410 results found
Zhu T, Kuang L, Piao C, et al., 2024, Population-Specific Glucose Prediction in Diabetes Care With Transformer-Based Deep Learning on the Edge., IEEE Trans Biomed Circuits Syst, Vol: PP
Leveraging continuous glucose monitoring (CGM) systems, real-time blood glucose (BG) forecasting is essential for proactive interventions, playing a crucial role in enhancing the management of type 1 diabetes (T1D) and type 2 diabetes (T2D). However, developing a model generalized to a population and subsequently embedding it within a microchip of a wearable device presents significant technical challenges. Furthermore, the domain of BG prediction in T2D remains under-explored in the literature. In light of this, we propose a population-specific BG prediction model, leveraging the capabilities of the temporal fusion Transformer (TFT) to adjust predictions based on personal demographic data. Then the trained model is embedded within a system-on-chip, integral to our low-power and low-cost customized wearable device. This device seamlessly communicates with CGM systems through Bluetooth and provides timely BG predictions using edge computing. When evaluated on two publicly available clinical datasets with a total of 124 participants with T1D or T2D, the embedded TFT model consistently demonstrated superior performance, achieving the lowest prediction errors when compared with a range of machine learning baseline methods. Executing the TFT model on our wearable device requires minimal memory and power consumption, enabling continuous decision support for more than 51 days on a single Li-Poly battery charge. These findings demonstrate the significant potential of the proposed TFT model and wearable device in enhancing the quality of life for people with diabetes and effectively addressing real-world challenges.
Malik FK, Panteli C, Goel K, et al., 2023, Improved Stability of Graphene-Coated CMOS ISFETs for Biosensing., IEEE Trans Biomed Circuits Syst, Vol: 17, Pages: 1293-1304
A polymer-assisted graphene transfer method is used to transfer sheets of monolayer and multilayer graphene onto the passivation layer of ion-sensitive field effect transistor arrays. The arrays are fabricated using commercial 0.35 μm complementary metal-oxide-semiconductor (CMOS) technology and contain 3874 pixels sensitive to pH changes on the top silicon nitride surface. By inhibiting dispersive ion transport and hydration of this underlying nitride layer, the transferred graphene sheets help address non-idealities in the sensor response while retaining some pH sensitivity due to the presence of ion adsorption sites. Improvements in hydrophilicity and electrical conductivity of the sensing surface after graphene transfer, as well as in-plane molecular diffusion along the graphene-nitride interface, also greatly improve spatial consistency across an array, allowing for ∼20% more pixels to remain within operating range and enhancing sensor reliability. Multilayer graphene offers a better performance trade-off than monolayer graphene, reducing drift rate by ∼25% and drift amplitude by ∼59% with minimal reduction in pH sensitivity. Monolayer graphene offers slightly better temporal and spatial uniformity in performance of a sensing array, which is associated with the consistency in layer thickness and a lower defect density.
Zhu T, Li K, Georgiou P, 2023, Offline Deep Reinforcement Learning and Off-Policy Evaluation for Personalized Basal Insulin Control in Type 1 Diabetes., IEEE J Biomed Health Inform, Vol: 27, Pages: 5087-5098
Recent advancements in hybrid closed-loop systems, also known as the artificial pancreas (AP), have been shown to optimize glucose control and reduce the self-management burdens for people living with type 1 diabetes (T1D). AP systems can adjust the basal infusion rates of insulin pumps, facilitated by real-time communication with continuous glucose monitoring. Deep reinforcement learning (DRL) has introduced new paradigms of basal insulin control algorithms. However, all the existing DRL-based AP controllers require extensive random online interactions between the agent and environment. While this can be validated in T1D simulators, it becomes impractical in real-world clinical settings. To this end, we propose an offline DRL framework that can develop and validate models for basal insulin control entirely offline. It comprises a DRL model based on the twin delayed deep deterministic policy gradient and behavior cloning, as well as off-policy evaluation (OPE) using fitted Q evaluation. We evaluated the proposed framework on an in silico dataset generated by the UVA/Padova T1D simulator, and the OhioT1DM dataset, a real clinical dataset. The performance on the in silico dataset shows that the offline DRL algorithm significantly increased time in range while reducing time below range and time above range for both adult and adolescent groups. Then, we used the OPE to estimate model performance on the clinical dataset, where a notable increase in policy values was observed for each subject. The results demonstrate that the proposed framework is a viable and safe method for improving personalized basal insulin control in T1D.
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: 27, Pages: 5122-5133
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.
Broomfield J, Kalofonou M, Franklin S, et al., 2023, Handheld ISFET Lab-on-Chip detection of TMPRSS2-ERG and AR mRNA for prostate cancer prognostics, IEEE Sensors Letters, Vol: 7, Pages: 1-4, ISSN: 2475-1472
Ion-sensitive field-effect transistors (ISFETs) in combination with unmodified complementary metal oxide semiconductors present a point-of-care platform for clinical diagnostics and prognostics. This work illustrates the sensitive and specific detection of two circulating mRNA markers for prostate cancer, the androgen receptor and the TMPRSS2-ERG fusion using a target-specific loop-mediated isothermal amplification method. TMPRSS2-ERG and androgen receptor RNA were detected down to 3x10 1 and 5x10 1 copies respectively in under 30 minutes. Administration of these assays onto the ISFET Lab-on-chip device was successful and the specificity of each marker was corroborated with mRNA extracted from prostate cancer cell lines.
Afentakis I, Unsworth R, Herrero P, et al., 2023, Development and Validation of Binary Classifiers to Predict Nocturnal Hypoglycemia in Adults With Type 1 Diabetes, JOURNAL OF DIABETES SCIENCE AND TECHNOLOGY, ISSN: 1932-2968
Malpartida-Cardenas K, Baum J, Cunnington A, et al., 2023, A dual paper-based nucleic acid extraction method from blood in under ten minutes for point-of-care diagnostics, The Analyst, Vol: 148, Pages: 3036-3044, ISSN: 0003-2654
Nucleic acid extraction (NAE) plays a crucial role for diagnostic testing procedures. For decades, dried blood spots (DBS) have been used for serology, drug monitoring, and molecular studies. However, extracting nucleic acids from DBS remains a significant challenge, especially when attempting to implement these applications to the point-of-care (POC). To address this issue, we have developed a paper-based NAE method using cellulose filter papers (DBSFP) that operates without the need for electricity (at room temperature). Our method allows for NAE in less than 7 min, and it involves grade 3 filter paper pre-treated with 8% (v/v) igepal surfactant, 1 min washing step with 1× PBS, and 5 min incubation at room temperature in 1× TE buffer. The performance of the methodology was assessed with loop-mediated isothermal amplification (LAMP), targeting the human reference gene beta-actin and the kelch 13 gene from P. falciparum. The developed method was evaluated against FTA cards and magnetic bead-based purification, using time-to-positive (min) for comparative analysis. Furthermore, we optimised our approach to take advantage of the dual functionality of the paper-based extraction, allowing for elution (eluted disk) as well as direct placement of the disk in the LAMP reaction (in situ disk). This flexibility extends to eukaryotic cells, bacterial cells, and viral particles. We successfully validated the method for RNA/DNA detection and demonstrated its compatibility with whole blood stored in anticoagulants. Additionally, we studied the compatibility of DBSFP with colorimetric and lateral flow detection, showcasing its potential for POC applications. Across various tested matrices, targets, and experimental conditions, our results were comparable to those obtained using gold standard methods, highlighting the versatility of our methodology. In summary, this manuscript presents a cost-effective solution for NAE from DBS, enabling molecular testing in virtually
Tripathi P, Gulli C, Broomfield J, et al., 2023, Classification of nucleic acid amplification on ISFET arrays using spectrogram-based neural networks., Computers in Biology and Medicine, Vol: 161, Pages: 1-11, ISSN: 0010-4825
The COVID-19 pandemic has highlighted a significant research gap in the field of molecular diagnostics. This has brought forth the need for AI-based edge solutions that can provide quick diagnostic results whilst maintaining data privacy, security and high standards of sensitivity and specificity. This paper presents a novel proof-of-concept method to detect nucleic acid amplification using ISFET sensors and deep learning. This enables the detection of DNA and RNA on a low-cost and portable lab-on-chip platform for identifying infectious diseases and cancer biomarkers. We show that by using spectrograms to transform the signal to the time-frequency domain, image processing techniques can be applied to achieve the reliable classification of the detected chemical signals. Transformation to spectrograms is beneficial as it makes the data compatible with 2D convolutional neural networks and helps gain significant performance improvement over neural networks trained on the time domain data. The trained network achieves an accuracy of 84% with a size of 30kB making it suitable for deployment on edge devices. This facilitates a new wave of intelligent lab-on-chip platforms that combine microfluidics, CMOS-based chemical sensing arrays and AI-based edge solutions for more intelligent and rapid molecular diagnostics.
Malpartida Cardenas K, Moser N, Ansah F, et al., 2023, Sensitive detection of asymptomatic and symptomatic malaria with seven novel parasite-specific LAMP assays and translation for use at point-of-care, Microbiology Spectrum, Vol: 11, Pages: 1-12, ISSN: 2165-0497
Human malaria is a life-threatening parasitic disease with high impact in the sub-Saharan Africa region, where 95% of global cases occurred in 2021. While most malaria diagnostic tools are focused on Plasmodium falciparum, there is a current lack of testing non-P. falciparum cases, which may be underreported and, if undiagnosed or untreated, may lead to severe consequences. In this work, seven species-specific loop-mediated isothermal amplification (LAMP) assays were designed and evaluated against TaqMan quantitative PCR (qPCR), microscopy, and enzyme-linked immunosorbent assays (ELISAs). Their clinical performance was assessed with a cohort of 164 samples of symptomatic and asymptomatic patients from Ghana. All asymptomatic samples with a parasite load above 80 genomic DNA (gDNA) copies per μL of extracted sample were detected with the Plasmodium falciparum LAMP assay, reporting 95.6% (95% confidence interval [95% CI] of 89.9 to 98.5) sensitivity and 100% (95% CI of 87.2 to 100) specificity. This assay showed higher sensitivity than microscopy and ELISA, which were 52.7% (95% CI of 39.7 to 67%) and 67.3% (95% CI of 53.3 to 79.3%), respectively. Nine samples were positive for P. malariae, indicating coinfections with P. falciparum, which represented 5.5% of the tested population. No samples were detected as positive for P. vivax, P. ovale, P. knowlesi, or P. cynomolgi by any method. Furthermore, translation to the point-of-care was demonstrated with a subcohort of 18 samples tested locally in Ghana using our handheld lab-on-chip platform, Lacewing, showing comparable results to a conventional fluorescence-based instrument. The developed molecular diagnostic test could detect asymptomatic malaria cases, including submicroscopic parasitemia, and it has the potential to be used for point-of-care applications.
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 JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, Vol: 27, Pages: 2536-2544, ISSN: 2168-2194
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, Vol: 25, Pages: 414-425, 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, Vol: 27, Pages: 3093-3103, 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
Ming D, Nguyen QH, An LP, et al., 2023, Mapping patient pathways and understanding clinical decision-making in dengue management to inform the development of digital health tools, BMC Medical Informatics and Decision Making, Vol: 23, Pages: 1-9, ISSN: 1472-6947
BackgroundDengue is a common viral illness and severe disease results in life-threatening complications. Healthcare services in low- and middle-income countries treat the majority of dengue cases worldwide. However, the clinical decision-making processes which result in effective treatment are poorly characterised within this setting. In order to improve clinical care through interventions relating to digital clinical decision-support systems (CDSS), we set out to establish a framework for clinical decision-making in dengue management to inform implementation.MethodsWe utilised process mapping and task analysis methods to characterise existing dengue management at the Hospital for Tropical Diseases, Ho Chi Minh City, Vietnam. This is a tertiary referral hospital which manages approximately 30,000 patients with dengue each year, accepting referrals from Ho Chi Minh city and the surrounding catchment area. Initial findings were expanded through semi-structured interviews with clinicians in order to understand clinical reasoning and cognitive factors in detail. A grounded theory was used for coding and emergent themes were developed through iterative discussions with clinician-researchers.ResultsKey clinical decision-making points were identified: (i) at the initial patient evaluation for dengue diagnosis to decide on hospital admission and the provision of fluid/blood product therapy, (ii) in those patients who develop severe disease or other complications, (iii) at the point of recurrent shock in balancing the need for fluid therapy with complications of volume overload. From interviews the following themes were identified: prioritising clinical diagnosis and evaluation over existing diagnostics, the role of dengue guidelines published by the Ministry of Health, the impact of seasonality and caseload on decision-making strategies, and the potential role of digital decision-support and disease scoring tools.ConclusionsThe study highlights the contemporary priorities i
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.
Arkell P, Mairiang D, Songjaeng A, et al., 2023, Analytical and diagnostic performance characteristics of reverse-transcriptase loop-mediated isothermal amplification assays for dengue virus serotypes 1-4: A scoping review to inform potential use in portable molecular diagnostic devices., PLOS Glob Public Health, Vol: 3
Dengue is a mosquito-borne disease caused by dengue virus (DENV) serotypes 1-4 which affects 100-400 million adults and children each year. Reverse-transcriptase (RT) quantitative polymerase chain reaction (qPCR) assays are the current gold-standard in diagnosis and serotyping of infections, but their use in low-middle income countries (LMICs) has been limited by laboratory infrastructure requirements. Loop-mediated isothermal amplification (LAMP) assays do not require thermocycling equipment and therefore could potentially be deployed outside laboratories and/or miniaturised. This scoping literature review aimed to describe the analytical and diagnostic performance characteristics of previously developed serotype-specific dengue RT-LAMP assays and evaluate potential for use in portable molecular diagnostic devices. A literature search in Medline was conducted. Studies were included if they were listed before 4th May 2022 (no prior time limit set) and described the development of any serotype-specific DENV RT-LAMP assay ('original assays') or described the further evaluation, adaption or implementation of these assays. Technical features, analytical and diagnostic performance characteristics were collected for each assay. Eight original assays were identified. These were heterogenous in design and reporting. Assays' lower limit of detection (LLOD) and linear range of quantification were comparable to RT-qPCR (with lowest reported values 2.2x101 and 1.98x102 copies/ml, respectively, for studies which quantified target RNA copies) and analytical specificity was high. When evaluated, diagnostic performance was also high, though reference diagnostic criteria varied widely, prohibiting comparison between assays. Fourteen studies using previously described assays were identified, including those where reagents were lyophilised or 'printed' into microfluidic channels and where several novel detection methods were used. Serotype-specific DENV RT-LAMP assays are high-perform
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, ACS Sensors, Vol: 7, Pages: 3389-3398, ISSN: 2379-3694
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. As such, the detection of relevant biomarkers in the blood for drug resistance in metastatic castration-resistant PCa patients could lead to personalized treatment options. mRNA detection is often limited by the low specificity of qPCR assays which are restricted to specialized laboratories. Here, we present a novel reverse-transcription loop-mediated isothermal amplification assay and have demonstrated its capability for sensitive detection of AR-V7 and YAP1 RNA (3 × 101 RNA copies per reaction). This work presents a foundation for the detection of circulating mRNA in PCa on a non-invasive lab-on-chip device for use at the point-of-care. This technique was implemented onto a lab-on-chip platform integrating an array of chemical sensors (ion-sensitive field-effect transistors) 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 (<15 min) of extracted RNA from prostate cancer cell lines 22Rv1s and DU145s.
Pennisi I, Moniri A, Miscourides N, et al., 2022, Discrimination of bacterial and viral infection using host-RNA signatures integrated in a lab-on-chip platform, BIOSENSORS & BIOELECTRONICS, Vol: 216, ISSN: 0956-5663
Le V-KD, Hai BH, Karolcik S, et al., 2022, vital_sqi: A Python package for physiological signal quality control, FRONTIERS IN PHYSIOLOGY, Vol: 13
Bolton WJ, Badea C, Georgiou P, et al., 2022, Developing moral AI to support decision-making about antimicrobial use, NATURE MACHINE INTELLIGENCE, Vol: 4, Pages: 912-915
Herrero Vinas P, Wilson R, Armiger R, et al., 2022, Closed-loop control of continuous piperacillin delivery: an in silico study, Frontiers in Bioengineering and Biotechnology, Vol: 10, ISSN: 2296-4185
Background and objective: Sub-therapeutic dosing of piperacillin-tazobactam in critically-ill patients is associated with poor clinical outcomes and may promote the emergence of drug-resistant infections. In this paper, an in silico investigation of whether closed-loop control can improve pharmacokinetic-pharmacodynamic (PK-PD) target attainment is described.Method: An in silico platform was developed using PK data from 20 critically-ill patients receiving piperacillin-tazobactam where serum and tissue interstitial fluid (ISF) PK were defined. Intra-day variability on renal clearance, ISF sensor error, and infusion constraints were taken into account. Proportional-integral-derivative (PID) control was selected for drug delivery modulation. Dose adjustment was made based on ISF sensor data with a 30-minute sampling period, targeting a serum piperacillin concentration between 32-64 mg/L. A single tuning parameter set was employed across the virtual population. The PID controller was compared to standard therapy, including bolus and continuous infusion of piperacillin-tazobactam.Results: Despite significant inter-subject and simulated intra-day PK variability and sensor error, PID demonstrated a significant improvement in target attainment compared to traditional bolus and continuous infusion approaches. Conclusion: A PID controller driven by ISF drug concentration measurements has the potential to precisely deliver piperacillin-tazobactam in critically-ill patients undergoing treatment for sepsis.
Miglietta L, Xu K, Chhaya P, et al., 2022, Adaptive filtering framework to remove nonspecific and low-efficiency reactions in multiplex digital PCR based on sigmoidal trends., Analytical Chemistry, Vol: 94, Pages: 14159-14168, ISSN: 0003-2700
Real-time digital polymerase chain reaction (qdPCR) coupled with machine learning (ML) methods has shown the potential to unlock scientific breakthroughs, particularly in the field of molecular diagnostics for infectious diseases. One promising application of this emerging field explores single fluorescent channel PCR multiplex by extracting target-specific kinetic and thermodynamic information contained in amplification curves, also known as data-driven multiplexing. However, accurate target classification is compromised by the presence of undesired amplification events and not ideal reaction conditions. Therefore, here, we proposed a novel framework to identify and filter out nonspecific 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 data-driven multiplexing method called amplification curve analysis (ACA), using available published data where the ACA is demonstrated to screen carbapenemase-producing organisms in clinical isolates. Furthermore, we developed a novel strategy, named adaptive mapping filter (AMF), to adjust the percentage of outliers removed according to the number of positive counts in qdPCR. From an overall total of 152,000 amplification events, 116,222 positive amplification reactions were evaluated before and after filtering by comparing against melting peak distribution, proving that abnormal amplification curves (outliers) are linked to shifted melting distribution or decreased PCR efficiency. The ACA was applied to assess classification performance before and after AMF, showing an improved sensitivity of 1.2% when using inliers compared to a decrement of 19.6% when using outliers (p-value < 0.0001), removing 53.5% of all wrong melting curves based only on the amplification shape. This work explores the correlation between the kinetics
Moser N, Yu L-S, Rodriguez Manzano J, et al., 2022, Quantitative detection of dengue serotypes using a smartphone-connected handheld Lab-on-Chip platform, Frontiers in Bioengineering and Biotechnology, Vol: 10, Pages: 1-14, ISSN: 2296-4185
Dengue is one of the most prevalent infectious diseases in the world. Rapid, accurate and scalable diagnostics are key to patient management and epidemiological surveillance of the dengue virus (DENV), however current technologies do not match required clinical sensitivity and specificity or rely on large laboratory equipment. In this work, we report the translation of our smartphone-connected handheld Lab-on-Chip (LoC) platform for the quantitative detection of twodengue serotypes. At its core, the approach relies on the combination of Complementary Metal Oxide-Semiconductor (CMOS) microchip technology to integrate an array of 78x56 potentiometric sensors, and a label-free reverse-transcriptase loop mediated isothermal amplification (RT-LAMP) assay. The platform communicates to a smartphone app which synchronises results in real time with a secure cloud server hosted by Amazon Web Services (AWS) for epidemiological surveillance. The assay on our LoC platform (RT-eLAMP) was shown to match performance on a gold-standard fluorescence-based real-time instrument (RT-qLAMP) with synthetic DENV-1 and DENV-2 RNA and extracted RNA from 9 DENV-2 clinical isolates, achieving quantitative detection in under 15 minutes. To validate the portability of the platform and the geo-tagging capabilities, we led our study in the laboratories at Imperial College London, UK, and Kaohsiung Medical Hospital, Taiwan. This approach carries high potential for application in low resource settings at the point-of-care (PoC).
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
Zhu T, Uduku C, Li K, et al., 2022, Enhancing self-management in type 1 diabetes with wearables and deep learning, npj Digital Medicine, Vol: 5, ISSN: 2398-6352
People living with type 1 diabetes (T1D) require lifelong selfmanagement to maintain glucose levels in a safe range. Failure to do socan lead to adverse glycemic events with short and long-term complications. Continuous glucose monitoring (CGM) is widely used in T1Dself-management for real-time glucose measurements, while smartphoneapps are adopted as basic electronic diaries, data visualization tools, andsimple decision support tools for insulin dosing. Applying a mixed effectslogistic regression analysis to the outcomes of a six-week longitudinalstudy in 12 T1D adults using CGM and a clinically validated wearablesensor wristband (NCT ID: NCT03643692), we identified several significant associations between physiological measurements and hypo- andhyperglycemic events measured an hour later. We proceeded to developa new smartphone-based platform, ARISES (Adaptive, Real-time, and Intelligent System to Enhance Self-care), with an embedded deep learning algorithm utilizing multi-modal data from CGM, daily entries of mealand bolus insulin, and the sensor wristband to predict glucose levels andhypo- and hyperglycemia. For a 60-minute prediction horizon, the proposed algorithm achieved the average root mean square error (RMSE)of 35.28±5.77 mg/dL with the Matthews correlation coefficients fordetecting hypoglycemia and hyperglycemia of 0.56±0.07 and 0.70±0.05,respectively. The use of wristband data significantly reduced the RMSEby 2.25 mg/dL (p < 0.01). The well-trained model is implemented onthe ARISES app to provide real-time decision support. These resultsindicate that the ARISES has great potential to mitigate the risk ofsevere complications and enhance self-management for people with T1D.
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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