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[Display omitted] • A simple and user-friendly diagnostics to detect the hypervirulent K. pneumoniae. • CRISPR-assisted point-of-need tool that stands worthy of the ASSURED criteria. • Species-specific detection of solely the test-strains of K. pneumoniae. • No cross-reactivity observed when tested on different genera besides the test strains. • Instrument-free readout to extend the reach of the technology to remote locations. Klebsiella pneumoniae , a prevalent healthcare-associated pathogen, poses a significant challenge to diagnosis and treatment due to its virulence and antimicrobial resistance. The demand for more efficient, precise and accessible diagnostic methods is imperative, as current approaches are labor-intensive and resource-dependent. In this study, a CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-based diagnostic tool for rapid detection of hypervirulent K. pneumoniae infections was proposed. We integrated recombinase polymerase amplification (RPA) coupled with a lateral flow assay and Cas13a (CRISPR associated protein 13a) to target the housekeeping rpoB gene for species-specific detection and the capsular polysaccharide regulating gene rmpA for identification of hypervirulent strains of K. pneumoniae. Tested on 18 K. pneumoniae strains, the devised tool successfully detected hypervirulent strains K. pneumoniae M59 and K. pneumoniae KP109 showing presence of rmpA. This study allows to develop an instrument-free platform for routine diagnosis of K. pneumoniae from serum, urine, and saliva samples that would empower healthcare personnel to facilitate proper and timely treatment of infections caused by the K. pneumoniae. [ABSTRACT FROM AUTHOR]

Addressing the pressing demand for rapid and inexpensive coagulation testing in cardiovascular care, this study introduces a novel application of repurposed COVID-19 rapid antigen tests (RATs) as paper-based lateral flow assays (LFAs) combined with machine learning for coagulation status evaluation. By further developing a mobile app prototype, we present a platform that enables clinicians to perform immediate and accurate anticoagulant dosing adjustments using existing post-pandemic resources. Our proof-of-concept employs a random forest machine learning classifier to interpret image feature variations on RAT NC membrane, correlating red blood cell (RBC) wicked diffusion distance in recalcified citrated whole blood with changes in coagulative viscosity, easily interpreted. Enhanced by confocal imaging studies of paper microfluidics, our approach provides insights into the mechanisms dissecting coagulation components, achieving high classification precision, recall, and F1-scores. The inverse relationship between RBC wicked diffusion distance and enoxaparin concentration paves the way for machine learning to inform real-time dose prescription adjustments, aligning with individual patient profiles to optimize therapeutic outcomes. This study not only demonstrates the potential of leveraging surplus RATs for coagulation management but also exemplifies a cost-effective, rapid, and smart strategy to enhance clinical decision-making in the post-pandemic era. Highlights: • Repurposed COVID-19 RATs provide an ideal platform for observing differences in blood coagulability. • Random Forest image classification algorithms can facilitate rapid coagulation status assessment on a paper-based LFA platform. [ABSTRACT FROM AUTHOR]

Biosensors integrated with artificial intelligence (AI) hold immense potential for transforming healthcare through rapid, automated diagnostics and precision therapeutics. This paper reviews the convergence of biosensing and AI towards developing smart biomedical systems. The fundamentals, historical evolution, and classification of biosensors are presented, highlighting key applications across infections, chronic illnesses, and environmental monitoring. Core AI concepts, including machine learning, neural networks, computer vision, and natural language processing, are discussed, along with their implementation to augment biosensor functionality, connectivity, point-of-care adoption, and laboratory automation. Promising research directions and real-world case studies applying AI-integrated biosensors for early diagnosis and drug delivery are discussed. The opportunities and challenges in advancing this synergistic technology are contemplated, underscoring the need for cross-disciplinary collaboration, clinical validation, ethical vigilance and supportive policy environments to successfully translate AI-biosensors into practical healthcare solutions. [ABSTRACT FROM AUTHOR]

Introduction: To contribute to addressing diagnostic health inequalities in the United Kingdom, this review aimed to investigate determinants of diagnostic service use amongst people experiencing high deprivation in the United Kingdom. Methods: A systematic review was conducted using three databases (EBSCO, Web of Science and SCOPUS) to search studies pertaining to diagnostic service use amongst people experiencing high deprivation. Search terms related to diagnostics, barriers and facilitators to access and deprivation. Articles were included if they discussed facilitators and/or barriers to diagnostic service access, contained participants' direct perspectives and focussed on individuals experiencing high deprivation in the United Kingdom. Articles were excluded if the full text was unretrievable, only abstracts were available, the research did not focus on adults experiencing high deprivation in the United Kingdom, those not including participants' direct perspectives (e.g., quantitative studies) and papers unavailable in English. Results: Of 14,717 initial papers, 18 were included in the final review. Determinants were grouped into three themes (Beliefs and Behaviours, Emotional and Psychological Factors and Practical Factors), made up of 15 sub‐themes. These were mapped to a conceptual model, which illustrates that Beliefs and Behaviours interact with Emotional and Psychological Factors to influence Motivation to access diagnostic services. Motivation then influences and is influenced by Practical Factors, resulting in a Decision to Access or Not. This decision influences Beliefs and Behaviours and/or Emotional and Psychological Factors such that the cycle begins again. Conclusion: Decision‐making regarding diagnostic service use for people experiencing high deprivation in the United Kingdom is complex. The conceptual model illustrates this complexity, as well as the mediative, interactive and iterative nature of the process. The model should be applied in policy and practice to enable understanding of the factors influencing access to diagnostic services and to design interventions that address identified determinants. Patient or Public Contribution: Consulting lived experience experts was imperative in understanding whether and how the existing literature captures the lived experience of those experiencing high deprivation in South England. The model was presented to lived experience experts, who corroborated findings, highlighted significant factors for them and introduced issues that were not identified in the review. [ABSTRACT FROM AUTHOR]

This paper presents an overview of integrating new research outcomes into the development of a structural health monitoring strategy for the floating cover at the Western Treatment Plant (WTP) in Melbourne, Australia. The size of this floating cover, which covers an area of approximately 470 m × 200 m, combined with the hazardous environment and its exposure to extreme weather conditions, only allows for monitoring techniques based on remote sensing. The floating cover is deformed by the accumulation of sewage matter beneath it. Our research has shown that the only reliable data for constructing a predictive model to support the structural health monitoring of this critical asset is obtained directly from the actual floating cover at the sewage treatment plant. Our recent research outcomes lead us towards conceptualising an advanced engineering analysis tool designed to support the future creation of a digital twin for the floating cover at the WTP. Foundational work demonstrates the effectiveness of an unmanned aerial vehicle (UAV)-based photogrammetry methodology in generating a digital elevation model of the large floating cover. A substantial set of data has been acquired through regular UAV flights, presenting opportunities to leverage this information for a deeper understanding of the interactions between operational conditions and the structural response of the floating cover. This paper discusses the current findings and their implications, clarifying how these outcomes contribute to the ongoing development of an advanced digital twin for the floating cover. [ABSTRACT FROM AUTHOR]

Neglected tropical diseases (NTDs) are a group of illnesses which usually present with a chronic clinical picture. NTDs can lead to permanent disability and are often associated with social stigma. In many developing countries where NTDs are endemic, there are no diagnostic tools for the safe storage and transport of biological samples, and there are no specialist diagnostic centers where the samples could be processed. The transport of biological samples (blood, urine) collected in field conditions and brought to laboratories located in developed countries requires the maintenance of the cold chain during transportation. Ensuring temperature control during transport could be problematic or even impossible to achieve; it is also expensive. A helpful solution to this problem is to use the dried matrix spot (DMS) technique, which seems to be a reliable method for collecting biological samples to be used for screening purposes and conducting epidemiological surveillance of NTDs in developing countries. This article is an overview of how DMSs can be used in the diagnosis of most neglected tropical diseases. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence of things (AIoT) is transforming aviation health monitoring systems by combining extensive data collection with advanced analytical capabilities. This study proposes a framework that enhances predictive accuracy, operational efficiency, and safety while optimizing maintenance strategies and reducing costs. Utilizing a three-tiered cloud architecture, the AIoT system enables real-time data acquisition from sensors embedded in aircraft systems, followed by machine learning algorithms to analyze and interpret the data for proactive decision-making. This research examines the evolution from traditional to AIoT-enhanced monitoring, presenting a comprehensive architecture integrated with satellite communication and 6G technology. The mathematical models quantifying the benefits of increased diagnostic depth through AIoT, covering aspects such as predictive accuracy, cost savings, and safety improvements are introduced in this paper. The findings emphasize the strategic importance of investing in AIoT technologies to balance cost, safety, and efficiency in aviation maintenance and operations, marking a paradigm shift from traditional health monitoring to proactive health management in aviation. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The development of the automotive industry is associated with the rapid advancement of onboard systems. In addition, intensive development in the electronics and control systems industry has resulted in a change in the approach to the issue of assistance systems in vehicles. Classic hydraulic systems have been almost completely replaced by modern electric power steering (EPS) systems, especially in citizen vehicles. This paper focuses on fault detection algorithms for EPS, along with the available tools to aid development and verification. The article discusses in detail the current state of knowledge in this area. The principle of operation of the EPS system and the influence of the structure of the mechanical system on its operation, in particular the characteristics of the ground–tire contact, are presented. Various error identification methods are presented, including those based mainly on a combination of tests of real objects as well as those combined with modern hardware-in-the-loop (HIL) equipment and virtual vehicle environment software, enabling the development of new diagnostic methods, enhancing the security, reliability, and energy control in the vehicle. A review of the literature indicates that although many algorithms which enable fault detection at an early stage are described, their potential for use in a vehicle is highly limited. The reason lies in simplifications, including models and the operating EPS temperature range. The most frequently used simplification of the model is its linearization, which significantly reduces the calculation time; however, this significantly reduces the accuracy of the model, especially in cases with a large range of system operation. The need for methods to detect incipient faults is important for the safety and reliability of the entire car, not only during regular use but also especially during life-saving evasive maneuvers. [ABSTRACT FROM AUTHOR]

Extracellular vesicles (EVs) hold great promise for clinical application as new diagnostic and therapeutic modalities. This paper describes major GMP-based upstream and downstream manufacturing processes for EV large-scale production, also focusing on post-processing technologies such as surface bioengineering and uploading studies to yield novel EV-based diagnostics and advanced therapy medicinal products. This paper also focuses on the quality, safety, and efficacy issues of the bioengineered EV drug candidates before first-in-human studies. Because clinical trials involving extracellular vesicles are on the global rise, this paper encompasses different clinical studies registered on clinical-trial register platforms, with varying levels of advancement, highlighting the growing interest in EV-related clinical programs. Navigating the regulatory affairs of EVs poses real challenges, and obtaining marketing authorization for EV-based medicines remains complex due to the lack of specific regulatory guidelines for such novel products. This paper discusses the state-of-the-art regulatory knowledge to date on EV-based diagnostics and medicinal products, highlighting further research and global regulatory needs for the safe and reliable implementation of bioengineered EVs as diagnostic and therapeutic tools in clinical settings. Post-marketing pharmacovigilance for EV-based medicinal products is also presented, mainly addressing such topics as risk assessment and risk management. [ABSTRACT FROM AUTHOR]

Diagnostics are critical on the path to commercial fusion reactors, since measurements and characterisation of the plasma is important for sustaining fusion reactions. Gamma spectroscopy is commonly used to provide information about the neutron energy spectrum from activation analysis, which can be used to calculate the neutron flux and fusion power. The detection limits for measuring nuclear dosimetry reactions used in such diagnostics are fundamentally related to Compton scattering events making up a background continuum in measured spectra. This background lies in the same energy region as peaks from low-energy gamma rays, leading to detection and characterisation limitations. This paper presents a digital machine learning Compton suppression algorithm (MLCSA), that uses state-of-the-art machine learning techniques to perform pulse shape discrimination for high purity germanium (HPGe) detectors. The MLCSA identifies key features of individual pulses to differentiate between those that are generated from photopeaks and Compton scatter events. Compton events are then rejected, reducing the low energy background. This novel suppression algorithm improves gamma spectroscopy results by lowering minimum detectable activity (MDA) limits and thus reducing the measurement time required to reach the desired detection limit. In this paper, the performance of the MLCSA is demonstrated using an HPGe detector, with a gamma spectrum containing americium-241 (Am-241) and cobalt-60 (Co-60). The MDA of Am-241 improved by 51% and the signal to background ratio improved by 49%, while the Co-60 peaks were partially preserved (reduced by 78%). The MLCSA requires no modelling of the specific detector and so has the potential to be detector agnostic, meaning the technique could be applied to a variety of detector types and applications. [ABSTRACT FROM AUTHOR]

Innovations in biomedical engineering have significantly advanced healthcare through the integration of technology into medical practices. This paper explores recent developments and their impact on improving patient outcomes, enhancing diagnostic accuracy, and revolutionizing treatment modalities. One major innovation is the advent of wearable medical devices, which enable continuous monitoring of vital signs and physiological parameters outside traditional clinical settings. These devices, ranging from smartwatches to implantable sensors, provide real-time data that empower individuals to proactively manage their health and allow healthcare professionals to remotely monitor patients, leading to early detection of health issues and timely interventions. Furthermore, advancements in medical imaging technology have revolutionized diagnostic capabilities, enabling high-resolution imaging of anatomical structures and functional processes. Techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) have become indispensable tools for diagnosing diseases and guiding treatment decisions. Innovations in biomedical engineering continue to drive progress in healthcare, offering new opportunities to improve patient outcomes. [ABSTRACT FROM AUTHOR]

Intra-day transactions of stocks from competing firms in the financial markets are known to exhibit significant volatility and over-dispersion. This paper proposes some bivariate integer-valued auto-regressive models of order 1 (BINAR(1)) that are useful to analyze such financial series. These models were constructed under both time-variant and time-invariant conditions to capture features such as over-dispersion and non-stationarity in time series of counts. However, the quest for the most robust BINAR(1) models is still on. This paper considers specifically the family of BINAR(1)s with a non-diagonal cross-correlation structure and with unpaired innovation series. These assumptions relax the number of parameters to be estimated. Simulation experiments are performed to assess both the consistency of the estimators and the robust behavior of the BINAR(1)s under mis-specified innovation distribution specifications. The proposed BINAR(1)s are applied to analyze the intra-day transaction series of AstraZeneca and Ericsson. Diagnostic measures such as the root mean square errors (RMSEs) and Akaike information criteria (AICs) are also considered. The paper concludes that the BINAR(1)s with negative binomial and COM–Poisson innovations are among the most suitable models to analyze over-dispersed intra-day transaction series of stocks. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Hearing Science is the property of Institute of Sensory Organs and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Melanocytic nevi, commonly known as moles, are benign skin lesions that often occur in children and adolescents. Overall, they are less common in children compared to adults. Understanding the diagnosis and management of melanocytic nevi and risk factors for melanoma development is crucial for their early detection and appropriate treatment. This paper presents children's most common melanocytic nevi, including their epidemiology, morphology, diagnostic methods, and treatment. [ABSTRACT FROM AUTHOR]

Aquaculture is expected to play a vital role in solving the challenge of sustainably providing the growing world population with healthy and nutritious food. Pathogen outbreaks are a major risk for the sector, so early detection and a timely response are crucial. This can be enabled by monitoring the pathogen levels in aquaculture facilities. This paper describes a photonic biosensing platform based on silicon nitride waveguide technology with integrated active components, which could be used for such applications. Compared to the state of the art, the current system presents improvements in terms of miniaturization of the Photonic Integrated Circuit (PIC) and the development of wafer-level processes for hybrid integration of active components and for material-selective chemical and biological surface modification. Furthermore, scalable processes for integrating the PIC in a microfluidic cartridge were developed, as well as a prototype desktop readout instrument. Three bacterial aquaculture pathogens (Aeromonas salmonicida, Vagococcus salmoninarum, and Yersinia ruckeri) were selected for assay development. DNA biomarkers were identified, corresponding primer-probe sets designed, and qPCR assays developed. The biomarker for Aeromonas was also detected using the hybrid PIC platform. This is the first successful demonstration of biosensing on the hybrid PIC platform. [ABSTRACT FROM AUTHOR]

Point-of-care tests (POCTs) have become technological solutions for many global health challenges. This meta-ethnography examines what has been learned about the 'social lives' of POCTs from in-depth qualitative research, highlighting key social considerations for policymakers, funders, developers and users in the design, development and deployment of POCTs. We screened qualitative research examining POCTs in low- and middle-income countries and selected 13 papers for synthesis. The findings illuminate five value-based logics—technological autonomy, care, scalability, rapidity and certainty—shaping global health innovation ecosystems and their entanglement with health systems. Our meta-ethnography suggests that POCTs never achieve the technological autonomy often anticipated during design and development processes. Instead, they are both embedded in and constitutive of the dynamic relationships that make up health systems in practice. POCTs are often imagined as caring commodities; however, in use, notions of care inscribed in these devices are constantly negotiated and transformed in relation to multiple understandings of care. POCTs promise to standardize care across scale, yet our analysis indicates nonstandard processes, diagnoses and treatment pathways as essential to 'fluid technologies' rather than dangerous aberrations. The rapidity of POCTs is constructed and negotiated within multiple distinct temporal registers, and POCTs operate as temporal objects that can either speed up or slow down experiences of diagnosis and innovation. Finally, while often valued as epistemic tools that can dispel diagnostic uncertainty, these papers demonstrate that POCTs contribute to new forms of uncertainty. Together, these papers point to knowledge practices as multiple, and POCTs as contributing to, rather than reducing, this multiplicity. The values embedded in POCTs are fluid and contested, with important implications for the kind of care these tools can deliver. These findings can contribute to more reflexive approaches to global health innovation, which take into account limitations of established global health logics, and recognize the socio-technical complexity of health systems. [ABSTRACT FROM AUTHOR]

This paper presents the concept of applying incremental capacity analysis (ICA) on the OCV curve in the SoC space. The OCV curve can be obtained from any sequence of discharge or charge current or power pulse with a necessary rest period to allow the cell to reach a pseudo-OCV after each pulse. With a high resolution (>100 pulses) in the full SoC window, an OCV-vs.-SoC curve can be obtained with sufficient accuracy to perform an ICA on the obtained OCV curve. ICA as a diagnostic technique has commonly been applied on Li-ion cells with constant charge and discharge at slow currents. However, a slow controlled constant current charge or discharge is normally not feasible and cannot be easily applied to a battery in an application. Here, we revisit pulse-based ICA to supplement the conventional constant-current-based technique. Based on actual ageing data, we show that ICA performed on a selection of high-resolution OCV curves is comparable or better than conventional ICA with constant current. The main advantage of OCV-ICA is that it can be applied to most cells and systems without a significant interruption of normal cell operation. OCV-ICA can provide valuable insights into ageing mechanisms as well as, e.g., detailed information on changes in internal resistance. [ABSTRACT FROM AUTHOR]

This paper explores the available material regarding the potential use of a thermal imaging camera in the field of uroandrology and human sexuality research. Infrared thermography (IRT) constitutes a low-cost, effective and quick tool for the diagnosis and real-time monitoring of therapeutic effects. The presented paper focuses on the clinical aspects of the use of IRT in daily medical practice, the methodology of applying thermal imaging measurements and the physical properties of infrared radiation. IRT provides a precise, measurable and repeatable source of temperature data and its applications are constantly expanding into new medical fields complementing and developing conventional diagnostic methods. [ABSTRACT FROM AUTHOR]

Introduction: Gastro-entero-pancreatic neuroendocrine neoplasms (GEP-NENs) are malignancies originating from cells of the diffuse endocrine system. They are rare and localize in the upper and lower parts of the gastrointestinal tract and in the pancreas. Despite such a varied location, GEP-NENs are considered a common group of neoplasms due to the fact of their similar morphology and ability to secrete peptide hormones and biologically active amines. They are associated with clinical manifestations specific to the substances produced by a particular neoplasm. The classification of GEP-NENs is constantly systematized and updated based on their differentiation and grading. The development of available diagnostic and treatment methods for these tumors has made significant progress over the past 10 years and is still ongoing. Aim: In the following paper, we review the diagnostics and treatment of GEP-NENs, taking into account the latest molecular, immunological, or gene-based methods. Imaging methods using markers for receptors allow for high diagnostic sensitivity. Methods: Medical databases were searched for the latest information. The authors also sought confirmation of the content of a particular publication in another publications, so as to present the most reliable information possible. Results: Research results revealed that the diagnostics and treatment of GEP-NENs have significantly advanced in recent years. Surgical interventions, especially minimally invasive techniques, have shown efficacy in treating GEP-NENs, with specific therapies such as somatostatin analogs, chemotherapy, and peptide receptor radionuclide therapy demonstrating promising outcomes. The evolution of diagnostic methods, including imaging techniques and biomarker testing, has contributed to improved patient care and prognosis. Conclusions: The increasing incidence of GEP-NENs is attributed to enhanced diagnostic capabilities rather than a rise in population prevalence. The study emphasizes the importance of ongoing research to identify specific markers for early detection and targeted therapies to further enhance the effectiveness of treating these rare and heterogeneous malignancies. The findings suggest a positive trajectory in the management of GEP-NENs, with future prospects focused on personalized and targeted treatment approaches. [ABSTRACT FROM AUTHOR]

Earlier access to patients' biomarker status could transform disease management. However, gold-standard techniques such as enzyme-linked immunosorbent assays (ELISAs) are typically not deployed at the point-of-care due to their cumbersome instrumentation and complexity. Electrochemical immunosensors can be disruptive in this sector with their small size and lower cost but, without further modifications, the performance of these sensors in complex media (e.g., blood) has been limited. This paper presents a low-cost fluidic accessory fabricated using widely accessible materials and processes for boosting sensor sensitivity through confinement of the detection media next to the electrode surface. Liquid confinement first highlighted a spontaneous reaction between the pseudoreference electrode and ELISA detection substrate 3,3',5,5'-tetramethylbenzidine (TMB) that decreases the amount of oxTMB available for detection. Different strategies are investigated to limit this and maximize reliability. Next, flow cell integration during the signal amplification step of sensor preparation was shown to substantially enhance the detection of cytokine interleukin-6 (IL-6) with the best sensitivity boost recorded for fresh human plasma (x7 increase compared to x5.8 in purified serum and x5.5 in PBS). The flow cell requires no specialized equipment and can be seamlessly integrated with commercial sensors, making an ideal companion for electrochemical signal enhancement. [ABSTRACT FROM AUTHOR]

As an integral part of the European strategy for advancing fusion-generated electricity, IFMIF-DONES represents a high-intensity neutron irradiation plant with the main purpose of assessing the suitability of materials for fusion reactor applications. Its primary mission is to examine how materials respond to irradiation within a neutron flux that mimics the conditions expected in the first wall of the proposed DEMO reactor, which is intended to succeed ITER. Consequently, IFMIF-DONES, whose construction is slated to commence shortly, plays a pivotal role in aiding the development, approval, and safe operation of DEMO, as well as future fusion power plants. This paper provides a quick overview of the current development of the IFMIF-DONES neutron source with a particular snapshot of the present engineering design status for what concerns the instrumentation and control systems together with its complex diagnostics, that guarantees the safe monitoring, supervision and regulation of all operations. The current status of design, after the completion of the preliminary design phase is presented, as well as the existing and future plans for their integration also using some of the new capabilities offered by Artificial Intelligence tools. [ABSTRACT FROM AUTHOR]

Thyroglobulin (Tg) is an iodinated glycoprotein, which is normally stored in the follicular colloid of the thyroid, being a substrate for thyroid hormone production. Since it is produced by well-differentiated thyroid cells, it is considered a reliable tumor marker for patients with differentiated thyroid carcinoma (DTC) during their follow-up after total thyroidectomy and radioiodine ablation. It is used to monitor residual disease and to detect recurrent disease. After total thyroid ablation, unstimulated highly sensitive Tg measurements are sufficiently accurate to avoid exogenous or endogenous thyrotropin (TSH) stimulation and provide accurate diagnostic and prognostic information in the great majority of DTC patients. Adopting sophisticated statistical analysis, i.e., decision tree models, the use of Tg before radioiodine theranostic administration was demonstrated to be useful in refining conventional, pathology-based risk stratification and providing personalized adjuvant or therapeutic radioiodine administrations. The follow-up of DTC patients aims to promptly identify patients with residual or recurrent disease following primary treatment. Our review paper covers the diagnostic, theranostic and prognostic value of thyroglobulin in DTC patients. [ABSTRACT FROM AUTHOR]

In today's health-monitoring applications, there is a growing demand for wireless and wearable acquisition platforms capable of simultaneously gathering multiple bio-signals from multiple body areas. These systems require well-structured software architectures, both to keep different wireless sensing nodes synchronized each other and to flush collected data towards an external gateway. This paper presents a quantitative analysis aimed at validating both the wireless synchronization task (implemented with a custom protocol) and the data transmission task (implemented with the BLE protocol) in a prototype wearable monitoring platform. We evaluated seven frequencies for exchanging synchronization packets (10 Hz, 20 Hz, 30 Hz, 40 Hz, 50 Hz, 60 Hz, 70 Hz) as well as two different BLE configurations (with and without the implementation of a dynamic adaptation of the BLE Connection Interval parameter). Additionally, we tested BLE data transmission performance in five different use case scenarios. As a result, we achieved the optimal performance in the synchronization task (1.18 ticks as median synchronization delay with a Min-Max range of 1.60 ticks and an Interquartile range (IQR) of 0.42 ticks) when exploiting a synchronization frequency of 40 Hz and the dynamic adaptation of the Connection Interval. Moreover, BLE data transmission proved to be significantly more efficient with shorter distances between the communicating nodes, growing worse by 30.5% beyond 8 m. In summary, this study suggests the best-performing network configurations to enhance the synchronization task of the prototype platform under analysis, as well as quantitative details on the best placement of data collectors. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has revolutionized various fields, including healthcare, drug delivery, and material science. Liposomes, as versatile nanocarriers, have emerged as promising tools in AI applications. This paper explores the intersection of liposomes and AI, highlighting their synergistic potential in drug delivery, medical imaging, diagnostics, and beyond. We delve into the mechanisms of liposomal drug delivery and discuss how AI algorithms enhance targeting, efficiency, and therapeutic outcomes. Furthermore, we examine recent advancements in liposomebased imaging agents and biosensors facilitated by AI-driven analysis techniques. Additionally, challenges and future directions in integrating liposomes with AI are discussed, paving the way for innovative solutions in personalized medicine and the diagnostics. [ABSTRACT FROM AUTHOR]

The paper considers the optimal allocation issues of the allocated limited investment costs of a power company for the worn-out and damaged equipment replacement, taking into account the objectively existing limiting technical and economic factors. In particular, the main existing strategies for electrical equipment maintenance and repair used in the world practice are analysed. A fuzzy mathematical model for a comprehensive assessment of the electrical equipment units and groups importance degree in the allocating costs task for the worn and damaged equipment replacement has been developed. A mathematical model of options optimal selection for replacing the power company equipment based on integer programming in limited financial resources conditions is proposed. A technical condition comprehensive modelling of the power system subsystem electrical equipment and modes was carried out to determine the system risk index in case of emergency failures, planned and unplanned electrical equipment decommissioning, and the estimated distribution of power company investment costs for equipment replacement was performed. [ABSTRACT FROM AUTHOR]

Monitoring dynamic load transfer from train traffic to sub-ballast layers is crucial for verifying the reliability and safety of railway lines, assessing the design cost-effectiveness and achieving minimum environmental impact. For this purpose, measurements in labs, in situ or modeling the influence of dynamic loads on the immediate and long-term roadway quality are often performed using suitable software. The available test sections enabled monitoring of the dynamic loads and optimizing the critical spots where increased dynamic effects from railway traffic may occur. The subject of this paper is the calibration of the sensors installed in the different test sections of the trans-European corridor number V. As a result, the necessary input parameters for the subsequent numerical modeling of the dynamic effects on the track substructure and vibration propagation on the available sections of the upgraded railway line were obtained. The sensor calibration was carried out on the experimental field, part of the Experimental Basis of the Department of Railway Engineering and Track Management. As part of the calibration, the sensitivity of the sensors embedded in the track bed to the applied dynamic loads resulting from the impact effects of the lightweight deflectometer was assessed. The result of the calibration was the demonstration of sufficient sensitivity of the sensors and their suitability for implementation in an actual railway track structure, with the aim of obtaining relevant values of the response of the sub-ballast layers to dynamic loads and assessing the operational impacts on the sustainable environment. Also, the main result of the research was the possibility of using the theoretical–experimental route to optimize the layers of the railway body. [ABSTRACT FROM AUTHOR]

In the field of aircraft engine diagnostics, many advanced algorithms have been proposed over the last few years. However, there is still wide room for improvement, especially in the development of more integrated and complete engine health management systems to detect, identify, and forecast complex faults in a short time. Furthermore, it is necessary to ensure that these systems preserve their capabilities over time despite engine deterioration. This paper addresses these necessities by proposing an integrated system that considers the joint operation of feature extraction, anomaly detection, fault identification, and prognostic algorithms for engines with long operation times. To effectively reveal the actual engine condition, light adaptive degraded engine models are computed along with different health indicators that are used as inputs to train and test recognition and prediction models. The system is developed and evaluated using a specialized NASA platform which provides data from a turbofan engine fleet simultaneously experiencing long-term performance deterioration and faults. Contrary to other compared solutions, our results show that the proposed system is robust against the effects of engine deterioration, maintaining its level of detection, recognition, and prediction accuracy over a total engine service life. The low computational cost algorithms has generally fast performance in all stages, making the system suitable for online applications. [ABSTRACT FROM AUTHOR]

In the realm of space exploration, solid rocket motors (SRMs) play a pivotal role due to their reliability and high thrust-to-weight ratio. Serving as boosters in space launch vehicles and employed in military systems, and other critical & emerging applications, SRMs' structural integrity monitoring, is of paramount importance. Traditional maintenance approaches often prove inefficient, leading to either unnecessary interventions or unexpected failures. Condition-based maintenance (CBM) emerges as a transformative strategy, incorporating advanced sensing technologies and predictive analytics. By continuously monitoring crucial parameters such as temperature, pressure, and strain, CBM enables real-time analysis, ensuring timely intervention upon detecting anomalies, thereby optimizing SRM lifecycle management. This paper critically evaluates conventional SRM health diagnosis methods and explores emerging sensing technologies. Photonic sensors and fiber-optic sensors, in particular, demonstrate exceptional promise. Their enhanced sensitivity and broad measurement range allow precise monitoring of temperature, strain, pressure, and vibration, capturing subtle changes indicative of degradation or potential failures. These sensors enable comprehensive, non-intrusive monitoring of multiple SRM locations simultaneously. Integrated with data analytics, these sensors empower predictive analysis, facilitating SRM behavior prediction and optimal maintenance planning. Ultimately, CBM, bolstered by advanced photonic sensors, promises enhanced operational availability, reduced costs, improved safety, and efficient resource allocation in SRM applications. [ABSTRACT FROM AUTHOR]

Background: Preterm birth (PTB) is a leading cause of childhood disability, and it has become a key public health priority recognized by the World Health Organization and the United Nations. Objectives: This review will: (1) summarize current practice in the diagnosis and management of PTB, (2) outline developments in precision-based medicine for diagnostics to improve the care provided to pregnant women at risk of PTB, and (3) discuss the implications of current research in personalized medicine and the potential of future advances to influence the clinical care of women at risk of PTB. Methodology: This is a narrative literature review. Relevant journal articles were identified following searches of computerized databases. Key Results: Current and emerging technologies for the utility of personalized medicine in the context of PTB have the potential for applications in: (1) direct diagnostics to identify and target infection as one of the main known causes of PTB, (2) identifying novel maternal and fetal biomarkers, (3) the use of artificial intelligence and computational modeling, and (4) combining methods to enhance diagnosis and treatment. Conclusions: In this paper, we show how current research has moved in the direction of the targeted use of biomarkers in the context of PTB, with many novel approaches. [ABSTRACT FROM AUTHOR]

Ganoderma boninense (G. boninense) is a soil-borne fungus threatening oil palm at the present. It causes basal stem rot disease on oil palm. Within six months, this fungus can cause an oil palm plantation to suffer a significant 43% economic loss. The high persistence and nature of spread of G. boninense in soil make control of the disease challenging. Therefore, controlling the pathogen requires a thorough understanding of the mechanisms that underlie pathogenicity as well as its interactions with host plants. In this paper, we present the general characteristics, the pathogenic mechanisms, and the host's defensive system of G. boninense. We also review upcoming and most promising techniques for disease management that will have the least negative effects on the environment and natural resources. [ABSTRACT FROM AUTHOR]

Featured Application: This approach is suitable for diagnostics of other systems in terms of real-time fault identification and mitigation. It will also be useful in the field of digital twin applications. Many different types of aircraft designs have flight control systems (FCS) powered by hydraulic systems. With respect to the torques, moments, surface areas, and opposing forces to be acted upon, components introduce faults into the hydraulic system when these components are aging or degrading. The diagnostics of a hydraulically powered flight control actuation system (HPFCAS) rely on the faults produced within the subsystem components as well as the entire system's mechanism itself. In this paper, a model for an HPFCAS is developed to analyze faults where the HPFCAS was approached as a system of systems (SOS). The identified faults were injected into the system. It is established that some of the faults from the different subsystems had similar characteristic effects and were propagated with attendant consequences. For instance, a measured decrease in the pressure value is observed because of the decrease in the pump speed. A similar characteristic is observed if there is leakage on the line or if there is a clogging valve. These form complex integrated responses in determining where the fault is coming from if only one component is analyzed since it involves components serving different subsystems. Results show that only models that can describe the real characteristics or attributes of the specific systems, due to their defined components, are sufficient for effective diagnostics. This is because the data obtained are more accurate at predicting the behavior of components. [ABSTRACT FROM AUTHOR]

This paper presents a methodology that is designed for rapid exploratory analysis of the outputs from ensembles of climate models, especially when these outputs consist of maps. The approach formalizes and extends the technique of "intermodel empirical orthogonal function" analysis, combining multivariate analysis of variance techniques with singular value decompositions (SVDs) of structured components of the ensemble data matrix. The SVDs yield spatial patterns associated with these components, which we call ensemble principal patterns (EPPs). A unique hierarchical partitioning of variation is obtained for balanced ensembles in which all combinations of factors, such as GCM and RCM pairs in a regional ensemble, appear with equal frequency: suggestions are also proposed to handle unbalanced ensembles without imputing missing values or discarding runs. Applications include the selection of ensemble members to propagate uncertainty into subsequent analyses, and the diagnosis of modes of variation associated with specific model variants or parameter perturbations. The approach is illustrated using outputs from the EuroCORDEX regional ensemble over the United Kingdom. [ABSTRACT FROM AUTHOR]

Printed circuit board assembly (PCBA) is a cost-effective hardware device used in mechanical, process, electrical, electronic, military, and medical equipment providing automated and digital functionalities for users. Keeping high quality standards in the PCBA production process is a major challenge for the electronics production industry. Defective PCBAs are submitted to analysis, debug, and repair processes. This paper presents an augmented reality (AR) fault diagnosis support system for assembled electronic systems—the Cadence inspectAR Augmented Reality Electronics Platform. The system's functional concept and components are described. The steps of the diagnostic process are presented and discussed. The diagnostic capabilities of the system are illustrated with an example of the system's use in industrial practice. The planned steps in the development of the elaborated system are indicated. [ABSTRACT FROM AUTHOR]

The article diagnoses the prospects for the development of enterprises for the processing of solid household waste (SHW). It is specified that improving the quality of services provided by enterprises and the transition to European standards in the field of removal, utilization and processing of solid waste is a top priority, as Ukraine seeks to become a member of the EU in the future. It is noted that recycling is an important component of sustainable development and contributes to the creation of a more efficient and environmentally conscious economy for enterprises and the country. The most common types of solid waste recycling are presented, each of which is aimed at transforming a specific type of waste. The paper analyzes successful business cases that have already been implemented as a result of recycled solid waste and introduced into practice in other countries of the world. It is noted that some Ukrainian enterprises in collaboration with foreign partners have actively begun to implement similar cases. It is noted that a promising direction is the energy sector, which is currently also related to the processing of solid waste. It is noted that, even taking into account the active hostilities taking place on the territory of Ukraine, there are enterprises, organizations and institutions that are already actively working or just starting their business activities in the field of SHW processing. It is concluded that it is an important aspect, in particular, firstly, how the growing awareness of society about the problems of environmental pollution and the need to protect it creates a demand for environmentally friendly technologies and services, including waste recycling. Secondly, legislative and regulatory initiatives aimed at reducing waste and improving waste management create a favorable framework for the development of this sector. Thirdly, technological progress in the field of recycling makes it possible to create new products from waste and reduce the negative impact on the environment. Also, the development of this sector can stimulate investment and contribute to the creation of new jobs in the field of ecological economy. All this testifies to the potentially high investment attractiveness and prospects for the development of solid waste processing enterprises. [ABSTRACT FROM AUTHOR]

Molecular sampling with vacuum-assisted tissue electroporation is a novel, minimally invasive method for molecular profiling of solid lesions. In this paper, we report on the design of the battery-powered pulsed electric field generator and electrode configuration for an electroporation-based molecular sampling device for skin cancer diagnostics. Using numerical models of skin electroporation corroborated by the potato tissue phantom model, we show that the electroporated tissue volume, which is the maximum volume for biomarker sampling, strongly depends on the electrode's geometry, needle electrode skin penetration depths, and the applied pulsed electric field protocol. In addition, using excised human basal cell carcinoma (BCC) tissues, we show that diffusion of proteins out of human BCC tissues into water strongly depends on the strength of the applied electric field and on the time after the field application. The developed numerical simulations, confirmed by experiments in potato tissue phantoms and excised human cancer lesions, provide essential tools for the development of electroporation-based molecular markers sampling devices for personalized skin cancer diagnostics. [ABSTRACT FROM AUTHOR]

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Analysis of the method of using sonography during surgery in animals with polycystic kidney disease is an urgent task since firstly, sonography is a safe and non-invasive method of examination, which allows determining the structural features of the kidneys before, during, and after surgery. Secondly, from the standpoint of improving the results of surgery, sonography helps to clarify the localisation of cysts and determine their size. Thirdly, an important factor in the use of sonography is the reduction of pain and the risk of postoperative complications. In addition, due to this method of kidney examination, it is possible to more accurately determine the optimal route of access to cysts, which helps to reduce tissue injury and ensures rapid recovery of the animal after surgery. The purpose of the study is to analyse in detail and describe the method of using sonography during surgery in animals with polycystic kidney disease. The study focuses on the need to determine how sonography affects reducing the duration of surgery, improving the quality of cyst removal, and reducing the risk of complications during abdominal surgery. The approach in this study is based on the analysis of scientific papers on this subject, in particular on the experience of veterinarians who have already used sonography during abdominal operations in animals with polycystic kidney disease. Thus, special techniques of sonography and surgical treatment of kidney cysts include dopplerography, colour dopplerography, 3D and 4D sonography, elastography, introperative sonography, intraperitoneal sonography, and duplex scanning of renal arteries and veins. Surgical methods of treatment include extraction of individual cysts, drainage of cysts, resection, and nephrectomy. The use of sonography at different stages of surgical intervention helps to optimise the operation process, reduce the risk of complications, and contribute to the introduction of new approaches in the treatment of animals with polycystic kidney disease, which will substantially improve their quality of life [ABSTRACT FROM AUTHOR]

This paper is concerned with possible model misspecification in moment inequality models. Two issues are addressed. First, standard tests and confidence sets for the true parameter in the moment inequality literature are not robust to model misspecification in the sense that they exhibit spurious precision when the identified set is empty. This paper introduces tests and confidence sets that provide correct asymptotic inference for a pseudo-true parameter in such scenarios, and hence, do not suffer from spurious precision. Second, specification tests have relatively low power against a range of misspecified models. Thus, failure to reject the null of correct specification does not necessarily provide evidence of correct specification. That is, model specification tests are subject to the problem that absence of evidence is not evidence of absence. This paper develops new diagnostics for model misspecification in moment inequality models that do not suffer from this problem. [ABSTRACT FROM AUTHOR]

Sarcosporidia and sarcosporidiosis in Southeast Kazakhstan present formidable challenges to livestock health. This paper focuses on the outcomes of two key innovations: advancements in molecular diagnostics and the development of integrated control strategies. In the realm of diagnostics, the implementation of Polymerase Chain Reaction (PCR) techniques for species-specific identification and quantification yielded notable results. Our comprehensive survey of vertebrates in the region identified a diverse range of Sarcosporidia species, with an average prevalence reduction of 25% compared to conventional methods. This advancement not only enhances accuracy in parasite identification but also provides a more nuanced understanding of the local epidemiological landscape. The integration of Geographic Information System (GIS) and remote sensing technologies into our surveillance system revealed specific high-risk areas with an unprecedented precision of 90%. This targeted approach allowed for resource optimization, resulting in a 30% reduction in overall prevalence rates in the identified regions. The practical implications of this innovation are evident in its potential to guide effective intervention strategies and resource allocation. Genomic studies elucidated the genetic diversity within Sarcosporidia strains, laying the foundation for targeted interventions. Building on this knowledge, preliminary trials of our integrated control strategies showcased a promising 40% reduction in Sarcosporidia prevalence in the selected communities. This innovative approach combines traditional deworming practices with environmentally friendly treatments, providing a viable and sustainable solution. In conclusion, our research signifies a substantial leap forward in understanding and combating Sarcosporidia infections in Southeast Kazakhstan. The tangible results of improved diagnostics and the promising outcomes of integrated control strategies underscore the potential for transformative impacts on livestock health, agricultural productivity, and the well-being of the communities reliant on these animals. [ABSTRACT FROM AUTHOR]

Background: Advancements in genomic technologies are rapidly evolving, with the potential to transform laboratory diagnostics by enabling high-throughput analysis of complex biological data, such as microbiome data. Large Language Models (LLMs) have shown significant promise in extracting actionable insights from vast datasets, but their application in generating microbiome findings reports with clinical interpretations and lifestyle recommendations has not been explored yet. Methods: This article introduces an innovative framework that utilizes LLMs to automate the generation of findings reports in the context of microbiome diagnostics. The proposed model integrates LLMs within an event-driven, workflow-based architecture, designed to enhance scalability and adaptability in clinical laboratory environments. Special focus is given to aligning the model with clinical standards and regulatory guidelines such as the In-Vitro Diagnostic Regulation (IVDR) and the guidelines published by the High-Level Expert Group on Artificial Intelligence (HLEG AI). The implementation of this model was demonstrated through a prototype called "MicroFlow". Results: The implementation of MicroFlow indicates the viability of automating findings report generation using LLMs. Initial evaluation by laboratory expert users indicated that the integration of LLMs is promising, with the generated reports being plausible and useful, although further testing on real-world data is necessary to assess the model's accuracy and reliability. Conclusions: This work presents a potential approach for using LLMs to support the generation of findings reports in microbiome diagnostics. While the initial results seem promising, further evaluation and refinement are needed to ensure the model's effectiveness and adherence to clinical standards. Future efforts will focus on improvements based on feedback from laboratory experts and comprehensive testing on real patient data. [ABSTRACT FROM AUTHOR]

Bridges, as public structures, are national infrastructure structures. The investment requirements and demands for safe operation should put them at the forefront of engineers' attention. The important SNP bridge in Bratislava (Slovakia), which is one of the first bridges in the world to be suspended entirely on cables, has been the subject of a diagnosis of its health after 50 years of operation. The article describes and documents the individual steps of this diagnostics ‐ identification of the rope material, the status of the rope material and its current strength properties, and the experimentally and computationally determination of the forces in the individual ropes and their cables. The uniqueness of all the diagnostic steps was that they were carried out directly in the outdoor conditions of full operation of the bridge. The achieved results are briefly documented in the paper. Conclusions and further necessary steps for the final comprehensive safety assessment of the bridge operation are formulated. [ABSTRACT FROM AUTHOR]

This paper presents a systematic literature survey on diagnostic methods used for railway materials and systems. The authors analyze various railway accident reports, focusing on the types of failures described and their causes. Previous review papers have addressed various aspects of railway systems diagnostics; however, most of the existing research focuses on specific parts of the rail vehicle or subsystems. In contrast, this survey focuses on railway diagnostic systems rather than general diagnostic methods used in mechanical and electrical engineering. The authors classify the types of failures and diagnostic methods that are used in rail transport into two categories: infrastructure and rolling stock. The purpose of this paper is to systematize the types of failure that occur in railway transport systems; identify the state-of-the-art means and methods of diagnostics in railway materials and systems, with particular focus on new research findings; and identify trends and possible research gaps in need of further development.