High Informative Value Research Articles

Item response theory for the color-picture version of Boston naming test in a Chinese sample with neurodegenerative diseases.

Although Boston Naming Test has been thoroughly validated at a global level, there is limited assessment of item-level properties using modern psychometric methods. This study aimed to investigate the construct validity and item-level properties of the color-picture version of Boston Naming Test (CP-BNT) in a Chinese cohort with neurodegenerative diseases. This retrospective study included 424 participants, consisting of 118 normal controls, 152 with Alzheimer's disease, 101 with primary progressive aphasia, and 53 with other neurodegenerative diseases. All participants underwent a comprehensive neuropsychological assessment that included the CP-BNT. Factor analysis and item response theory were conducted. The CP-BNT exhibits a multidimensional structure with three factors: Factor 1, consisting of nine items with moderate difficulty levels, demonstrated peak measurement function for mild anomia (the highest information value = 33.7, ability estimated value = -0.8, reliability = 0.97); Factor 2, comprising eleven items with lower difficulty levels, performed well in cases of mild to moderate anomia (the highest information value = 34.1, ability estimated value = -1.2, reliability = 0.97); and Factor 3, including ten items with higher difficulty levels, provided the most measurement information for normal naming (the highest information value = 9.9, ability estimated value = 0, reliability = 0.90). All items, except item igloo, showed good discrimination (discrimination parameter ranged from 5.46 to 1.15). Most items had a different difficulty position versus the original version, thereby generating a novel item sequence with an ascending difficulty hierarchy for Chinese samples. These findings support that the CP-BNT has good validity, reliability, and cultural appropriateness in the Chinese context, improving its utility in clinical assessments and interventions.

Use of mobile communication technology to overcome vaccine skepticism

Despite significant advances in clinical medicine, infectious diseases and their prevention remain the leading problem of our time. Low literacy of the population reggarding immunization, combined with high activity of the anti-vaccination movement, creates unhealthy skepticism about immunoprophylaxis and the risks of epidemics. The work uses theoretical analysis of literature, methods of anonymous questionnaires of students of medical and non-medical specialties, methods of statistical and bioinformatics data processing, and modern internet technologies: the platform of the social network “Vkontakte” and the program for creating a chatbot “Senler”. The analysis of commitment to vaccination of medical and non-medical students showed that 65.8% to 96.2% of respondents have a positive attitude to preventive health measures. From 1.9% to 6% believe the disease is easier to treat than to prevent, and almost 30% of respondents do not think about prevention and diseases in general, considering themselves young. Almost 10% of non-medical students (1 group) consider vaccination useless, 8.4% dangerous for immunity, 18.3% know nothing about vaccinations and cannot compare the benefits of vaccination with the risk of infectious pathology. In order to form medical literacy of the population, including in strategic issues of preserving the health of the nation, the team of authors developed and implemented through the social network “Vkontakte” chatbot “Vaccine and point” containing up-to-date information about vaccines, methods multiplicity and age features of their use. People [n = 156] used the chatbot during the month of work, noting its high informational value, motivating to vaccination. Analysis of commitment to vaccination of students of medical and non-medical specialties showed an insufficient level of trust in domestic preventive medicine, which forms an unhealthy attitude to vaccine prevention. Insufficient vaccine coverage creates real threats to the spread of infectious diseases at the population level. Developed and implemented in real practice, the “Vaccine and point” chatbot is an effective tool for increasing the knowledge of compatriots about the real possibilities of protection against the main vaccine-controlled infections and an additional attempt to overcome anti-vaccination skepticism.

Quantitative and spatially resolved detection of multiplexed microRNA from plant tissue via hybridization to hydrogel-bound DNA probes in nanoliter well arrays

Understanding complex regulatory networks in plant systems requires elucidating the roles of various gene regulators under a spatial landscape. MicroRNA are key regulators that impart high information value through their tissue specificity and stability when using expression patterns for evaluating network outcomes. However, current techniques that utilize spatial multiplexing and quantitation of microRNA are limited to primarily mammalian systems. Here, we present a method to spatially resolve and quantify multiple endogenous microRNA in situ using ethanol fixed, paraffin embedded model plant species. This method utilizes target-specific microRNA capture along with universal ligating and labelling, all within functionalized hydrogel posts containing DNA probes in nanoliter well arrays. We demonstrate the platform’s multiplexing capabilities through analyzing three endogenous microRNA in Arabidopsis thaliana rosettes which provide useful answers to fundamental plant growth and development from the unique expression patterns. The spatial tissue technique is also validated using non-spatial small RNA assays to demonstrate the versatility of the well array platform. Our new platform expands the toolkit of spatial omics technologies for plants.

Assessing health-related quality of life using the Wound-QoL-17 and the Wound-QoL-14-Results of the cross-sectional European HAQOL study using item response theory.

For assessing health-related quality of life in patients with chronic wounds, the Wound-QoL questionnaire has been developed. Two different versions exist: the Wound-QoL-17 and the Wound-QoL-14. For international and cross-cultural comparisons, it is necessary to demonstrate psychometric properties in an international study. Therefore, the aim of this study was to test both questionnaires in a European sample, using item response theory (IRT). Participants were recruited in eight European countries. Item characteristic curves (ICC), item information curves (IIC) and differential item functioning (DIF) were calculated. In both questionnaires, ICCs for most items were well-ordered and sufficiently distinct. For items, in which adjacent response categories were not sufficiently distinct, response options were merged. IICs showed that items on sleep and on pain, on worries as well as on day-to-day and leisure activities had considerably high informational value. In the Wound-QoL-14, the item on social activities showed DIFs regarding the country and age. The same applied for the Wound-QoL-17, in which also the item on stairs showed DIFs regarding age. Our study showed comparable results across both versions of the Wound-QoL. We established a new scoring method, which could be applied in international research projects. For clinical practice, the original scoring can be maintained.

Assessment of the degree of inflammatory reaction in uveitis: diagnostic capabilities of clinical methods

Inflammatory diseases of the uvea demonstrate a high incidence and a significant prevalence among people of working age. Uveitis complications often lead to blindness and low vision in patients of various age groups. Diagnostics and monitoring the efficacy of uveitis therapy requires the use of quantitative criteria to assess the degree of inflammatory reaction. The aim of this review is to analyze the informative value and accuracy of various clinical methods for subjective and objective assessment of the inflammatory reaction in the anterior and posterior segments of the eye in uveitis. To assess the inflammatory reaction in uveitis in everyday clinical practice, biomicroscopy is most often used, which is characterized by subjectivity associated with the level of qualification of the ophthalmologist. Other clinical methods, such as fluorescein angiography, optical coherence tomography-angiography, have high sensitivity and informative value along with independence from the subjective impression of the examiner. However, the first of them is invasive, and the second is still not available to a wide range of practicing ophthalmologists today, especially in the “angio” mode. Laser flare photometry is of special importance in the diagnosis of uveitis. It is both a non-invasive and sensitive method that makes it possible to obtain quantitative indicators characterizing the activity of uveal inflammation. The prospects for the application and development of this method are important both for clinical practice and for research purposes.

A spin on active learning analysis for health monitoring.

Decisions on the operation and maintenance of high-valued structures are critical, collecting and analysing data that can inform these decisions motivates the development of structural health monitoring (SHM) and tool monitoring (TM) systems. Descriptive health-state information labels for measured data points are crucial for successful monitoring systems. However, the labels can be expensive to collect and are often unavailable. Due to this problem, fully-supervised machine learning is limited in it’s application to monitoring systems which gives rise to unsupervised and semi-supervised approaches, one of these approaches is active learning. Active learning aims to highlight which unlabelled data points have the highest value of information and would be most informative to the model if they were labelled. For risk-based active learning in SHM, the model is used to advise some maintenance decision process where querying a data point means an expert inspecting the structure to determine its health-state. The current paper applies an active learning regression technology to a machining tool health case study. The algorithm queries data points that are most important in helping determine the health-state of the tool. Querying the most informative datapoints can drastically reduce the costs of a monitoring system. The regression analysis results are used to estimate tool wear and inform a risk-based maintenance decision process. This addresses the problem of when to replace a tool based on its health status, which has applications for many monitoring system problems.

Modelling wild boar abundance at high resolution

Abstract By using the latest available data, we provide estimates of wild boar (Sus scrofa) distribution and abundance pre‐African Swine Fever (ASF) based on occurrence data in Europe. Secondly, as a basis for the calibration model output into densities, we used the predictions of relative abundance, and hunting yield‐based model (hunted individuals per km2), at 2x2 km for wild boar (by ENETWILD Consortium) and local wild boar densities (individuals per km2) considered reliable and obtained in the framework of the European Observatory of Wildlife (EOW), as well as some from recent literature (2015 onwards). Hunting yield predictions were considered at different spatial scales namely 5, 10 and 15 km radii buffer around localities with density estimations. The calibration of hunting yield‐based model into densities are a better fit for 15 km radius buffer and a significant relationship between model predictions of hunting yield and reliable density values at European level. This calibration of wild boar hunting yield‐based model into densities will offer the possibility to predict density values of wild boar. This will be useful to incorporate into risk factor analyses for African Swine Fever at the selected spatial range. This is the first time that absolute density estimates have been made using these two approaches for Europe, which demonstrates the added value of the observatory approach (a number of study areas where reliable density values are obtained, such as from the EOW) to generate novel information of high value for epidemiological assessment. During an ASF outbreak hunting effort will change dramatically and will take a few years to return to similar pre‐ASF levels, so post‐ASF estimates of density would be limited to areas where ASF has been present for a while. However, there will be relatively limited effect on sighting data as these rely on a number of different actors, many of whom may be expected to return to normal activities relatively soon after ASF arrives. Thus, relative post‐ASF wild boar density may be more reliable in the short term. These relative post‐ASF densities were calculated but with the limited sighting data available at the chosen locations the uncertainty was high. We advocate for the developing this nework of wildlife monitoring across Europe, and in general, harmioized wildlife monitoring programs, ensuring standardisation and consistency in the data generated and collected, which is essential for assessing management and risks related not only to ASF but other wildlife diseases.

Diagnostic Efficacy of Trephine Biopsy and Core Biopsy of Bone and Soft Tissue Tumors

Verification of pathomorphological diagnosis is a key stage in selection of treatment tactics in patients with neoplasms. Understanding of the nature of pathological process is especially important in context of primary tumors of the bones, soft tissues and metastases as despite active development of diagnostic techniques, they do not allow to obtain full information on histological nature of the tissues, differentiation grade, as well as molecular and genetic characteristics of the tumor. These data are associated with selection of optimal treatment tactics at every stage and disease prognosis. The optimal approach to obtaining histological materials in tumor of the bones and soft tissues are radiologically guided (X-ray, computed tomography and magnetic resonance imaging) trephine biopsy or ultra-sound-guided core biopsy.Aim. To determine information value of trephine and core biopsies of neoplasms of the bones and soft tissues of various locations, identify mistakes during manipulation affecting results of morphological diagnosis.Materials and methods. Between 2015 and 2023 at the Russian Scientific Center of Roentgenoradiology, 788 patients aged 18–85 years (380 men, 408 women) underwent 602 trephine biopsies and 248 core biopsies (n = 850) of neo-plasms of the soft tissues and bones of various locations. Absence of informational value of morphological study required repeat trephine biopsies in 19 patients, core biopsies in 2 patients, open biopsies in 13 patients.Results. Informational value of trephine biopsy of the bones was 90.5 % (long bones – 96.8 %, flat bones – 91.6 %, spine bones – 82.1 %), core biopsy of the soft tissues 98.8 %. Sufficient amount of material for immunohistochemical testing was obtained in 89 % biopsies. Interventions in cases of bone neoplasms were performed using CT (computed tomography) navigation; for core biopsies of the soft tissues, ultrasound was used. Absence of informational value in trephine bio psies was caused by insufficient amount of material (48 (7.97 %) cases), retrieval of trephine biopsy from the area of tumor necrosis (4 (0.67 %) cases), incorrect selection of access (4 (0.67 %) cases) and needle type (1 (0.17 %) case).Conclusion. Results of analysis of trephine and core biopsy data of bone and soft tissue neoplasms showed high information value, which is supported by literature data. This allows to consider these methods of histological material retrieval the “golden standard” of bone and soft tissue tumor diagnosis.

Review on Verifiable Outsourcing of Industrial Internet Data in Securely Shared

With the rapid development of information technology, industrial Internet has also entered a rapid development stage. As cloud servers have huge storage space as well as powerful computing power, more and more resource-constrained factories outsource their data to cloud servers for computation and storage to reduce the burden of data owners. Industrial data contains information about industrially produced products, and the high value of such information attracts attacks from illegal users, which may lead to data leakage. In addition, cloud servers are not fully trustworthy; therefore, it is important to ensure the security and correctness of industrial Internet data during outsourced computation and storage for the healthy development of the industrial Internet. In this paper, we explore technical solutions to guarantee the security and correctness of industrial Internet data when outsourcing computation, to achieve the privacy protection of industrial Internet data.

Path to autonomous soil sampling and analysis by ground-based robots

Good site characterization is essential for the selection of remediation alternatives for impacted soils. The value of site characterization is critically dependent on the quality and quantity of the data collected. Current methods for characterizing impacted soils rely on expensive manual sample collection and off-site analysis. However, recent advances in terrestrial robotics and artificial intelligence offer a potentially revolutionary set of tools and methods that will help to autonomously explore natural environments, select sample locations with the highest value of information, extract samples, and analyze the data in real-time without exposing humans to potentially hazardous conditions. A fundamental challenge to realizing this potential is determining how to design an autonomous system for a given investigation with many, and often conflicting design criteria. This work presents a novel design methodology to navigate these criteria. Specifically, this methodology breaks the system into four components – sensing, sampling, mobility, and autonomy – and connects design variables to the investigation objectives and constraints. These connections are established for each component through a survey of existing technology, discussion of key technical challenges, and highlighting conditions where generality can promote multi-application deployment. An illustrative example of this design process is presented for the development and deployment of a robotic platform characterizing salt-impacted oil & gas reserve pits. After calibration, the relationship between the in situ robot chloride measurements and laboratory-based chloride measurements had a good linear relationship (R2-value = 0.861) and statistical significance (p-value = 0.003).

Biochemical changes in the blood of domestic dogs with cardiomyopathy in the setting of obesity

In clinical settings, cardiac biomarkers with high sensitivity and specificity can be used to predict or diagnose heart disease. In humane clinical practice, troponins I and T have been used as predictors of heart disease, and the concentration of N-terminal pro-B-type natriuretic peptide (NT-proBNP) is also considered the most reliable predictor of heart disease. Veterinary cardiac biomarkers are not yet widely used by practitioners in the diagnosis of heart disease, but this does not negate the high informative value of specific cardiac parameters such as canine N-terminal pro-B-type natriuretic peptide (cNT-proBNP) and canine cardiac troponin I (ccTnI). Given the above, there is a need to conduct biochemical blood tests for cardiac pathology in dogs. The aim of the study was to determine specific parameters (cNT-proBNP and ccTnI) and blood lipidogram of domestic dogs with cardiomyopathy in the setting of obesity. The analysis of blood tests showed an increase in the content of ccTnI by more than 20 times (Р < 0.001) and cNT-pro BNP by 17.6 times (Р < 0.001) in domestic dogs with cardiomyopathy in the setting of obesity. According to the results of lipidogram determination, the dogs of the experimental group showed an increase in total cholesterol by 66.9 %, low-density lipoprotein (LDL) by 8.4 times, and very low-density lipoprotein (VLDL) by 4.2 times. The studies showed significant changes in blood biochemical parameters in cardiomyopathy in domestic dogs with obesity, which confirms the diagnostic value of these biomarkers. The research results deserve the attention of the veterinary community of practitioners and can be used in the diagnosis and subsequent prognosis of heart disease in the future.

Genome-wide sequencing in children with epilepsy and developmental disorders

The progress of genetic diagnostic methods and a significant improvement in the quality of next-generation sequencing (NGS) have led to a revolution in the study of the genetics of epilepsy. Genome-wide sequencing (PSG) is the «gold standard» in genetic research in epilepsy.Material and methods. Genome-wide sequencing was performed in 168 probands aged from 1 month to 18 years with a suspected diagnosis of genetic epilepsy. PSG was prescribed to patients who, alongside with epilepsy, had delayed intellectual/speech development and/or motor disorders and behavioral disorders.Results. According to the results of PSG, genetic variants related to the phenotype of the disease were detected in 137 out of 168 (81.5%) children, variations in the number of DNA copies were noted in 14 out of 168 (8.3%) patients. Variants with unclear clinical significance were described in 35 of 137 (25.54%). In the remaining 102 out of 137 (74.45%) patients, the identified causative genetic variants were described as probably pathogenic and pathogenic. Monogenic developmental and epileptic encephalopathies (DEE) were detected in 37/137 or 27% of all patients, while the spectrum of these genetic encephalopathies was extremely wide (from DEE type 1 to DEE type 97). In 52/137 (37.9%) children, the presence of a specific genetic syndrome outside the framework of the DEE, classified in OMIM, was confirmed.Conclusion. The results confirm the high informative value of genome-wide sequencing in a group of children with a combination of epilepsy, intellectual, speech, motor and behavioral disorders. In most cases, the results allow either to prescribe a genotype-oriented symptomatic (less often pathogenetic) treatment, or rationally justify the tactics of further observation and examination, as well as to increase the effectiveness of medical and genetic counseling. The authors express their sincere gratitude to the Charity foundation for medical and social genetic aid projects «Life Genome” for assistance in conducting genome-wide sequencing of most of the described patients.

Predictors of chronic heart failure in occupational pulmonology

The issue of occupational lung diseases, primarily dust lung diseases caused by exposure to industrial fibrogenic aerosols of varying degrees of fibrogenicity, remains relevant not only for the Russian Federation, but also for most of the world's leading economies. Purpose of the study: to investigate echocardiographic parameters and constitution features, assess the state of carbohydrate metabolism, and evaluate the concentration of leptin and resistin in patients with occupational respiratory diseases. Results of the study: fiberoptic bronchoscopy revealed both subatrophic changes in the bronchial mucosa and phenomena of focal atrophy of the bronchial mucosa and diffuse atrophy in all study groups. According to the otorhinolaryngologist’s examination, there is a high detection rate of chronic atrophic rhino-pharyngolaryngitis in the study groups, which, taking into account the data of fiberop tic bronchoscopy, indicates the systemic and descending nature of the pathological process. Furthermore, our studies demonstrated the high informative value and diagnostic significance of computer spirography for occupational respiratory diseases. Conclusion. As a result of the study, novel clinical and pathogenetic features were identified, and myocardial con tractility was assessed in chronic dust bronchitis, occupational chronic obstructive pulmo nary disease, and interstitial pneumoconiosis. This allows recommending the determination of these indicators (leptin, resistin), as well as echocardiographic assessment of the state and function of the myocardium during periodic medical examinations of workers exposed to industrial fibrogenic aerosols of varying degrees of fibrogenicity in order to assess the risk of developing and predict chronic heart failure in occupational respiratory diseases associated with exposure to highly fibrogenic, moderately fibrogenic, and low fibrogenic industrial aerosols.

Assessing Machine Learning Techniques for Predicting Banking Crises in India

The historical prevalence of banking crises and their profound impact on global economies underscores the imperative for policy makers to refine their crisis forecasting frameworks. Against this backdrop, the present study endeavors to predict potential banking crises in India by leveraging a spectrum of artificial intelligence and machine learning techniques (AI-ML). These techniques encompass logistic regression, random forest, naïve Bayes, gradient boosting, support vector machine, neural networks, K-nearest neighbors, and decision trees. Initially, a banking fragility index was constructed utilizing monthly banking data spanning 2002 to 2023, demarcating the periods of crisis and stability. Subsequently, an extensive array of early warning indicators (EWIs) encompassing asset prices, macroeconomic factors, external influences, and credit-related variables were employed to forecast crisis periods. Our findings reveal that AI-ML models exhibit reasonable accuracy in predicting banking crises. Moreover, advanced model performance metrics highlight neural networks and random forest models as particularly effective in crisis prediction, surpassing other methodologies. Notably, among the EWIs, variables related to credit, interest rates, and liquidity emerge as possessing relatively higher information value in discerning fragilities within the Indian banking system. Importantly, the methodological framework presented herein can be extrapolated for banking crisis prediction in other economies.

Energy Consumption Displays in Electric Vehicles: Differential Effects on Estimating Consumption and Experienced Energy Dynamics Awareness.

The effects of three prototypical designs of energy consumption displays on energy-specific situation awareness were examined. Energy efficiency is crucial for the sustainability of technical systems. However, without accurate situation awareness of energy dynamics (energy dynamics awareness, EDA) it can be challenging for humans to optimize the use of energy resources of electric vehicles (EVs) through their behavior. We examined three prototypical energy display designs that varied by their informational value to support EDA. Furthermore, we investigated the differential effects on EDA measured by (1) a newly constructed scale (experienced EDA), (2) estimating energy consumption, and (3) identifying efficient trips in an online experiment. Participants (N = 82) watched standardized driving scenes (videos) of EV trips presenting the energy displays. We found a strong effect of display type on experienced EDA, with the trace display being the most supportive. The EDA scale showed excellent internal consistency. The consumption estimation and efficient trip identification indicators were not affected by the display type. The study indicates that experienced EDA is immediately affected by displays with higher information value, but performance might need more time and training. More research is needed to investigate the cognitive processes related to EDA and to examine how distinct display elements enhance EDA. Results from this research can be used as guidance for the design of energy displays, especially in EVs. The EDA scale can be used as an evaluation measure in the human-centered design process of energy displays.

Harnessing the power of Microscale AcoustoFluidics: A perspective based on BAW cancer diagnostics.

Cancer directly affects one in every three people, and mortality rates strongly correlate with the stage at which diagnosis occurs. Each of the multitude of methods used in cancer diagnostics has its own set of advantages and disadvantages. Two common drawbacks are a limited information value of image based diagnostic methods and high invasiveness when opting for methods that provide greater insight. Microfluidics offers a promising avenue for isolating circulating tumor cells from blood samples, offering high informational value at predetermined time intervals while being minimally invasive. Microscale AcoustoFluidics, an active method capable of manipulating objects within a fluid, has shown its potential use for the isolation and measurement of circulating tumor cells, but its full potential has yet to be harnessed. Extensive research has focused on isolating single cells, although the significance of clusters should not be overlooked and requires attention within the field. Moreover, there is room for improvement by designing smaller and automated devices to enhance user-friendliness and efficiency as illustrated by the use of bulk acoustic wave devices in cancer diagnostics. This next generation of setups and devices could minimize streaming forces and thereby enable the manipulation of smaller objects, thus aiding in the implementation of personalized oncology for the next generation of cancer treatments.

Assessment of the dynamics of concentration of biomarkers of acute kidney injury in remote shock wave lithotripsy in children

BACKGROUND: For several decades, remote shock wave lithotripsy has been considered a universally recognized gold standard for the treatment of upper urinary tract concrements. Despite its noninvasiveness, each lithotripsy session causes acute kidney injury which cannot be reliably assessed with traditional indicators used in nephrourology. Currently, new modern indicators found in the urine and serum are thought to be more informative biomarkers. In this paper, we investigated the effectiveness of some of them for possible potentials in the diagnostics of acute kidney injury in remote lithotripsy.
 AIM: To evaluate changes in acute kidney injury biomarkers during remote shock wave lithotripsy in children.
 MATERIALS AND METHODS: 54 children with urolithiasis, who had a session of remote shock wave lithotripsy, were enrolled in the study. In all patients, samples of urine and blood serum were taken three times for assessing biomarkers concentration: before lithotripsy session, after 45 min and after 24 h.
 RESULTS: Statistically significant changes in the concentration of all urine biomarkers (NGAL, L-FABP, TIMP-2, calbindin-D, KIM-1) were registered at the basal level and 45 min after the procedure. A number of markers studied by us in the blood serum showed more significant changes 24 h after the procedure (IL-18, TNF-α). Although IGFBP-1 concentration increased slightly after 45 min, this change was not statistically significant (p 0.781). The level of cystatin C did not increase after lithotripsy.
 CONCLUSION: The performed analysis of changes in biomarkers concentration has revealed a sufficiently high informative value of biomarkers in assessing the degree of acute kidney injury during remote lithotripsy in children. It also allows to suggest that the studied biomarkers may be promising indicators characterizing such an injury.

CLINICAL, ULTRASOUND AND CYTOGENETIC CHARACTERISTICS OF FETUSES WITH INCREASED NUCHAL TRANSLUCENCY THICKNESS IN THE FIRST TRIMESTER OF PREGNANCY

Background: Improving programs for early prenatal detection of congenital malformations remains a relevant scientific and practical problem. The prevalence of congenital anomalies in Ukraine is 23.7:1000 among live-born babies, and there is no significant decrease in it. The aim of this study was to compare clinical, ultrasound and cytogenetic data in swollen fetuses with increased nuchal translucency (NT) thicknesses from the group of pregnant women in the first trimester at high genetic risk to optimize the algorithm of prenatal diagnostics.
 Materials and Methods: Clinical examinations, ultrasound diagnostics, invasive methods of prenatal diagnostics (chorion biopsy, amniocentesis), genetic testing techniques, such as karyotyping and FISH, genetic counseling and statistical analysis were carried out. The results of complex examinations of 127 fetuses with an increased NT thickness from the group of pregnant women were analyzed. Fetuses were divided into two groups with an NT thickness of 2.5–3.5 mm (group 1) (38 cases) and with an NT above 3.5 mm (group 2)(89 cases).
 Results: Among pregnancies with fetuses with an increased NT thickness, there were 65.4% cases of adverse outcomes with chromosomal pathology (69.9%), congenital malformations of non-chromosomal etiology (25.3%) and pregnancy loss (4.8%). The frequency of chromosomal abnormalities in fetuses of group 1 was 55.3% and 41.6% in group 2. Congenital malformations of various systems and organs in fetuses, the most frequent of which were cardiac defects, were diagnosed. The ratio of congenital heart defects in the fetuses of groups 1 and 2 was 23.7% and 43.8%, respectively (p=0.03; OR=0.40).
 Conclusion: There is no significant difference between the frequency of chromosomal abnormalities in the fetuses of group 1 compared to group 2, which indicates a high informative value of an increased NT thickness, including the thickness of 2.5–3.5 mm in fetuses in the first trimester as a marker of chromosomal pathology. A significantly higher incidence of congenital malformations of non-chromosomal etiology was found in fetuses with venous duct pathology and NT thickness over 3.5 mm compared to fetuses with the same pathology and NT thickness of 2.5–3.5 mm. Proposed changes to the management algorithm for pregnant women with swollen fetuses include mandatory congenital heart defects screening in the first trimester.

Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.

In recent years, health data collected during the clinical care process have been often repurposed for secondary use through clinical data warehouses (CDWs), which interconnect disparate data from different sources. A large amount of information of high clinical value is stored in unstructured text format. Natural language processing (NLP), which implements algorithms that can operate on massive unstructured textual data, has the potential to structure the data and make clinical information more accessible. The aim of this review was to provide an overview of studies applying NLP to textual data from CDWs. It focuses on identifying the (1) NLP tasks applied to data from CDWs and (2) NLP methods used to tackle these tasks. This review was performed according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. We searched for relevant articles in 3 bibliographic databases: PubMed, Google Scholar, and ACL Anthology. We reviewed the titles and abstracts and included articles according to the following inclusion criteria: (1) focus on NLP applied to textual data from CDWs, (2) articles published between 1995 and 2021, and (3) written in English. We identified 1353 articles, of which 194 (14.34%) met the inclusion criteria. Among all identified NLP tasks in the included papers, information extraction from clinical text (112/194, 57.7%) and the identification of patients (51/194, 26.3%) were the most frequent tasks. To address the various tasks, symbolic methods were the most common NLP methods (124/232, 53.4%), showing that some tasks can be partially achieved with classical NLP techniques, such as regular expressions or pattern matching that exploit specialized lexica, such as drug lists and terminologies. Machine learning (70/232, 30.2%) and deep learning (38/232, 16.4%) have been increasingly used in recent years, including the most recent approaches based on transformers. NLP methods were mostly applied to English language data (153/194, 78.9%). CDWs are central to the secondary use of clinical texts for research purposes. Although the use of NLP on data from CDWs is growing, there remain challenges in this field, especially with regard to languages other than English. Clinical NLP is an effective strategy for accessing, extracting, and transforming data from CDWs. Information retrieved with NLP can assist in clinical research and have an impact on clinical practice.

Toward optimizing measurement of self‐perceived cognitive function ‐ a global expert survey on behalf of the SCD‐I Working Group

AbstractBackgroundAssessment of self‐perceived cognitive decline (SCD) is relevant in the context of Alzheimer’s disease and related disorders, but heterogeneity in measurement limits its interpretability and usability. Selection of optimal content for a questionnaire measuring SCD requires statistical information and expert opinion to ensure reliability as well as acceptability and feasibility. This study aims to survey experts about their perspectives on essential features of SCD measurement.MethodThe survey was distributed through the ISTAART Subjective Cognitive Decline PIA, International Neuropsychological Society’s Dementia Special Interest Group, and authors’ professional networks. Targeted experts included professionals working with older adults with SCD through research and/or clinical practice. Main topics were item content, item format, and questionnaire features. Topics were evaluated with multiple choice questions and open‐ended questions. Furthermore, experts were shown items from existing SCD questionnaires with relatively high information value as identified in a previous statistical harmonization effort using Item Response Theory analyses (see Rabin et al., 2021), and asked to select items they viewed as most relevant. Frequency analyses were performed.ResultNinety‐one experts (66% female) across four continents completed the questionnaire (Table 1). Thirty‐nine experts (43%) indicated routinely using an instrument to assess SCD (17 different instruments were mentioned). Seventy‐nine experts (87%) perceived a need for a harmonized SCD instrument. Whereas most experts (72%) responded that a questionnaire should measure both current ability and change in ability, views on the most relevant timeframe varied substantially (top choices: past year (33%) and past six months (25%)). Figure 1a and 1b show that items with the highest statistical information value were not necessarily selected as most relevant by experts. Preferred practical features of a questionnaire were availability of an informant version and coverage of multiple cognitive domains (chosen by 13% and 14% respectively). Preferred validation approaches were distinguishing psychiatric illness from neurodegenerative disorders and the correlation with neuropsychological test performance (both chosen by 20%).ConclusionDespite agreeing on the need for a harmonized SCD instrument, experts express varying views on optimal content, format, and validation approaches. The complementary information derived from statistical results and expert opinion can facilitate more comprehensive measurement of SCD.