Abnormality Detection inside Blood Vessels with Mobile Nanomachines

Motivated by the numerous healthcare applications of molecular communication within Internet of Bio-Nano Things (IoBNT), this work addresses the problem of abnormality detection in a blood vessel using multiple biological embedded computing devices called cooperative biological nanomachines (CNs), and a common receiver called the fusion center (FC). Due to blood flow inside a vessel, each CN and the FC are assumed to be mobile. In this work, each of the CNs perform abnormality detection with certain probabilities of detection and false alarm by counting the number of molecules received from a source, e.g., infected tissue. These CNs subsequently report their local decisions to a FC over a diffusion-advection blood flow channel using different types of molecules in the presence of inter-symbol interference, multi-source interference, and counting errors. Due to limited computational capability at the FC, OR and AND logic based fusion rules are employed to make the final decision after obtaining each local decision based on the optimal likelihood ratio test. For the aforementioned system, probabilities of detection and false alarm at the FC are derived for OR and AND fusion rules. Finally, simulation results are presented to validate the derived analytical results, which provide important insights.Comment: Submitted to IEEE Transactions on Molecular, Biological, and Multi-Scale Communications Letters for possible publicatio

Sequential decision fusion for abnormality detection via diffusive molecular communications

This paper considers the task of abnormality detection in a fluid medium, employing a molecular communications (MC) based network of nanoscale sensors. This task entails sensing, detection and reporting of abnormal changes in the environment that may characterize a disorder or an abnormal event. Such distributed detection (DD) problems are of paramount interest, especially in applications such as health monitoring, disease diagnosis, targeted drug delivery, environmental sensing and monitoring, contaminant detection and removal, and environmental remediation. This letter proposes, for the first time in the literature, to employ a sequential probability ratio test based approach to the decision fusion in diffusive MC based DD. The proposed approach leads to considerable gains in the average number of samples required for the decision compared to its fixed-sample size counterparts, resulting in a significant improvement in the average decision delay. In the investigated DD scenarios, we observe savings of up to 50% in the number of samples required for decision fusion

Neural network based decision fusion for abnormality detection via molecular communications

Abnormality detection is one of the most highly anticipated application areas of Molecular Communication (MC) based nanonetworks. This task entails sensing, detection, and reporting of abnormal changes in a fluid medium that may characterize a disease or disorder using a network of collaborating nanoscale sensors. Existing strategies for such distributed collaborative detection problems require a complete statistical characterization of the underlying communication channel between the sensors and the fusion centre (FC), with the assumption of perfectly-known or accurately estimated channel parameters. This assumption is usually impractical both due to mathematical intractability of the analytical channel models for MC except in a few ideal cases, and the slow and dispersive signal propagation characteristics that make the channel estimation a difficult task even in these ideal cases. This work, for the first time in the literature, proposes to employ a machine learning approach to this task and shows that this approach provides the robustness and flexibility required for practical implementation. We focus on detection based on deep learning, specifically on a feed-forward neural network and a recurrent neural network structure that learn the underlying model from data. This study shows that the proposed decision fusion strategy can perform well without any knowledge of the communication channel

Abnormality detection using molecular communications based nano-scale sensor networks

Abnormality detection is one of the most highly anticipated application areas of Molecular Communication (MC) based nanonetworks. .is task entails sensing, detection, and reporting of abnormal changes in a fluid medium that may characterize a disease or disorder using a network of collaborating nanoscale sensors. Such distributed detection (DD) problems are of paramount interest in applications of nanonetworks. For the first time in literature, we proposed to employ sequential probability ratio test (SPRT) to decision fusion (DF). .e proposed approach yields considerable gains in the average number of samples required for the decision resulting in significant improvement in decision delay, which is one of the main challenges encountered in a molecular communications based sensor network. Existing strategies for such distributed collaborative detection problems require a complete statistical characterization of the underlying communication channel between the sensors and the fusion centre (FC), with the assumption of perfectly-known or accurately estimated channel parameters. .is assumption is usually impractical both due to mathematical intractability of the analytical channel models for MC except in a few ideal cases, and the slow and dispersive signal propagation characteristics that make the channel estimation a difficult task even in these ideal cases. .is work, for the first time in the literature, proposes to employ a machine learning (ML) approach to this task and shows that this approach provides the robustness and flexibility required for practical implementation. We focus on detection based on deep learning, specifically on a feed-forward neural network and a recurrent neural network structure that learn the underlying model from data. .is study shows that the proposed DF strategy can perform well without any knowledge of the communication channel

Electrochemical Biosensing and Deep Learning-Based Approaches in the Diagnosis of COVID-19: A Review

COVID-19 caused by the transmission of SARS-CoV-2 virus taking a huge toll on global health and caused life-threatening medical complications and elevated mortality rates, especially among older adults and people with existing morbidity. Current evidence suggests that the virus spreads primarily through respiratory droplets emitted by infected persons when breathing, coughing, sneezing, or speaking. These droplets can reach another person through their mouth, nose, or eyes, resulting in infection. The gold standard\u27\u27 for clinical diagnosis of SARS-CoV-2 is the laboratory-based nucleic acid amplification test, which includes the reverse transcription-polymerase chain reaction (RT-PCR) test on nasopharyngeal swab samples. The main concerns with this type of test are the relatively high cost, long processing time, and considerable false-positive or false-negative results. Alternative approaches have been suggested to detect the SARS-CoV-2 virus so that those infected and the people they have been in contact with can be quickly isolated to break the transmission chains and hopefully, control the pandemic. These alternative approaches include electrochemical biosensing and deep learning. In this review, we discuss the current state-of-the-art technology used in both fields for public health surveillance of SARS-CoV-2 and present a comparison of both methods in terms of cost, sampling, timing, accuracy, instrument complexity, global accessibility, feasibility, and adaptability to mutations. Finally, we discuss the issues and potential future research approaches for detecting the SARS-CoV-2 virus utilizing electrochemical biosensing and deep learning

Monitoring and modelling the dynamics of the cellular glycolysis pathway: A review and future perspectives

Background The dynamics of the cellular glycolysis pathway underpin cellular function and dysfunction, and therefore ultimately health, disease, diagnostic and therapeutic strategies. Evolving our understanding of this fundamental process and its dynamics remains critical. Scope of review This paper reviews the medical relevance of glycolytic pathway in depth and explores the current state of the art for monitoring and modelling the dynamics of the process. The future perspectives of label free, vibrational microspectroscopic techniques to overcome the limitations of the current approaches are considered. Major conclusions Vibrational microspectroscopic techniques can potentially operate in the niche area of limitations of other omics technologies for non-destructive, real-time, in vivo label-free monitoring of glycolysis dynamics at a cellular and subcellular level

Nanochips and medical applications

Ο όρος «νανοτσιπ» αναφέρεται σε ένα ολοκληρωμένο κύκλωμα (τσιπ) με νανοϋλικά και δομές στη νανοκλίμακα (1-100nm). Ένα ολοκληρωμένο κύκλωμα είναι μια συλλογή ηλεκτρονικών εξαρτημάτων, όπως τρανζίστορ, δίοδοι, πυκνωτές και αντιστάσεις. Τα σημερινά τρανζίστορ είναι στη νανοκλίμακα, αλλά μπορούν να τροποποιηθούν με νανοδομές για την κατασκευή βιοαισθητήρων που μπορούν να πραγματοποιούν ανίχνευση βιομορίων, όπως ιόντα, μόρια DNA, αντισώματα και αντιγόνα με μεγάλη ευαισθησία. Υλικά και Μέθοδοι: Πραγματοποιήθηκε συστηματική αναζήτηση βιβλιογραφίας με χρήση των ηλεκτρονικών βάσεων δεδομένων PubMed, Google Scholar και Scopus για την ανάπτυξη και χρήση νανοτσίπ σε ιατρικές εφαρμογές. Για τον προσδιορισμό των σχετικών εργασιών, τα κριτήρια συμπερίληψης αναφέρονται σε άρθρα στην αγγλική γλώσσα, άρθρα βιβλιογραφικού περιεχομένου ή/και έρευνών. Τα κριτήρια αποκλεισμού ήταν άρθρα εφημερίδων, περιλήψεις συνεδρίων και επιστολές. Αποτελέσματα: Τεχνικές in-vivo και in-vitro έχουν χρησιμοποιηθεί για την ανίχνευση μορίων DNA, ιόντων, αντισωμάτων, σημαντικών πρωτεϊνών και καρκινικών δεικτών, όχι μόνο από δείγματα αίματος αλλά και από ιδρώτα, σάλιο και άλλα βιολογικά υγρά. Διαγνωστική εφαρμογή των νανοτσίπ αποτελεί και η ανίχνευση πτητικών οργανικών ενώσεων μέσω τεστ εκπνεόμενης αναπνοής. Υπάρχουν και αρκετές θεραπευτικές εφαρμογές αυτών των συσκευών ημιαγωγών όπως τσιπ διασύνδεσης εγκεφάλου-υπολογιστή για παραλυτικές ή επιληπτικές καταστάσεις, κατασκευή «βιονικών» οργάνων όπως τεχνητός αμφιβληστροειδής, τεχνητό δέρμα και ρομποτικά προθετικά άκρα για ακρωτηριασμένους ή ρομποτική χειρουργική. Συμπέρασμα: Η χρήση των νανοτσίπ στην ιατρική είναι ένας αναδυόμενος τομέας με αρκετές θεραπευτικές εφαρμογές όπως η διάγνωση, η παρακολούθηση της υγείας και της φυσικής κατάστασης και η κατασκευή «βιονικών» οργάνων.Background: The term “nanochip” pertains to an integrated circuit (chip) with nanomaterials and components in the nano-dimension (1-100nm). An integrated circuit is essentially a collection of electronic components, like transistors, diodes, capacitors, and resistors. Current transistors are in the nanoscale but can also be modified with nanostructures like nanoribbons and nanowires to manufacture biosensors that can perform label-free, ultrasensitive detection of biomolecules like ions, DNA molecules, antibodies and antigens. Materials and Methods: A systematic literature search was conducted using the electronic databases PubMed, Google Scholar and Scopus for the development and use of nanochips in medical applications. For the identification of relevant papers, the inclusion criteria referred to articles in the English language, review and/or research articles. The exclusion criteria were newspaper articles, conference abstracts and letters. Results: In-vivo and In-vitro techniques have been used for detection of DNA molecules, ions, antibodies, important proteins, and tumor markers, not only from blood samples but also from sweat, saliva and other biological fluids. Another diagnostic application of nanochips is detection of volatile organic compounds via a breath test. There are also several therapeutic applications of these semiconductor devices like brain-computer interface chips for paralytic or epileptic conditions, manufacture of “bionic” organs like artificial retinas, artificial skin and robotic prostheses for amputees or robotic surgery. Conclusion: The use of nanochips in medicine is an emerging field with several therapeutic applications like diagnostics, health and fitness monitoring, and manufacture of “bionic” organs

Gold nanoparticles for nanotheranostics in leukemia – Addressing Chronic Myeloid Leukemia

Leukemia is a type of cancer that initiates in the bone marrow and results in the unregulated production of immature white blood cells (leukemic cells). The most homogenous subgroup of the disease is chronic myeloid leukemia (CML) accounting for nearly 1.5 million patients worldwide. Virtually all cases harbor the genetic translocation t(9;22)(q34.1;q11.2) resulting in the BCR-ABL1 gene fusion, that encodes for BCR-ABL1 tyrosine kinase. CML treatment success relies on an early diagnosis and the intense research towards developing effective tyrosine kinase inhibitors (TKI). Nanotechnology offers unprecedent advantages to tackle the shortcomings of conventional procedures for the management of CML. Gold nanoparticles (AuNPs) have unique optical properties suitable for ex vivo biosensing applications, but can also function in vivo as nanocarriers in a theranostic approach that links treatment with diagnosis according to patient’s molecular profile. A gold nanoprobe (Au-nanoprobe) colorimetric assay was optimized for the detection of the most frequent BCR-ABL1 isoform (e14a2) and was validated on fully characterized clinical samples. This simple and cheap method enabled the direct detection of e14a2-expressing RNA samples, with accuracy and high specificity. The Au-nanoprobe assay was translated onto a microfluidics chip, resulting in a faster outcome with smaller sample volumes, due to the scale and design of the device. Additionally, a new therapeutic strategy was designed to overcome CML resistance to first line therapy, such as imatinib (IM). BCR-ABL1 gene silencing was effectively achieved in vitro, using AuNPs functionalized with polyethylene glycol and a hairpin-shaped antisense single stranded DNA (ssDNA) oligonucleotide. Furthermore, the nanoconstruct allowed to reduce the dose of IM, when tested in a combined approach, and potentiated a viability decrease of K562 cells resistant to IM. The results of this thesis strongly suggest that AuNPs are a suitable and flexible tool for CML nanotheranostics, improving detection and a personalized treatment strategy