Ultrasensitive Silicon Nanowire for Real-World Gas Sensing: Noninvasive Diagnosis of Cancer from Breath Volatolome

We report on an ultrasensitive, molecularly modified silicon nanowire field effect transistor that brings together the lock-and-key and cross-reactive sensing worlds for the diagnosis of (gastric) cancer from exhaled volatolome. The sensor is able to selectively detect volatile organic compounds (VOCs) that are linked with gastric cancer conditions in exhaled breath and to discriminate them from environmental VOCs that exist in exhaled breath samples but do not relate to the gastric cancer per se. Using breath samples collected from actual patients with gastric cancer and from volunteers who do not have cancer, blind analysis validated the ability of the reported sensor to discriminate between gastric cancer and control conditions with >85% accuracy, irrespective of important confounding factors such as tobacco consumption and gender. The reported sensing approach paves the way to use the power of silicon nanowires for simple, inexpensive, portable, and noninvasive diagnosis of cancer and other disease conditions

Accuracy of two plasma antibody tests and faecal antigen test for non-invasive detection of <i>H. pylori</i> in middle-aged Caucasian general population sample

<p><b>Objective:</b> The aim of the study was to assess the accuracy of two plasma <i>Helicobacter pylori (H. pylori)</i> antibody test-systems and a stool antigen test (SAT) system in a general population sample in Latvia.</p> <p><b>Materials and methods:</b> Blood and faecal samples were analysed in healthy individuals (40–64 years), referred for upper gastrointestinal endoscopy according to pilot study protocol within a population-based study investigating gastric cancer prevention strategies (GISTAR pilot study). Antibodies to <i>H. pylori</i> were assessed in plasma by latex-agglutination test and enzyme-linked immunosorbent assay (ELISA). <i>H. pylori</i> antigen in faecal samples was detected by a monoclonal enzyme immunoassay-based SAT. Histological assessment of <i>H. pylori</i> based on a modified Giemsa staining method was used as the gold standard. Individuals having received <i>H. pylori</i> eradication within one year prior to enrolment were excluded. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and overall accuracy were calculated. Receiver-operating characteristic curves were designed to estimate the optimal diagnostic cut-off value of tests.</p> <p><b>Results:</b> The analysis included 779 participants for latex-agglutination test, 1002 for ELISA and 672 individual samples for SAT. The sensitivity, specificity, PPV, NPV and overall accuracy were as follows: latex-agglutination test (86;81;87;80;84%), ELISA (97;72;83;94;86%) and SAT (87;81;87;81;85%), respectively. The optimal diagnostic cut-off value for ELISA test was ≥50.26 g/L.</p> <p><b>Conclusions:</b> Although the performance of the three tests was comparable to each other, the three test systems showed suboptimal accuracy, with important implications for public health programs based on <i>‘test-and-treat’</i> strategy.</p

Genotype frequencies of <i>miR-27a</i>, <i>miR-146a</i>, <i>miR-196a-2</i>, <i>miR-492</i>, <i>miR-608</i> SNPs in controls, gastric cancer and high risk gastritis patients.

1<p>GC – gastric cancer; HRAG – high risk gastritis; aOR – adjusted odds ratio (age, sex, country); CI – confidence interval.</p>2<p>six patients with missing values on rs895819 were excluded from analysis.</p>3<p>five patients with missing values on rs2910164 were excluded from analysis.</p>4<p>one patient with missing values on rs11614913 was excluded from analysis.</p>5<p>three patients with missing values on rs4919510 were excluded from analysis.</p

Characteristics of subject groups.

1<p>GC – gastric cancer; HRAG – high risk gastritis; <i>H. pylori</i> – <i>Helicobacter pylori.</i></p>2<p>Statistical analysis was performed globally for all three groups.</p

Silicon Nanowire Sensors Enable Diagnosis of Patients <i>via</i> Exhaled Breath

Two of the biggest challenges in medicine today are the need to detect diseases in a noninvasive manner and to differentiate between patients using a single diagnostic tool. The current study targets these two challenges by developing a molecularly modified silicon nanowire field effect transistor (SiNW FET) and showing its use in the detection and classification of many disease breathprints (lung cancer, gastric cancer, asthma, and chronic obstructive pulmonary disease). The fabricated SiNW FETs are characterized and optimized based on a training set that correlate their sensitivity and selectivity toward volatile organic compounds (VOCs) linked with the various disease breathprints. The best sensors obtained in the training set are then examined under real-world clinical conditions, using breath samples from 374 subjects. Analysis of the clinical samples show that the optimized SiNW FETs can detect and discriminate between almost all binary comparisons of the diseases under examination with >80% accuracy. Overall, this approach has the potential to support detection of many diseases in a direct harmless way, which can reassure patients and prevent numerous unpleasant investigations

Genotype frequencies of <i>miR-27a</i>, <i>miR-146a</i>, <i>miR-196a-2</i>, <i>miR-492</i>, <i>miR-608</i> SNPs in controls, intestinal and diffuse-type GC subjects.

1<p>GC – gastric cancer; HRAG – high risk gastritis; aOR – adjusted odds ratio (age, sex, country); CI – confidence interval.</p

Diagnosis and Classification of 17 Diseases from 1404 Subjects <i>via</i> Pattern Analysis of Exhaled Molecules

We report on an artificially intelligent nanoarray based on molecularly modified gold nanoparticles and a random network of single-walled carbon nanotubes for noninvasive diagnosis and classification of a number of diseases from exhaled breath. The performance of this artificially intelligent nanoarray was clinically assessed on breath samples collected from 1404 subjects having one of 17 different disease conditions included in the study or having no evidence of any disease (healthy controls). Blind experiments showed that 86% accuracy could be achieved with the artificially intelligent nanoarray, allowing both detection and discrimination between the different disease conditions examined. Analysis of the artificially intelligent nanoarray also showed that each disease has its own unique breathprint, and that the presence of one disease would not screen out others. Cluster analysis showed a reasonable classification power of diseases from the same categories. The effect of confounding clinical and environmental factors on the performance of the nanoarray did not significantly alter the obtained results. The diagnosis and classification power of the nanoarray was also validated by an independent analytical technique, <i>i.e.</i>, gas chromatography linked with mass spectrometry. This analysis found that 13 exhaled chemical species, called volatile organic compounds, are associated with certain diseases, and the composition of this assembly of volatile organic compounds differs from one disease to another. Overall, these findings could contribute to one of the most important criteria for successful health intervention in the modern era, viz. easy-to-use, inexpensive (affordable), and miniaturized tools that could also be used for personalized screening, diagnosis, and follow-up of a number of diseases, which can clearly be extended by further development