tumor(s) and from females that have benign genital tract neoplasia, using exhaled breath samples. The study is based on clinical samples and data from 182 females, as follows: 48 females with OC, 48 tumor‐free controls and 86 females with benign gynecological neoplasia. Analysis of the breath samples with gas chromatography linked with mass spectrometry shows that decanal, nonanal, styrene, 2‐butanone and hexadecane could serve as potential volatile markers for OC. Analysis of the same samples with tailor‐made nanoarrays shows good discrimination between females with OC and females that have either no tumor or benign genital tract neoplasia (71% for accuracy, sensitivity and specificity). Conversely, the nanoarray output shows excellent discrimination between the OC patients and the tumor‐free controls (79% sensitivity, 100% specificity and 89% accuracy). These results suggest that the nanoarray approach might be useful to avoid unnecessary complicated or expensive tests for tumor‐free females in case of a negative result. In the case of positive result, the test will indicate with high probability the presence of OC.