Publications
410 results found
Wormald BW, Moser N, deSouza NM, et al., 2022, Lab-on-Chip assay of tumour markers and Human Papilloma Virus for cervical cancer detection at the point-of-care, Scientific Reports, Vol: 12, ISSN: 2045-2322
Cervical cancer affects over half a million people worldwide each year, the majority of whom are in resource-limited settingswhere cytology screening is not available. As persistent human papilloma virus (HPV) infections are a key causative factor,detection of HPV strains now complements cytology where screening services exist. This work demonstrates the efficacy ofa handheld Lab-on-Chip (LoC) device, with an external sample extraction process, in detecting cervical cancer from biopsysamples. The device is based on Ion-Sensitive Field-Effect Transistor (ISFET) sensors used in combination with loop-mediatedisothermal amplification (LAMP) assays, to amplify HPV DNA and human telomerase reverse transcriptase (hTERT) mRNA.These markers were selected because of their high levels of expression in cervical cancer cells, but low to nil expression innormal cervical tissue. The achieved analytical sensitivity for the molecular targets resolved down to a single copy per reactionfor the mRNA markers, achieving a limit of detection of 102for hTERT. In the tissue samples, HPV-16 DNA was present in 4/5malignant and 2/5 benign tissues, with HPV-18 DNA being present in 1/5 malignant and 1/5 benign tissues. hTERT mRNA wasdetected in all malignant and no benign tissues, with the demonstrated pilot data to indicate the potential for using the LoC incervical cancer screening in resource-limited settings on a large scale.
Zhu T, Li K, Herrero P, et al., 2022, RECURRENT GENERATIVE ADVERSARIAL NETWORKS FOR GLUCOSE TIME SERIES GENERATION, Publisher: MARY ANN LIEBERT, INC, Pages: A229-A229, ISSN: 1520-9156
Afentakis I, Herrero P, Unsworth R, et al., 2022, PREDICTION OF NOCTURNAL HYPOGLYCAEMIA IN ADULTS WITH TYPE 1 DIABETES USING MACHINE LEARNING CLASSIFIERS, Publisher: MARY ANN LIEBERT, INC, Pages: A226-A226, ISSN: 1520-9156
Miglietta L, Xu K, Chhaya P, et al., 2022, An adaptive filtering framework for non-specific and inefficient reactions in multiplex digital PCR based on sigmoidal trends
<jats:title>ABSTRACT</jats:title><jats:p>Real-time digital PCR (qdPCR) coupled with artificial intelligence has shown the potential of unlocking scientific breakthroughs, particularly in the field of molecular diagnostics for infectious diseases. One of the most promising applications is the use of machine learning (ML) methods to enable single fluorescent channel PCR multiplex by extracting target-specific kinetic and thermodynamic information contained in amplification curves. However, the robustness of such methods can be affected by the presence of undesired amplification events and nonideal reaction conditions. Therefore, here we proposed a novel framework to filter non-specific and low efficient reactions from qdPCR data using outlier detection algorithms purely based on sigmoidal trends of amplification curves. As a proof-of-concept, this framework is implemented to improve the classification performance of the recently reported ML-based Amplification Curve Analysis (ACA), using available data from a previous publication where the ACA method was used to screen carbapenemase-producing organisms in clinical isolates. Furthermore, we developed a novel strategy, named Adaptive Mapping Filter (AMF), to consider the variability of positive counts in digital PCR. Over 152,000 amplification events were analyzed. For the positive reactions, filtered and unfiltered amplification curves were evaluated by comparing against melting peak distribution, proving that abnormalities (filtered out data) are linked to shifted melting distribution or decreased PCR efficiency. The ACA was applied to compare classification accuracies before and after AMF, showing an improved sensitivity of 1.18% for inliers and 20% for outliers (p-value < 0.0001). This work explores the correlation between kinetics of amplification curves and thermodynamics of melting curves and it demonstrates that filtering out non-specific or low efficient reactions can significantly impr
Stringer OW, Li Y, Bosse JT, et al., 2022, Rapid detection of A. pleuropneumoniae from clinical samples using recombinase polymerase amplification, Frontiers in Veterinary Science, Vol: 9, ISSN: 2297-1769
Actinobacillus pleuropneumoniae (APP) is the causative agent of porcine pleuropneumonia, resulting in high economic impact worldwide. There are currently 19 known serovars of APP, with different ones being predominant in specific geographic regions. Outbreaks of pleuropneumonia, characterized by sudden respiratory difficulties and high mortality, can occur when infected pigs are brought into naïve herds, or by those carrying different serovars. Good biosecurity measures include regular diagnostic testing for surveillance purposes. Current gold standard diagnostic techniques lacksensitivity (bacterial culture), require expensive thermocycling machinery (PCR) and are time consuming (culture and PCR). Here we describe the development of an isothermal point-of-care diagnostic test - utilizing recombinase polymerase amplification (RPA) for the detection of APP,targeting the species-specific apxIVA gene. Our APP-RPA diagnostic test achieved a sensitivity of 10 copies/µL using a strain of APP serovar 8, which is the most prevalent serovar in the UK. Additionally, our APP-RPA assay achieved a clinical sensitivity and specificity of 84.3% and 100%, respectively,across 61 extracted clinical samples obtained from farms located in England and Portugal. Using a small subset (n = 14) of the lung tissue samples, we achieved a clinical sensitivity and specificity of 76.9% and 100%, respectively) using lung imprints made on FTA cards tested directly in the APP- RPA reaction. Our results demonstrate that our APP-RPA assay enables a suitable rapid and sensitive screening tool for this important veterinary pathogen.
Ming DK, Hernandez B, Sangkaew S, et al., 2022, Applied machine learning for the risk-stratification and clinical decision support of hospitalised patients with dengue in Vietnam, PLOS Digital Health, Vol: 1, Pages: e0000005-e0000005
BackgroundIdentifying patients at risk of dengue shock syndrome (DSS) is vital for effective healthcare delivery. This can be challenging in endemic settings because of high caseloads and limited resources. Machine learning models trained using clinical data could support decision-making in this context.MethodsWe developed supervised machine learning prediction models using pooled data from adult and paediatric patients hospitalised with dengue. Individuals from 5 prospective clinical studies in Ho Chi Minh City, Vietnam conducted between 12th April 2001 and 30th January 2018 were included. The outcome was onset of dengue shock syndrome during hospitalisation. Data underwent random stratified splitting at 80:20 ratio with the former used only for model development. Ten-fold cross-validation was used for hyperparameter optimisation and confidence intervals derived from percentile bootstrapping. Optimised models were evaluated against the hold-out set.FindingsThe final dataset included 4,131 patients (477 adults and 3,654 children). DSS was experienced by 222 (5.4%) of individuals. Predictors were age, sex, weight, day of illness at hospitalisation, indices of haematocrit and platelets over first 48 hours of admission and before the onset of DSS. An artificial neural network model (ANN) model had best performance with an area under receiver operator curve (AUROC) of 0.83 (95% confidence interval [CI], 0.76–0.85) in predicting DSS. When evaluated against the independent hold-out set this calibrated model exhibited an AUROC of 0.82, specificity of 0.84, sensitivity of 0.66, positive predictive value of 0.18 and negative predictive value of 0.98.InterpretationThe study demonstrates additional insights can be obtained from basic healthcare data, when applied through a machine learning framework. The high negative predictive value could support interventions such as early discharge or ambulatory patient management in this population. Work is underway to incorporate t
Daniels J, Herrero P, Georgiou P, 2022, A Multitask Learning Approach to Personalized Blood Glucose Prediction, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 26, Pages: 436-445, ISSN: 2168-2194
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- Citations: 8
Daniels J, Herrero P, Georgiou P, 2022, A Deep Learning Framework for Automatic Meal Detection and Estimation in Artificial Pancreas Systems, SENSORS, Vol: 22
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- Citations: 6
Armiger R, Reddy M, Oliver NS, et al., 2022, An In Silico Head-to-Head Comparison of the Do-It-Yourself Artificial Pancreas Loop and Bio-Inspired Artificial Pancreas Control Algorithms, JOURNAL OF DIABETES SCIENCE AND TECHNOLOGY, Vol: 16, Pages: 29-39, ISSN: 1932-2968
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- Citations: 1
Yao T, Tripathi P, Keeble L, et al., 2022, A Linear Weighted Neuromorphic ISFET Array with Offset Compensation, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 1072-1076, ISSN: 0271-4302
Rawson TM, Wilson RC, O'Hare D, et al., 2021, Optimizing antimicrobial use: challenges, advances and opportunities, NATURE REVIEWS MICROBIOLOGY, Vol: 19, Pages: 747-758, ISSN: 1740-1526
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- Citations: 45
Wormald B, Rodriguez-Manzano J, Moser N, et al., 2021, Loop-mediated isothermal amplification assay for detecting tumor markers and human papillomavirus: accuracy and supplemental diagnostic value to endovaginal MRI in cervical cancer, Frontiers in Oncology, Vol: 11, Pages: 1-13, ISSN: 2234-943X
Objective: To establish the sensitivity and specificity of a human papillomavirus (HPV) and tumor marker DNA/mRNA assay for detecting cervical cancer that is transferrable to a Lab-on-a-chip platform and determine its diagnostic benefit in early stage disease when used in conjunction with high-resolution endovaginal magnetic resonance imaging (MRI).Methods: Forty-one patients (27 with Stage1 cervical cancer [Group1] and 14 non-cancer HPV negative controls [Group2]) had DNA and RNA extracted from cervical cytology swab samples. HPV16, HPV18, hTERT, TERC/GAPDH and MYC/GAPDH concentration was established using a loop mediated isothermal amplification (LAMP) assay. Thresholds for tumor marker detection for Group1 were set from Group2 analysis (any hTERT, TERC/GAPDH 3.12, MYC/GAPDH 0.155). Group 1 participants underwent endovaginal MRI. Sensitivity and specificity for cancer detection by LAMP and MRI individually and combined was documented by comparison to pathology.Results: Sensitivity and specificity for cancer detection was 68.8% and 77.8% if any tumor marker was positive regardless of HPV status (scenario1), and 93.8% and 55.8% if tumor marker or HPV were positive (scenario 2). Adding endovaginal MRI improved specificity to 88.9% in scenario 1 (sensitivity 68.8%) and to 77.8%% in scenario2 (sensitivity 93.8%).Conclusion: Specificity for cervical cancer detection using a LAMP assay is superior with tumor markers; low sensitivity is improved by HPV detection. Accuracy for early stage cervical cancer detection is optimal using a spatially multiplexed tumor marker/HPV LAMP assay together with endovaginal MRI.
Li HK, Kaforou M, Rodriguez-Manzano J, et al., 2021, Discovery and validation of a 3-gene signature to distinguish COVID-19 and other viral infections in emergency infectious disease presentations; a case-control then observational cohort study, The Lancet Microbe, Vol: 2, Pages: 594-603, ISSN: 2666-5247
Background: Emergency admissions for infection often lack initial diagnostic certainty. COVID-19 has highlighted a need for novel diagnostic approaches to indicate likelihood of viral infection in a pandemic setting. We sought to derive and validate a blood transcriptional signature to detect viral infections including COVID-19 among adults with suspected infection presenting to the Emergency Department (ED).Methods: Blood RNA sequencing was performed on a discovery cohort of adults attending the ED with suspected infection who had subsequently-confirmed viral, bacterial, or no infection diagnoses. Differentially expressed host genes were subjected to feature selection to derive the most parsimonious discriminating signature. RT-qPCR validation of the signature was then performed in a prospective cohort of ED patients presenting with undifferentiated fever, and a second case-control cohort of ED patients with COVID-19 or bacterial infection. Signature performance was assessed by calculating area under receiver-operating characteristic curves (AUC-ROCs), sensitivities, and specificities.Findings: A 3-gene transcript signature was derived from the discovery cohort of 56 bacterial and 27 viral infection cases. In the validation cohort of 200 cases, the signature differentiated bacterial from viral infections with an AUC-ROC of 0.976 (95% CI: 0.919-1.000), sensitivity 97.3% and specificity of 100%. The AUC-ROC for C-reactive protein (CRP) and leucocyte count (WCC) was 0.833 (95% CI: 0.694-0.944) and 0.938 (95% CI: 0.840-0.986) respectively. The signature achieved higher net benefit in decision curve analysis than either CRP or WCC for discriminating viral infections from all other cases. In the second validation analysis the signature discriminated 35 bacterial infections from 34 SARS-CoV-2 positive COVID-19 infections with AUC-ROC of 0.953 (95% CI: 0.893-0.992), sensitivity 88.6% and specificity of 94.1%.Interpretation: This novel 3-gene signature discriminates viral i
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, Frontiers in Molecular Biosciences, Vol: 8, Pages: 1-11, ISSN: 2296-889X
Rapid and accurate identification of patients colonised with carbapenemase-producing organisms (CPOs) is essential to adopt prompt prevention measures to reduce the risk of transmission. Recent studies have demonstrated the ability to combine machine learning (ML) algorithms with real-time digital PCR (dPCR) instruments to increase classification accuracy of multiplex PCR assays when using synthetic DNA templates. We sought to determine if this novel methodology could be applied to improve identification of the five major carbapenem-resistant genes in clinical CPO-isolates, which would represent a leap forward in the use of PCR-based data-driven diagnostics for clinical applications. We collected 3 clinical isolates (including 221 CPO-positive samples) and developed a novel 5-plex PCR assay for detection of blaIMP, blaKPC, blaNDM, blaOXA-48 and blaVIM. Combining the recently reported ML method ‘Amplification and Melting Curve Analysis’ (AMCA) with the abovementioned multiplex assay, we assessed the performance of the AMCA methodology in detecting these genes. The improved classification accuracy of AMCA relies on the usage of real-time data from a single fluorescent channel and benefits from the kinetic/thermodynamic information encoded in the thousands of amplification events produced by high throughput real-time dPCR. The 5-plex showed a lower limit of detection of 10 DNA copies per reaction for each primer set and no cross-reactivity with other carbapenemase genes. The AMCA classifier demonstrated excellentpredictive performance with 99.6% (CI 97.8-99.9%) accuracy (only one misclassified sample out of the 253, with a total of 160,041 positive amplification events), which represents a 7.9% increase (p value < 0.05) compared to conventional melting curve analysis. This work demonstrates the use of the AMCA method to increase the throughput and performance of state-of-the-art molecular diagnostic platforms, without hardware modifications and additiona
Douthwaite M, Moser N, Georgiou P, 2021, CMOS ISFET Arrays for Integrated Electrochemical Sensing and Imaging Applications: A Tutorial, IEEE SENSORS JOURNAL, Vol: 21, Pages: 22155-22169, ISSN: 1530-437X
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- Citations: 8
Charani E, McKee M, Ahmad R, et al., 2021, Optimising antimicrobial use in humans-review of current evidence and an interdisciplinary consensus on key priorities for research, The Lancet Regional Health - Europe, Vol: 7, Pages: 1-10, ISSN: 2666-7762
Addressing the silent pandemic of antimicrobial resistance (AMR) is a focus of the 2021 G7 meeting. A major driver of AMR and poor clinical outcomes is suboptimal antimicrobial use. Current research in AMR is inequitably focused on new drug development. To achieve antimicrobial security we need to balance AMR research efforts between development of new agents and strategies to preserve the efficacy and maximise effectiveness of existing agents.Combining a review of current evidence and multistage engagement with diverse international stakeholders (including those in healthcare, public health, research, patient advocacy and policy) we identified research priorities for optimising antimicrobial use in humans across four broad themes: policy and strategic planning; medicines management and prescribing systems; technology to optimise prescribing; and context, culture and behaviours. Sustainable progress depends on: developing economic and contextually appropriate interventions; facilitating better use of data and prescribing systems across healthcare settings; supporting appropriate and scalable technological innovation. Implementing this strategy for AMR research on the optimisation of antimicrobial use in humans could contribute to equitable global health security.
Zeng J, Kuang L, Cacho-Soblechero M, et al., 2021, An Ultra-High Frame Rate Ion Imaging Platform Using ISFET Arrays With Real-Time Compression, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 15, Pages: 820-833, ISSN: 1932-4545
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- Citations: 5
Alexandrou G, Moser N, Mantikas K-T, et al., 2021, Detection of Multiple Breast Cancer ESR1 mutations on an ISFET based Lab-on-Chip Platform., IEEE Trans Biomed Circuits Syst, Vol: PP
ESR1 mutations are important biomarkers in metastatic breast cancer. Specifically, p.E380Q and p.Y537S mu- tations arise in response to hormonal therapies given to patients with hormone receptor positive (HR+) breast cancer (BC). This paper demonstrates the efficacy of an ISFET based CMOS integrated Lab-on-Chip (LoC) system, coupled with variant- specific isothermal amplification chemistries, for detection and discrimination of wild type (WT) from mutant (MT) copies of the ESR1 gene. Hormonal resistant cancers often lead to increased chances of metastatic disease which leads to high mortality rates, especially in low-income regions and areas with low healthcare coverage. Design and optimization of bespoke primers was carried out and tested on a qPCR instrument and then benchmarked versus the LoC platform. Assays for detection of p.Y537S and p.E380Q were developed and tested on the LoC platform, achieving amplification in under 25 minutes and sensitivity of down to 1000 copies of DNA per reaction for both target assays. The LoC system hereby presented, is cheaper and smaller than other standard industry equivalent technologies such as qPCR and sequencing. The LoC platform proposed, has the potential to be used at a breast cancer point-of-care testing setting, offering mutational tracking of circulating tumour DNA in liquid biopsies to assist patient stratification and metastatic monitoring.
Panteli C, Georgiou P, Fobelets K, 2021, Reduced drift of CMOS ISFET pH sensors using graphene sheets, IEEE Sensors Journal, Vol: 21, ISSN: 1530-437X
Reduction of drift in Complementary Metal Oxide-Semiconductor (CMOS) Ion-Sensitive Field-Effect Transistor (ISFET) pH sensors is demonstrated using monolayer and multilayer graphene sheets. Graphene blocks the ion penetration in the CMOS passivation layers and provides the physisorption sites needed for electrical double layer formation allowing sensing. With an in-house polymer-assisted graphene transfer (PAGT) process, monolayer and multilayer graphene sheets were manually transferred on top of the sensing membrane of CMOS ISFET sensors on a 2 by 4 mm chip. Experiments with pH buffers on five different chips were performed to extract the average performance parameters of capacitive attenuation, trapped charge, sensitivity, drift and noise. The stretched exponential function, that describes dispersion processes in amorphous solids such as silicon dioxide and silicon nitride, was modified to model the dynamic drift behaviour and analyse the effect of graphene on the performance of the sensors. The results show that on average the graphene coated ISFET sensors experience about 50% reduction in drift amplitude, up to 3 times slower surface modification and perform overall better compared to the plain unmodified devices.
Zhu T, Li K, Herrero P, et al., 2021, Deep Learning for Diabetes: A Systematic Review, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 25, Pages: 2744-2757, ISSN: 2168-2194
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- Citations: 53
Rawson TM, Hernandez B, Moore L, et al., 2021, A real-world evaluation of a case-based reasoning algorithm to support antimicrobial prescribing decisions in acute care, Clinical Infectious Diseases, Vol: 72, Pages: 2103-2111, ISSN: 1058-4838
BackgroundA locally developed Case-Based Reasoning (CBR) algorithm, designed to augment antimicrobial prescribing in secondary care was evaluated.MethodsPrescribing recommendations made by a CBR algorithm were compared to decisions made by physicians in clinical practice. Comparisons were examined in two patient populations. Firstly, in patients with confirmed Escherichia coli blood stream infections (‘E.coli patients’), and secondly in ward-based patients presenting with a range of potential infections (‘ward patients’). Prescribing recommendations were compared against the Antimicrobial Spectrum Index (ASI) and the WHO Essential Medicine List Access, Watch, Reserve (AWaRe) classification system. Appropriateness of a prescription was defined as the spectrum of the prescription covering the known, or most-likely organism antimicrobial sensitivity profile.ResultsIn total, 224 patients (145 E.coli patients and 79 ward patients) were included. Mean (SD) age was 66 (18) years with 108/224 (48%) female gender. The CBR recommendations were appropriate in 202/224 (90%) compared to 186/224 (83%) in practice (OR: 1.24 95%CI:0.392-3.936;p=0.71). CBR recommendations had a smaller ASI compared to practice with a median (range) of 6 (0-13) compared to 8 (0-12) (p<0.01). CBR recommendations were more likely to be classified as Access class antimicrobials compared to physicians’ prescriptions at 110/224 (49%) vs. 79/224 (35%) (OR: 1.77 95%CI:1.212-2.588 p<0.01). Results were similar for E.coli and ward patients on subgroup analysis.ConclusionsA CBR-driven decision support system provided appropriate recommendations within a narrower spectrum compared to current clinical practice. Future work must investigate the impact of this intervention on prescribing behaviours more broadly and patient outcomes.
Uduku C, Zhu T, Li K, et al., 2021, INDEPENDENT PREDICTORS OF HYPOGLYCAEMIA AND IMPENDING HYPOGLYCAEMIA USING A WEARABLE PHYSIOLOGICAL DATA ACQUISITION SENSOR, Publisher: MARY ANN LIEBERT, INC, Pages: A56-A57, ISSN: 1520-9156
Daniels J, Herrero P, Georgiou P, 2021, AUTOMATIC MEAL DETECTION AND ESTIMATION USING NEURAL NETWORKS, Publisher: MARY ANN LIEBERT, INC, Pages: A96-A96, ISSN: 1520-9156
Herrero P, Armiger R, Daniels J, et al., 2021, AI UPGRADES AUTOMATED INSULIN DELIVERY TOWARDS A FULLY CLOSED-LOOP, Publisher: MARY ANN LIEBERT, INC, Pages: A4-A4, ISSN: 1520-9156
Herrero P, Reddy M, Georgiou P, et al., 2021, IDENTIFYING CGM DATA USING MACHINE LEARNING; A CGM DIGITAL 'FINGERPRINT', Publisher: MARY ANN LIEBERT, INC, Pages: A36-A36, ISSN: 1520-9156
Zhu T, Li K, Herrero P, et al., 2021, BLOOD GLUCOSE PREDICTION FOR TYPE 1 DIABETES WITH POPULATION DATA AND MODEL-AGNOSTIC META-LEARNING, Publisher: MARY ANN LIEBERT, INC, Pages: A100-A100, ISSN: 1520-9156
Zhu T, Li K, Herrero P, et al., 2021, PERSONALIZED BLOOD GLUCOSE PREDICTION FOR TYPE 1 DIABETES WITH DEEP NEURAL NETWORKS AND ATTENTION MECHANISM, Publisher: MARY ANN LIEBERT, INC, Pages: A100-A101, ISSN: 1520-9156
Thomas K, Lazarini A, Kaltsonoudis E, et al., 2021, Incidence, risk factors and validation of the RABBIT score for serious infections in a cohort of 1557 patients with rheumatoid arthritis, RHEUMATOLOGY, Vol: 60, Pages: 2223-2230, ISSN: 1462-0324
Tu W, Cacho-Soblechero M, Terracina D, et al., 2021, A 4-channel sEMG ASIC with real-time muscle fatigue feature extraction, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, Pages: 1-5, ISSN: 0271-4302
This paper presents a 4 channel ASIC for sEMG sensing with in-built muscle fatigue and activity feature extraction. Each channel filters and conditions the electrode signal in parallel, while extracting key features for Low Back Pain (LBP) fatigue monitoring and forecasting - Zero Crossing rate and Root Mean Square through sEMG Envelope. The channels are integrated with a Transimpedance Amplifier, an 10-Bit ADC and a Digital Control Unit to digitise and enable transmission of extracted features. Fabricated in TSMC 180nm, these channels present a compact form factor (90μm× 630μm,) and a low power consumption (42.61 μw), ideal characteristic for wearable devices utilised for long-term monitoring of activities.
Ma D, Chen Y, Ghoreishizadeh SS, et al., 2021, SPACEMan: wireless SoC for concurrent potentiometry and amperometry, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302
Abstract:This work describes the implementation of SPACEMan, a wireless electrochemical system with concurrent potentiometric and amperometric sensing that can be utilised for saliva, sweat or point of care diagnostics. This system is designed with the vision of simpler interfaces for biofluid analysis. With a complete system-on-chip including electrochemical sensing, power management and data transmission, conventional interfaces like wirebonds will no longer be required in post-processing steps. The proposed architecture consists of a sensor front-end with four electrodes for concurrent amperometric and potentiometric sensing. This front-end outputs square wave signals mixed together with varying frequencies dependent on the sensed input, with the output type switchable with a state machine. A power management system consisting of a low dropout regulator (LDO) band gap reference (BGR), and a rectifier bridge is utilised for supplying power from an inductive link at 433MHz. Sensor data is transmitted wirelessly to a base station using LSK (Load-Shift Keying). The sensor front-end consumes 18μW, which the power management system more than adequately provides. The core area of the electronics without the coil is a conservative size of 0.41mm 2 .
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