Publications
360 results found
Zhu T, Li K, Herrero P, et al., 2023, GluGAN: Generating Personalized Glucose Time Series Using Generative Adversarial Networks., IEEE J Biomed Health Inform, Vol: PP
Time series data generated by continuous glucose monitoring sensors offer unparalleled opportunities for developing data-driven approaches, especially deep learning-based models, in diabetes management. Although these approaches have achieved state-of-the-art performance in various fields such as glucose prediction in type 1 diabetes (T1D), challenges remain in the acquisition of large-scale individual data for personalized modeling due to the elevated cost of clinical trials and data privacy regulations. In this work, we introduce GluGAN, a framework specifically designed for generating personalized glucose time series based on generative adversarial networks (GANs). Employing recurrent neural network (RNN) modules, the proposed framework uses a combination of unsupervised and supervised training to learn temporal dynamics in latent spaces. Aiming to assess the quality of synthetic data, we apply clinical metrics, distance scores, and discriminative and predictive scores computed by post-hoc RNNs in evaluation. Across three clinical datasets with 47 T1D subjects (including one publicly available and two proprietary datasets), GluGAN achieved better performance for all the considered metrics when compared with four baseline GAN models. The performance of data augmentation is evaluated by three machine learning-based glucose predictors. Using the training sets augmented by GluGAN significantly reduced the root mean square error for the predictors over 30 and 60-minute horizons. The results suggest that GluGAN is an effective method in generating high-quality synthetic glucose time series and has the potential to be used for evaluating the effectiveness of automated insulin delivery algorithms and as a digital twin to substitute for pre-clinical trials.
Noaro G, Zhu T, Cappon G, et al., 2023, A Personalized and Adaptive Insulin Bolus Calculator Based on Double Deep Q- Learning to Improve Type 1 Diabetes Management., IEEE J Biomed Health Inform, Vol: 27, Pages: 2536-2544
Mealtime insulin dosing is a major challenge for people living with type 1 diabetes (T1D). This task is typically performed using a standard formula that, despite containing some patient-specific parameters, often leads to sub-optimal glucose control due to lack of personalization and adaptation. To overcome the previous limitations here we propose an individualized and adaptive mealtime insulin bolus calculator based on double deep Q-learning (DDQ), which is tailored to the patient thanks to a personalization procedure relying on a two-step learning framework. The DDQ-learning bolus calculator was developed and tested using the UVA/Padova T1D simulator modified to reliably mimic real-world scenarios by introducing multiple variability sources impacting glucose metabolism and technology. The learning phase included a long-term training of eight sub-population models, one for each representative subject, selected thanks to a clustering procedure applied to the training set. Then, for each subject of the testing set, a personalization procedure was performed, by initializing the models based on the cluster to which the patient belongs. We evaluated the effectiveness of the proposed bolus calculator on a 60-day simulation, using several metrics representing the goodness of glycemic control, and comparing the results with the standard guidelines for mealtime insulin dosing. The proposed method improved the time in target range from 68.35% to 70.08% and significantly reduced the time in hypoglycemia (from 8.78% to 4.17%). The overall glycemic risk index decreased from 8.2 to 7.3, indicating the benefit of our method when applied for insulin dosing compared to standard guidelines.
Unsworth R, Armiger R, Jugnee N, et al., 2023, Safety and efficacy of an adaptive bolus calculator for Type 1 diabetes: a randomised control cross over study, Diabetes Technology and Therapeutics, ISSN: 1520-9156
Background The Advanced Bolus Calculator for Type 1 Diabetes (ABC4D) is a decision support system employing the artificial intelligence technique of case-based reasoning to adapt and personalise insulin bolus doses. The integrated system comprises a smartphone application and clinical web portal. We aimed to assess safety and efficacy of the ABC4D (intervention) compared to a non-adaptive bolus calculator (control). Methods This was a prospective randomised controlled crossover study. Following a 2-week run-in period, participants were randomised to ABC4D or control for 12 weeks. After a 6-week washout period, participants crossed over for 12 weeks. The primary outcome was difference in percentage (%) time in range (TIR) (3.9-10.0 mmol/L (70-180mg/dL)) change during the daytime (07:00-22:00) between groups. Results 37 adults with type 1 diabetes on multiple daily injections of insulin were randomised, median (IQR) age 44.7 (28.2-55.2) years, diabetes duration 15.0 (9.5-29.0) years, HbA1C 61.0 (58.0-67.0) mmol/mol (7.7 (7.5-8.3)%). Data from 33 participants were analysed. There was no significant difference in daytime %TIR change with ABC4D compared to control (median (IQR) +0.1 (-2.6 to + 4.0)% versus +1.9 (-3.8 to + 10.1)%; p = 0.53). Participants accepted fewer meal dose recommendations in the intervention compared to control (78.7 (55.8-97.6)% versus 93.5 (73.8-100)%; p = 0.009) with a greater reduction in insulin dosage from that recommended. Conclusion The ABC4D is safe for adapting insulin bolus doses and provided the same level of glycaemic control as the non-adaptive bolus calculator. Results suggest that participants did not follow ABC4D recommendations as frequently as control, impacting its effectiveness.
Mao Y, Miglietta L, Kreitmann L, et al., 2023, Deep domain adaptation enhances Amplification Curve Analysis for single-channel multiplexing in real-time PCR, IEEE Journal of Biomedical and Health Informatics, ISSN: 2168-2208
Data-driven approaches for molecular diagnostics are emerging as an alternative to perform an accurate and inexpensive multi-pathogen detection. A novel technique called Amplification Curve Analysis (ACA) has been recently developed by coupling machine learning and real-time Polymerase Chain Reaction (qPCR) to enable the simultaneous detection of multiple targets in a single reaction well. However, target classification purely relying on the amplification curve shapes currently faces several challenges, such as distribution discrepancies between different data sources of synthetic DNA and clinical samples (i.e., training vs testing). Optimisation of computational models is required to achieve higher performance of ACA classification in multiplex qPCR through the reduction of those discrepancies. Here, we proposed a novel transformer-based conditional domain adversarial network (T-CDAN) to eliminate data distribution differences between the source domain (synthetic DNA data) and the target domain (clinical isolate data). The labelled training data from the source domain and unlabelled testing data from the target domain are fed into the T-CDAN, which learns both domains' information simultaneously. After mapping the inputs into a domain-irrelevant space, T-CDAN removes the feature distribution differences and provides a clearer decision boundary for the classifier, resulting in a more accurate pathogen identification. Evaluation of 198 clinical isolates containing three types of carbapenem-resistant genes ( bla NDM , bla IMP and bla OXA-48 ) illustrates a curve-level accuracy of 93.1% and a sample-level accuracy of 97.0% using T-CDAN, showing an accuracy improvement of 20.9% and 4.9% respectively, compared with previous methods. This research emphasises the importance of deep domain adaptation to enable high-level multiplexing in a single qPCR reaction, providing a solid approach to extend qPCR instruments' capabilities without hardware modification in real-world cli
Zhu T, Kuang L, Daniels J, et al., 2023, IoMT-Enabled Real-Time Blood Glucose Prediction With Deep Learning and Edge Computing, IEEE INTERNET OF THINGS JOURNAL, Vol: 10, Pages: 3706-3719, ISSN: 2327-4662
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- Citations: 10
Zhu T, Li K, Herrero P, et al., 2023, Personalized blood glucose prediction for Type 1 diabetes using evidential deep learning and meta-learning., IEEE Transactions on Biomedical Engineering, Vol: 70, Pages: 193-204, ISSN: 0018-9294
The availability of large amounts of data from continuous glucose monitoring (CGM), together with the latest advances in deep learning techniques, have opened the door to a new paradigm of algorithm design for personalized blood glucose (BG) prediction in type 1 diabetes (T1D) with superior performance. However, there are several challenges that prevent the widespread implementation of deep learning algorithms in actual clinical settings, including unclear prediction confidence and limited training data for new T1D subjects. To this end, we propose a novel deep learning framework, Fast-adaptive and Confident Neural Network (FCNN), to meet these clinical challenges. In particular, an attention-based recurrent neural network is used to learn representations from CGM input and forward a weighted sum of hidden states to an evidential output layer, aiming to compute personalized BG predictions with theoretically supported model confidence. The model-agnostic meta-learning is employed to enable fast adaptation for a new T1D subject with limited training data. The proposed framework has been validated on three clinical datasets. In particular, for a dataset including 12 subjects with T1D, FCNN achieved a root mean square error of 18.64±2.60 mg/dL and 31.07±3.62 mg/dL for 30 and 60-minute prediction horizons, respectively, which outperformed all the considered baseline methods with significant improvements. These results indicate that FCNN is a viable and effective approach for predicting BG levels in T1D. The well-trained models can be implemented in smartphone apps to improve glycemic control by enabling proactive actions through real-time glucose alerts.
Broomfield J, Kalofonou M, Pataillot-Meakin T, et al., 2022, Detection of YAP1 and AR-V7 mRNA for Prostate Cancer prognosis using an ISFET Lab-On-Chip platform, Publisher: Cold Spring Harbor Laboratory
<jats:title>Abstract</jats:title><jats:p>Prostate cancer (PCa) is the second most common cause of male cancer-related death worldwide. The gold standard of treatment for advanced PCa is androgen deprivation therapy (ADT). However, eventual failure of ADT is common and leads to lethal metastatic castration resistant PCa (mCRPC). As such, the detection of relevant biomarkers in the blood for drug resistance in mCRPC patients could lead to personalized treatment options. mRNA detection is often limited by the low specificity of qPCR assays which are restricted to specialised laboratories. Here, we present a novel reversetranscription loop-mediated isothermal amplification (RT-LAMP) assay and have demonstrated its capability for sensitive detection of AR-V7 and YAP1 RNA (3×10<jats:sup>1</jats:sup> RNA copies per reaction). This work presents a foundation for the detection of circulating mRNA in PCa on a non-invasive Lab-on-chip (LoC) device for use at point-of-care. This technique was implemented onto a Lab-on-Chip platform integrating an array of chemical sensors (ion-sensitive field-effect transistors - ISFETs) for real-time detection of RNA. Detection of RNA presence was achieved through the translation of chemical signals into electrical readouts. Validation of this technique was conducted with rapid detection (<jats:italic><</jats:italic>15 min) of extracted RNA from prostate cancer cell lines 22Rv1s and DU145s.</jats:p>
Zeng J, Kuang L, Cicatiello C, et al., 2022, A LoC Ion Imaging Platform for Spatio-Temporal Characterisation of Ion-Selective Membranes, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 16, Pages: 545-556, ISSN: 1932-4545
Malpartida-Cardenas K, Baum J, Cunnington A, et al., 2022, Electricity-free nucleic acid extraction method from dried blood spots on filter paper for point-of-care diagnostics
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Nucleic acid extraction is a crucial step for molecular biology applications, being a determinant for any diagnostic test procedure. Dried blood spots (DBS) have been used for decades for serology, drug monitoring, environmental investigations, and molecular studies. Nevertheless, nucleic acid extraction from DBS remains one of the main challenges to translate them to the point-of-care (POC).</jats:p></jats:sec><jats:sec><jats:title>Method</jats:title><jats:p>We have developed a fast nucleic acid extraction (NAE) method from DBS which is electricity-free and relies on cellulose filter papers (DBSFP). The performance of NAE was assessed with loop-mediated isothermal amplification (LAMP), targeting the human reference gene beta-actin. The developed method was evaluated against FTA cards and magnetic bead-based purification, using time-to-positive (min) for comparative analysis. We optimised and validated the developed method for elution (<jats:italic>eluted disk</jats:italic>) and disk directly in the reaction (<jats:italic>in-situ disk)</jats:italic>, RNA and DNA detection, and whole blood stored in anticoagulants (K<jats:sub>2</jats:sub>EDTA and lithium heparin). Furthermore, the compatibility of DBSFP with colourimetric detection was studied to show the transferability to the POC.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>The proposed DBSFP is based on grade 3 filter paper pre-treated with 8% (v/v) igepal surfactant, 1 min washing step with PBS 1X and elution in TE 1X buffer after 5 min incubation at room temperature, enabling NAE under 7 min. Obtained results were comparable to gold standard methods across tested matrices, targets and experimental conditions, demonstrating the versatility of the methodology. Las
Pennisi I, Moniri A, Miscourides N, et al., 2022, Discrimination of bacterial and viral infection using host-RNA signatures integrated in a lab-on-a-chip technology, Publisher: MedRxiv
<h4>ABSTRACT</h4> The unmet clinical need for accurate point-of-care (POC) diagnostic tests able to discriminate bacterial from viral infection demands a solution that can be used both within healthcare settings and in the field and that can also stem the tide of antimicrobial resistance. Our approach to solve this problem is to combine the use of Host-gene signatures with our Lab-on-a-chip (LoC) technology enabling low-cost LoC expression analysis to detect Infectious Disease.Host-gene expression signatures have been extensively study as a potential tool to be implemented in the diagnosis of infectious disease. On the other hand LoC technologies using Ion-sensitive field-effect transistor (ISFET) arrays, in conjunction with isothermal chemistries, are offering a promising alternative to conventional lab-based nucleic acid amplification instruments, owing to their portable and affordable nature. Currently, the data analysis of ISFET arrays are restricted to established methods by averaging the output of every sensor to give a single time-series. This simple approach makes unrealistic assumptions, leading to insufficient performance for applications that require accurate quantification such as RNA host transcriptomics. In order to reliably quantify host-gene expression on our LoC platform enabling the classification of bacterial and viral infection on chip, we propose a novel data-driven algorithm for extracting time-to-positive values from ISFET arrays. The algorithm proposed is based on modelling sensor drift with adaptive signal processing and clustering sensors based on their behaviour with unsupervised learning methods. Results show that the approach correctly outputs a time-to-positive for all the reactions, with a high correlation to RT-qLAMP (0.85, R2 = 0.98, p < 0.01), resulting in a classification accuracy of 100 % (CI, 95 - 100). By leveraging more advanced data processing methods for ISFET arrays, this work aims to bridge the gap between tr
Beykou M, Arias-Garcia M, Roumeliotis TI, et al., 2022, Proteomic characterisation of triple negative breast cancer cells following CDK4/6 inhibition, SCIENTIFIC DATA, Vol: 9
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- Citations: 1
Miglietta L, Chen Y, Luo Z, et al., 2022, Smart-Plexer: a breakthrough workflow for hybrid development of multiplex PCR assays
<jats:title>Abstract</jats:title> <jats:p>Developing multiplex PCR assays requires an extensive amount of experimental testing, the number of which exponentially increases by the number of multiplexed targets. Dedicated efforts must be devoted to the design of optimal multiplex assays for specific and sensitive identification of multiple analytes in a single well reaction. Inspired by data-driven approaches, we reinvent the way of designing and developing multiplex assays by proposing a hybrid, easy-to-use workflow, named Smart-Plexer, which couples empirical testing of singleplex assays and computer simulation of multiplexing. The Smart-Plexer leverages kinetic inter-target distances among amplification curves to generate optimal multiplex PCR primer sets for accurate multi-pathogen identification. The optimal single-channel assays, together with a novel data-driven approach, Amplification Curve Analysis (ACA), were demonstrated to be capable of classifying the presence of desired targets in a single test for seven common respiratory infection pathogens.</jats:p>
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.
Herrero P, Reddy M, Georgiou P, et al., 2022, Identifying Continuous Glucose Monitoring Data Using Machine Learning, DIABETES TECHNOLOGY & THERAPEUTICS, Vol: 24, Pages: 403-408, ISSN: 1520-9156
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- Citations: 3
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
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: 4
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: 2
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: 3
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.
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: 24
Panteli C, Georgiou P, Fobelets K, 2021, Reduced Drift of CMOS ISFET pH Sensors Using Graphene Sheets, IEEE SENSORS JOURNAL, Vol: 21, Pages: 14609-14618, ISSN: 1530-437X
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- Citations: 1
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.
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
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 In Silico Validation, IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 25, Pages: 1223-1232, ISSN: 2168-2194
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- Citations: 19
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.
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