Haitham Amal

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.

The scent fingerprint of hepatocarcinoma: in-vitro metastasis prediction with volatile organic compounds (VOCs).

Background: Hepatocellular carcinoma (HCC) is a common and aggressive form of cancer. Due to a high rate of postoperative recurrence, the prognosis for HCC is poor. Subclinical metastasis is the major cause of tumor recurrence and patient mortality. Currently, there is no reliable prognostic method of invasion. Aim: To investigate the feasibility of fingerprints of volatile organic compounds (VOCs) for the in-vitro prediction of metastasis. Methods: Headspace gases were collected from 36 cell cultures (HCC with high and low metastatic potential and normal cells) and analyzed using nanomaterial-based sensors. Predictive models were built by employing discriminant factor analysis pattern recognition, and the classification success was determined using leave-one-out cross-validation. The chemical composition of each headspace sample was studied using gas chromatography coupled with mass spectrometry (GC-MS). Results: Excellent discrimination was achieved using the nanomaterial-based sensors between (i) all HCC and normal controls; (ii) low metastatic HCC and normal controls; (iii) high metastatic HCC and normal controls; and (iv) high and low HCC. Several HCC-related VOCs that could be associated with biochemical cellular processes were identified through GC-MS analysis. Conclusion: The presented results constitute a proof-of-concept for the in-vitro prediction of the metastatic potential of HCC from VOC fingerprints using nanotechnology. Further studies on a larger number of more diverse cell cultures are needed to evaluate the robustness of the VOC patterns. These findings could benefit the development of a fast and potentially inexpensive laboratory test for subclinical HCC metastasis.

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).

Assessment of ovarian cancer conditions from exhaled breath.

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.

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 exhaled breath for diagnosing head and neck squamous cell carcinoma: a feasibility study

Background:Squamous cell carcinoma of the head and neck (HNSCC) are wide-spread cancers that often lead to disfigurement and loss of important functions such as speech and ingestion. To date, HNSCC has no adequate method for early detection and screening.Methods:Exhaled breath samples were collected from 87 volunteers; 62 well-defined breath samples from 22 HNSCC patients (larynx and pharynx), 21 patients with benign tumours (larynx and pharynx) and 19 healthy controls were analysed in a dual approach: (i) chemical analysis using gas chromatography/mass spectrometry (GC–MS) and (ii) breath-print analysis using an array of nanomaterial-based sensors, combined with a statistical algorithm.Results:Gas chromatography/mass spectrometry identified ethanol, 2-propenenitrile and undecane as potential markers for HNSCC and/or benign tumours of the head and neck. The sensor-array-based breath-prints could clearly distinguish HNSCC both from benign tumours and from healthy states. Within the HNSCC group, patients could be classified according to tumour site and stage.Conclusions:We have demonstrated the feasibility of a breath test for a specific, clinically interesting application: distinguishing HNSCC from tumour-free or benign tumour states, as well as for staging and locating HNSCC. The sensor array used here could form the basis for the development of an urgently needed non-invasive, cost-effective, fast and reliable point-of-care diagnostic/screening tool for HNSCC.

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

UNLABELLED 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.

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