81 results found
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, ISSN: 2296-889X
Rodriguez Manzano J, Moniri A, Miglietta L, et al., 2021, Method of assay design
Charani E, McKee M, Ahmad R, et al., 2021, Optimising antimicrobial use in humans-review of current evidence and an interdisciplinary consensus on key priorities for research, LANCET REGIONAL HEALTH-EUROPE, Vol: 7, ISSN: 2666-7762
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.
Li HK, Kaforou M, Rodriguez-Manzano J, et al., 2021, Discovery and validation of a 3-gene signature to distinguish COVID-19 and other viral infections in emergency infectious disease presentations; a case-control then observational cohort study, The Lancet Microbe, ISSN: 2666-5247
Background: Emergency admissions for infection often lack initial diagnostic certainty. COVID-19 has highlighted a need for novel diagnostic approaches to indicate likelihood of viral infection in a pandemic setting. We sought to derive and validate a blood transcriptional signature to detect viral infections including COVID-19 among adults with suspected infection presenting to the Emergency Department (ED).Methods: Blood RNA sequencing was performed on a discovery cohort of adults attending the ED with suspected infection who had subsequently-confirmed viral, bacterial, or no infection diagnoses. Differentially expressed host genes were subjected to feature selection to derive the most parsimonious discriminating signature. RT-qPCR validation of the signature was then performed in a prospective cohort of ED patients presenting with undifferentiated fever, and a second case-control cohort of ED patients with COVID-19 or bacterial infection. Signature performance was assessed by calculating area under receiver-operating characteristic curves (AUC-ROCs), sensitivities, and specificities.Findings: A 3-gene transcript signature was derived from the discovery cohort of 56 bacterial and 27 viral infection cases. In the validation cohort of 200 cases, the signature differentiated bacterial from viral infections with an AUC-ROC of 0.976 (95% CI: 0.919-1.000), sensitivity 97.3% and specificity of 100%. The AUC-ROC for C-reactive protein (CRP) and leucocyte count (WCC) was 0.833 (95% CI: 0.694-0.944) and 0.938 (95% CI: 0.840-0.986) respectively. The signature achieved higher net benefit in decision curve analysis than either CRP or WCC for discriminating viral infections from all other cases. In the second validation analysis the signature discriminated 35 bacterial infections from 34 SARS-CoV-2 positive COVID-19 infections with AUC-ROC of 0.953 (95% CI: 0.893-0.992), sensitivity 88.6% and specificity of 94.1%.Interpretation: This novel 3-gene signature discriminates viral i
Li HK, Kaforou M, Rodriguez-Manzano J, et al., 2021, Discovery and Validation of a 3-Gene Transcriptional Signature to Distinguish COVID-19 and Other Viral Infections from Bacterial Sepsis in Adults; A Case-Control then Observational Cohort Study, SSRN Electronic Journal
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, medRxiv
<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
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.
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.
Cavuto M, Pennisi I, Rodriguez Manzano J, et al., 2021, Lid assembly for a sample tube, method of using the same to collect magnetic beads, and sample processing kit
Pennisi I, Rodriguez Manzano J, Moniri A, et al., 2021, Translation of a host blood RNA Signature distinguishing bacterial from viral infection into a platform suitable for development as a point-of-care test, JAMA Pediatrics, Vol: 175, Pages: 417-419, ISSN: 2168-6203
Moser N, Rodriguez-Manzano J, Georgiou P, 2021, ProtonDx: Accurate, Rapid and Lab-Free Detection of SARS-CoV-2 and Other Respiratory Pathogens, IEEE CIRCUITS AND SYSTEMS MAGAZINE, Vol: 21, Pages: 84-88, ISSN: 1531-636X
Yu L-S, Rodriguez-Manzano J, Moser N, et al., 2020, Rapid detection of azole-resistant Aspergillus fumigatus in clinical and environmental isolates using lab-on-a-chip diagnostic system, Journal of Clinical Microbiology, Vol: 58, Pages: 1-11, ISSN: 0095-1137
Aspergillus fumigatus has widely evolved resistance to the most commonly used class of antifungal chemicals, the azoles. Current methods for identifying azole resistance are time-consuming and depend on specialized laboratories. There is an urgent need for rapid detection of these emerging pathogens at point-of-care to provide the appropriate treatment in the clinic and to improve management of environmental reservoirs to mitigate the spread of antifungal resistance. Our study demonstrates the rapid and portable detection of the two most relevant genetic markers linked to azole resistance, the mutations TR34 and TR46, found in the promoter region of the gene encoding the azole target, cyp51A. We developed a lab-on-a-chip platform consisting of: (1) tandem-repeat loop-mediated isothermal amplification, (2) state-of-the-art complementary metal-oxide-semiconductor microchip technology for nucleic-acid amplification detection and, (3) and a smartphone application for data acquisition, visualization and cloud connectivity. Specific and sensitive detection was validated with isolates from clinical and environmental samples from 6 countries across 5 continents, showing a lower limit-of-detection of 10 genomic copies per reaction in less than 30 minutes. When fully integrated with a sample preparation module, this diagnostic system will enable the detection of this ubiquitous fungus at the point-of-care, and could help to improve clinical decision making, infection control and epidemiological surveillance.
Keeble L, Moser N, Rodriguez-Manzano J, et al., 2020, ISFET-Based Sensing and Electric Field Actuation of DNA for On-Chip Detection: A Review, IEEE SENSORS JOURNAL, Vol: 20, Pages: 11044-11065, ISSN: 1530-437X
Moniri A, Miglietta L, Holmes A, et al., 2020, High-level multiplexing in digital PCR with intercalating dyes by coupling real-time kinetics and melting curve analysis., Analytical Chemistry, Vol: 92, Pages: 14181-14188, ISSN: 0003-2700
Digital polymerase chain reaction (dPCR) is a mature technique that has enabled scientific breakthroughs in several fields. However, this technology is primarily used in research environments with high-level multiplexing representing a major challenge. Here, we propose a novel method for multiplexing, referred to as amplification and melting curve analysis (AMCA), which leverages the kinetic information in real-time amplification data and the thermodynamic melting profile using an affordable intercalating dye (EvaGreen). The method trains a system comprised of supervised machine learning models for accurate classification, by virtue of the large volume of data from dPCR platforms. As a case study, we develop a new 9-plex assay to detect mobilised colistin resistant (mcr) genes as clinically relevant targets for antimicrobial resistance. Over 100,000 amplification events have been analysed, and for the positive reactions, the AMCA approach reports a classification accuracy of 99.33 ± 0.13%, an increase of 10.0% over using melting curve analysis. This work provides an affordable method of high-level multiplexing without fluorescent probes, extending the benefits of dPCR in research and clinical settings.
Moniri A, Miglietta L, Malpartida Cardenas K, et al., 2020, Amplification curve analysis: Data-driven multiplexing using real-time digital PCR, Analytical Chemistry, Vol: 92, Pages: 13134-13143, ISSN: 0003-2700
Information about the kinetics of PCR reactions are encoded in the amplification curve. However, in digital PCR (dPCR), this information is typically neglected by collapsing each amplification curve into a binary output (positive/negative). Here, we demonstrate that the large volume of raw data obtained from realtime dPCR instruments can be exploited to perform data-driven multiplexing in a single fluorescent channel using machine learning methods, by virtue of the information in the amplification curve. This new approach, referred to as amplification curve analysis (ACA), was shown using an intercalating dye (EvaGreen), reducing the cost and complexity of the assay and enabling the use of melting curve analysis for validation. As a case study, we multiplexed 3 carbapenem-resistant genes to show the impact of this approach on global challenges such as antimicrobial resistance. In the presence of single targets, we report a classification accuracy of 99.1% (N = 16188) which represents a 19.7% increase compared to multiplexing based on the final fluorescent intensity. Considering all combinations of amplification events (including coamplifications), the accuracy was shown to be 92.9% (N = 10383). To support the analysis, we derived a formula to estimate the occurrence of co-amplification in dPCR based on multivariate Poisson statistics, and suggest reducing the digital occupancy in the case of multiple targets in the same digital panel. The ACA approach takes a step towards maximizing the capabilities of existing real-time dPCR instruments and chemistries, by extracting more information from data to enable data-driven multiplexing with high accuracy. Furthermore, we expect that combining this method with existing probe-based assays will increase multiplexing capabilities significantly. We envision that once emerging point-of-care technologies can reliably capture real-time data from isothermal chemistries, the ACA method will facilitate the implementation of dPCR outs
Pennisi I, Rodriguez Manzano J, Miscourides N, et al., 2020, A method for determining a diagnostic outcome
Rodriguez Manzano J, Moniri A, Miglietta L, et al., 2020, Identifying a target nucleic acid
Moniri A, Terracina D, Rodriguez-Manzano J, et al., 2020, Real-time forecasting of sEMG features for trunk muscle fatigue using machine learning, IEEE Transactions on Biomedical Engineering, Vol: 68, Pages: 718-727, ISSN: 0018-9294
Objective: Several features of the surface electromyography (sEMG) signal are related to muscle activity and fatigue. However, the time-evolution of these features are non-stationary and vary between subjects. The aim of this study is to investigate the use of adaptive algorithms to forecast sMEG feature of the trunk muscles. Methods: Shallow models and a deep convolutional neural network (CNN) were used to simultaneously learn and forecast 5 common sEMG features in real-time to provide tailored predictions. This was investigated for: up to a 25 second horizon; for 14 different muscles in the trunk; across 13 healthy subjects; while they were performing various exercises. Results: The CNN was able to forecast 25 seconds ahead of time, with 6.88% mean absolute percentage error and 3.72% standard deviation of absolute percentage error, across all the features. Moreover, the CNN outperforms the best shallow model in terms of a figure of merit combining accuracy and precision by at least 30% for all the 5 features. Conclusion: Even though the sEMG features are non-stationary and vary between subjects, adaptive learning and forecasting, especially using CNNs, can provide accurate and precise forecasts across a range of physical activities. Significance: The proposed models provide the groundwork for a wearable device which can forecast muscle fatigue in the trunk, so as to potentially prevent low back pain. Additionally, the explicit realtime forecasting of sEMG features provides a general model which can be applied to many applications of muscle activity monitoring, which helps practitioners and physiotherapists improve therapy.
Rodriguez Manzano J, Moser N, Malpartida Cardenas K, et al., 2020, Rapid detection of mobilized colistin resistance using a nucleic acid based lab-on-a-chip diagnostic system, Scientific Reports, Vol: 10, ISSN: 2045-2322
The increasing prevalence of antimicrobial resistance is a serious threat to global public health. One of the most concerning trends is the rapid spread of Carbapenemase-Producing Organisms (CPO), where colistin has become the last-resort antibiotic treatment. The emergence of colistin resistance, including the spread of mobilized colistin resistance (mcr) genes, raises the possibility of untreatable bacterial infections and motivates the development of improved diagnostics for the detection of colistin-resistant organisms. This work demonstrates a rapid response for detecting the most recently reported mcr gene, mcr−9, using a portable and affordable lab-on-a-chip (LoC) platform, offering a promising alternative to conventional laboratory-based instruments such as real-time PCR (qPCR). The platform combines semiconductor technology, for non-optical real-time DNA sensing, with a smartphone application for data acquisition, visualization and cloud connectivity. This technology is enabled by using loop-mediated isothermal amplification (LAMP) as the chemistry for targeted DNA detection, by virtue of its high sensitivity, specificity, yield, and manageable temperature requirements. Here, we have developed the first LAMP assay for mcr−9 - showing high sensitivity (down to 100 genomic copies/reaction) and high specificity (no cross-reactivity with other mcr variants). This assay is demonstrated through supporting a hospital investigation where we analyzed nucleic acids extracted from 128 carbapenemase-producing bacteria isolated from clinical and screening samples and found that 41 carried mcr−9 (validated using whole genome sequencing). Average positive detection times were 6.58 ± 0.42 min when performing the experiments on a conventional qPCR instrument (n = 41). For validating the translation of the LAMP assay onto a LoC platform, a subset of the samples were tested (n = 20), showing average detection times o
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.
Cacho-Soblechero M, Malpartida-Cardenas K, Cicatiello C, et al., 2020, A dual-sensing thermo-chemical ISFET array for DNA-based diagnostics., IEEE Transactions on Biomedical Circuits and Systems, Vol: 14, Pages: 477-489, ISSN: 1932-4545
This paper presents a 32x32 ISFET array with in-pixel dual-sensing and programmability targeted for on-chip DNA amplification detection. The pixel architecture provides thermal and chemical sensing by encoding temperature and ion activity in a single output PWM, modulating its frequency and its duty cycle respectively. Each pixel is composed of an ISFET-based differential linear OTA and a 2-stage sawtooth oscillator. The operating point and characteristic response of the pixel can be programmed, enabling trapped charge compensation and enhancing the versatility and adaptability of the architecture. Fabricated in 0.18 μm standard CMOS process, the system demonstrates a quadratic thermal response and a highly linear pH sensitivity, with a trapped charge compensation scheme able to calibrate 99.5% of the pixels in the target range, achieving a homogeneous response across the array. Furthermore, the sensing scheme is robust against process variations and can operate under various supply conditions. Finally, the architecture suitability for on-chip DNA amplification detection is proven by performing Loop-mediated Isothermal Amplification (LAMP) of phage lambda DNA, obtaining a time-to-positive of 7.71 minutes with results comparable to commercial qPCR instruments. This architecture represents the first in-pixel dual thermo-chemical sensing in ISFET arrays for Lab-on-a-Chip diagnostics.
Keeble L, Moser N, Rodriguez-Manzano J, et al., 2020, A Combined ISFET-Electric Field Actuation System for Enhanced Detection of DNA: A Proof-of-Concept, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, ISSN: 0271-4302
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, ISSN: 0271-4302
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
Witters D, Jue E, Schoepp NG, et al., 2020, Autonomous and portable device for rapid sample-to-answer molecular diagnostics at the point-of-care, Pages: 1219-1220
In this work, we demonstrate a novel prototype of an autonomous, equipment-free, and handheld device that integrates sample preparation, on-board storage of liquid and dry reagents, nucleic acid amplification, and cellphone based detection in point-of-care (POC) and limited resource settings (LRS). We demonstrate sample preparation from urine samples within 4 min and minimal hands-on time (<1 min), as well as amplification of target nucleic acids within 15 min on-device. The proposed prototype was validated for the detection of Neisseria gonorrhoeae in urine samples within 20 min.
Georgiou P, Malpartida Cardenas K, Yu L-S, et al., 2019, Method for detecting a single nucleotide polymorphism (snp) using lamp and blocking primers, WO2019234251A1
The present application relates to methods for detecting a first allele of a single nucleotide polymorphism (SNP) in a nucleic acid sequence under isothermal conditions using primers specific for said first allele, in particular using Loop mediated isothermal amplification (LAMP), wherein the amplification of a second allele is prevented by using blocking primers.
Georgiou P, Yu L-S, Malpartida-Cardenas K, et al., 2019, Method for detecting a tandem repeat, WO2019234252A1
The present application relates to methods for detecting a tandem repeat in a nucleic acid sequence under isothermal conditions using primers.
Georgiou P, Moniri A, Rodriguez Manzano J, 2019, A method for analysis of real-time amplification data, WO2019234247A1
This disclosure relates to methods, systems, computer programs and computer- readable media for the multidimensional analysis of real-time amplification data. A framework is presented that shows that the benefits of standard curves extend beyond absolute quantification when observed in a multidimensional environment. Relating to the field of Machine Learning, the disclosed method combines multiple extracted features (e.g. linear features) in order to analyse real-time amplification data using a multidimensional view. The method involves two new concepts: the multidimensional standard curve and its 'home', the feature space. Together they expand the capabilities of standard curves, allowing for simultaneous absolute quantification, outlier detection and providing insights into amplification kinetics. The new methodology thus enables enhanced quantification of nucleic acids, single-channel multiplexing, outlier detection, characteristic patterns in the multidimensional space related to amplification kinetics and increased robustness for sample identification and quantification.
Georgiou P, Moniri A, Moser N, et al., 2019, Devices and method for detecting an amplification event, WO2019234451A1
A method is disclosed herein for detecting an amplification reaction in a solution containing a biological sample using an array of ion sensors. The amplification reaction is indicative of the presence of a nucleic acid. The method comprises monitoring a signal from each respective sensor of the array of ion sensors, detecting a change in the signal from a first sensor of the array of ion sensors, and comparing the signal from the first sensor with the signal of at least one neighbouring sensor, the at least one neighbouring sensor being proximate to the first sensor in the array. The method further comprises determining, based on the comparing, that an amplification event has occurred in the solution in the vicinity of the first sensor.
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