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Context Use of artificial intelligence (AI) to predict clinical outcomes in thyroid nodule diagnostics has grown exponentially over the past decade. The greatest challenge is in understanding the best model to apply to one's own patient population, and how to operationalize such a model in practice. Evidence Acquisition A literature search of PubMed and IEEE Xplore was conducted for English-language publications between January 1, 2015 and January 1, 2023, studying diagnostic tests on suspected thyroid nodules that used AI. We excluded articles without prospective or external validation, nonprimary literature, duplicates, focused on nonnodular thyroid conditions, not using AI, and those incidentally using AI in support of an experimental diagnostic outside standard clinical practice. Quality was graded by Oxford level of evidence. Evidence Synthesis A total of 61 studies were identified; all performed external validation, 16 studies were prospective, and 33 compared a model to physician prediction of ground truth. Statistical validation was reported in 50 papers. A diagnostic pipeline was abstracted, yielding 5 high-level outcomes: (1) nodule localization, (2) ultrasound (US) risk score, (3) molecular status, (4) malignancy, and (5) long-term prognosis. Seven prospective studies validated a single commercial AI; strengths included automating nodule feature assessment from US and assisting the physician in predicting malignancy risk, while weaknesses included automated margin prediction and interobserver variability. Conclusion Models predominantly used US images to predict malignancy. Of 4 Food and Drug Administration–approved products, only S-Detect was extensively validated. Implementing an AI model locally requires data sanitization and revalidation to ensure appropriate clinical performance. [ABSTRACT FROM AUTHOR]

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.

Children with gifted intellects often earn lower scores on measures of processing speed than their nongifted counterparts. However, neuropsychological research indicates such a profile of scores is likely not due to a true innate neurocognitive processing speed deficit but is rather a statistical artifact resulting from the interference of common cognitive and behavioral idiosyncrasies inherent to giftedness, such as perfectionism and an aversion to completing paper and pencil tasks. The first aim of this study is to identify and explain the relative underperformance of children with gifted intellects on measures of processing speed. The second aim is to develop a neuropsychologically informed framework for clinically assessing processing speed in children with gifted intellects using alternative measures from the WISC–V. Clinical implications are discussed as they relate to the relative disabling effects of common gifted-related idiosyncrasies on the expression of their otherwise intact neuropsychological capabilities. [ABSTRACT FROM AUTHOR]

The progression and refinement of traction electric motors play a pivotal role in the enhancement of vehicles. With constant advancements in power efficiency, diagnostic tools, and reliability, electric traction motors are becoming more economical in terms of operational costs. This article underscores the significance of methods used in assessing the technical proficiency of traction electric motors, coupled with their testing techniques. The vibration characteristics of these motors serve as a prime criterion in assessing their quality throughout their lifecycle – from design and manufacturing to operation. A groundbreaking proposition made in this article is the evaluation of electric traction motors' health based on their vibration acceleration within an expansive frequency range of 5 Hz to 10 kHz. This contrasts the conventional approach which focuses on vibration speed in a restricted frequency spectrum up to 1000 Hz, as outlined by current standards. Such permissible vibration classes are essential in ensuring the high-quality design, technical finesse, and dependability of traction electric motors. Exceeding these vibration thresholds can result in rapid wear and tear, diminishing the motor's reliability and lifespan. Hence, motors that surpass these permissible vibrations are not advised for vehicular use. Additionally, the article sheds light on requisite equipment controls for accurately gauging vibrations and noise levels in electric motors. Delving deeper into the nuances, the methods for mounting vibration transducers and electric motors on testing platforms are elucidated. These methods are pivotal in obliterating any hindrances that could impede the precise measurement of vibrations induced by an electric motor. [ABSTRACT FROM AUTHOR]

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]

[Display omitted] • Original Partial Discharge Echo (PDE) measurement methodology is presented. • Extension of PDE potential refers to the detection and extraction of echo attributes. • Measurement methodology can be applied to various high-voltage objects and insulation systems. • Experimental results showed dependence of gaseous inclusion surface conditions on the echo pattern. • Results extend the insight into PD – especially with a focus on diagnostic applications. In this article, an original measurement methodology is presented for insight into the partial discharge (PD) mechanism − being one of the key indicators of high-voltage insulation system integrity. This approach is based on riveting measurements of PD echo (PDE) that are acquired by applying a chopped sequence. The new extension of PDE potential, presented in this paper, refers to the detection and extraction of echo attributes, providing an additional quantitative assessment of the insulation condition. In this way a noninvasive insight into source of defect can be provided, which is not possible by conventional PD measurements. Hence, intension and future development direction of this method is to provide insulation deterioration imaging. The detection of echo discharges occurs during the voltage-less time break between consecutive periods of excitation voltage, therefore the further advantage of PDE is higher immunity to excitation-driven disturbances. The whole measurement sequence can be subdivided in to AC and DC parts. The measurement methodology can be applied to various high-voltage objects and insulation systems. As an example, measurement results obtained on polymeric material that contained an embedded defect are presented. A comparison of PD echo effects on both untreated and aged specimens revealed the dependence of gaseous inclusion surface conditions on the echo pattern. Presented new measurement methodology allowed for extraction and evaluation of PD echo time constant, which yielded distinct values in the cases of new and aged specimens. The echo discharges were attributed to the mechanisms in the dielectric that started in the voltage-less part after the transition point. These effects represent the interplay between the remnant electric field and the decay of the electric field that was accumulated from the previous PDs. The aim of this paper was to show advanced PD measurement approach to investigate the effects of an internal embedded electrical insulation defect on an externally measurable indicator. The paper also presents an attempt to extract additional information from phase-resolved PD images. In this context, further research is needed to provide directions toward a measurement technique and a potential future diagnostic approach. [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.

A novel approach for cost-effective and temporally resolved in-line combustion gas diagnostics based on spontaneous Stokes Raman spectroscopy is presented in this paper. The proposed instrument uses a multipass configuration designed to increase the scattering generation, giving information about gas species concentrations, including H2 and N2 that are not commonly available from analysis with absorption spectroscopy techniques. The system performs calibrated analysis providing both qualitative and quantitative information about the gas composition. Depending on the application, the device can work with spectra integration time from 0.15 s up to 10 s, with a Raman spectrum ranging from the H2 rotational peak at Raman shift of 587 cm−1 up to the H2 vibrational peak at 4156 cm−1, covering all the Raman emissions of major combustion species. The device response was characterized by a working pressure from 0.7 to 7.5 bar. The instrument prototype has been made completely transportable, designed to operate using a gas sampling system, and ready to be operated in relevant industrial in-line environments. [ABSTRACT FROM AUTHOR]

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]

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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]

A new Phase II control chart, which is based on the change-point model and combined with the exponentially weighted moving average (EWMA) mechanism, is proposed to monitor general linear profiles. The new control chart can be used to monitor general linear profiles when the true in-control parameters are unknown, and only a few historical data are available. In addition, when the chart triggers an out-of-control signal, not only can it estimate the location of the change point, it can also identify which of the parameters have changed as well as the change directions. Using Monte-Carlo simulations, the proposed chart is shown to be effective and has good diagnostic performance. Furthermore, the simulation results show that the proposed chart has better performance than the existing charts in most of the out-of-control scenarios considered. An example is used to illustrate how the proposed chart can be implemented in practical applications. [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]

Preliminary evidence from the Childhood Joint Attention Rating Scale (C-JARS; Mundy et al., 2017) suggests symptoms related to diminished joint attention and the spontaneous sharing of experience with others can be assessed with a parent-report measure in children and adolescents with autism. This study was designed to expand on the previous study by examining the validity of both a Social Symptom (SS) and a Prosocial (PS) scale of the C-JARS in a study of school-aged autistic children (n = 89) with and without co-occurring intellectual disability (ID), as well as an age matched neurotypical sample (n = 62). Results indicated that both C-JARS scales were sensitive and specific with respect to identifying the diagnostic status of the children. In addition, the PS scale was sensitive to differences in cognitive abilities (IQ) and sex differences in the autism group. These results are consistent with the hypothesis that joint attention and spontaneous sharing of experience symptoms are not only characteristic of preschool children with autism but may also constitute a developmentally continuous dimension of the social phenotype of autism that can be measured in school-aged children. [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]

Metabolomics aims for comprehensive characterization and measurement of small molecule metabolites (<1700 Da) in complex biological matrices. This study sought to assess the current understanding and usage of metabolomics in laboratory medicine globally and evaluate the perception of its promise and future implementation. A survey was conducted by the IFCC metabolomics working group that queried 400 professionals from 79 countries. Participants provided insights into their experience levels, knowledge, and usage of metabolomics approaches, along with detailing the applications and methodologies employed. Findings revealed a varying level of experience among respondents, with varying degrees of familiarity and utilization of metabolomics techniques. Targeted approaches dominated the field, particularly liquid chromatography coupled to a triple quadrupole mass spectrometer, with untargeted methods also receiving significant usage. Applications spanned clinical research, epidemiological studies, clinical diagnostics, patient monitoring, and prognostics across various medical domains, including metabolic diseases, endocrinology, oncology, cardiometabolic risk, neurodegeneration and clinical toxicology. Despite optimism for the future of clinical metabolomics, challenges such as technical complexity, standardization issues, and financial constraints remain significant hurdles. The study underscores the promising yet intricate landscape of metabolomics in clinical practice, emphasizing the need for continued efforts to overcome barriers and realize its full potential in patient care and precision medicine. [ABSTRACT FROM AUTHOR]

Emerging technology in laboratory medicine can be defined as an analytical method (including biomarkers) or device (software, applications, and algorithms) that by its stage of development, translation into broad routine clinical practice, or geographical adoption and implementation has the potential to add value to clinical diagnostics. Paediatric laboratory medicine itself may be considered an emerging area of specialisation that is established relatively recently following increased appreciation and understanding of the unique physiology and healthcare needs of the children. Through four clinical (neonatal hypoglycaemia, neonatal hyperbilirubinaemia, sickle cell disorder, congenital adrenal hyperplasia) and six technological (microassays, noninvasive testing, alternative matrices, next generation sequencing, exosome analysis, machine learning) illustrations, key takeaways of application of emerging technology for each area are summarised. Additionally, nine key considerations when applying emerging technology in paediatric laboratory medicine setting are discussed. [ABSTRACT FROM AUTHOR]

Introduction: Sensorineural hearing impairment (SNHI), a common childhood disorder with heterogeneous genetic causes, can lead to delayed language development and psychosocial problems. Next-generation sequencing (NGS) offers high-throughput screening and high-sensitivity detection of genetic etiologies of SNHI, enabling clinicians to make informed medical decisions, provide tailored treatments, and improve prognostic outcomes. Areas covered: This review covers the diverse etiologies of HHI and the utility of different NGS modalities (targeted sequencing and whole exome/genome sequencing), and includes HHI-related studies on newborn screening, genetic counseling, prognostic prediction, and personalized treatment. Challenges such as the trade-off between cost and diagnostic yield, detection of structural variants, and exploration of the non-coding genome are also highlighted. Expert opinion: In the current landscape of NGS-based diagnostics for HHI, there are both challenges (e.g. detection of structural variants and non-coding genome variants) and opportunities (e.g. the emergence of medical artificial intelligence tools). The authors advocate the use of technological advances such as long-read sequencing for structural variant detection, multi-omics analysis for non-coding variant exploration, and medical artificial intelligence for pathogenicity assessment and outcome prediction. By integrating these innovations into clinical practice, precision medicine in the diagnosis and management of HHI can be further improved. [ABSTRACT FROM AUTHOR]

Background: Whole blood host transcript signatures show great potential for diagnosis of infectious and inflammatory illness, with most published signatures performing binary classification tasks. Barriers to clinical implementation include validation studies, and development of strategies that enable simultaneous, multiclass diagnosis of febrile illness based on gene expression. Methods: We validated five distinct diagnostic signatures for paediatric infectious diseases in parallel using a single NanoString nCounter® experiment. We included a novel 3-transcript signature for childhood tuberculosis, and four published signatures which differentiate bacterial infection, viral infection, or Kawasaki disease from other febrile illnesses. Signature performance was assessed using receiver operating characteristic curve statistics. We also explored conceptual frameworks for multiclass diagnostic signatures, including additional transcripts found to be significantly differentially expressed in previous studies. Relaxed, regularised logistic regression models were used to derive two novel multiclass signatures: a mixed One-vs-All model (MOVA), running multiple binomial models in parallel, and a full-multiclass model. In-sample performance of these models was compared using radar-plots and confusion matrix statistics. Results: Samples from 91 children were included in the study: 23 bacterial infections (DB), 20 viral infections (DV), 14 Kawasaki disease (KD), 18 tuberculosis disease (TB), and 16 healthy controls. The five signatures tested demonstrated cross-platform performance similar to their primary discovery-validation cohorts. The signatures could differentiate: KD from other diseases with area under ROC curve (AUC) of 0.897 [95% confidence interval: 0.822–0.972]; DB from DV with AUC of 0.825 [0.691–0.959] (signature-1) and 0.867 [0.753–0.982] (signature-2); TB from other diseases with AUC of 0.882 [0.787–0.977] (novel signature); TB from healthy children with AUC of 0.910 [0.808–1.000]. Application of signatures outside of their designed context reduced performance. In-sample error rates for the multiclass models were 13.3% for the MOVA model and 0.0% for the full-multiclass model. The MOVA model misclassified DB cases most frequently (18.7%) and TB cases least (2.7%). Conclusions: Our study demonstrates the feasibility of NanoString technology for cross-platform validation of multiple transcriptomic signatures in parallel. This external cohort validated performance of all five signatures, including a novel sparse TB signature. Two exploratory multi-class models showed high potential accuracy across four distinct diagnostic groups. [ABSTRACT FROM AUTHOR]

Ethics Review began a decade ago with a mission to identify ethical concerns that hold back innovation and to promote solutions that would move the field forward. Over this time, blood biomarkers for brain pathology and medications that treat that pathology promise to transform research and care. A central problem is that the evidence needed to guide test interpretation and practice is accumulating and there are unanswered questions. At the same time, people living with and at risk for dementia want access to their test results and involvement in their care. We promote dialog among diverse people across many institutions through collaboration with the Advisory Group on Risk Evidence Education for Dementia (AGREEDementia.org). Over the years Ethics Review continues to publish these dialogs and solutions to overcome the paralysis of indecision and ethical concerns. [ABSTRACT FROM AUTHOR]

Diabetic cardiomyopathy (DCM) is a severe secondary complication of type 2 diabetes mellitus (T2DM) that is diagnosed as a heart disease occurring in the absence of any previous cardiovascular pathology in diabetic patients. Although it is still lacking an exact definition as it combines aspects of both pathologies - T2DM and heart failure, more evidence comes forward that declares DCM as one complex disease that should be treated separately. It is the ambiguous pathological phenotype, symptoms or biomarkers that makes DCM hard to diagnose and screen for its early onset. This re-view provides an updated look on the novel advances in DCM diagnosis and treatment in the experimental and clinical settings. Management of patients with DCM proposes a challenge by itself and we aim to help navigate and advice clinicians with early screening and pharmacotherapy of DCM. [ABSTRACT FROM AUTHOR]

Power conversion systems for wireless power transfer (WPT) applications have demanding requirements for continuity of service, besides being operated with stressing environmental conditions. Diagnostic and prognostic programs are thus quite useful and this work shows a novel approach based on the analysis of spectra of an autoregressive (AR) model to recognize a wide range of faulty devices, including incipient faults, when deviations from nominal parameters begin to manifest. AR modeling provides cleaner and easier to interpret spectra, where only the salient features remain, and they are more sensitive to variations in the corresponding time domain waveforms. A log spectral distance is calculated that successfully separates healthy and faulty states of the feeding single-phase inverter, even in challenging scenarios of poor signal-to-noise ratio. [ABSTRACT FROM AUTHOR]

Healthcare Internet of Things (IoT) initiatives that aim to integrate technology and medicine are shaking the sector to its foundations. The revolutionary potential of the proposed strategy is shown here as we investigate the far-reaching consequences of the Healthcare IoT on remote patient monitoring. The beginning sets the stage by underlining the significance of bridging the gap between technology and medicine. Our multi-pronged approach comprises Internet of Things (IoT) remote monitoring, cloud-based analysis, artificial intelligence (AI) integrated diagnostics, real-time alerts, and predictive analytics. Our study's results demonstrate that the proposed approach is superior to the status quo. The area of remote patient monitoring has profited considerably from the employment of traditional approaches, such as the fusion of data from wearable sensors, analysis in the cloud, diagnostics that utilize artificial intelligence, real-time monitoring, predictive modeling, and smart alarm systems. The suggested strategy, however, performs very well across all of the most important measures of assessment. Comparatively, the accuracy rate of the conventional wearable sensor fusion approach was only 76%, whereas our suggested method reached 89%. Our strategy was also more accurate than the standard approach (88% vs. 73%). When compared to the recall rate of 68% produced by conventional methods, our suggested strategy significantly outperformed the competition. It's a great option for hospitals and clinics since it improves diagnostic precision and speed without breaking the bank. [ABSTRACT FROM AUTHOR]

Efforts to control and eliminate measles and rubella are aided by high-quality surveillance data—supported by laboratory confirmation—to guide decision-making on routine immunization strategies and locations for conducting preventive supplementary immunization activities (SIAs) and outbreak response. Important developments in rapid diagnostic tests (RDTs) for measles and rubella present new opportunities for the global measles and rubella surveillance program to greatly improve the ability to rapidly detect and respond to outbreaks. Here, we review the status of RDTs for measles and rubella Immunoglobulin M (IgM) testing, as well as ongoing questions and challenges regarding the operational use and deployment of RDTs as part of global measles and rubella surveillance. Efforts to develop IgM RDTs that can be produced at scale are underway. Once validated RDTs are available, clear information on the benefits, challenges, and costs of their implementation will be critical for shaping deployment guidance and informing country plans for sustainably deploying such tests. The wide availability of RDTs could provide new programmatic options for measles and rubella elimination efforts, potentially enabling improvements and flexibility for testing, surveillance, and vaccination. [ABSTRACT FROM AUTHOR]

Permanent grasslands are characterized by herbaceous flora adapted to local conditions, with deep root systems that facilitate resource uptake and provide resistance to anthropogenic and abiotic stresses. This study aimed to develop and implement efficient diagnostic and agronomic management tools for farmers. In order to demonstrate the methodology, we selected five diverse grasslands with different characteristics. The research tested the grass typology method to diagnose these areas and establish optimal management practices based on floristic composition. The method was applied to achieve the rational management of the grasslands studied. The results provided valuable data on floristic composition, species frequency, and specific functional indices. The characterization of the five grasslands in Moșnița Nouă in Timiș County enabled us to recognize optimal grassland strategies for each area, maximizing production based on the grass typology. Thus, the study demonstrated the impact of using simplified tools to improve grassland diagnosis and management, significantly contributing to the more sustainable maintenance of the permanent grasslands for farmers. [ABSTRACT FROM AUTHOR]

This paper discusses a new method for measuring and calculating the angle of inclination of a railway support or a contact network support, using an unmanned aerial vehicle flying along a straight path, parallel to the railway track. A review of existing measurement methods is carried out, their advantages and disadvantages are indicated. In the method under consideration, it is proposed to measure angles and distances with six laser scanning rangefinders installed in threes on horizontal and inclined planes on an unmanned aerial vehicle. This allows you to increase the speed and accuracy of determining the angle of inclination of vertical supports. The calculations use the minimum distances from the laser scanning range finder to the top and bottom of the support surface. The formulas use geometric relationships and the cosine theorem to calculate the roll of supports taking into account their taper. Measuring distances and angles three times allows for averaging over them, which significantly increases the accuracy of calculations. A model experiment is carried out on a model of a reinforced concrete contact network support in four orientations. A comparison is made between theoretically calculated and experimentally measured distances and inclination angles. The accuracy of parameter determination complies with regulatory requirements.

Abstract Context Prostate‐specific antigen is non‐specific for prostate cancer. This is improved by multiparametric MRI but a significant amount of indolent prostate cancer is detected by the current MRI pathway and data is emerging that clinically significant cancers maybe missed using a standard PSA threshold. Volatile organic compound (VOC) analysis may offer novel biomarkers for prostate cancer and clinically significant disease. Objective To perform a systematic review of the literature to evaluate the current evidence for the use of VOCs as novel biomarkers for prostate cancer and clinically significant prostate cancer. Evidence Acquisition A systematic search of MEDLINE, Scopus, Web of Science and the Cochrane Library was undertaken by two independent reviewers and papers were assessed for inclusion in the review. Study characteristics, sensitivity and specificity of GC–MS or eNose were extracted. Risk of bias and applicability issues were determined using QUADAS 2 and the quality of reporting using the STARD checklist. Evidence Synthesis Nineteen studies were included, of which 6 utilised eNose and 13 GC–MS. eNose sensitivity and specificity were 0.71–0.95 and 0.79–0.96, respectively, and GC–MS found a sensitivity and specificity of 0.66–1.00 and 0.53–0.97, respectively. There were concerns about bias in patient recruitment due to differences in the timing of the index test relative to the reference standard. Conclusion This review has found promising early results for urinary metabolomics in the detection of prostate cancer. However, there is a need for larger, high‐quality studies to validate this. Future work should focus on the detection of clinically significant prostate cancer.