Roberts Skapars

Diagnosis and Classification of 17 Diseases from 1404 Subjects via 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.e., 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.

Detection of precancerous gastric lesions and gastric cancer through exhaled breath

Objectives Timely detection of gastric cancer (GC) and the related precancerous lesions could provide a tool for decreasing both cancer mortality and incidence. Design 968 breath samples were collected from 484 patients (including 99 with GC) for two different analyses. The first sample was analysed by gas chromatography linked to mass spectrometry (GCMS) while applying t test with multiple corrections (p value<0.017); the second by cross-reactive nanoarrays combined with pattern recognition. For the latter, 70% of the samples were randomly selected and used in the training set while the remaining 30% constituted the validation set. The operative link on gastric intestinal metaplasia (OLGIM) assessment staging system was used to stratify the presence/absence and risk level of precancerous lesions. Patients with OLGIM stages III–IV were considered to be at high risk. Results According to the GCMS results, patients with cancer as well as those at high risk had distinctive breath-print compositions. Eight significant volatile organic compounds (p value<0.017) were detected in exhaled breath in the different comparisons. The nanoarray analysis made it possible to discriminate between the patients with GC and the control group (OLGIM 0–IV) with 73% sensitivity, 98% specificity and 92% accuracy. The classification sensitivity, specificity, and accuracy between the subgroups was as follows: GC versus OLGIM 0–II—97%, 84% and 87%; GC versus OLGIM III–IV—93%, 80% and 90%; but OLGIM I–II versus OLGIM III–IV and dysplasia combined—83%, 60% and 61%, respectively. Conclusions Nanoarray analysis could provide the missing non-invasive screening tool for GC and related precancerous lesions as well as for surveillance of the latter. Trial registration number Clinical Trials.gov number, NCT01420588 (3/11/2013).

Breath testing as potential colorectal cancer screening tool

Although colorectal cancer (CRC) screening is included in organized programs of many countries worldwide, there is still a place for better screening tools. In this study, 418 breath samples were collected from 65 patients with CRC, 22 with advanced or nonadvanced adenomas, and 122 control cases. All patients, including the controls, had undergone colonoscopy. The samples were analysed with two different techniques. The first technique relied on gas chromatography coupled with mass spectrometry (GC‐MS) for identification and quantification of volatile organic compounds (VOCs). The T‐test was used to identify significant VOCs (p values < 0.017). The second technique relied on sensor analysis with a pattern recognition method for building a breath pattern to identify different groups. Blind analysis or leave‐one‐out cross validation was conducted for validation. The GC‐MS analysis revealed four significant VOCs that identified the tested groups; these were acetone and ethyl acetate (higher in CRC), ethanol and 4‐methyl octane (lower in CRC). The sensor‐analysis distinguished CRC from the control group with 85% sensitivity, 94% specificity and 91% accuracy. The performance of the sensors in identifying the advanced adenoma group from the non‐advanced adenomas was 88% sensitivity, 100% specificity, and 94% accuracy. The performance of the sensors in identifying the advanced adenoma group was distinguished from the control group was 100% sensitivity, 88% specificity, and 94% accuracy. For summary, volatile marker testing by using sensor analysis is a promising noninvasive approach for CRC screening.

Breath Testing as Potential Colorectal Cancer Screening Tool

Although colorectal cancer (CRC) screening is included in organized programs of many countries worldwide, there is still a place for better screening tools. In this study, 418 breath samples were collected from 65 patients with CRC, 22 with advanced or nonadvanced adenomas, and 122 control cases. All patients, including the controls, had undergone colonoscopy. The samples were analysed with two different techniques. The first technique relied on gas chromatography coupled with mass spectrometry (GC‐MS) for identification and quantification of volatile organic compounds (VOCs). The T‐test was used to identify significant VOCs (p values < 0.017). The second technique relied on sensor analysis with a pattern recognition method for building a breath pattern to identify different groups. Blind analysis or leave‐one‐out cross validation was conducted for validation. The GC‐MS analysis revealed four significant VOCs that identified the tested groups; these were acetone and ethyl acetate (higher in CRC), ethanol and 4‐methyl octane (lower in CRC). The sensor‐analysis distinguished CRC from the control group with 85% sensitivity, 94% specificity and 91% accuracy. The performance of the sensors in identifying the advanced adenoma group from the non‐advanced adenomas was 88% sensitivity, 100% specificity, and 94% accuracy. The performance of the sensors in identifying the advanced adenoma group was distinguished from the control group was 100% sensitivity, 88% specificity, and 94% accuracy. For summary, volatile marker testing by using sensor analysis is a promising noninvasive approach for CRC screening.

Analysis of the effects of microbiome-related confounding factors on the reproducibility of the volatolomic test.

Volatile organic compound (VOC) testing in breath has potential in gastric cancer (GC) detection. Our objective was to assess the reproducibility of VOCs in GC, and the effects of conditions modifying gut microbiome on the test results. Ten patients with GC were sampled for VOC over three consecutive days; 17 patients were sampled before and after H. pylori eradication therapy combined with a yeast probiotic; 61 patients were sampled before and after bowel cleansing (interventions affecting the microbiome). The samples were analyzed by: (1) gas chromatography linked to mass spectrometry (GC-MS), applying the non-parametric Wilcoxon test (level of significance p  <  0.05); (2) by cross-reactive nanoarrays combined with pattern recognition. Discriminant function analysis (DFA) was used to build the classification models; and leave-one-out cross-validation analysis was used to classify the findings. Exhaled VOCs profiles were stable for GC patients over a three day period. Alpha pinene (p  =  0.028) and ethyl acetate (p  =  0.030) increased after the antibiotic containing eradication regimen; acetone (p  =  0.0001) increased following bowel cleansing prior to colonoscopy. We further hypothesize that S. boulardii given with the standard eradication regimen to re-establish the gut microbiome was the source for long-term ethyl acetate production. Differences between the initial and the follow-up sample were also revealed in the DFA analysis of the sensor data. VOC measurement results are well-reproducible in GC patients indicating a useful basis for potential disease diagnostics. However, interventions with a potential effect on the gut microbiome may have an effect upon the VOC results, and therefore should be considered for diagnostic accuracy.

Sa1896 Nanomaterial-Based Sensor Technology Can Detect Gastric Cancer and Peptic Ulcer Disease With a High Accuracy From an Exhaled Air Sample

BACKGROUND. Early and non-invasive detection of gastric cancer (GC) is remaining a challenge in many high-risk areas. OBJECTIVES. To investigate the feasibility of gastric cancer and peptic ulcer disease detection by measuring volatile organic compounds (VOCs) in the exhaled breath by a nanomaterial-based sensor technology. METHODS. Alveolar exhaled breath samples were collected from 99 GC patients, 53 peptic ulcer disease (PUD) patients and 342 controls in Latvia (Caucasian population) having been investigated by upper endoscopy. Nanomaterial-based sensors Gold nano particles (GNP) and single-wall carbon nanotube (SWCNT) were used to discriminate the GC and the PUD groups from the healthy controls by loading discriminant factor analysis (DFA) pattern recognition. Classification success was calculated by (i) building an algorithm for 70% of the samples as a training set and (ii) randomly blinding 30% of the samples as a validation set. RESULTS. The blind DFA models showed: (i) An excellent discrimination between the GC patients and controls (91% accuracy); (ii) An excellent discrimination between the GC patients and PUD patients (86% accuracy); (iii) Good discrimination between the PUD patients and controls (80% accuracy). CONCLUSIONS: The obtained results are demonstrating the good potential of VOC detection in exhaled breath by nanomaterial-based sensor technology in diagnosing GC and PUD. Performance characteristics of nanomaterial-based sensor technology to detect gastric cancer and peptic ulcer disease