Artificially intelligent nanosensors for personalized diagnosis and monitoring

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Charge transport across metal/molecular (alkyl) monolayer-Si junctions is dominated by the LUMO level

We compare the charge transport characteristics of heavy doped p- and n-Si-alkyl chain/Hg junctions. Photoelectron spectroscopy (UPS, IPES and XPS) results for the molecule-Si band alignment at equilibrium show the Fermi level to LUMO energy difference to be much smaller than the corresponding Fermi level to HOMO one. This result supports the conclusion we reach, based on negative differential resistance in an analogous semiconductor-inorganic insulator/metal junction, that for both p- and n-type junctions the energy difference between the Fermi level and LUMO, i.e., electron tunneling, controls charge transport. The Fermi level-LUMO energy difference, experimentally determined by IPES, agrees with the non-resonant tunneling barrier height deduced from the exponential length-attenuation of the current

Modular Point-of-Care Breath Analyzer and Shape Taxonomy-Based Machine Learning for Gastric Cancer Detection

Funding Information: The development of the analysis approach and its evaluation and analysis were supported by a postdoctoral grant within the Activity 1.1.1.2 “Post-doctoral Research Aid” co-funded by the European Regional Development Fund (postdoctoral project numbers: 1.1.1.2/VIAA/2/18/270 and 1.1.1.2/VIAA/3/19/495). Publisher Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Background: Gastric cancer is one of the deadliest malignant diseases, and the non-invasive screening and diagnostics options for it are limited. In this article, we present a multi-modular device for breath analysis coupled with a machine learning approach for the detection of cancer-specific breath from the shapes of sensor response curves (taxonomies of clusters). Methods: We analyzed the breaths of 54 gastric cancer patients and 85 control group participants. The analysis was carried out using a breath analyzer with gold nanoparticle and metal oxide sensors. The response of the sensors was analyzed on the basis of the curve shapes and other features commonly used for comparison. These features were then used to train machine learning models using Naïve Bayes classifiers, Support Vector Machines and Random Forests. Results: The accuracy of the trained models reached 77.8% (sensitivity: up to 66.54%; specificity: up to 92.39%). The use of the proposed shape-based features improved the accuracy in most cases, especially the overall accuracy and sensitivity. Conclusions: The results show that this point-of-care breath analyzer and data analysis approach constitute a promising combination for the detection of gastric cancer-specific breath. The cluster taxonomy-based sensor reaction curve representation improved the results, and could be used in other similar applications.publishersversionPeer reviewe

Silicon Nanowire Sensors Enable Diagnosis of Patients via 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