Raneen Jeries

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.

Detecting active pulmonary tuberculosis with a breath test using nanomaterial-based sensors

To the Editor: Detecting active tuberculosis (TB) remains a major global public health challenge [1]. The tuberculin skin test does not distinguish latent from active TB [2]. The interferon-γ release assays have similar limitations [3]. Acid-fast bacilli staining of sputum has a high false-negative rate (up to 50%) [4]. Nucleic acid amplification tests (NAATs), such as GeneXpert MTB/RIF (Cepheid, Sunnyvale, CA, USA), are accurate but require a good infrastructure and the necessity to obtain a good quality sputum sample, which is often unobtainable in more than a third of HIV-infected persons [5, 6]. Given these unmet needs, we explored the use of a novel, rapid, simple and inexpensive point-of-care test for the diagnosis of TB [7]. The approach is based on the detection of volatile organic compounds (VOCs) that are emitted from infected cells and released in exhaled breath [8, 9]. Using gas chromatography linked with mass spectrometry, researchers have previously reported identification of TB-related VOCs in the exhaled breath, though there has been low accuracy in detection (80–85%) [10]. In this study, we explore the possibility of active TB detection via the analysis of exhaled breath using a novel technology of organically modified nanomaterial-based sensors. Such cross-reactive sensors are highly sensitive to the collective changes in the VOCs spectrum [8]. Therefore, we designed a case–control study, in which breath and sputum sampling was performed in 210 adult participants, after informed consent, at three sites in Cape Town, South Africa, between November 2011 and March 2012. The study population consisted of two main subgroups. The first subgroup included those in …

Unique volatolomic signatures of TP53 and KRAS in lung cells

Background:Volatile organic compounds (VOCs) are potential biomarkers for cancer detection in breath, but it is unclear if they reflect specific mutations. To test this, we have compared human bronchial epithelial cell (HBEC) cell lines carrying the KRASV12 mutation, knockdown of TP53 or both with parental HBEC cells.Methods:VOC from headspace above cultured cells were collected by passive sampling and analysed by thermal desorption gas chromatography mass spectrometry (TD-GC–MS) or sensor array with discriminant factor analysis (DFA).Results:In TD-GC–MS analysis, individual compounds had limited ability to discriminate between cell lines, but by applying DFA analysis combinations of 20 VOCs successfully discriminated between all cell types (accuracies 80–100%, with leave-one-out cross validation). Sensor array detection DFA demonstrated the ability to discriminate samples based on their cell type for all comparisons with accuracies varying between 77% and 93%.Conclusions:Our results demonstrate that minimal genetic changes in bronchial airway cells lead to detectable differences in levels of specific VOCs identified by TD-GC–MS or of patterns of VOCs identified by sensor array output. From the clinical aspect, these results suggest the possibility of breath analysis for detection of minimal genetic changes for earlier diagnosis or for genetic typing of lung cancers.

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.

Distinguishing idiopathic Parkinson's disease from other parkinsonian syndromes by breath test

INTRODUCTION Diagnosis of different parkinsonian syndromes is linked with high misdiagnosis rates and various confounding factors. This is particularly problematic in its early stages. With this in mind, the current pilot study aimed to distinguish between Idiopathic Parkinsons Disease (iPD), other Parkinsonian syndromes (non-iPD) and healthy subjects, by a breath test that analyzes the exhaled volatile organic compounds using a highly sensitive nanoarray. METHODS Breath samples of 44 iPD, 16 non-iPD patients and 37 healthy controls were collected. The samples were passed over a nanoarray and the resulting electrical signals were analyzed with discriminant factor analysis as well as by a K-fold cross-validation method, to test the accuracy of the model. RESULTS Comparison of non-iPD with iPD states yielded 88% sensitivity, 88% accuracy, and 88% Receiver Operating Characteristic area under the curve in the training set samples with known identity. The validation set of this comparison scored 81% sensitivity and accuracy and 92% negative predictive value. Comparison between atypical parkinsonism states and healthy subjects scored 94% sensitivity and 85% accuracy in the training set samples with known identity. The validation set of this comparison scored 81% sensitivity and 78% accuracy. The obtained results were not affected by l-Dopa or MAO-B inhibitor treatment. CONCLUSIONS Exhaled breath analysis with nanoarray is a promising approach for a non-invasive, inexpensive, and portable technique for differentiation between different Parkinsonian states. A larger cohort is required in order to establish the clinical usefulness of the method.

Impact of hemodialysis on exhaled volatile organic compounds in end-stage renal disease: a pilot study

AIM To demonstrate the feasibility of nanomaterial-based sensors for identifying patterns of exhaled volatile organic compound of end-stage renal disease (ESRD) and study the impact of hemodialysis (HD) on these patterns. PATIENTS & METHODS Exhaled breath samples were collected from a group of 37 volunteers (26 ESRD HD patients; 11 healthy controls); a third of the samples were randomly blinded for determining the sensitivity/specificity of the method. Discriminant function analysis was used to build a model for discriminating ESRD patients and healthy controls (classification accuracy for blind samples: 80%), based on the signals of the nanomaterial sensors. RESULTS & CONCLUSION The breath pattern of the ESRD patients approached the healthy pattern during the HD treatment, without reaching it completely. Gas chromatography/mass spectrometry identified four volatile organic compounds as potential ESRD biomarkers. Although this pilot study has yielded encouraging results, additional large-scale clinical studies are required to develop a fast, noninvasive breath test for monitoring HD adequacy in real time.

Programmed Nanoparticles for Tailoring the Detection of Inflammatory Bowel Diseases and Irritable Bowel Syndrome Disease via Breathprint

Chemical sensors based on programmable molecularly modified gold nanoparticles are tailored for the detection and discrimination between the breathprint of irritable bowel syndrome (IBS) and inflammatory bowel diseases (IBD). The sensors are examined in both lab- and real-world clinical conditions. The results reveal a discriminative power accuracy of 81% between IBD and IBS and 75% between Crohns and Colitis states.

Exhaled Breath Markers for Nonimaging and Noninvasive Measures for Detection of Multiple Sclerosis

Multiple sclerosis (MS) is the most common chronic neurological disease affecting young adults. MS diagnosis is based on clinical characteristics and confirmed by examination of the cerebrospinal fluids (CSF) or by magnetic resonance imaging (MRI) of the brain or spinal cord or both. However, neither of the current diagnostic procedures are adequate as a routine tool to determine disease state. Thus, diagnostic biomarkers are needed. In the current study, a novel approach that could meet these expectations is presented. The approach is based on noninvasive analysis of volatile organic compounds (VOCs) in breath. Exhaled breath was collected from 204 participants, 146 MS and 58 healthy control individuals. Analysis was performed by gas-chromatography mass-spectrometry (GC-MS) and nanomaterial-based sensor array. Predictive models were derived from the sensors, using artificial neural networks (ANNs). GC-MS analysis revealed significant differences in VOC abundance between MS patients and controls. Sensor data analysis on training sets was able to discriminate in binary comparisons between MS patients and controls with accuracies up to 90%. Blinded sets showed 95% positive predictive value (PPV) between MS-remission and control, 100% sensitivity with 100% negative predictive value (NPV) between MS not-treated (NT) and control, and 86% NPV between relapse and control. Possible links between VOC biomarkers and the MS pathogenesis were established. Preliminary results suggest the applicability of a new nanotechnology-based method for MS diagnostics.

Sensor Array for Detection of Early Stage Parkinson’s Disease before Medication

Early diagnosis of Parkinsons disease (PD) is important because it affects the choice of therapy and is subject to a relatively high degree of error. In addition, early detection of PD can potentially enable the start of neuroprotective therapy before extensive loss of dopaminergic neurons of the substantia nigra occurs. However, until now, studies for early detection of PD using volatile biomarkers sampled only treated and medicated patients. Therefore, there is a great need to evaluate untreated patients for establishing a real world screening and diagnostic technology. Here we describe for the first time a clinical trial to distinguish between de novo PD and control subjects using an electronic system for detection of volatile molecules in exhaled breath (sensor array). We further determine for the first time the association to other common tests for PD diagnostics as smell, ultrasound, and nonmotor symptoms. The test group consisted of 29 PD patients after initial diagnosis by an experienced neurologist, compared with 19 control subjects of similar age. The sensitivity, specificity, and accuracy values of the sensor array to detect PD from controls were 79%, 84%, and 81% respectively, in comparison with midbrain ultrasonography (93%, 90%, 92%) and smell detection (62%, 89%, 73%). The results confirm previous data showing the potential of sensor arrays to detect PD.