Silicon Nanowire Sensors Enable Diagnosis of Patients via Exhaled Breath

Abstract

Abstract:Two of the biggest challenges in medicine today are the need to detect diseases in a non-invasive manner, and to differentiate between patients using a single diagnostic tool. The current study targets these two challenges by developing a molecularlymodified Silicon Nanowire Field Effect Transistors (SiNW FETs) 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 correlated their sensitivity and selectivity towards volatile organic compounds (VOCs) linked with diseased states. 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 showed that the optimized SiNW FETs can detectand 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 positive way, which can reassure patients and prevent numerous negative investigations. 3Physicians are always challenged by the need to give the correct diagnosis as early in the onset of a disease is possible, whether the disease-related symptoms are absent or not evident.1 Symptoms are not always characteristic of one particular disease; overlap of symptoms is common in, for example, lung diseases. 2 Patients with different respiratory diseases, such as malignant or benign tumors, or substantially less severe diseases, may have similar symptoms, e.g. cough, chest pain, difficulty to breathe, etc. These symptoms may be characteristic of lung cancer (LC), pneumonia, asthma, and chronic obstructive pulmonary disease (COPD). 1,2Therefore, it is of particular clinical importance to find a diagnostic tool capable of distinguishing between these diseases. A diagnostic tool that involves no needle, surgery and/or active materials and/or radioactive exposure would have a benefit.A highly promising approach that could meet the aforementioned need is based on the detection and classification of the disease breathprint, viz. the chemical profiles of highly-and semi-VOCs in exhaled breath linked with disease. [3][4][5][6][7][8][9][10][11][12][13][14][15] The rationale behind this approach relies on the fact that VOCs generated by cellular metabolic pathways during a specific disease circulate in the blood stream and diffuse into exhaled breath, which is easily sampled. 4,16,17 In certain instances, analysis of breathprints offers several potential advantages, such as: (a) breath samples are non-invasive and easy to obtain; (b) breath contains less complicated mixtures than either serum or urine; and (c) breath testing has the potential for direct and real-time diagnosis and monitoring. 3,18-21Several mass-spectrometry and spectroscopy studies have shown that the breathprint of a specific disease differs from that of healthy control...

Diagnosis and Classification of 17 Diseases from 1404 Subjects via Pattern Analysis of Exhaled Molecules

“…, semiselective) nanotechnology-based sensor arrays, using pattern recognition; 3,7,36−38 we refer to this approach as using an “artificially intelligent nanoarray”. 43−46 In contrast to the selective method, an artificially intelligent nanoarray is more suitable for rapid diagnostic methods in which evaluation of a VOC compendium is qualitative and semiquantitative, with selectivity being achieved through pattern recognition of the compendium. 3,7,36,37 Due to cross-reactivity, each sensor responds to a variety of VOCs, thereby allowing sensing and analysis of individual components from multicomponent samples.…”

“…Although these sensors may have a sensitivity to a specific analyte (or VOC) lower than that of a selective sensor, they are more versatile in detecting multicomponent and complex VOC mixtures in different atmospheres (including those for which the (nano)arrays were not originally designed). 32,43,44,47,48 Artificially intelligent nanoarrays of different composition were assessed in a series of separate laboratory (preclinical) and clinical studies for the detection of a wide range of cancerous and noncancerous diseases. 8,14,23−32,43,45,47,49−57 In these studies, disease detection was mostly carried out with reference to healthy control groups, without examining correlated and uncorrelated clinical confounding factors.…”

Gating Hysteresis as an Indicator for Silicon Nanowire FET Biosensors

“…The indication of certain diseases can also be further improved by using several bioreceptors and neural networks for fingerprinting of the samples [65,66]. …”

Deployment and exploitation of nanotechnology nanomaterials and nanomedicine

“…Amongst these are biobarcode assays based on gold or semiconductor nanoparticles [100] and FETs based on molecularly-modified Si nanowires for the detection of volatile organic compounds (VOCs) in breath [101]. A relatively recent development is the implementation of digital quantitation (digital PCR, digital ELISA) based on a binary output: a sample is subdivided into smaller aliquots each containing from none to few molecules of the target analyte, and each aliquot is tested just for the presence or absence of the target irrespective of its amount.…”