Leveraging a 7-Layer Long Short-Term Memory Model for Early Detection and Prevention of Diabetes in Oman: An Innovative Approach.

This study develops a 7-layer Long Short-Term Memory (LSTM) model to enhance early diabetes detection in Oman, aligning with the theme of 'Artificial Intelligence in Healthcare'. The model focuses on addressing the increasing prevalence of Type 2 diabetes, projected to impact 23.8% of Oman's population by 2050. It employs LSTM neural networks to manage factors contributing to this rise, including obesity and genetic predispositions, and aims to bridge the gap in public health awareness and prevention. The model's performance is evaluated through various metrics. It achieves an accuracy of 99.40%, specificity and sensitivity of 100% for positive cases, a recall of 99.34% for negative cases, an F1 score of 96.24%, and an AUC score of 94.51%. These metrics indicate the model's capability in diabetes detection. The implementation of this LSTM model in Oman's healthcare system is proposed to enhance early detection and prevention of diabetes. This approach reflects an application of AI in addressing a significant health concern, with potential implications for similar healthcare challenges relating to globally diagnostic capabilities, representing a significant leap forward in healthcare technology in Oman.

A comparison of different methods to maximise signal extraction when using central venous pressure to optimise atrioventricular delay after cardiac surgery.

Objective: Our group has shown that central venous pressure (CVP) can optimise atrioventricular (AV) delay in temporary pacing (TP) after cardiac surgery. However, the signal-to-noise ratio (SNR) is influenced both by the methods used to mitigate the pressure effects of respiration and the number of heartbeats analysed. This paper systematically studies the effect of different analysis methods on SNR to maximise the accuracy of this technique. Methods: We optimised AV delay in 16 patients with TP after cardiac surgery. Transitioning rapidly and repeatedly from a reference AV delay to different tested AV delays, we measured pressure differences before and after each transition. We analysed the resultant signals in different ways with the aim of maximising the SNR: (1) adjusting averaging window location (around versus after transition), (2) modifying window length (heartbeats analysed), and (3) applying different signal filtering methods to correct respiratory artefact. Results: (1) The SNR was 27 % higher for averaging windows around the transition versus post-transition windows. (2) The optimal window length for CVP analysis was two respiratory cycle lengths versus one respiratory cycle length for optimising SNR for arterial blood pressure (ABP) signals. (3) Filtering with discrete wavelet transform improved SNR by 62 % for CVP measurements. When applying the optimal window length and filtering techniques, the correlation between ABP and CVP peak optima exceeded that of a single cycle length (R = 0.71 vs. R = 0.50, p < 0.001). Conclusion: We demonstrated that utilising a specific set of techniques maximises the signal-to-noise ratio and hence the utility of this technique.

Revolutionizing Early Disease Detection: A High-Accuracy 4D CNN Model for Type 2 Diabetes Screening in Oman.

The surge of diabetes poses a significant global health challenge, particularly in Oman and the Middle East. Early detection of diabetes is crucial for proactive intervention and improved patient outcomes. This research leverages the power of machine learning, specifically Convolutional Neural Networks (CNNs), to develop an innovative 4D CNN model dedicated to early diabetes prediction. A region-specific dataset from Oman is utilized to enhance health outcomes for individuals at risk of developing diabetes. The proposed model showcases remarkable accuracy, achieving an average accuracy of 98.49% to 99.17% across various epochs. Additionally, it demonstrates excellent F1 scores, recall, and sensitivity, highlighting its ability to identify true positive cases. The findings contribute to the ongoing effort to combat diabetes and pave the way for future research in using deep learning for early disease detection and proactive healthcare.

Corrigendum: VIDIIA Hunter: a low-cost, smartphone connected, artificial intelligence-assisted COVID-19 rapid diagnostic platform approved for medical use in the UK.

[This corrects the article DOI: 10.3389/fmolb.2023.1144001.].

Liquid resistivity of pharmaceutical propellants using novel resistivity cell.

Metered-dose inhalers employ propellants to produce pharmaceutical aerosols for treating respiratory conditions like asthma. In the liquid phase, the DC volume resistivity of pharmaceutical propellants, including R134a, R152a, and R227ea, was studied at saturation pressures and room temperature (not vapour phase). These measurements are essential for industries like refrigerants. Aerosols from metered dose inhalers (MDIs) with these propellants become electrically charged, affecting medicament deposition in lung. The resistivity was measured using a novel concentric cylinder-type capacitance cell designed in-house. The resistivity for the propellants (R134a, R152a, and R227ea) was found to be 3.02 × 1010 Ωm, 2.37 × 109 Ωm and 1.31 × 1010 Ωm, respectively. The electrical resistivity data obtained was found to be at least two orders of magnitude higher than the limited data available in the literature. Challenges in the resistivity cell's development and performance are discussed, with a focus on various propellants and their mixtures with ethanol and moisture concentrations. The resistivity of propellant mixtures containing moisture concentrations ranging from 5 to 500 ppm and ethanol concentrations ranging between 1000 and 125,000 ppm was determined. The resistivity was tested across 10-min and 1-h periods and was performed in accordance with the contemporary IEC 60247 standard.

VIDIIA Hunter diagnostic platform: a low-cost, smartphone connected, artificial intelligence-assisted COVID-19 rapid diagnostics approved for medical use in the UK.

Introduction: Accurate and rapid diagnostics paired with effective tracking and tracing systems are key to halting the spread of infectious diseases, limiting the emergence of new variants and to monitor vaccine efficacy. The current gold standard test (RT-qPCR) for COVID-19 is highly accurate and sensitive, but is time-consuming, and requires expensive specialised, lab-based equipment. Methods: Herein, we report on the development of a SARS-CoV-2 (COVID-19) rapid and inexpensive diagnostic platform that relies on a reverse-transcription loop-mediated isothermal amplification (RT-LAMP) assay and a portable smart diagnostic device. Automated image acquisition and an Artificial Intelligence (AI) deep learning model embedded in the Virus Hunter 6 (VH6) device allow to remove any subjectivity in the interpretation of results. The VH6 device is also linked to a smartphone companion application that registers patients for swab collection and manages the entire process, thus ensuring tests are traced and data securely stored. Results: Our designed AI-implemented diagnostic platform recognises the nucleocapsid protein gene of SARS-CoV-2 with high analytical sensitivity and specificity. A total of 752 NHS patient samples, 367 confirmed positives for coronavirus disease (COVID-19) and 385 negatives, were used for the development and validation of the test and the AI-assisted platform. The smart diagnostic platform was then used to test 150 positive clinical samples covering a dynamic range of clinically meaningful viral loads and 250 negative samples. When compared to RT-qPCR, our AI-assisted diagnostics platform was shown to be reliable, highly specific (100%) and sensitive (98-100% depending on viral load) with a limit of detection of 1.4 copies of RNA per µL in 30 min. Using this data, our CE-IVD and MHRA approved test and associated diagnostic platform has been approved for medical use in the United Kingdom under the UK Health Security Agency's Medical Devices (Coronavirus Test Device Approvals, CTDA) Regulations 2022. Laboratory and in-silico data presented here also indicates that the VIDIIA diagnostic platform is able to detect the main variants of concern in the United Kingdom (September 2023). Discussion: This system could provide an efficient, time and cost-effective platform to diagnose SARS-CoV-2 and other infectious diseases in resource-limited settings.

Analytical Simulation of the Microbubble Collapsing in a Welding Fusion Pool.

This paper explains the use of remote ultrasound vibration at the optimum position and frequencies to vibrate plates under welding, with the aim of initiating cavitation in the molten pool area. It has been shown in the literature that ultrasound cavitation changes microstructure morphology and refines the grain of the weld. In practice, the plates are excited through narrow-band high-power ultrasound transducers (HPUTs). Therefore, a theoretical investigation is carried out to identify the plate-mode shapes due to the ultrasound vibration aligned with the frequency bandwidth of HPUTs available in the marketplace. The effect of exciting the plate at different locations and frequencies is studied to find the optimum position and frequencies to achieve the maximum pressure at the area of the fusion zone. It was shown that applying the excitation from the side of the plate produces an order of 103 higher vibration displacement amplitude, compared with excitation from the corner. The forced vibration of cavitation and bursting time are studied to identify vibration amplitude and the time required to generate and implode cavities, hence specifying the vibration-assisted welding time. Thus, the proposed computational platform enables efficient multiparametric analysis of cavitation, initiated by remote ultrasound excitation, in the molten pool under welding.

Techniques to aid prediction of pacing dependence at 30 days in patients requiring pacemaker implantation after cardiac surgery.

Permanent pacemaker (PPM) implantation occurs in up to 5 % of patients after cardiac surgery but there is little consensus on how long to wait between surgery and PPM insertion. Predicting the likelihood of a patient being pacing dependent 30 days after implant can aid with this timing decision and avoid unnecessary observation time waiting for intrinsic conduction to recover. In this paper, we introduce a new approach for the prediction of PPM dependency at 30 days after implant in patients who have undergone recent cardiac surgery. The aim is to create an automatic detection model able to support clinicians in the decision-making process. We first applied Synthetic Minority Oversampling Technique (SMOTE) and Bayesian Networks (BN) to the dataset, to balance the inherently imbalanced data and create additional synthetic data respectively. The six resultant datasets were then used to train four different classifiers to predict pacing dependence at 30 days, all using the same testing set. The Bagged Trees classifier achieved the best results, reaching an area under the receiver operating curve (AUC) of 90 % in the train phase, and 83 % in the test phase. The overall classification performance was clearly enhanced when using SMOTE and synthetic data created with BN to create a combined and balanced dataset. This technique could be of great use in answering clinical questions where the original dataset is imbalanced.

Artificial Intelligence-Assisted Loop Mediated Isothermal Amplification (AI-LAMP) for Rapid Detection of SARS-CoV-2.

Until vaccines and effective therapeutics become available, the practical solution to transit safely out of the current coronavirus disease 19 (CoVID-19) lockdown may include the implementation of an effective testing, tracing and tracking system. However, this requires a reliable and clinically validated diagnostic platform for the sensitive and specific identification of SARS-CoV-2. Here, we report on the development of a de novo, high-resolution and comparative genomics guided reverse-transcribed loop-mediated isothermal amplification (LAMP) assay. To further enhance the assay performance and to remove any subjectivity associated with operator interpretation of results, we engineered a novel hand-held smart diagnostic device. The robust diagnostic device was further furnished with automated image acquisition and processing algorithms and the collated data was processed through artificial intelligence (AI) pipelines to further reduce the assay run time and the subjectivity of the colorimetric LAMP detection. This advanced AI algorithm-implemented LAMP (ai-LAMP) assay, targeting the RNA-dependent RNA polymerase gene, showed high analytical sensitivity and specificity for SARS-CoV-2. A total of ~200 coronavirus disease (CoVID-19)-suspected NHS patient samples were tested using the platform and it was shown to be reliable, highly specific and significantly more sensitive than the current gold standard qRT-PCR. Therefore, this system could provide an efficient and cost-effective platform to detect SARS-CoV-2 in resource-limited laboratories.

Development of Ultrasonic Guided Wave Transducer for Monitoring of High Temperature Pipelines.

High-temperature (HT) ultrasonic transducers are of increasing interest for structural health monitoring (SHM) of structures operating in harsh environments. This article focuses on the development of an HT piezoelectric wafer active sensor (HT-PWAS) for SHM of HT pipelines using ultrasonic guided waves. The PWAS was fabricated using Y-cut gallium phosphate (GaPO4) to produce a torsional guided wave mode on pipes operating at temperatures up to 600 °C. A number of confidence-building tests on the PWAS were carried out. HT electromechanical impedance (EMI) spectroscopy was performed to characterise piezoelectric properties at elevated temperatures and over long periods of time (>1000 h). Laser Doppler vibrometry (LDV) was used to verify the modes of vibration. A finite element model of GaPO4 PWAS was developed to model the electromechanical behaviour of the PWAS and the effect of increasing temperatures, and it was validated using EMI and LDV experimental data. This study demonstrates the application of GaPO4 for guided-wave SHM of pipelines and presents a model that can be used to evaluate different transducer designs for HT applications.

Recombinase polymerase amplification-nucleic acid lateral flow immunoassays for Newcastle disease virus and infectious bronchitis virus detection.

Newcastle disease virus (NDV) and infectious bronchitis virus (IBV) are two poultry pathogens affecting the respiratory tract of chickens, and cause major economic losses in the industry. Rapid detection of these viruses is crucial to inform implementation of appropriate control measures. The objective of our study is developing a simple, rapid and field applicable recombinase polymerase amplification (RPA)-nucleic acid lateral flow (NALF) immunoassay for detection of NDV and IBV. Isothermal amplification of the matrix protein (M) gene of NDV and the nucleoprotein (N) gene of IBV was implemented via recombinase polymerase amplification at 38 °C for 40 min and 20 min, respectively using modified labeled primers. NALF device used in this study utilizes antibodies for detection of labeled RPA amplicons. The results revealed that RPA-NALF immunoassays can detect both viruses after 40 min at 38 °C and only NDV after 20 min. The limit of detection (LOD) was 10 genomic copies/RPA reaction. The assays results on clinical samples collected from diseased chicken farms demonstrated a good performance in comparison with quantitative real time reverse transcription polymerase chain reaction (qRT-PCR). The assays established in this study can facilitate rapid, on-site molecular diagnosis of suspected cases of ND and IB viral infections as the results can be detected by the naked eye without the need for measuring fluorescence. Furthermore, the NALF device could be adapted to detect other infectious agents.

Measurement of Core Body Temperature Using Graphene-Inked Infrared Thermopile Sensor.

Continuous and reliable measurements of core body temperature (CBT) are vital for studies on human thermoregulation. Because tympanic membrane directly reflects the temperature of the carotid artery, it is an accurate and non-invasive method to record CBT. However, commercial tympanic thermometers lack portability and continuous measurements. In this study, graphene inks were utilized to increase the accuracy of the temperature measurements from the ear by coating graphene platelets on the lens of an infrared thermopile sensor. The proposed ear-based device was designed by investigating ear canal geometry and developed with 3D printing technology using the Computer-Aided Design (CAD) Software, SolidWorks 2016. It employs an Arduino Pro Mini and a Bluetooth module. The proposed system runs with a 3.7 V, 850 mAh rechargeable lithium-polymer battery that allows long-term, continuous monitoring. Raw data are continuously and wirelessly plotted on a mobile phone app. The test was performed on 10 subjects under resting and exercising in a total period of 25 min. Achieved results were compared with the commercially available Braun Thermoscan, Original Thermopile, and Cosinuss One ear thermometers. It is also comprehended that such system will be useful in personalized medicine as wearable in-ear device with wireless connectivity.

RPA using a multiplexed cartridge for low cost point of care diagnostics in the field.

A point of care device utilising Lab-on-a-Chip technologies that is applicable for biological pathogens was designed, fabricated and tested showing sample in to answer out capabilities. The purpose of the design was to develop a cartridge with the capability to perform nucleic acid extraction and purification from a sample using a chitosan membrane at an acidic pH. Waste was stored within the cartridge with the use of sodium polyacrylate to solidify or gelate the sample in a single chamber. Nucleic acid elution was conducted using the RPA amplification reagents (alkaline pH). Passive valves were used to regulate the fluid flow and a multiplexer was designed to distribute the fluid into six microchambers for amplification reactions. Cartridges were produced using soft lithography of silicone from 3D printed moulds, bonded to glass substrates. The isothermal technique, RPA is employed for amplification. This paper shows the results from two separate experiments: the first using the RPA control nucleic acid, the second showing successful amplification from Chlamydia Trachomatis. Endpoint analysis conducted for the RPA analysis was gel electrophoresis that showed 143 base pair DNA was amplified successfully for positive samples whilst negative samples did not show amplification. End point analysis for Chlamydia Trachomatis samples was fluorescence detection that showed successful detection of 1 copy/µL and 10 copies/µL spiked in a MES buffer.

Chemically Roughened Solid Silver: A Simple, Robust and Broadband SERS Substrate.

Surface-enhanced Raman spectroscopy (SERS) substrates manufactured using complex nano-patterning techniques have become the norm. However, their cost of manufacture makes them unaffordable to incorporate into most biosensors. The technique shown in this paper is low-cost, reliable and highly sensitive. Chemical etching of solid Ag metal was used to produce simple, yet robust SERS substrates with broadband characteristics. Etching with ammonium hydroxide (NH4OH) and nitric acid (HNO3) helped obtain roughened Ag SERS substrates. Scanning electron microscopy (SEM) and interferometry were used to visualize and quantify surface roughness. Flattened Ag wires had inherent, but non-uniform roughness having peaks and valleys in the microscale. NH4OH treatment removed dirt and smoothened the surface, while HNO3 treatment produced a flake-like morphology with visibly more surface roughness features on Ag metal. SERS efficacy was tested using 4-methylbenzenethiol (MBT). The best SERS enhancement for 1 mM MBT was observed for Ag metal etched for 30 s in NH4OH followed by 10 s in HNO3. Further, MBT could be quantified with detection limits of 1 pM and 100 µM, respectively, using 514 nm and 1064 nm Raman spectrometers. Thus, a rapid and less energy intensive method for producing solid Ag SERS substrate and its efficacy in analyte sensing was demonstrated.

Modular development of a prototype point of care molecular diagnostic platform for sexually transmitted infections.

This paper presents the design of a modular point of care test platform that integrates a proprietary sample collection device directly with a microfluidic cartridge. Cell lysis, within the cartridge, is conducted using a chemical method and nucleic acid purification is done on an activated cellulose membrane. The microfluidic device incorporates passive mixing of the lysis-binding buffers and sample using a serpentine channel. Results have shown extraction efficiencies for this new membrane of 69% and 57% compared to the commercial Qiagen extraction method of 85% and 59.4% for 0.1ng/µL and 100ng/µL salmon sperm DNA respectively spiked in phosphate buffered solution. Extraction experiments using the serpentine passive mixer cartridges incorporating lysis and nucleic acid purification showed extraction efficiency around 80% of the commercial Qiagen kit. Isothermal amplification was conducted using thermophillic helicase dependant amplification and recombinase polymerase amplification. A low cost benchtop real-time isothermal amplification platform has been developed capable of running six amplifications simultaneously. Results show that the platform is capable of detecting 1.32×10(6) of sample DNA through thermophillic helicase dependant amplification and 1×10(5) copy numbers Chlamydia trachomatis genomic DNA within 10min through recombinase polymerase nucleic acid amplification tests.

High Temperature Shear Horizontal Electromagnetic Acoustic Transducer for Guided Wave Inspection.

Guided Wave Testing (GWT) using novel Electromagnetic Acoustic Transducers (EMATs) is proposed for the inspection of large structures operating at high temperatures. To date, high temperature EMATs have been developed only for thickness measurements and they are not suitable for GWT. A pair of water-cooled EMATs capable of exciting and receiving Shear Horizontal (SH0) waves for GWT with optimal high temperature properties (up to 500 °C) has been developed. Thermal and Computational Fluid Dynamic (CFD) simulations of the EMAT design have been performed and experimentally validated. The optimal thermal EMAT design, material selection and operating conditions were calculated. The EMAT was successfully tested regarding its thermal and GWT performance from ambient temperature to 500 °C.

Graphene-Enabled Electrodes for Electrocardiogram Monitoring.

The unique parameters of graphene (GN)-notably its considerable electron mobility, high surface area, and electrical conductivity-are bringing extensive attention into the wearable technologies. This work presents a novel graphene-based electrode for acquisition of electrocardiogram (ECG). The proposed electrode was fabricated by coating GN on top of a metallic layer of a Ag/AgCl electrode using a chemical vapour deposition (CVD) technique. To investigate the performance of the fabricated GN-based electrode, two types of electrodes were fabricated with different sizes to conduct the signal qualities and the skin-electrode contact impedance measurements. Performances of the GN-enabled electrodes were compared to the conventional Ag/AgCl electrodes in terms of ECG signal quality, skin-electrode contact impedance, signal-to-noise ratio (SNR), and response time. Experimental results showed the proposed GN-based electrodes produced better ECG signals, higher SNR (improved by 8%), and lower contact impedance (improved by 78%) values than conventional ECG electrodes.

A Simple, Low-Cost Platform for Real-Time Isothermal Nucleic Acid Amplification.

Advances in microfluidics and the introduction of isothermal nucleic acid amplification assays have resulted in a range of solutions for nucleic acid amplification tests suited for point of care and field use. However, miniaturisation of instrumentation for such assays has not seen such rapid advances and fluorescence based assays still depend on complex, bulky and expensive optics such as fluorescence microscopes, photomultiplier tubes and sensitive lens assemblies. In this work we demonstrate a robust, low cost platform for isothermal nucleic acid amplification on a microfluidic device. Using easily obtainable materials and commercial off-the-shelf components, we show real time fluorescence detection using a low cost photodiode and operational amplifier without need for lenses. Temperature regulation on the device is achieved using a heater fabricated with standard printed circuit board fabrication methods. These facile construction methods allow fabrications at a cost compatible with widespread deployment to resource poor settings.

Frequency-sweep examination for wave mode identification in multimodal ultrasonic guided wave signal.

Ultrasonic guided waves can be used to assess and monitor long elements of a structure from a single position. The greatest challenges for any guided wave system are the plethora of wave modes arising from the geometry of the structural element which propagate with a range of frequency-dependent velocities and the interpretation of these combined signals reflected by discontinuities in the structural element. In this paper, a novel signal processing technique is presented using a combination of frequency-sweep measurement, sampling rate conversion, and Fourier transform. The technique is applied to synthesized and experimental data to identify different modes in complex ultrasonic guided wave signals. It is demonstrated throughout the paper that the technique also has the capability to derive the time of flight and group velocity dispersion curve of different wave modes in field inspections.

Isothermal nucleic acid amplification technologies for point-of-care diagnostics: a critical review.

Nucleic Acid Testing (NAT) promises rapid, sensitive and specific diagnosis of infectious, inherited and genetic disease. The next generation of diagnostic devices will interrogate the genetic determinants of such conditions at the point-of-care, affording clinicians prompt reliable diagnosis from which to guide more effective treatment. The complex biochemical nature of clinical samples, the low abundance of nucleic acid targets in the majority of clinical samples and existing biosensor technology indicate that some form of nucleic acid amplification will be required to obtain clinically relevant sensitivities from the small samples used in point-of-care testing (POCT). This publication provides an overview and thorough review of existing technologies for nucleic acid amplification. The different methods are compared and their suitability for POCT adaptation are discussed. Current commercial products employing isothermal amplification strategies are also investigated. In conclusion we identify the factors impeding the integration of the methods discussed in fully automated, sample-to-answer POCT devices.