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Alexandrou G, Mantikas K-T, Allsopp R, et al., 2023, The evolution of affordable technologies in liquid biopsy diagnostics: the key to clinical implementation, Cancers, Vol: 15, ISSN: 2072-6694
Cancer remains a leading cause of death worldwide, despite many advances in diagnosis and treatment. Precision medicine has been a key area of focus, with research providing insights and progress in helping to lower cancer mortality through better patient stratification for therapies and more precise diagnostic techniques. However, unequal access to cancer care is still a global concern, with many patients having limited access to diagnostic tests and treatment regimens. Noninvasive liquid biopsy (LB) technology can determine tumour-specific molecular alterations in peripheral samples. This allows clinicians to infer knowledge at a DNA or cellular level, which can be used to screen individuals with high cancer risk, personalize treatments, monitor treatment response, and detect metastasis early. As scientific understanding of cancer pathology increases, LB technologies that utilize circulating tumour DNA (ctDNA) and circulating tumour cells (CTCs) have evolved over the course of research. These technologies incorporate tumour-specific markers into molecular testing platforms. For clinical translation and maximum patient benefit at a wider scale, the accuracy, accessibility, and affordability of LB tests need to be prioritized and compared with gold standard methodologies in current use. In this review, we highlight the range of technologies in LB diagnostics and discuss the future prospects of LB through the anticipated evolution of current technologies and the integration of emerging and novel ones. This could potentially allow a more cost-effective model of cancer care to be widely adopted.
Cavallo FR, Toumazou C, 2023, Personalised lifestyle recommendations for type 2 diabetes: Design and simulation of a recommender system on UK Biobank Data., PLOS Digit Health, Vol: 2
Mobile health applications, which employ wireless technology for healthcare, can aid behaviour change and subsequently improve health outcomes. Mobile health applications have been developed to increase physical activity, but are rarely grounded on behavioural theory and employ simple techniques for personalisation, which has been proven effective in promoting behaviour change. In this work, we propose a theoretically driven and personalised behavioural intervention delivered through an adaptive knowledge-based system. The behavioural system design is guided by the Behavioural Change Wheel and the Capability-Opportunity-Motivation behavioural model. The system exploits the ever-increasing availability of health data from wearable devices, point-of-care tests and consumer genetic tests to issue highly personalised physical activity and sedentary behaviour recommendations. To provide the personalised recommendations, the system firstly classifies the user into one of four diabetes clusters based on their cardiometabolic profile. Secondly, it recommends activity levels based on their genotype and past activity history, and finally, it presents the user with their current risk of developing cardiovascular disease. In addition, leptin, a hormone involved in metabolism, is included as a feedback biosignal to personalise the recommendations further. As a case study, we designed and demonstrated the system on people with type 2 diabetes, since it is a chronic condition often managed through lifestyle changes, such as physical activity increase and sedentary behaviour reduction. We trained and simulated the system using data from diabetic participants of the UK Biobank, a large-scale clinical database, and demonstrate that the system could help increase activity over time. These results warrant a real-life implementation of the system, which we aim to evaluate through human intervention.
Alexandrou G, Moser N, Ali S, et al., 2023, Distinguishing <i>PIK3CA</i> p.E545K mutational status from pseudogene DNA with a next-generation ISFET sensor array, 56th IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302
Cavallo FR, Toumazou C, Nikolic K, 2022, Unsupervised Classification of Human Activity with Hidden Semi-Markov Models, APPLIED SYSTEM INNOVATION, Vol: 5
Cavallo FR, Mirza KB, de Mateo S, et al., 2022, A Point-of-Care Device for Fully Automated, Fast and Sensitive Protein Quantification via qPCR, BIOSENSORS-BASEL, Vol: 12
Allsopp R, Alexandrou G, Toumazou C, et al., 2022, A comparison between Mini-loop mediated isothermal amplification and polymerase spiral reaction for selective amplification of short template DNA, bioRxiv
Pallett SJ, Jones R, Abdulaal A, et al., 2022, Variability in detection of SARS-CoV-2-specific antibody responses following mild infection: a prospective multicentre cross-sectional study, London, United Kingdom, 17 April to 17 July 2020, Eurosurveillance, Vol: 27, ISSN: 1025-496X
IntroductionImmunoassays targeting different SARS-CoV-2-specific antibodies are employed for seroprevalence studies. The degree of variability between immunoassays targeting anti-nucleocapsid (anti-NP; the majority) vs the potentially neutralising anti-spike antibodies (including anti-receptor-binding domain; anti-RBD), particularly in mild or asymptomatic disease, remains unclear.AimsWe aimed to explore variability in anti-NP and anti-RBD antibody detectability following mild symptomatic or asymptomatic SARS-CoV-2 infection and analyse antibody response for correlation with symptomatology.MethodsA multicentre prospective cross-sectional study was undertaken (April–July 2020). Paired serum samples were tested for anti-NP and anti-RBD IgG antibodies and reactivity expressed as binding ratios (BR). Multivariate linear regression was performed analysing age, sex, time since onset, symptomatology, anti-NP and anti-RBD antibody BR.ResultsWe included 906 adults. Antibody results (793/906; 87.5%; 95% confidence interval: 85.2–89.6) and BR strongly correlated (ρ = 0.75). PCR-confirmed cases were more frequently identified by anti-RBD (129/130) than anti-NP (123/130). Anti-RBD testing identified 83 of 325 (25.5%) cases otherwise reported as negative for anti-NP. Anti-NP presence (+1.75/unit increase; p < 0.001), fever (≥ 38°C; +1.81; p < 0.001) or anosmia (+1.91; p < 0.001) were significantly associated with increased anti-RBD BR. Age (p = 0.85), sex (p = 0.28) and cough (p = 0.35) were not. When time since symptom onset was considered, we did not observe a significant change in anti-RBD BR (p = 0.95) but did note decreasing anti-NP BR (p < 0.001).ConclusionSARS-CoV-2 anti-RBD IgG showed significant correlation with anti-NP IgG for absolute seroconversion and BR. Higher BR were seen in symptomatic indiv
Cavallo FR, Golden C, Pearson-Stuttard J, et al., 2022, The association between sedentary behaviour, physical activity and type 2 diabetes markers: A systematic review of mixed analytic approaches, PLOS ONE, Vol: 17, ISSN: 1932-6203
Burdett A, Toumazou C, Sahoo R, et al., 2021, Pooled sputum to optimise the efficiency and utility of rapid, point-of-care molecular SARS-CoV-2 testing, BMC Infectious Diseases, Vol: 21, Pages: 1-10, ISSN: 1471-2334
BackgroundAs SARS-CoV-2 testing expands, particularly to widespread asymptomatic testing, high sensitivity point-of-care PCR platforms may optimise potential benefits from pooling multiple patients’ samples.MethodWe tested patients and asymptomatic citizens for SARS-CoV-2, exploring the efficiency and utility of CovidNudge (i) for detection in individuals’ sputum (compared to nasopharyngeal swabs), (ii) for detection in pooled sputum samples, and (iii) by modelling roll out scenarios for pooled sputum testing.ResultsAcross 295 paired samples, we find no difference (p = 0.1236) in signal strength for sputum (mean amplified replicates (MAR) 25.2, standard deviation (SD) 14.2, range 0–60) compared to nasopharyngeal swabs (MAR 27.8, SD 12.4, range 6–56). At 10-sample pool size we find some drop in absolute strength of signal (individual sputum MAR 42.1, SD 11.8, range 13–60 vs. pooled sputum MAR 25.3, SD 14.6, range 1–54; p < 0.0001), but only marginal drop in sensitivity (51/53,96%). We determine a limit of detection of 250 copies/ml for an individual test, rising only four-fold to 1000copies/ml for a 10-sample pool. We find optimal pooled testing efficiency to be a 12–3-1-sample model, yet as prevalence increases, pool size should decrease; at 5% prevalence to maintain a 75% probability of negative first test, 5-sample pools are optimal.ConclusionWe describe for the first time the use of sequentially dipped sputum samples for rapid pooled point of care SARS-CoV-2 PCR testing. The potential to screen asymptomatic cohorts rapidly, at the point-of-care, with PCR, offers the potential to quickly identify and isolate positive individuals within a population “bubble”.
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.
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.
Cavallo FR, Mirza KB, de Mateo S, et al., 2021, Aptasensor for quantification of leptin through PCR amplification of short DNA-aptamers., ACS Sensors, Vol: 6, Pages: 709-715, ISSN: 2379-3694
Protein quantification is traditionally performed through enzyme-linked immunosorbent assay (ELISA), which involves long preparation times. To overcome this, new approaches use aptamers as an alternative to antibodies. In this paper, we present a new approach to quantify proteins with short DNA aptamers through polymerase chain reaction (PCR) resulting in shorter protocol times with comparatively improved limits of detection. The proposed method includes a novel way to quantify both the target protein and the corresponding short DNA-aptamers simultaneously, which also allows us to fully characterize the performance of aptasensors. Human leptin is used as a target protein to validate this technique, because it is considered an important biomarker for obesity-related studies. In our experiments, we achieved the lowest limit of detection of 100 pg/mL within less than 2 h, a limit affected by the dissociation constant of the leptin aptamer, which could be improved by selecting a more specific aptamer. Because of the simple and inexpensive approach, this technique can be employed for Lab-On-Chip implementations and for rapid "on-site" quantification of proteins.
Occhipinti E, Mirza KB, Toumazou C, 2021, Personalised trainer recommendation based on physical activity and genetic profile
Foot-care specialists recommend shoes by analysing the patient's gait cycle and looking for any structural or functional problems. Such methods are time consuming, inaccurate and unable to identify any risk factors that may lead to development of foot-related diseases in the future. This work presents a footwear recommendation algorithm based on genetic predispositions i.e. the genetic profile associated to selected Single Nucleotide Polymorphisms (SNPs), and the individual activity level, in addition to age, body mass index (BMI) and pronation. The algorithm, built on an Artificial Neural Network (ANN), returns a personalised recommendation for four different commercially available shoe categories (Minimalist, Stability, Motion Control, Cushioned). The activity profiles are generated based on features extracted from actual users' step count data collected via the wearable device DnaBand™, which are then combined with users' physical information and genetic profile. The Gaussian Mixture Model (GMM) has been found to best identify the relevant activity profiles' clusters. 5 case studies have been selected and used to validate the ANN output.
Han Z, Francesca C, Nikolic K, et al., 2021, Signal identification of DNA amplification curves in custom-PCR platforms, ISSN: 0271-4310
Custom-made, point-of-care PCR platforms are a necessary tool for rapid, point-of-care diagnostics in situations such as the current Covid-19 pandemic. However, a common issue faced by them is noisy fluorescence signals, which consist of a drifting baseline or noisy sigmoidal curve. This makes automated detection difficult and requires human verification. In this paper, we have tried to use nonlinear fitting for automated classification of PCR waveforms to identify whether amplification has taken place or not. We have presented several novel signal reconstruction techniques based on nonlinear fitting which will enable better pre-processing and automated differentiation of a valid or invalid PCR amplification curve. We have also tried to perform this classification at lower PCR cycles to reduce decision times in diagnostic tests.
Cavallo FR, Mirza KB, de Mateo S, et al., 2021, A Point-of-Care Device for Sensitive Protein Quantification, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302
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Han Z, Francesca C, Nikolic K, et al., 2021, Signal Identification of DNA Amplification Curves in Custom-PCR Platforms, IEEE International Symposium on Circuits and Systems (IEEE ISCAS), Publisher: IEEE, ISSN: 0271-4302
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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
Zhang J, Alexandrou G, Toumazou C, et al., 2021, Automating the Design of Cancer Specific DNA Probes Using Computational Algorithms, 43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC), Publisher: IEEE, Pages: 1852-1856, ISSN: 1557-170X
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Wildner K, Mirza KB, De La Franier B, et al., 2020, Iridium oxide based potassium sensitive microprobe with anti-fouling properties, IEEE Sensors Journal, Vol: 20, Pages: 12610-12619, ISSN: 1530-437X
Here, we present a new type of potassium sensor which possesses a combination of potassium sensing and anti-biofouling properties. Two major advancements were required to be developed with respect to the current technology; Firstly, design of surface linkers for this type of coating that would allow deposition of the potassiumselective coating on Iridium (Ir) wire or micro-spike surface for chronic monitoring for the first time. As this has never been done before, even for flat Ir surfaces, the material’s small dimensions and surface area render this challenging. Secondly, the task of transformation of the coated wire into a sensor. Here we develop and bench-test the electrode sensitivity to potassium and determine its specificity to potassium versus sodium interference. For this purpose we also present a novel characterisation platform which enables dynamic characterization of the sensor including step and sinusoidal response to analyte changes. The developed sensor shows good sensitivity (<1 mM concentrations of K+ ions) and selectivity (up to approximately 10 times more sensitive to K+ than Na+ concentration changes, depending on concentrations and ionic environment). In addition, the sensor displays very good mechanical properties for the small diameter involved (sub 150 μm), which in combination with anti-biofouling properties, renders it an excellent potential tool for the chemical monitoring of neural and other physiological activities using implantable devices.
Liu X, Chen C-H, Karvela M, et al., 2020, A DNA-Based Intelligent Expert System for Personalised Skin-Health Recommendations, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 24, Pages: 3276-3284, ISSN: 2168-2194
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- Citations: 7
Gibani MM, Toumazou C, Sohbati M, et al., 2020, Assessing a novel, lab-free, point-of-care test for SARS-CoV-2 (CovidNudge): a diagnostic accuracy study., The Lancet Microbe, Vol: 1, Pages: e300-e307, ISSN: 2666-5247
Background: Access to rapid diagnosis is key to the control and management of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Laboratory RT-PCR testing is the current standard of care but usually requires a centralised laboratory and significant infrastructure. We describe our diagnostic accuracy assessment of a novel, rapid point-of-care real time RT-PCR CovidNudge test, which requires no laboratory handling or sample pre-processing. Methods: Between April and May, 2020, we obtained two nasopharyngeal swab samples from individuals in three hospitals in London and Oxford (UK). Samples were collected from three groups: self-referred health-care workers with suspected COVID-19; patients attending emergency departments with suspected COVID-19; and hospital inpatient admissions with or without suspected COVID-19. For the CovidNudge test, nasopharyngeal swabs were inserted directly into a cartridge which contains all reagents and components required for RT-PCR reactions, including multiple technical replicates of seven SARS-CoV-2 gene targets (rdrp1, rdrp2, e-gene, n-gene, n1, n2 and n3) and human ribonuclease P (RNaseP) as sample adequacy control. Swab samples were tested in parallel using the CovidNudge platform, and with standard laboratory RT-PCR using swabs in viral transport medium for processing in a central laboratory. The primary analysis was to compare the sensitivity and specificity of the point-of-care CovidNudge test with laboratory-based testing. Findings: We obtained 386 paired samples: 280 (73%) from self-referred health-care workers, 15 (4%) from patients in the emergency department, and 91 (23%) hospital inpatient admissions. Of the 386 paired samples, 67 tested positive on the CovidNudge point-of-care platform and 71 with standard laboratory RT-PCR. The overall sensitivity of the point-of-care test compared with laboratory-based testing was 94% (95% CI 86-98) with an overall specificity of 100% (99-100). The sensitivity of the test varied
Roever P, Mirza KB, Nikolic K, et al., 2020, Convolutional neural network for classification of nerve activity based on action potential induced neurochemical signatures, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 1-5, ISSN: 0271-4302
Neural activity results in chemical changes in theextracellular environment such as variation in pH or potassium/sodium ion concentration. Higher signal to noise ratio makeneurochemical signals an interesting biomarker for closed-loopneuromodulation systems. For such applications, it is importantto reliably classify pH signatures to control stimulationtiming and possibly dosage. For example, the activity of thesubdiaphragmatic vagus nerve (sVN) branch can be monitoredby measuring extracellular neural pH. More importantly, guthormone cholecystokinin (CCK)-specific activity on the sVN canbe used for controllably activating sVN, in order to mimic thegut-brain neural response to food intake. In this paper, we presenta convolutional neural network (CNN) based classification systemto identify CCK-specific neurochemical changes on the sVN,from non-linear background activity. Here we present a novelfeature engineering approach which enables, after training, ahigh accuracy classification of neurochemical signals using CNN.
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.
Gibani M, Toumazou C, Sohbati M, et al., 2020, CovidNudge: diagnostic accuracy of a novel lab-free point-of-care diagnostic for SARS-CoV-2, Publisher: Cold Spring Harbor Laboratory
Background Access to rapid diagnosis is key to the control and management of SARS-CoV-2. Reverse Transcriptase-Polymerase Chain Reaction (RT-PCR) testing usually requires a centralised laboratory and significant infrastructure. We describe the development and diagnostic accuracy assessment of a novel, rapid point-of-care RT-PCR test, the DnaNudge® platform CovidNudge test, which requires no laboratory handling or sample pre-processing.Methods Nasopharyngeal swabs are inserted directly into a cartridge which contains all reagents and components required for RT-PCR reactions, including multiple technical replicates of seven SARS-CoV-2 gene targets (rdrp1, rdrp2, e-gene, n-gene, n1, n2 and n3) and human ribonuclease P (RNaseP) as positive control. Between April and May 2020, swab samples were tested in parallel using the CovidNudge direct-to-cartridge platform and standard laboratory RT-PCR using swabs in viral transport medium. Samples were collected from three groups: self-referred healthcare workers with suspected COVID-19 (Group 1, n=280/386; 73%); patients attending the emergency department with suspected COVID-19 (Group 2, n=15/386; 4%) and hospital inpatient admissions with or without suspected COVID-19 (Group 3, n=91/386; 23%).Results Of 386 paired samples tested across all groups, 67 tested positive on the CovidNudge platform and 71 with standard laboratory RT-PCR. The sensitivity of the test varied by group (Group 1 93% [84-98%], Group 2 100% [48-100%] and Group 3 100% [29-100%], giving an average sensitivity of 94.4% (95% confidence interval 86-98%) and an overall specificity of 100% (95%CI 99-100%; Group 1 100% [98-100%]; Group 2 100% [69-100%] and Group 3 100% [96-100%]). Point of care testing performance was comparable during a period of high (25%) and low (3%) background prevalence. Amplification of the viral nucleocapsid (n1, n2, n3) targets were most sensitive for detection of SARS-CoV2, with the assay able to detect 1×104 viral particles in
Kalofonou M, Malpartida-Cardenas K, Alexandrou G, et al., 2020, A novel hotspot specific isothermal amplification method for detection of thecommon PIK3CA p.H1047R breast cancer mutation, Scientific Reports, Vol: 10, ISSN: 2045-2322
Breast cancer (BC) is a common cancer in women worldwide. Despite advances in treatment, up to 30% of women eventually relapse and die of metastatic breast cancer. Liquid biopsy analysis of circulating cell-free DNA fragments in the patients’ blood can monitor clonality and evolving mutations as a surrogate for tumour biopsy. Next generation sequencing platforms and digital droplet PCR can be used to profile circulating tumour DNA from liquid biopsies; however, they are expensive and time consuming for clinical use. Here, we report a novel strategy with proof-of-concept data that supports the usage of loop-mediated isothermal amplification (LAMP) to detect PIK3CA c.3140 A > G (H1047R), a prevalent BC missense mutation that is attributed to BC tumour growth. Allele-specific primers were designed and optimized to detect the p.H1047R variant following the USS-sbLAMP method. The assay was developed with synthetic DNA templates and validated with DNA from two breast cancer cell-lines and two patient tumour tissue samples through a qPCR instrument and finally piloted on an ISFET enabled microchip. This work sets a foundation for BC mutational profiling on a Lab-on-Chip device, to help the early detection of patient relapse and to monitor efficacy of systemic therapies for personalised cancer patient management.
Alexandrou G, Rodriguez-Manzano J, Malpartida-Cardenas K, et al., 2020, In-silico automated allele-specific primer design for loop-mediated isothermal amplification, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302
Mirza KB, Golden C, Nikolic K, et al., 2019, Closed-loop implantable therapeutic neuromodulation systems based on neurochemical monitoring, Frontiers in Neuroscience, Vol: 13, ISSN: 1662-4548
Closed-loop or intelligent neuromodulation allows adjustable, personalised neuromodulation which usually incorporates the recording of a biomarker, followed by implementation of an algori5 thm which decides the timing (when ?) and strength (how much ?) of stimulation. Closed-loop neuromodulation has been shown to have greater benefits compared to open-loop neuromodu lation, particularly for therapeutic applications such as pharmacoresistant epilepsy, movement disorders and potentially for psychological disorders such as depression or drug addiction. How ever, an important aspect of the technique is selection of an appropriate, preferably neural biomarker. Neurochemical sensing can provide high resolution biomarker monitoring for various neurological disorders as well as offer deeper insight into neurological mechanisms. The chemicals of interest being measured, could be ions such as potassium (K+), sodium (Na+ 12 ), calcium(Ca2+), chloride (Cl−), hydrogen (H+ 13 ) or neurotransmitters such as dopamine, serotonin and glutamate. This review focusses on the different building blocks necessary for a neurochemi cal, closed-loop neuromodulation system including biomarkers, sensors and data processing algorithms. Furthermore, it also highlights the merits and drawbacks of using this biomarker modality.
Toumazou C, Baig Mirza K, Rodriguez Manzano J, 2019, Molecule detection using aptamer nucleic acid duplex, WO2019138255A1
The present invention relates to the detection of molecules of a target type in a sample. A method of detecting molecules of a target type in a sample comprises providing an aptamer-nucleic acid duplex, contacting the sample with the aptamer-nucleic acid duplex, wherein the aptamer is capable of selectively dissociating from the nucleic acid to selectively bind to a molecule of the target type in the sample, amplifying any dissociated nucleic acid and detecting any amplified nucleic acid. The method further comprises using the detected result to indicate the presence of molecules of the target type and/or quantify an amount of molecules of the target type. Also provided is a system for detecting molecules of a target type in a sample.
Mirza KB, Kulasekeram N, Liu Y, et al., 2019, System on chip for closed loop neuromodulation based on dual mode biosignals, 2019 IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: Institute of Electrical and Electronics Engineers (IEEE), ISSN: 2158-1525
Closed loop neuromodulation, where the stimulation is controlled autonomously based on physiological events, has been more effective than open loop techniques. In the few existing closed loop implementations which have a feedback, indirect non-neurophysiological biomarkers have been typically used (e.g. heart rate, stomach distension). Although these biomarkers enable automatic initiation of neural stimulation, they do not enable intelligent control of stimulation dosage. In this paper, we present a novel closed loop neuromodulation System-on-Chip (SoC) based on a dual signal mode that is detecting both electrical and chemical signatures of neural activity. We use vagus nerve stimulation (VNS) as a design case here. Vagal chemical (pH) signal is detected and used for initiating VNS and vagal compound nerve action potential (CNAP) signals are used to determine the stimulation dosage and pattern. Although we used the paradigm of appetite control and neurometabolic therapies for developing the algorithms for neurostimulation control, the SoC described here can be utilised for other types of closed loop neuromodulation implants.
Rawson TM, Hernandez B, Moore L, et al., 2019, Supervised machine learning for the prediction of infection on admission to hospital: a prospective observational cohort study, Journal of Antimicrobial Chemotherapy, Vol: 74, Pages: 1108-1115, ISSN: 0305-7453
BackgroundInfection diagnosis can be challenging, relying on clinical judgement and non-specific markers of infection. We evaluated a supervised machine learning (SML) algorithm for diagnosing bacterial infection using routinely available blood parameters on presentation to hospital.MethodsAn SML algorithm was developed to classify cases into infection versus no infection using microbiology records and six available blood parameters (C-reactive protein, white cell count, bilirubin, creatinine, ALT and alkaline phosphatase) from 160 203 individuals. A cohort of patients admitted to hospital over a 6 month period had their admission blood parameters prospectively inputted into the SML algorithm. They were prospectively followed up from admission to classify those who fulfilled clinical case criteria for a community-acquired bacterial infection within 72 h of admission using a pre-determined definition. Predictive ability was assessed using receiver operating characteristics (ROC) with cut-off values for optimal sensitivity and specificity explored.ResultsOne hundred and four individuals were included prospectively. The median (range) cohort age was 65 (21–98) years. The majority were female (56/104; 54%). Thirty-six (35%) were diagnosed with infection in the first 72 h of admission. Overall, 44/104 (42%) individuals had microbiological investigations performed. Treatment was prescribed for 33/36 (92%) of infected individuals and 4/68 (6%) of those with no identifiable bacterial infection. Mean (SD) likelihood estimates for those with and without infection were significantly different. The infection group had a likelihood of 0.80 (0.09) and the non-infection group 0.50 (0.29) (P < 0.01; 95% CI: 0.20–0.40). ROC AUC was 0.84 (95% CI: 0.76–0.91).ConclusionsAn SML algorithm was able to diagnose infection in individuals presenting to hospital using routinely available blood parameters.
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