Search

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]

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]

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]

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]

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]

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]

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]

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 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]

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]

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]

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]

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]

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]

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]

Copyright of FME Transactions is the property of University of Belgrade, Faculty of Mechanical Engineering 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.)

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]

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.

In this paper, a robust fault detection methodology for complex analog CMOS integrated filters is presented. It is based on combining the two types of testing methodologies, Oscillation-Based Testing (OBT) and IDDQ testing, i.e., measuring of the power-supply current ( I D D ). The proposed methodology is applied to the Bi-quad Sallen–Key band-pass (BP) filter cell with relatively complex, two-stage, class-AB-output, operational amplifier (opamp) topology. The filter is custom designed targeting the 180-nm CMOS technology. Hundreds of time-domain simulations and analyses of the circuit output signal are performed in order to obtain the fault dictionary. The presented results show that the proposed hybrid OBT–IDDQ methodology is significantly more efficient in the defects coverage than any of the particular test methodologies alone. Subsequently, the specific algorithm for the defects classification is proposed. Based on the classification, certain degree of diagnosis of the individual defect, or a group of defects, can be achieved. [ABSTRACT FROM AUTHOR]

ABSTRACT Metagenomic shotgun sequencing (mNGS) can serve as a generic molecular diagnostic tool. An mNGS proficiency test (PT) was performed in six European veterinary and public health laboratories to detect porcine astroviruses in fecal material and the extracted RNA. While different mNGS workflows for the generation of mNGS data were used in the different laboratories, the bioinformatic analysis was standardized using a metagenomic read classifier as well as read mapping to selected astroviral reference genomes to assess the semiquantitative representation of astrovirus species mixtures. All participants successfully identified and classified most of the viral reads to the two dominant species. The normalized read counts obtained by aligning reads to astrovirus reference genomes by Bowtie2 were in line with Kraken read classification counts. Moreover, participants performed well in terms of repeatability when the fecal sample was tested in duplicate. However, the normalized read counts per detected astrovirus species differed substantially between participants, which was related to the different laboratory methods used for data generation. Further modeling of the mNGS data indicated the importance of selecting appropriate reference data for mNGS read classification. As virus- or sample-specific biases may apply, caution is needed when extrapolating this swine feces-based PT for the detection of other RNA viruses or using different sample types. The suitability of experimental design to a given pathogen/sample matrix combination, quality assurance, interpretation, and follow-up investigation remain critical factors for the diagnostic interpretation of mNGS results.IMPORTANCEMetagenomic shotgun sequencing (mNGS) is a generic molecular diagnostic method, involving laboratory preparation of samples, sequencing, bioinformatic analysis of millions of short sequences, and interpretation of the results. In this paper, we investigated the performance of mNGS on the detection of porcine astroviruses, a model for RNA viruses in a pig fecal material, among six European veterinary and public health laboratories. We showed that different methods for data generation affect mNGS performance among participants and that the selection of reference genomes is crucial for read classification. Follow-up investigation remains a critical factor for the diagnostic interpretation of mNGS results. The paper contributes to potential improvements of mNGS as a diagnostic tool in clinical settings.

ABSTRACT 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.

The paper presents the importance of electricity parameters in transient states for assessing the ability of an aircraft's power grid to self-regulate. It has been shown that the quality of electrical energy of an aircraft's power system is influenced by its operating time. Aging changes resulting from long-term operation of the power system cause deterioration of electrical energy parameters, which directly affects airworthiness and flying safety. Maintaining standard energy parameters in transient states is very important to ensure proper operation of the aircraft. The article describes the method of measuring the quality of electricity in transient states, and using the example of the DC power system of the Su 22 aircraft, the essence of its network diagnostics in transient states is presented. Also presented are sample results of research conducted by the authors regarding changes in electrical energy parameters in transient states of the Su 22 aircraft, obtained during its many years of operation. [ABSTRACT FROM AUTHOR]

In the framework of generalized linear models (GLM), this paper explores the design and applicability of partial residual (PRES), augmented partial residual (APRES), and conditional expectation and residuals (CERES) plots for visualizing an outlier's diagnostics as a function of selected variables. Here, a geometric regression as a GLM is thoroughly described. Additionally, plots for PRES, APRES, and CERES have been built. Due to how the response variable and the associated link function interact with various covariates, the effectiveness of these plots for creating an appealing visual impression may vary. On the cervical cancer data, specific methodologies are used to identify trends for effective modelling. When compared to other approaches, the power of the tests for various plots demonstrates that PRES, CERES (L) and CERES (K) have the greatest endurance for the outlier's diagnostics. On the basis of the power of residual plots, the use is recommended for outlier diagnostics in presence of conventional tests. [ABSTRACT FROM AUTHOR]

Ankle sprains are exceedingly common injuries in both athletes and the general population. They account for 10 to 30% of all sports injuries. Although the vast majority of lateral ankle ligament injuries respond successfully to conservative management, the absolute number of those that progress to chronic lateral ankle instability (CLAI) remains considerably important. This condition is characterized by persistent symptoms and may be associated with short-term and long-term complications and functional deficits. There is still a lack of ideal postoperative management of CLAI patients. Furthermore, an evidence-based rehabilitation phasing does not exist and most of the published studies regarding this subject suggest some protocols based on a wide variety of functional assessment scores and other modalities that are not accurate enough. Moreover, the literature that assesses the ability to return to work (RTW) and return to sport (RTS) in the general population and athletes operated for CLAI most commonly shows aggregated results with global rates of RTW or RTS without describing a detailed timeline based on the readiness of patients to return to each level of activity. Although stress radiographs and MRI have been assessed as potential tools to improve postoperative management of CLAI patients, the first modality is limited by its low sensitivity to detect laxity and the second one by its static character and its inability to predict neither the healing process phase nor the mechanical properties of the repaired/reconstructed ligaments. Bioelectrical impedance, mechanical impedance and near-infrared spectroscopy are non-invasive methods of measurement that could be potential assessment tools to help surgeons improve the postoperative management of patients after CLAI surgery. [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.

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.

The integration of biochips with AI opened up new possibilities and is expected to revolutionize smart healthcare tools within the next five years. The combination of miniaturized, multi-functional, rapid, high-throughput sample processing and sensing capabilities of biochips, with the computational data processing and predictive power of AI, allows medical professionals to collect and analyze vast amounts of data quickly and efficiently, leading to more accurate and timely diagnoses and prognostic evaluations. Biochips, as smart healthcare devices, offer continuous monitoring of patient symptoms. Integrated virtual assistants have the potential to send predictive feedback to users and healthcare practitioners, paving the way for personalized and predictive medicine. This review explores the current state-of-the-art biochip technologies including gene-chips, organ-on-a-chips, and neural implants, and the diagnostic and therapeutic utility of AI-assisted biochips in medical practices such as cancer, diabetes, infectious diseases, and neurological disorders. Choosing the appropriate AI model for a specific biomedical application, and possible solutions to the current challenges are explored. Surveying advances in machine learning models for biochip functionality, this paper offers a review of biochips for the future of biomedicine, an essential guide for keeping up with trends in healthcare, while inspiring cross-disciplinary collaboration among biomedical engineering, medicine, and machine learning fields. [Display omitted] • Biochips are key for diagnosis, drug delivery, disease modeling, and neurostimulation. • AI-assisted biochip technology offers advances in novel diagnostics and therapeutics. • Useful in cancer, diabetes, infectious diseases, and neurological disorders. • Predictive analytics in AI-biochips shed light on predictive and precision medicine. [ABSTRACT FROM AUTHOR]

Fusion reactors are complex systems of interconnected parts, many of which rely on novel technologies. Understanding the patterns of connectivity is essential for many design aspects: assessing technical risks, leveraging modularity, preventing rework and enabling functional integration. The enabling design artefact is the system's architecture, its conceptual model of functions, components and interfaces. In this paper, we present two architecture modelling and analysis methods based on Dependency Structure Matrices (DSM). Firstly, the bottom-up method builds an architecture model from decentralized documentation of all ITER diagnostics and their interfaces. The DSM highlights a modular system topology, integration challenges and opportunities for managing complexity. The limitations of this method, however, motivate the development of a top-down alternative. The top-down method is suited for early-stage design definition, therefore generating valuable insights throughout the development lifecycle. The architecture model serves as a central storage of design information, from which viewpoints can be automatically generated: DSMs, functional flow diagrams, cabling diagrams, interface reports and requirements propagation tables. This model-based information paradigm drastically improves design quality, project planning, change management and stakeholder communication. The top-down method is demonstrated for the Visible Spectroscopy Reference System, one of ITER's first diagnostics, but promises a more widespread application. • A modelling method to define the multi-level components, functions and interfaces of fusion devices. • Automatically generated design documents that improve communication with multidisciplinary stakeholders. • Recommendations for the organization and management of sets of diagnostic systems. [ABSTRACT FROM AUTHOR]

• The nanophotonic field has advanced to be used as a method to detect infectious diseases. • Multiplex nanophotonic diagnostics enable rapid and simultaneous detection of viral pathogens. • High sensitivity and specificity of multiplex nanophotonic diagnostics enable real-time identification of viruses without the need for nucleic acid amplification. • Photonic diagnostics such as localized surface plasmon resonance (LSPR), magnetic resonance imaging (MRI), optical coherence tomography (OCT), and photoacoustic imaging (PAI), can be developed as point-of-care diagnostic tools and used as procedures for treating viral diseases. The field of nanophotonics has advanced and can be utilized as a method to detect different infectious diseases. The introduction of multiplex nanophotonic diagnostics has enabled the speedy and simultaneous detection of viral infections and specific biomarkers. The quick reaction times, high sensitivity, and specificity of multiplex nanophotonic diagnostics enable real-time identification of viruses without the need for nucleic acid amplification. This review presents an overview of nanophotonic tools used to identify diseases and particular biomarkers. The paper also examines possible research areas for the development of unique, cutting-edge multiplex nanophotonic diagnostics capable of concurrently detecting various diseases or biomarkers/biomolecules. Furthermore, it discusses barriers to further advancement and offers insight into anticipated trends. [Display omitted] [ABSTRACT FROM AUTHOR]

Objective biomarkers are crucial for early diagnosis to promote treatment and raise survival rates for diseases. With the smallest non-coding RNAs—piwi-RNAs (piRNAs)—and their transcripts, we sought to identify if these piRNAs could be used as biomarkers for colorectal cancer (CRC). Using previously published data from serum samples of patients with CRC, 13 differently expressed piRNAs were selected as potential biomarkers. With this data, we developed a machine learning (ML) algorithm and created 1020 different piRNA sequence descriptors. With the Naïve Bayes Multinomial classifier, we were able to isolate the 27 most influential sequence descriptors and achieve an accuracy of 96.4%. To test the validity of our model, we used data from piRBase with known associations with CRC that we did not use to train the ML model. We were able to achieve an accuracy of 85.7% with these new independent data. To further validate our model, we also tested data from unrelated diseases, including piRNAs with a correlation to breast cancer and no proven correlation to CRC. The model scored 44.4% on these piRNAs, showing that it can identify a difference between biomarkers of CRC and biomarkers of other diseases. The final results show that our model is an effective tool for diagnosing colorectal cancer. We believe that in the future, this model will prove useful for colorectal cancer and other diseases diagnostics. [ABSTRACT FROM AUTHOR]

Inflammatory syndromes, including those caused by infection, are a major cause of hospital admissions among children and are often misdiagnosed because of a lack of advanced molecular diagnostic tools. In this study, we explored the utility of circulating cell-free RNA (cfRNA) in plasma as an analyte for the differential diagnosis and characterization of pediatric inflammatory syndromes. We profiled cfRNA in 370 plasma samples from pediatric patients with a range of inflammatory conditions, including Kawasaki disease (KD), multisystem inflammatory syndrome in children (MIS-C), viral infections, and bacterial infections. We developed machine learning models based on these cfRNA profiles, which effectively differentiated KD from MIS-C--two conditions presenting with overlapping symptoms--with high performance [test area under the curve = 0.98]. We further extended this methodology into a multiclass machine learning framework that achieved 80% accuracy in distinguishing among KD, MIS-C, viral, and bacterial infections. We further demonstrated that cfRNA profiles can be used to quantify injury to specific tissues and organs, including the liver, heart, endothelium, nervous system, and the upper respiratory tract. Overall, this study identified cfRNA as a versatile analyte for the differential diagnosis and characterization of a wide range of pediatric inflammatory syndromes. [ABSTRACT FROM AUTHOR]

To mitigate the use of antibiotics for many of the multifactorial diseases seen in pigs, horses and cattle, new diagnostic tools are needed. Acute phase protein (APP) measurements can, in humans, be used to guide antibiotic treatment initiation, evaluate treatment efficacy, and make a prognosis. The aim of this review is to collect evidence on the clinical functionality of APP measurements as a tool to guide antibiotic treatment in pigs, horses, and cattle. Literature was retrieved using Medline, CAB Abstracts and Google Scholar. The acute phase response has been investigated for a plethora of diseases and clinical signs and the major acute phase proteins are elevated in diseased compared to healthy animals. Few studies correlated acute phase response with aetiology, antibiotic treatment efficacy, prognosis, or severity of disease. The existing research does not support that APP can be used to guide antibiotic treatment, but the reported studies indicate that C-reactive protein (CRP) might be able to differentiate between bacterial and non-bacterial causes of disease in pigs. Serum amyloid A (SAA) might reflect underlying aetiology in horses and infectious or non-infectious cases of mastitis in cows. [ABSTRACT FROM AUTHOR]

Diffuse large B cell lymphoma, not otherwise specified (DLBCL, NOS) is the most common type of non-Hodgkin lymphoma (NHL). Significant efforts have been focused on utilizing advanced genomic technologies to further subclassify DLBCL, NOS into clinically relevant subtypes. These efforts have led to the implementation of novel algorithms to support optimal risk-oriented therapy and improvement in the overall survival of DLBCL patients. The pathogenesis of DLBCL at the molecular level indicates copy number variation (CNV) as one of the major forms of genetic alterations in the somatic mutational landscape. Random deregulation that results in complex breaks of chromosomes and restructuring of shattered chromosomal segments is called chromothripsis. Gene expression changes influenced by chromothripsis have been reported in cancer and congenital diseases. This chaotic phenomenon results in complex CNV, gene fusions, and amplification and loss of tumor suppressor genes. We present herein a summary of the most clinically relevant genomic aberrations, with particular focus on copy number aberrations in a case that highlights DLBCL, NOS arising from relapsed Hodgkin lymphoma. The focus of our study was to understand the relationship between the clinical, morphological, and genomic abnormalities in DLBCL, NOS through multiple techniques for therapeutic considerations. [ABSTRACT FROM AUTHOR]

Autoimmune diseases of the liver and biliary tract require timely and accurate diagnosis. This study evaluates the D-tek panel (D-Tek, Mons, Belgium) of 10 immunodot antigens for its effectiveness in diagnosing autoimmune hepatitis (AIH) and primary biliary cholangitis (PBC). We retrospectively analysed serum samples from 111 patients who had undergone routine testing, including indirect immunofluorescence (IIF) and enzyme-linked immunosorbent assays (ELISA), to confirm or exclude autoimmune liver or biliary tract disease. The panel tested for M2/nPDC, M2/OGDC-E2, M2/BCOADC-E2, M2/PDC-E2, gp210, sp100, LKM1, LC1, SLA, and F-actin antigens. Results showed that all positive IIF+ELISA results were confirmed by the immunodot panel, except for two samples from patients who had never been diagnosed with AIH. The immunodot test identified over 20 additional autoantibodies in samples initially negative by IIF, corroborated by laboratory imaging and medical history. The immunodot technique proved to be a quick, sensitive, and specific method with high overall accuracy. This study suggests that the immunodot technique may be an effective screening and confirmatory method for autoimmune liver diseases, potentially improving diagnostic efficiency and accuracy in clinical practice. [ABSTRACT FROM AUTHOR]

This review explores the impact of Artificial Intelligence (AI) in dentistry, reflecting on its potential to reshape traditional practices and meet the increasing demands for high-quality dental care. The aim of this research is to examine how AI has evolved in dentistry over the past two decades, driven by two pivotal questions: "What are the current emerging trends and developments in AI in dentistry?" and "What implications do these trends have for the future of AI in the dental field?". Utilizing the Scopus database, a bibliometric analysis of the literature from 2000 to 2023 was conducted to address these inquiries. The findings reveal a significant increase in AI-related publications, especially between 2018 and 2023, underscoring a rapid expansion in AI applications that enhance diagnostic precision and treatment planning. Techniques such as Deep Learning (DL) and Neural Networks (NN) have transformed dental practices by enhancing diagnostic precision and reducing workload. AI technologies, particularly Convolutional Neural Networks (CNNs) and Artificial Neural Networks (ANNs), have improved the accuracy of radiographic analysis, from detecting dental pathologies to automating cephalometric evaluations, thereby optimizing treatment outcomes. This advocacy is underpinned by the need for AI applications in dentistry to be both efficacious and ethically sound, ensuring that they not only improve clinical outcomes but also adhere to the highest standards of patient care. [ABSTRACT FROM AUTHOR]

The term long COVID (LC) effectively describes the broad long-term disease burden of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infections, encompassing individual suffering and significant socioeconomic impacts. However, its general use hampers precise epidemiological research, diagnostics and therapeutic strategies. Misinterpretations occur, for example, when population surveys are compared to studies using health record data, because both refer to these data as LC. This also emphasizes the need for different terminology. The National Institute for Health and Care Excellence (NICE) rapid guideline differentiates ongoing symptomatic COVID-19 from post-COVID conditions, yet real-world observations challenge these two subgroup definitions. We propose refining the term LC into three subgroups: ongoing symptomatic COVID-19, SARS-CoV-2 induced or exacerbated diseases and post-acute COVID condition. This stratification aids targeted diagnostics, treatment and epidemiological research. Subgroup-specific documentation using the International Classification of Diseases, Tenth Revision (ICD-10) codes ensures accurate tracking and understanding of long-term effects. The subgroup of post-acute COVID condition again includes various symptoms, syndromes and diseases like post-exertional malaise (PEM), dysautonomia or cognitive dysfunctions. In this regard, differentiation, especially considering PEM, is crucial for effective diagnostics and treatment. [ABSTRACT FROM AUTHOR]