Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

Matrix metalloproteinase-9 activity and a downregulated Hedgehog pathway impair blood-brain barrier function in an <i>in vitro</i> model of CNS tuberculosis

Central nervous system tuberculosis (CNS TB) has a high mortality and morbidity associated with severe inflammation. The blood-brain barrier (BBB) protects the brain from inflammation but the mechanisms causing BBB damage in CNS TB are uncharacterized. We demonstrate that Mycobacterium tuberculosis (Mtb) causes breakdown of type IV collagen and decreases tight junction protein (TJP) expression in a co-culture model of the BBB. This increases permeability, surface expression of endothelial adhesion molecules and leukocyte transmigration. TJP breakdown was driven by Mtb-dependent secretion of matrix metalloproteinase (MMP)-9. TJP expression is regulated by Sonic hedgehog (Shh) through transcription factor Gli-1. In our model, the hedgehog pathway was downregulated by Mtb-stimulation, but Shh levels in astrocytes were unchanged. However, Scube2, a glycoprotein regulating astrocyte Shh release was decreased, inhibiting Shh delivery to brain endothelial cells. Activation of the hedgehog pathway by addition of a Smoothened agonist or by addition of exogenous Shh, or neutralizing MMP-9 activity, decreased permeability and increased TJP expression in the Mtb-stimulated BBB co-cultures. In summary, the BBB is disrupted by downregulation of the Shh pathway and breakdown of TJPs, secondary to increased MMP-9 activity which suggests that these pathways are potential novel targets for host directed therapy in CNS TB

Beating the reaction limits of biosensor sensitivity with dynamic tracking of single binding events

The clinical need for ultrasensitive molecular analysis has motivated the development of several endpoint-assay technologies capable of single-molecule readout. These endpoint assays are now primarily limited by the affinity and specificity of the molecular-recognition agents for the analyte of interest. In contrast, a kinetic assay with single-molecule readout could distinguish between low-abundance, high-affinity (specific analyte) and high-abundance, low-affinity (nonspecific background) binding by measuring the duration of individual binding events at equilibrium. Here, we describe such a kinetic assay, in which individual binding events are detected and monitored during sample incubation. This method uses plasmonic gold nanorods and interferometric reflectance imaging to detect thousands of individual binding events across a multiplex solid-phase sensor with a large area approaching that of leading bead-based endpoint-assay technologies. A dynamic tracking procedure is used to measure the duration of each event. From this, the total rates of binding and debinding as well as the distribution of binding-event durations are determined. We observe a limit of detection of 19 fM for a proof-of-concept synthetic DNA analyte in a 12-plex assay format.First author draf

Supervised machine learning based multi-task artificial intelligence classification of retinopathies

Artificial intelligence (AI) classification holds promise as a novel and affordable screening tool for clinical management of ocular diseases. Rural and underserved areas, which suffer from lack of access to experienced ophthalmologists may particularly benefit from this technology. Quantitative optical coherence tomography angiography (OCTA) imaging provides excellent capability to identify subtle vascular distortions, which are useful for classifying retinovascular diseases. However, application of AI for differentiation and classification of multiple eye diseases is not yet established. In this study, we demonstrate supervised machine learning based multi-task OCTA classification. We sought 1) to differentiate normal from diseased ocular conditions, 2) to differentiate different ocular disease conditions from each other, and 3) to stage the severity of each ocular condition. Quantitative OCTA features, including blood vessel tortuosity (BVT), blood vascular caliber (BVC), vessel perimeter index (VPI), blood vessel density (BVD), foveal avascular zone (FAZ) area (FAZ-A), and FAZ contour irregularity (FAZ-CI) were fully automatically extracted from the OCTA images. A stepwise backward elimination approach was employed to identify sensitive OCTA features and optimal-feature-combinations for the multi-task classification. For proof-of-concept demonstration, diabetic retinopathy (DR) and sickle cell retinopathy (SCR) were used to validate the supervised machine leaning classifier. The presented AI classification methodology is applicable and can be readily extended to other ocular diseases, holding promise to enable a mass-screening platform for clinical deployment and telemedicine.Comment: Supplemental material attached at the en

Design and development of a microfluidic platform for use with colorimetric gold nanoprobe assays

Due to the importance and wide applications of the DNA analysis, there is a need to make genetic analysis more available and more affordable. As such, the aim of this PhD thesis is to optimize a colorimetric DNA biosensor based on gold nanoprobes developed in CEMOP by reducing its price and the needed volume of solution without compromising the device sensitivity and reliability, towards the point of care use. Firstly, the price of the biosensor was decreased by replacing the silicon photodetector by a low cost, solution processed TiO2 photodetector. To further reduce the photodetector price, a novel fabrication method was developed: a cost-effective inkjet printing technology that enabled to increase TiO2 surface area. Secondly, the DNA biosensor was optimized by means of microfluidics that offer advantages of miniaturization, much lower sample/reagents consumption, enhanced system performance and functionality by integrating different components. In the developed microfluidic platform, the optical path length was extended by detecting along the channel and the light was transmitted by optical fibres enabling to guide the light very close to the analysed solution. Microfluidic chip of high aspect ratio (~13), smooth and nearly vertical sidewalls was fabricated in PDMS using a SU-8 mould for patterning. The platform coupled to the gold nanoprobe assay enabled detection of Mycobacterium tuberculosis using 3 8l on DNA solution, i.e. 20 times less than in the previous state-of-the-art. Subsequently, the bio-microfluidic platform was optimized in terms of cost, electrical signal processing and sensitivity to colour variation, yielding 160% improvement of colorimetric AuNPs analysis. Planar microlenses were incorporated to converge light into the sample and then to the output fibre core increasing 6 times the signal-to-losses ratio. The optimized platform enabled detection of single nucleotide polymorphism related with obesity risk (FTO) using target DNA concentration below the limit of detection of the conventionally used microplate reader (i.e. 15 ng/μl) with 10 times lower solution volume (3 μl). The combination of the unique optical properties of gold nanoprobes with microfluidic platform resulted in sensitive and accurate sensor for single nucleotide polymorphism detection operating using small volumes of solutions and without the need for substrate functionalization or sophisticated instrumentation. Simultaneously, to enable on chip reagents mixing, a PDMS micromixer was developed and optimized for the highest efficiency, low pressure drop and short mixing length. The optimized device shows 80% of mixing efficiency at Re = 0.1 in 2.5 mm long mixer with the pressure drop of 6 Pa, satisfying requirements for the application in the microfluidic platform for DNA analysis.Portuguese Science Foundation - (SFRH/BD/44258/2008), “SMART-EC” projec

Nanomotion technology in combination with machine learning: a new approach for a rapid antibiotic susceptibility test for Mycobacterium tuberculosis.

Nanomotion technology is a growth-independent approach that can be used to detect and record the vibrations of bacteria attached to cantilevers. We have developed a nanomotion-based antibiotic susceptibility test (AST) protocol for Mycobacterium tuberculosis (MTB). The protocol was used to predict strain phenotype towards isoniazid (INH) and rifampicin (RIF) using a leave-one-out cross-validation (LOOCV) and machine learning techniques. This MTB-nanomotion protocol takes 21 h, including cell suspension preparation, optimized bacterial attachment to functionalized cantilever, and nanomotion recording before and after antibiotic exposure. We applied this protocol to MTB isolates (n = 40) and were able to discriminate between susceptible and resistant strains for INH and RIF with a maximum sensitivity of 97.4% and 100%, respectively, and a maximum specificity of 100% for both antibiotics when considering each nanomotion recording to be a distinct experiment. Grouping recordings as triplicates based on source isolate improved sensitivity and specificity to 100% for both antibiotics. Nanomotion technology can potentially reduce time-to-result significantly compared to the days and weeks currently needed for current phenotypic ASTs for MTB. It can further be extended to other anti-TB drugs to help guide more effective TB treatment

Challenges and opportunities of centrifugal microfluidics for extreme point-of-care testing

The advantages offered by centrifugal microfluidic systems have encouraged its rapid adaptation in the fields of in vitro diagnostics, clinical chemistry, immunoassays, and nucleic acid tests. Centrifugal microfluidic devices are currently used in both clinical and point-of-care settings. Recent studies have shown that this new diagnostic platform could be potentially used in extreme point-of-care settings like remote villages in the Indian subcontinent and in Africa. Several technological inventions have decentralized diagnostics in developing countries; however, very few microfluidic technologies have been successful in meeting the demand. By identifying the finest difference between the point-of-care testing and extreme point-of-care infrastructure, this review captures the evolving diagnostic needs of developing countries paired with infrastructural challenges with technological hurdles to healthcare delivery in extreme point-of-care settings. In particular, the requirements for making centrifugal diagnostic devices viable in developing countries are discussed based on a detailed analysis of the demands in different clinical settings including the distinctive needs of extreme point-of-care settings.ope

ALTERNATING CURRENT ELECTROKINETICS BASED CAPACITIVE AFFINITY BIOSENSOR: A POINT-OF-CARE DIAGNOSTIC PLATFORM

Capacitive bioaffinity detection using microelectrodes is considered as a promising label-free method for point-of-care diagnosis, though with challenges in sensitivity, specificity and the time “from sample to result.” This work presents an alternating current (AC)-electrokinetic based capacitive affinity sensing method that is capable of realizing rapid in-situ detection of specific biomolecular interactions such as probe-analyte binding. The capacitive biosensor presented here employs elevated AC potentials at a fixed frequency for impedimetric interrogation of the microelectrodes. Such an AC signal is capable of inducing dielectrophoresis (DEP) and AC electrothermal (ACET) effects, so as to realize in-situ enrichment of macro and even small molecules at microelectrodes and hence accelerated detection. Experimental study of the DEP/ACET-enhanced capacitive sensing method was conducted, and the results corroborate our hypothesis. This capacitive sensing method has been shown to work with various types and sizes of biomolecules (such as antibodies, virus and small molecules) to differentiate disease-positive samples from negative samples within or less than two minutes, while conventional assay would require multiple processing steps and take hours to complete. The results showed high accuracy and sensitivity. Overall, this capacitive affinity biosensor may form a basis for the development of a feasible point-of-care diagnostic platform for the detection of infectious diseases in the future

Bayesian latent class estimation of sensitivity and specificity parameters of diagnostic tests for bovine tuberculosis in chronically infected herds in Northern Ireland

Publication history: Accepted - 26 April 2018; Published online - 1 May 2018.In the European Union, the recommended ante-mortem diagnostic methods for bovine tuberculosis (bTB) include the single intradermal cervical comparative tuberculin (SICCT) test and the interferon-gamma (IFN- g) test as an ancillary test. The SICCT test has a moderate sensitivity (Se) and high specificity (Sp), while the IFN-g test has good Se, but a lower Sp than the SICCT test. A retrospective Bayesian latent class analysis was conducted on 71,185 cattle from 806 herds chronically infected with bTB distributed across Northern Ireland (NI) to estimate the Se and Sp of the common ante-mortem tests and meat inspection. Analyses were also performed on data stratified by farming type and herd location to explore possible differences in test performance given the heterogeneity in the population. The mean estimates in chronically infected herds were: (1) ‘standard’ SICCT: Se 40.5–57.7%, Sp 96.3–99.7%; (2) ‘severe’ SICCT: Se 49.0%–60.6%, Sp 94.4–99.4%; (3) IFN-g(bovine–avian) using a NI optical density (OD) cut-off difference of 0.05: IFN-g(B–A)NI: Se 85.8– 93.0%, Sp 75.6–96.2%; (4) IFN-g(bovine–avian) using a standard ‘commercial’ OD cut-off difference of 0.1: IFN-g(B–A)0.1: Se 83.1–92.1%, Sp 83.1–97.3%; and (5) meat inspection: Se 49.0–57.1% Se, Sp 99.1–100%. Se estimates were lower in cattle from dairy farms than from beef farms. There were no notable differences in estimates by location of herds. Certain population characteristics, such as production type, might influence the ability of bTB tests to disclose truly infected cases.This study is part of a larger project on the evaluation of the performance characteristics of the test in chronic bTB herds in NI from 2004 to 2010. It was financed by DAERA (E&I grant code 11/ 03/10)