Publications
410 results found
Chen Y, Ma D, Georgiou P, 2021, A wireless power management unit with a novel self-tuned LDO for system-on-chip sensors, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, Pages: 1-5, ISSN: 0271-4302
A wireless power management unit is presented in this work. The system size of 0.4mm 2 with the low power consumption of 87μW allow it to be applied in implantable biomedical applications and ex-vivo systems. The proposed system optimises the conventional slew-rate detection low dropout (LDO) circuit by using a self-tuned controlling block that reduces the response time to 100ns. The static power required for this LDO is reduced to 2μW. The band gap reference (BGR) is designed with a high power supply rejection ratio (PSRR) of 60dB at 100kHz, along with an output reference voltage deviation of 1mV, to deal with the power supply ripple caused by the load shift keying (LSK). The RF power is transmitted at 433MHz and the internal power management circuit is able to provide a 1.4V stable DC supply for a front end on-chip sensor.
Miglietta L, Moniri A, Pennisi I, et al., 2021, Coupling machine learning and high throughput multiplex digital PCR enables accurate detection of carbapenem-resistant genes in clinical isolates, Publisher: Cold Spring Harbor Laboratory
<jats:p>Background: The emergence and spread of carbapenemase-producing organisms (CPO) are a significant clinical and public health concern. Rapid and accurate identification of patients colonised with CPO is essential to adopt prompt prevention measures in order to reduce the risk of transmission. Recent proof-of-concept studies have demonstrated the ability to combine machine learning (ML) algorithms with real-time digital PCR (dPCR) instruments to increase classification accuracy of multiplex assays. From this, we sought to determine if this ML based methodology could accurately identify five major carbapenem-resistant genes in clinical CPO-isolates.Methods: We collected 253 clinical isolates (including 221 CPO-positive samples) and developed a novel 5-plex assay for detection of blaVIM, blaOXA-48, blaNDM, blaIMP and blaKPC. Combining the recently reported ML method "Amplification and Melting Curve Analysis" (AMCA) with the abovementioned multiplex assay, we assessed the performance of the methodology in detecting these five carbapenem-resistant genes. The classification accuracy relies on the usage of real-time data from a single fluorescent channel and benefits from the kinetic and thermodynamic information encoded in the thousands of amplification events produced by high throughput dPCR.Results: The 5-plex showed a lower limit of detection of 100 DNA copies per reaction for each primer set and no cross-reactivity with other carbapenemase genes. The AMCA classifier demonstrated excellent predictive performance with 99.6% (CI 97.8-99.9%) accuracy (only one misclassified sample out of the 253, with a total of 163,966 positive amplification events), which represents a 7.9% increase compared to the conventional ML-based melting curve analysis (MCA) method.Conclusion: This work demonstrates the utility of the AMCA method to increase the throughput and performance of state-of-the-art molecular diagnostic platforms, reducing costs without any changes
Zhu T, Li K, Herrero P, et al., 2021, Basal Glucose Control in Type 1 Diabetes Using Deep Reinforcement Learning: An <i>In Silico</i> Validation, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 25, Pages: 1223-1232, ISSN: 2168-2194
- Author Web Link
- Cite
- Citations: 33
Ma D, Ghoreishizadeh SS, Georgiou P, 2021, Concurrent potentiometric and amperometric sensing with shared reference electrodes, IEEE Sensors Journal, Vol: 21, Pages: 5720-5727, ISSN: 1530-437X
Potentiometry and amperometry are the two most common electrochemical sensing methods. They are conventionally performed at different times, although new applications are emerging that require their simultaneous usage in a single electrochemical cell. This paper investigates the feasibility and potential drawbacks of such a setup. We use a potentiometric and an amperometric sensor to compare their output signals when they are used individually, as well as when they are combined together with a shared reference electrode. Our results in particular show that potentiometric readings with a shared reference electrode show a high correlation of 0.9981 with conventional potentiometry. In the case of amperometric sensing, the cross correlation of the simultaneous versus individual measurement is 0.9959. Furthermore, we also demonstrate concurrent measurement for potentiometry in the presence of cell current through the design of innovative test systems. This is done through measuring both varying pH values and varying concentrations of H2O2 to showcase the operation of the circuit.
Rawson TM, Hernandez B, Wilson R, et al., 2021, Supervised machine learning to support the diagnosis of bacterial infection in the context of COVID-19, JAC-Antimicrobial Resistance, Vol: 3, Pages: 1-4, ISSN: 2632-1823
Background: Bacterial infection has been challenging to diagnose in patients with COVID-19. We developed and evaluated supervised machine learning algorithms to support the diagnosis of secondary bacterial infection in hospitalized patients during COVID-19.Methods: Inpatient data at three London hospitals for the first COVD-19 wave in March and April 2020 were extracted. Demographic, blood test, and microbiology data for individuals with and without SARS-CoV-2 positive PCR were obtained. A Gaussian-Naïve Bayes (GNB), Support Vector Machine (SVM), and Artificial Neuronal Network (ANN) were trained and compared using the area under the receiver operating characteristic curve (AUCROC). The best performing algorithm (SVM with 21 blood test variables) was prospectively piloted in July 2020. AUCROC was calculated for the prediction of a positive microbiological sample within 48 hours of admission. Results: A total of 15,599 daily blood profiles for 1,186 individual patients were identified to train the algorithms. 771/1186 (65%) individuals were SARS-CoV-2 PCR positive. Clinically significant microbiology results were present for 166/1186 (14%) patients during admission. A SVM algorithm trained with 21 routine blood test variables and over 8000 individual profiles had the best performance. AUCROC was 0.913, sensitivity 0.801, and specificity 0.890. Prospective testing on 54 patients on admission (28/54, 52% SARS-CoV-2 PCR positive) demonstrated an AUCROC of 0.960 (0.90-1.00). Conclusion: A SVM using 21 routine blood test variables had excellent performance at inferring the likelihood of positive microbiology. Further prospective evaluation of the algorithms ability to support decision making for the diagnosis of bacterial infection in COVID-19 cohorts is underway.
Rodriguez-Manzano J, Malpartida-Cardenas K, Moser N, et al., 2021, Handheld point-of-care system for rapid detection of SARS-CoV-2 extracted RNA in under 20 min, ACS Central Science, Vol: 7, Pages: 307-317, ISSN: 2374-7943
The COVID-19 pandemic is a global health emergency characterized by the high rate of transmission and ongoing increase of cases globally. Rapid point-of-care (PoC) diagnostics to detect the causative virus, SARS-CoV-2, are urgently needed to identify and isolate patients, contain its spread and guide clinical management. In this work, we report the development of a rapid PoC diagnostic test (<20 min) based on reverse transcriptase loop-mediated isothermal amplification (RT-LAMP) and semiconductor technology for the detection of SARS-CoV-2 from extracted RNA samples. The developed LAMP assay was tested on a real-time benchtop instrument (RT-qLAMP) showing a lower limit of detection of 10 RNA copies per reaction. It was validated against extracted RNA from 183 clinical samples including 127 positive samples (screened by the CDC RT-qPCR assay). Results showed 91% sensitivity and 100% specificity when compared to RT-qPCR and average positive detection times of 15.45 ± 4.43 min. For validating the incorporation of the RT-LAMP assay onto our PoC platform (RT-eLAMP), a subset of samples was tested (n = 52), showing average detection times of 12.68 ± 2.56 min for positive samples (n = 34), demonstrating a comparable performance to a benchtop commercial instrument. Paired with a smartphone for results visualization and geolocalization, this portable diagnostic platform with secure cloud connectivity will enable real-time case identification and epidemiological surveillance.
Pennisi I, Rodriguez Manzano J, Moniri A, et al., 2021, Translation of a host blood RNA Signature distinguishing bacterial from viral infection into a platform suitable for development as a point-of-care test, JAMA Pediatrics, Vol: 175, Pages: 417-419, ISSN: 2168-6203
Zhu T, Li K, Georgiou P, 2021, Personalized Dual-Hormone Control for Type 1 Diabetes Using Deep Reinforcement Learning, Pages: 45-53, ISSN: 1860-949X
We introduce a dual-hormone control algorithm for people with Type 1 Diabetes (T1D) which uses deep reinforcement learning (RL). Specifically, double dilated recurrent neural networks are used to learn the control strategy, trained by a variant of Q-learning. The inputs to the model include the real-time sensed glucose and meal carbohydrate content, and the outputs are the actions necessary to deliver dual-hormone (basal insulin and glucagon) control. Without prior knowledge of the glucose-insulin metabolism, we develop a data-driven model using the UVA/Padova Simulator. We first pre-train a generalized model using long-term exploration in an environment with average T1D subject parameters provided by the simulator, then adopt importance sampling to train personalized models for each individual. In-silico, the proposed algorithm largely reduces adverse glycemic events, and achieves time in range, i.e., the percentage of normoglycemia, for the adults and for the adolescents, which outperforms previous approaches significantly. These results indicate that deep RL has great potential to improve the treatment of chronic diseases such as diabetes.
Zhang Y, Ma D, Carrara S, et al., 2021, Design of Low-Power Highly Accurate CMOS Potentiostat Using the g<sub>m</sub>/I<sub>D</sub> Methodology, 16th IEEE International Symposium on Medical Measurements and Applications (IEEE MeMeA), Publisher: IEEE
Moser N, Rodriguez-Manzano J, Georgiou P, 2021, ProtonDx: Accurate, Rapid and Lab-Free Detection of SARS-CoV-2 and Other Respiratory Pathogens, IEEE CIRCUITS AND SYSTEMS MAGAZINE, Vol: 21, Pages: 84-88, ISSN: 1531-636X
Hua Q, Cacho-Soblechero M, Georgiou P, 2021, A Multi-sensing ISFET Array for Simultaneous In-pixel Detection of Light, Temperature, Moisture and Ions, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302
Zeng J, Georgiou P, 2021, A 1000fps Programmable Gain CMOS ISFET SoC with Array-level Offset Compensation for Real Time Ion Imaging, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302
- Author Web Link
- Cite
- Citations: 1
Kuang L, Zeng J, Georgiou P, 2021, Live Demonstration: Real-Time and High-Speed Ion Imaging Using CMOS ISFET Arrays, IEEE Biomedical Circuits and Systems Conference (IEEE BioCAS), Publisher: IEEE
Jahin M, Fenech-Salerno B, Moser N, et al., 2021, Detection of <i>MGMT</i> methylation status using a Lab-on-Chip compatible isothermal amplification method, 43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC), Publisher: IEEE, Pages: 7385-7389, ISSN: 1557-170X
Kuang L, Zhu T, Li K, et al., 2021, Live Demonstration: An IoT Wearable Device for Real-time Blood Glucose Prediction with Edge AI, IEEE Biomedical Circuits and Systems Conference (IEEE BioCAS), Publisher: IEEE
- Author Web Link
- Cite
- Citations: 1
Tripathi P, Moser N, Georgiou P, 2021, Multiple Ion-channel ISFET Neuron for Lab-on-chip applications, IEEE Biomedical Circuits and Systems Conference (IEEE BioCAS), Publisher: IEEE
Emmolo G, Ma D, Demarchi D, et al., 2021, Multiple Input, Single Output Frequency Mixing Communication Technique for Low Power Data Transmission, 16th IEEE International Symposium on Medical Measurements and Applications (IEEE MeMeA), Publisher: IEEE
Wang Z, Keeble L, Moser N, et al., 2021, A Dual-sensing CMOS Array for Combined Impedance-pH Detection of DNA with Integrated Electric Field Manipulation, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302
Kuang L, Zeng J, Georgiou P, 2021, A USB 3.0 High Speed Digital Readout System with Dynamic Frame Rate Processing for ISFET Lab-on-Chip Platforms, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302
- Author Web Link
- Cite
- Citations: 2
Zhu T, Kuang L, Li K, et al., 2021, Blood Glucose Prediction in Type 1 Diabetes Using Deep Learning on the Edge, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302
- Author Web Link
- Cite
- Citations: 1
Han J, Cacho-Soblechero M, Douthwaite M, et al., 2021, A Digital ISFET Sensor with In-Pixel ADC, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302
- Author Web Link
- Cite
- Citations: 18
Thirumalaikumar S, Douthwaite M, Georgiou P, 2021, Design of a Calorimetric Flow Rate Sensor for On-Body Sweat Monitoring, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302
Troppoli T, Zanos P, Georgiou P, et al., 2020, An α5-Containing Benzodiazepine Site on the GABAAR is Required for the Fast Antidepressant-Like Actions of MRK-016 on Stress-Induced Anhedonia and Weakened Synaptic Function, 59th Annual Meeting of the American-College-of-Neuropsychopharmacology (ACNP), Publisher: SPRINGERNATURE, Pages: 111-111, ISSN: 0893-133X
Karolcik S, Miscourides N, Cacho-Soblechero M, et al., 2020, A High-Performance Raspberry Pi-Based Interface for Ion Imaging Using ISFET Arrays, IEEE SENSORS JOURNAL, Vol: 20, Pages: 12837-12847, ISSN: 1530-437X
- Author Web Link
- Cite
- Citations: 3
Yu L-S, Rodriguez-Manzano J, Moser N, et al., 2020, Rapid detection of azole-resistant Aspergillus fumigatus in clinical and environmental isolates using lab-on-a-chip diagnostic system, Journal of Clinical Microbiology, Vol: 58, Pages: 1-11, ISSN: 0095-1137
Aspergillus fumigatus has widely evolved resistance to the most commonly used class of antifungal chemicals, the azoles. Current methods for identifying azole resistance are time-consuming and depend on specialized laboratories. There is an urgent need for rapid detection of these emerging pathogens at point-of-care to provide the appropriate treatment in the clinic and to improve management of environmental reservoirs to mitigate the spread of antifungal resistance. Our study demonstrates the rapid and portable detection of the two most relevant genetic markers linked to azole resistance, the mutations TR34 and TR46, found in the promoter region of the gene encoding the azole target, cyp51A. We developed a lab-on-a-chip platform consisting of: (1) tandem-repeat loop-mediated isothermal amplification, (2) state-of-the-art complementary metal-oxide-semiconductor microchip technology for nucleic-acid amplification detection and, (3) and a smartphone application for data acquisition, visualization and cloud connectivity. Specific and sensitive detection was validated with isolates from clinical and environmental samples from 6 countries across 5 continents, showing a lower limit-of-detection of 10 genomic copies per reaction in less than 30 minutes. When fully integrated with a sample preparation module, this diagnostic system will enable the detection of this ubiquitous fungus at the point-of-care, and could help to improve clinical decision making, infection control and epidemiological surveillance.
Moscardo V, Herrero P, Reddy M, et al., 2020, Assessment of Glucose Control Metrics by Discriminant Ratio, DIABETES TECHNOLOGY & THERAPEUTICS, Vol: 22, Pages: 719-726, ISSN: 1520-9156
Keeble L, Moser N, Rodriguez-Manzano J, et al., 2020, ISFET-Based Sensing and Electric Field Actuation of DNA for On-Chip Detection: A Review, IEEE SENSORS JOURNAL, Vol: 20, Pages: 11044-11065, ISSN: 1530-437X
Alexandrou G, Moser N, Rodriguez-Manzano J, et al., 2020, Detection of breast cancer ESR1 p.E380Q mutation on an ISFET lab-on-chip platform, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 1-5, ISSN: 0271-4302
This paper presents a method for detection of ESR1 p.E380Q, a common Breast Cancer (BC) mutation, using an ISFET (Ion-Sensitive Field-Effect Transistor) based Lab-on-Chip (LoC) platform. The LoC contains an ISFET array that can detect pH changes during DNA amplification, specifically Loop-Mediated Isothermal Amplification (LAMP). Synthetic ESR1 DNA was detected in a comparison pH-LAMP assay, carried out on the LoC platform as well as a conventional qPCR instrument. Positive detection of the allele arises due to bespoke allele-specific primers that target one base-pair difference between the wild-type and mutant alleles. The LoC and qPCR demonstrate comparable results detecting the mutant allele with mutant primers in around 25 minutes. The sensing microchip technology coupled with the molecular methods of isothermal chemistries and primer design allow this platform to be tested at a Point-of-Care setting for breast cancer patients, offering mutational tracking platform of circulating tumour DNA in liquid biopsies to assist patient stratification and allow tailored treatments.
Ma D, Ghoreishizadeh SS, Georgiou P, 2020, DAPPER: a low Power, dual amperometric and potentiometric single-channel front end, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302
DAPPER is a front end system capable of simultaneous amperometric and potentiometric sensing proposed for low-power multi-parameter analysis of bio-fluids such as saliva. The system consists of two oscillator circuits, generating a frequency relative to their sensed current and voltage signals. These signals are then mixed together to produce a single channel output that can be transmitted through backscattering (load-shift keying). The entire system consumes 40μW from a 1.4V supply. The linear ranges of potentiometry and amperometry circuits are 0.4V - 1V and 250pA - 5.6μA (87dB), and their input referred noise is 1.7μV and 44.6fA, respectively.
Moniri A, Miglietta L, Holmes A, et al., 2020, High-level multiplexing in digital PCR with intercalating dyes by coupling real-time kinetics and melting curve analysis., Analytical Chemistry, Vol: 92, Pages: 14181-14188, ISSN: 0003-2700
Digital polymerase chain reaction (dPCR) is a mature technique that has enabled scientific breakthroughs in several fields. However, this technology is primarily used in research environments with high-level multiplexing representing a major challenge. Here, we propose a novel method for multiplexing, referred to as amplification and melting curve analysis (AMCA), which leverages the kinetic information in real-time amplification data and the thermodynamic melting profile using an affordable intercalating dye (EvaGreen). The method trains a system comprised of supervised machine learning models for accurate classification, by virtue of the large volume of data from dPCR platforms. As a case study, we develop a new 9-plex assay to detect mobilised colistin resistant (mcr) genes as clinically relevant targets for antimicrobial resistance. Over 100,000 amplification events have been analysed, and for the positive reactions, the AMCA approach reports a classification accuracy of 99.33 ± 0.13%, an increase of 10.0% over using melting curve analysis. This work provides an affordable method of high-level multiplexing without fluorescent probes, extending the benefits of dPCR in research and clinical settings.
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.