Diagnostic Devices Research Articles

Friction in soft biological systems and surface self-organization: the role of viscoelasticity

AbstractFriction is a critical factor in the proper functioning of human organs as well as in the potential development of disease. It is also important for the design of diagnostic and interventional medical devices. Nanoscale surface roughness, viscoelastic or plastic deformations, wear, and lubrication all influence the functions of individual cells. The effects of friction in soft matter systems are quantified using different types of frictional coefficients, including the dynamic friction coefficient, friction-skin drag, and pressure drag. These coefficients are determined by the viscoelastic properties of the two systems in contact and their relative velocity. In this review, several biological systems are considered, including (i) epithelial tissues in contact with soft hydrogel-like implants, (ii) the collective migration of epithelial monolayers on substrate matrices, (iii) blood flow through blood vessels, and (iv) the movement of cancer cells past epithelial clusters along with the migration of epithelial cells within the cluster.

Using Bland-Altman plot-based harmonization algorithm to optimize the harmonization for immunoassays.

Harmonization has been recommended by theInternational Organization for Standard (ISO) to achieve equivalent results across invitro diagnostic measurement devices (IVD-MDs). We aim to evaluate the effectiveness of Bland-Altman plot-based harmonization algorithm (BA-BHA) created in this study and compare it with weighted Deming regression-based harmonization algorithm (WD-BHA) proposed in ISO 21151:2020. Eighty patient sera were used as the harmonization reference material (HRM) to develop IVD-MD-specific harmonization algorithms. Another panel of 40 patient sera was used to validate the effectiveness of harmonization algorithms. We compared regression slopes, intercepts, Bland-Altman plot layouts, percent differences, limits of agreement (LoAs), between-method coefficients of variation (CV) before and after harmonization. After harmonization by WD-BHA, acceptable slopes and intercepts between measured values and HRM targets were observed in weighted Deming regression, but not in Passing-Bablok analysis. Mean differences were-5.5 to 5.0 % and differences at specific levels were-33.9 to 23.9 %. LoAs were-64.6 to 74.6 %. Between-method CV was 22.9 % (±12.9 %). However, after harmonization by BA-BHA, both weighted Deming and Passing-Bablok regressions equations presented harmonized results. Mean differences were-0.3 to 0.2 % and differences at specific levels were-1.1 to 1.6 %. LoAs were-23.3 to 23.2 %. Between-method CV was 8.4 % (±4.0 %). The data points were evenly distributed at both sides of the mean in Bland-Altman plots. The inequivalence of test results between different methods can be improved but unacceptable analytical differences at specific levels may be hidden in terms of an acceptable slope and intercept on WD-BHA. The new protocol BA-BHA may be a viable alternative to optimize the harmonization for immunoassays.

Zwitterionic Polymers for Biomedical Applications: Antimicrobial and Antifouling Strategies toward Implantable Medical Devices and Drug Delivery.

Poly(ethylene glycol) (PEG) is extensively utilized in biomedical applications due to its biocompatibility; however, its thermal instability and susceptibility to oxidative degradation significantly constrain its long-term effectiveness. Zwitterionic polymers, characterized by their distinctive structure, enhanced stability, and superior biocompatibility, offer a more advantageous alternative. These polymers exhibit super hydrophilicity, resist nonspecific protein adsorption, and maintain stability in biological environments due to their charge-neutral ionic nature. Zwitterionic polymers enhance anticancer drug delivery by precisely targeting tumor cells and facilitating an efficient drug release. Their inherent antifouling properties and prolonged circulation within the bloodstream render them highly suitable for redox-sensitive drug carriers, thereby augmenting the antitumor efficacy. Moreover, zwitterionic polymers markedly mitigate biofouling in implants, biosensors, and wound dressings, thereby improving both their functionality and their therapeutic outcomes. These advantages arise from the formation of robust hydration layers, which significantly enhance the hemocompatibility and inhibit the adhesion of proteins, platelets, and bacteria. Zwitterionic polymers, including sulfobetaine (SB), phosphorylcholine (PC), and carboxybetaine (CB), are increasingly employed in blood-contacting devices and as effective coating materials for implantable devices. This mini-review paper aims to explore the recent diverse biomedical applications of zwitterionic polymers and highlight their advantageous properties compared with unmodified polymers. We will cover their use in drug delivery systems, tumor targeting nanocarriers, antibiofouling and antibacterial activities in implantable devices, tissue engineering, and diagnostic devices, demonstrating how their unique properties can translate into different applications. Through this exploration, this Perspective will display the potential of zwitterionic polymers as innovative polymer materials in the field of biomedical engineering and beyond.

Public evidence on AI products for digital pathology

Novel products applying artificial intelligence (AI)-based methods to digital pathology images are touted to have many uses and benefits. However, publicly available information for products can be variable, with few sources of independent evidence. This review aimed to identify public evidence for AI-based products for digital pathology. Key features of products on the European Economic Area/Great Britain (EEA/GB) markets were examined, including their regulatory approval, intended use, and published validation studies. There were 26 AI-based products that met the inclusion criteria and, of these, 24 had received regulatory approval via the self-certification route as General in vitro diagnostic (IVD) medical devices. Only 10 of the products (38%) had peer-reviewed internal validation studies and 11 products (42%) had peer-reviewed external validation studies. To support transparency an online register was developed using identified public evidence (https://osf.io/gb84r/), which we anticipate will provide an accessible resource on novel devices and support decision making.

Deep transfer learning with improved crayfish optimization algorithm for oral squamous cell carcinoma cancer recognition using histopathological images

Oral Squamous Cell Carcinoma (OSCC) causes a severe challenge in oncology due to the lack of diagnostic devices, leading to delays in detecting the disorder. The OSCC diagnosis through histopathology demands a pathologist expert because the cellular presentation is variable and highly complex. Existing diagnostic approaches for OSCC have specific efficiency and accuracy restrictions, highlighting the necessity for more reliable techniques. The increase of deep neural networks (DNN) model and their applications in medical imaging have been instrumental in disease diagnosis and detection. Automatic detection systems using deep learning (DL) approaches show tremendous promise in investigating medical imagery with speed, efficiency, and accuracy. In terms of OSCC, this system allows the diagnostic method to be streamlined, facilitating earlier diagnosis and enhancing survival rates. Automatic analysis of histopathological image (HI) can assist in accurately detecting and identifying tumorous tissue, reducing diagnostic turnaround times and increasing the efficacy of pathologists. This study presents a Squeeze-Excitation with Hybrid Deep Learning for Oral Squamous Cell Carcinoma Recognition (SEHDL-OSCCR) on HIs. The presented SEHDL-OSCCR technique mainly focuses on detecting oral cancer (OC) using hybrid DL models. The bilateral filtering (BF) technique is initially used to remove the noise. Next, the SEHDL-OSCCR technique employs the SE-CapsNet model to recognize the feature extractors. An improved crayfish optimization algorithm (ICOA) technique is utilized to improve the performance of the SE-CapsNet model. At last, the classification of the OSCC technique is performed by employing a convolutional neural network with a bidirectional long short-term memory (CNN-BiLSTM) model. The simulation results obtained using the SEHDL-OSCCR technique are investigated using a benchmark medical image dataset. The experimental validation of the SEHDL-OSCCR technique illustrated a greater accuracy outcome of 98.75% compared to recent approaches.

CFTrack: Advanced Diagnostic, Monitoring, and Tracking Device for Cystic Fibrosis Care.

Cystic fibrosis (CF) is a genetic disorder that primarily affects the respiratory, digestive, and reproductive systems. In the United States, approximately 32,000 individuals, spanning both children and adults, suffer from CF, and roughly 1,000 new cases are diagnosed annually. The current gold standard for CF diagnosis is the sweat test, yet this method is plagued by issues such as being time-consuming, expensive, challenging to replicate, and lacking treatment monitoring capabilities. In contrast, the emerging field of wearable sweat biosensors has gained significant attention due to their potential for noninvasive health monitoring. Despite this, there remains a conspicuous absence of a wearable sweat biosensor tailored specifically for CF diagnosis and monitoring. Here, this study introduces a flexible wearable sweat biosensor, named CFTrack, designed to address the unique challenges associated with CF. This proposed CFTrack biosensor not only facilitates CF diagnosis but also enables the monitoring of medication treatment effectiveness and tracks therapy activities. In addition, it operates in a self-powered and customized manner, ensuring seamless integration into the daily lives of individuals with CF. Given that sweat tests and fitness routines are the predominant methods for diagnosing and treating cystic fibrosis patients, respectively, the proposed CFTrack biosensor leverages ion concentration in sweat for diagnostic purposes. Additionally, it incorporates a motion-tracking function to monitor physical activity, providing a comprehensive approach to CF management. To evaluate the feasibility of the proposed CFTrack biosensor, a comprehensive evaluation has been performed including numerical simulations, theoretical analyses, and experimental tests. The results demonstrate the efficacy of the proposed CFTrack biosensor in diagnosing and monitoring CF conditions while also showcasing its ability to effectively track the progress of patients undergoing physical therapy. The proposed CFTrack biosensor resolves key issues associated with existing sweat sensors including high energy consumption, intricate fabrication procedures, and the absence of continuous monitoring capabilities. By addressing these challenges, the proposed sweat biosensor aims to revolutionize CF diagnosis and monitoring, offering a more efficient and user-friendly alternative to current methods.

Ultrasonic A-Scan Signals Data Augmentation Using Electromechanical System Modelling to Enhance Cataract Classification Methods

The use of artificial intelligence in diverse diagnosis areas has significantly increased in the past few years because of the advantages it represents in clinical routine. Among the diverse diagnostic techniques, the use of ultrasounds is often preferred because of their simplicity, low cost, non-invasiveness, and non-ionizing characteristic. However, obtaining an adequate number of patients and data for training and testing machine learning models is challenging. To overcome this limitation, a novel approach is proposed for simulating data produced by ultrasonic diagnostic devices. The implemented method was based on a clinical prototype for eye cataract diagnosis, although the method can be extended to other applications as well. The proposed model encompasses the electric-to-acoustic signal conversion in the ultrasonic transducer, the wave propagation through the biological medium, and the subsequent acoustic-to-electric signal conversion in the transducer. Electrical modelling of the transducer was performed using a two-port network approach, while the acoustic wave propagation was modelled by using the k-Wave MATLAB toolbox. It was verified that the holistic modelling approach enabled the generation of synthetic data augmentation, presenting high similarity with real data.

Afamitresgene Autoleucel: First Approval.

Afamitresgene autoleucel (TECELRA®), a genetically modified human leukocyte antigen (HLA)-restricted autologous melanoma-associated antigen 4 (MAGE-A4)-directed T cell immunotherapy, is being developed by Adaptimmune Therapeutics plc, for the treatment of solid tumours expressing the MAGE-A4 antigen. In August 2024, afamitresgene autoleucel was approved in the USA under accelerated approval for the treatment of adults with unresectable or metastatic synovial sarcoma who have received prior chemotherapy, are HLA-A*02:01P, -A*02:02P, -A*02:03P or -A*02:06P positive and whose tumour expresses the MAGE-A4 antigen as determined by FDA-approved or cleared companion diagnostic devices. This article summarizes the milestones in the development of afamitresgene autoleucel leading to this first approval for the treatment of advanced synovial sarcoma.

3D Polymeric Lattice Microstructure-Based Microneedle Array for Transdermal Electrochemical Biosensing.

Microneedles (MNs) or microneedle arrays (MNAs) are critical components of minimally invasive devices comprised of a single or a series of micro-scale projections. MNs can bypass the outermost layer of the skin and painlessly access microcirculation of the epidermis and dermis layers, attracting great interest in the development of personalized healthcare monitoring and diagnostic devices. However, MN technology has not yet reached its full potential since current micro- and nanofabrication methods do not address the need of fabricating MNs with complex surfaces to facilitate the development of clinically adequate devices. This work presents a new approach that combines 3D printing technology based on two-photon polymerization with soft lithography for cost-effective and time-saving fabrication of complex MNAs. Specifically, this method relies on printing complex 3D objects efficiently replicated into polymeric substrates via soft lithography, resulting in a free-standing polymeric lattice (PL) membrane that can be transferred onto gold-coated MNs and used for electrochemical biosensing. This platform shows excellent electrochemical performance in detecting metabolite (glucose) and protein (insulin) biomarkers with a dynamic linear range sufficient for detecting biomarkers in healthy individuals and patients. The approach holds great potential for fabricating next-generationMNs, including their transfer into clinically adequate devices.

A ring resonators optical sensor for multiple biomarkers detection

In the recent years, the number of Point-Of-Care-Tests (POCTs) available for clinical diagnostic has steadily increased. POCTs provide a near-patient testing with the potential to generate a result quickly so that appropriate treatment can be implemented, leading to improved clinical outcomes compared to traditional laboratory testing. Technological advances, such as miniaturization of sensors and improved instrumentation, have revolutionized POCTs, enabling the development of smaller and more accurate devices. In this context, it has also gained increasing importance the screening of various analytes simultaneously to increase specificity and improve the characterization of the disease. This study is aimed at developing and characterizing a photonic integrated circuit for multiple markers detection, which represents the functional core towards a full developed POCT device for clinical pathology applications. The photonic sensor, based on microring resonators (MRRs), is functionalized by immobilizing specific antibodies on a copolymer layer deposited on the MRR’s surfaces. Surface chemical techniques were employed to analyse the surface chemical characteristics while fluorescence microscopy was involved to analyse the resulting bioreceptor surface density. The photonic sensor is characterized for the parallel detection of two biomarkers, the C-Reactive Protein (CRP) and the Creatine-Kinase-MB (CK-MB). The analyte-antibody binding curves were obtained both in buffer and in filtered un-diluted artificial saliva showing promising results both in terms of sensitivity, with limit of detection (LOD) of 103 pM for CRP and 140 pM for CK-MB, and in terms of specificity. These encouraging results let the assembly of a highly sensitive POC device for molecular diagnostics.

Amplification-free detection of Ascochyta blight in chickpea using a simple molecular beacon assay

Ascochyta blight is a major biotic stress that limits chickpea production globally. Fungicide application remains one of the effective control measures for the endemic spread. Due to the serious threat that synthetic fungicides pose to crop quality, early diagnosis of the pathogen is imperative. Whilst there have previously been several conventional lab-based diagnostic methods developed for early detection of Ascochyta rabiei, they require long assay times, specialised equipment and facilities, and trained personnel to process the samples. To overcome this challenge, a rapid amplification-free detection assay using a molecular beacon probe was developed. The method consists of a simple assembly assay that accurately detects pathogen within 30 min. The developed assay is species-specific and has a similar sensitivity level as conventional amplification-based methods. Although it is still a lab-based technique, considering the simplicity of the assay, it has a great potential to be developed further as a reliable in-field diagnostic device for early detection and quantification of fungal pathogen spores.

Transitions in Immunoassay Leading to Next-Generation Lateral Flow Assays and Future Prospects.

In the field of clinical testing, the traditional focus has been on the development of large-scale analysis equipment designed to process high volumes of samples with fully automatic and high-sensitivity measurements. However, there has been a growing demand in recent years for the development of analytical reagents tailored to point-of-care testing (POCT), which does not necessitate a specific location or specialized operator. This trend is epitomized using the lateral flow assay (LFA), which became a cornerstone during the 2019 pandemic due to its simplicity, speed of delivering results-within about 10 min from minimal sample concentrations-and user-friendly design. LFAs, with their paper-based construction, combine cost-effectiveness with ease of disposal, addressing both budgetary and environmental concerns comprehensively. Despite their compact size, LFAs encapsulate a wealth of technological ingenuity, embodying years of research and development. Current research is dedicated to further evolving LFA technology, paving the way for the next generation of diagnostic devices. These advancements aim to redefine accessibility, empower individuals, and enhance responsiveness to public health challenges. The future of LFAs, now unfolding, promises even greater integration into routine health management and emergency responses, underscoring their critical role in the evolution of decentralized and patient-centric healthcare solutions. In this review, the historical development of LFA and several of the latest LFA technologies using catalytic amplification, surface-enhanced Raman scattering, heat detection, electron chemical detections, magnetoresistance, and detection of reflected electrons detection are introduced to inspire readers for future research and development.

Xpert Breast Cancer STRAT4 Assay using fine-needle aspiration biopsy samples in a resource-constrained setting: a prospective diagnostic accuracy study

Use of fine-needle aspiration biopsy (FNAB) specimens on Xpert Breast Cancer STRAT4 Assay (STRAT4; Cepheid, Sunnyvale, CA, USA), a CE-marked in-vitro diagnostic medical device, could potentially increase access to breast cancer biomarker testing in resource-constrained settings. We aimed to assess the performance of a research use-only version of STRAT4 using FNAB specimens in Tanzania. In this prospective diagnostic accuracy study, patients aged 18 years or older with palpable breast masses presenting to the FNAB Clinic at Muhimbili National Hospital (Dar es Salaam, Tanzania) were recruited consecutively. Patients who were pregnant, lactating, or had a previous diagnosis of breast cancer were excluded. STRAT4 testing was performed on off-label FNAB samples using four protocols: the 1 × protocol (using the standard lysate method) on FNAB smears (1 × FNAB), quick lysis and Maui protocols (both on FNAB smears), and the 1 × protocol on formalin-fixed paraffin-embedded (FFPE) cell block material (1 × cell block). For 1 × FNAB and 1 × cell block, tissue was processed using FFPE lysis reagent, incubated at 80°C with proteinase K, and followed by addition of 95% or higher ethanol. Quick lysis was processed using FFPE lysis reagent and 95% or higher ethanol, whereas Maui was processed using a proprietary research-use only lysis reagent. The primary outcomes were overall concordance, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of STRAT4 as compared with immunohistochemistry or immunohistochemistry plus fluorescence in-situ hybridisation performed on cell blocks using clinically validated protocols in a Clinical Laboratory Improvement Amendments-accredited laboratory at the University of California, San Francisco (San Francisco, CA, USA). Between Nov 29, 2017, and Dec 17, 2020, 208 patients were enrolled. Of 208 cases, 51 (25%) were excluded from analysis because of insufficient tissue in the cell block or absent cell blocks, leaving 157 participants (all female) for analysis. For oestrogen receptor, 1 × FNAB had the best performance, with an overall concordance of 95% (95% CI 90-100), sensitivity of 94% (85-100), specificity of 97% (90-100), and AUC of 0·96 (0·81-1·00). For progesterone receptor, 1 × cell block had the best overall performance (overall concordance 89% [95% CI 84-95], sensitivity 91% [82-99], and specificity 89% [81-97], with an AUC of 0·93 [0·89-0·99]) and 1 × FNAB performed the best among the smear protocols, with a concordance of 84% (95% CI 74-93), sensitivity of 63% (43-82), specificity of 97% (92-100), and AUC of 0·91 (0·72-0·97). For HER2, Maui had the highest agreement, with an overall concordance of 93% (95% CI 89-98), sensitivity of 96% (88-100), specificity of 92% (87-98), and AUC of 0·95 (0·98-1·00). For Ki67, Maui had the best performance of smear protocols, with a concordance of 73% (95% CI 64-82), sensitivity of 70% (58-81), specificity of 81% (66-96), and AUC of 0·80 (0·54-0·82). Processing FNAB samples with STRAT4 is feasible in Tanzania, and performance for the oestrogen receptor is robust. Further optimisation of STRAT4 for FNAB has the potential to improve timely access to breast cancer diagnostics in resource-constrained settings. US National Institutes of Health; UCSF Global Cancer Program, Helen Diller Family Comprehensive Cancer Center; UCSF Department of Pathology; and Cepheid.

Synthesis of Pd-decorated ZnO nanocomposites with improved gas-sensitive properties for acetone detection

ZnO/Pd nanocomposites containing 0.5–3.0 mol.% palladium and possessing improved gas-sensitive properties for VOC’s detection were prepared using solvothermal synthesis. The synthesised powders have been characterised using various methods of physicochemical analysis (DSC/TGA, XRD, SET/TEM, XPS). It is shown that Pd@PdO nanoparticles (4–7 nm in diameter) are localised on the surface of ZnO nanoparticles (40–50 nm in size). According to the combined XRD and XPS data, it was found that palladium is in partially oxidised state, and the nanocomposite has a structure close to "core@shell": Pd@PdO. The influence of palladium concentration in the ZnO/Pd composite on the complex of chemoresistive gas-sensitive properties was studied. It is shown that at 250 °C the obtained nanocomposites demonstrate a high (up to 57.8 a.u.) response to 100 ppb–20 ppm acetone, which can be used as a part of supersensitive devices for noninvasive diagnostics of diseases.

A-243 Quantitative Bacteriology Parameters for Seal-Packaged Agar Plates Challenged with Experimental Low-resource Tropical Field Clinic Stress

Abstract Background InTray® Mueller Hinton Chocolate (MHC) and Colorex™ Screen (CS) plates are in vitro diagnostic devices combining prepared bacteriological agar-media with Biomed’s ‘InTray®’ format (a double sealed-packaging cassette system that provides extended shelf-life 12 months past the date of manufacture). The devices are intended for use with clinical specimens to culture, isolate and identify potentially pathogenic bacteria. This study evaluated MHC and CS device performance at 7 and 13 months past the date of manufacture after product was experimentally challenged with stress replicating a low-resource tropical field clinic scenario. Methods MHC and CS plates were exposed to high temperature (40.5°C ± 0.5) and high humidity (74.5 ± 2.5% relative humidity) for an extended time-period (18 days). After the 18 days of heat treatment, the stress-group plates were stored in the lab at working room temperature (20-25°C) for the remainder of the study. Quantitative differences between control-groups (stored under refrigeration: 2-8°C) and heat-stress treatment groups for both MHC and CS were examined via CFU/mL measurements comparing three different lab-strain bacteria chosen based on clinical sub-culturing prevalence from blood bottles; Escherichia coli (ATCC 25922), Streptococcus pneumoniae (ATCC 6305), and Staphylococcus aureus (ATCC 25923). Briefly, 24-hour (18-30 h) old bacterial colony suspensions (grown on MHC) were prepared in 0.85% saline solution, adjusted to a 0.5 McFarland standard (here defined as 0.08-0.96 Absorbance @ 625 nm), measured in triplicate and diluted. A 20 μL volume from the lowest dilution (2.5 x 103 CFU/mL) was then spread-plated to each plate in order to count colony-forming units (CFU) in the acceptable range of 25-250. Results Quantitative CFU/mL differences between control-groups (stored under refrigeration: 2-8°C) and heat-stress-treatment groups (>40°C and high humidity) for MHC and CS for three different test bacteria (listed above) were not statistically significant when tested at 13 months. The CS stress and control group comparison for S. pneumoniae with a p value < 0.05 at 7 months had a p value > 0.05 at 13 months, suggesting that the difference between the CFU counts may not be significant in biological terms. Conclusions In conclusion, control and heat-stress treatment groups for both MHC and CS met acceptable performance criteria as expected for the products, 13 months after the date of manufacture.

Revolution in malaria detection: unveiling current breakthroughs and tomorrow's possibilities in biomarker innovation.

The ongoing battle against malaria has seen significant advancements in diagnostic methodologies, particularly through the discovery and application of novel biomarkers. Traditional diagnostic techniques, such as microscopy and rapid diagnostic tests, have their limitations in terms of sensitivity, specificity, and the ability to detect low-level infections. Recent breakthroughs in biomarker research promise to overcome these challenges, providing more accurate, rapid, and non-invasive detection methods. These advancements are critical in enhancing early detection, guiding effective treatment, and ultimately reducing the global malaria burden. Innovative approaches in biomarker detection are leveraging cutting-edge technologies like next-generation sequencing, proteomics, and metabolomics. These techniques have led to the identification of new biomarkers that can be detected in blood, saliva, or urine, offering less invasive and more scalable options for widespread screening. For instance, the discovery of specific volatile organic compounds in the breath of infected individuals presents a revolutionary non-invasive diagnostic tool. Additionally, the integration of machine learning algorithms with biomarker data is enhancing the precision and predictive power of malaria diagnostics, making it possible to distinguish between different stages of infection and identify drug-resistant strains. Looking ahead, the future of malaria detection lies in the continued exploration of multi-biomarker panels and the development of portable, point-of-care diagnostic devices. The incorporation of smartphone-based technologies and wearable biosensors promises to bring real-time monitoring and remote diagnostics to even the most resource-limited settings.

Brain tumor detection and classification in MRI using hybrid ViT and GRU model with explainable AI in Southern Bangladesh

Brain tumor, a leading cause of uncontrolled cell growth in the central nervous system, presents substantial challenges in medical diagnosis and treatment. Early and accurate detection is essential for effective intervention. This study aims to enhance the detection and classification of brain tumors in Magnetic Resonance Imaging (MRI) scans using an innovative framework combining Vision Transformer (ViT) and Gated Recurrent Unit (GRU) models. We utilized primary MRI data from Bangabandhu Sheikh Mujib Medical College Hospital (BSMMCH) in Faridpur, Bangladesh. Our hybrid ViT-GRU model extracts essential features via ViT and identifies relationships between these features using GRU, addressing class imbalance and outperforming existing diagnostic methods. We extensively processed the dataset, and then trained the model using various optimizers (SGD, Adam, AdamW) and evaluated through rigorous 10-fold cross-validation. Additionally, we incorporated Explainable Artificial Intelligence (XAI) techniques-Attention Map, SHAP, and LIME-to enhance the interpretability of the model’s predictions. For the primary dataset BrTMHD-2023, the ViT-GRU model achieved precision, recall, and F1-score metrics of 97%. The highest accuracies obtained with SGD, Adam, and AdamW optimizers were 81.66%, 96.56%, and 98.97%, respectively. Our model outperformed existing Transfer Learning models by 1.26%, as validated through comparative analysis and cross-validation. The proposed model also shows excellent performances with another Brain Tumor Kaggle Dataset outperforming the existing research done on the same dataset with 96.08% accuracy. The proposed ViT-GRU framework significantly improves the detection and classification of brain tumors in MRI scans. The integration of XAI techniques enhances the model’s transparency and reliability, fostering trust among clinicians and facilitating clinical application. Future work will expand the dataset and apply findings to real-time diagnostic devices, advancing the field.

Simulation and commissioning of a Faraday cup for absolute charge measurements of very high-energy electrons in-air at PEER

The Pulsed Energetic Electrons for Research (PEER) beamline at the ANSTO Australian Synchrotron comprises a 100 MeV linac injector that is currently being developed for ultra-high dose-rate, very high-energy electron radiotherapy research. Previously, dosimetry studies discovered a lack of reliable charge measurement to the in-air end station, though no change in response was recorded in fast current transformer measurements, the only available diagnostic device for measuring charge. This work describes the process of simulating and then commissioning a purpose-built Faraday cup to ascertain absolute in-air charge measurements at PEER. By combining simulation data with experimental results, the PEER Faraday cup is shown to possess a primary electron capture efficiency of (99.22 ± 0.10)%, with a net capture efficiency due to secondary electron emission of (97.87 ± 0.24)%. These results show the PEER Faraday cup performs as intended and, when scaled against simulations, will be suitable for measuring absolute charge at PEER.

Design and Analysis of Wireless Power Transmission (2X1) MIMO Antenna at 5G - Frequencies for Applications of Rectenna Circuits in Biomedical

This research presents the design and analysis of a 5G harvesting energy Circuit (MIMO-rectenna) to receive wireless power at Sub-6 frequencies (5.6 GHz) as a power source for some medical devices carried by a patient and moved from one place to another, whether they are diagnostic or therapeutic devices. This concept aims (MIMO-rectenna) to increase the likelihood of obtaining electricity from the ambient field by harvesting energy at 5G- frequencies. The tiny MIMO (2x1) antenna measuring 30 by 40 mm2 is the antenna portion of this rectenna. It has been built and tested to operate at Sub-6 frequency, or 5.6 GHz, for wireless power transmission applications related to 5G technology. The MIMO antenna has parameters of E = 4.4, h = 1.6 mm, and tand = 0.025 when printed on an FR4 substrate. A significant section of the antenna's rear was removed to carry out the broadband process, and the material's front side was composed of a series of circular slits. In this antenna, the parasitic approach was employed to decrease the mutual coupling between two ports by creating an inverted T with precise dimensions. The CST software 2024 was used to assist with the design and simulation results of this MIMO antenna. It was discovered through the simulation that the mutual coupling for these ports, 𝑆12and 𝑆21, is equal to -51.476 dB, and that the S-parameters, 𝑆11, 𝑆22, equal -22 dB. That is, there is very little signal loss while switching from the first port to the second and vice versa. This antenna's rectifier comprised an AC-to-DC conversion circuit, a DC filter, and an impedance-matching network. With the use of ADS software 2024, this rectenna's design and simulation results were completed. The greatest conversion efficiency of this rectenna at the frequency of 5.6 GHz is determined to be between 65 % and 65.01% for load resistance between 12 KΩ and 15 KΩ at an input power of 14 dBm.

Advances in wearable electronics for monitoring human organs: Bridging external and internal health assessments

Devices used for diagnosing disease are often large, expensive, and require operation by trained professionals, which can result in delayed diagnosis and missed opportunities for timely treatment. However, wearable devices are being recognized as a new approach to overcoming these difficulties, as they are small, affordable, and easy to use. Recent advancements in wearable technology have made monitoring information possible from the surface of organs like the skin and eyes, enabling accurate diagnosis of the user's internal status. In this review, we categorize the body's organs into external (e.g., eyes, oral cavity, neck, and skin) and internal (e.g., heart, brain, lung, stomach, and bladder) organ systems and introduce recent developments in the materials and designs of wearable electronics, including electrochemical and electrophysiological sensors applied to each organ system. Further, we explore recent innovations in wearable electronics for monitoring of deep internal organs, such as the heart, brain, and nervous system, using ultrasound, electrical impedance tomography, and temporal interference stimulation. The review also addresses the current challenges in wearable technology and explores future directions to enhance the effectiveness and applicability of these devices in medical diagnostics. This paper establishes a framework for correlating the design and functionality of wearable electronics with the physiological characteristics and requirements of various organ systems.