Search Stage Research Articles

Диагностика врожденных дефектов иммунитета в многопрофильном стационаре: обзор клинических наблюдений

Inborn errors of immunity (IEI) are rare heterogeneous diseases with a wide range of clinical manifestations that makes its diagnosis difficult for practitioners of different specialties. The purpose of this research was to describe a series of clinical case reports of primary immunodeficiencies (PIDs) diagnosis using the TREC/KREC assay in a multidisciplinary hospital. In order to verify the diagnosis, TREC/KREC determination, immunophenotyping and molecular genetic studies were performed. Thirty-nine patients with alarming signs of PIDs were examined within a period of 18 months. Primary immunodeficiencies were identified in 6 (31.6%) children with congenital malformations, in 5 (50%) with resistant cytopenias and in 3 (30%) with severe infections. Syndromal IDs were diagnosed in 8 (57.1%), combined immunodeficiency in a single patient (7.1%), immune regulation defects in 2 (14.3%) and antibody formation disorders in 3 (21.5%). Abnormal TREC/KREC values were established in 9 (64.3%) patients with IEI at the early stages of diagnostic search. Thus, TREC/KREC assay is an effective method for IEI diagnosis within a multidisciplinary hospital.

Labor biographies of russian workers: from search to survival

The article aims to study labor biography of industrial workers. It is based on the materials of in-depth interviews conducted in the Ulyanovsk region in 2020–2021. The authors propose a new approach to the study of labor biographies, taking the concept of work capacity. They distinguish three main stages: labor search, labor balance and labor survival. This division is based on the logic of workers building their own professional path, therefore, considering stages do not have certain age or time limits. The authors give main characteristic of the worker’s behavior for each of this stage. The purpose of labor search is to choose such type of employment or working conditions that would suit the physical capabilities, personality and salary expectations of the worker, as well as their ability to learn new professional skills. The stage of labor balance is characterized by achieving a balance between different types of employment that allows workers to cope with high physical exertion and provide the desired standard of living. The stage of labor survival is aimed at preserving the workplace, adapting the labor process to the current state of health and reduced physical capabilities. The authors emphasize that the work capacity (in the form of health, professional experience and knowledge) is not the resources that are valued by both employers and workers. As a consequence, the stages of labor search and balance can quite quickly be replaced by the stage of labor survival.

Construal level theory and online reviews: A search stage perspective

Drawing on Construal Level Theory, we propose that consumer search stage (i.e., screening or selecting), not psychological distance, interacts with review features to influence review helpfulness. Through an online experiment, we test the interaction effects between textual concreteness, review valence and consumer search on review helpfulness at close and far temporal distances. Our findings support a congruency effect between consumer search stage and textual concreteness, showing that positive, abstract reviews are more helpful in the screening stage, while negative, concrete reviews are more helpful in the selection stage. Conversely, temporal distance had a negligible effect. Our results suggest that consumer search stages may confound previous findings on psychological distance and review helpfulness and have practical implications regarding review relevance for consumers.

Manualised Attachment-Based Interventions for Improving Caregiver-Infant Relationships: A Two-Stage Systematic Review

Abstract As attachment-based interventions can improve caregiver-infant relationships and their subsequent psychological outcomes, the identification of relevant and effective interventions can facilitate their implementation into clinical practice. This systematic review aimed to a) provide an overview of manualised attachment-based interventions, without video-feedback as the main component, for caregivers and infants from conception to two years, and b) determine which of these interventions were effective in demonstrating improvements in caregiver-infant relational outcomes. To identify eligible interventions and their empirical evidence base, two search stages were conducted for 1) relevant interventions and 2) studies of interventions identified in the first stage that focussed on caregiver-infant relational outcomes. All studies included in Stage 2 were quality assessed and findings analysed. Twenty-six interventions were eligible for inclusion at Stage 1 but studies reporting on relational outcomes were identified for 16 interventions only. Forty studies reporting on those 16 interventions met inclusion criteria and were synthesised at Stage 2. Most studies were of good quality. Observer-rated measures were used in 90% of studies. There was evidence for these interventions in relation to improving caregiver-infant relational outcomes: 80% of studies reported a statistically significant positive change in a relational outcome for the intervention compared to pre-intervention or control group. The most promising evidence was identified for Attachment and Biobehavioral Catch-Up (ABC), Minding the Baby (MTB) and Circle of Security (COS). This systematic review offers guidance to healthcare professionals, commissioners and policymakers within perinatal sectors in relation to the training, delivery and implementation of evidenced manualised attachment-based interventions.

The Role of Religious Education in Dealing with the Issue of Radicalization among Students

Radicalization has become a serious threat in the world of education, especially among students who are in the developing stage of self-identity search, where curiosity and emotional instability make them vulnerable to the influence of radical ideology. Religious education, in this context, plays a central role in shaping students' character by instilling values of moderation, tolerance, and respect for diversity. This study aims to explore the role of religious education in preventing radicalization, using a descriptive qualitative method with data collection techniques through in-depth observation and interviews involving teachers, students, principals, and parents. The focus of the research is on the implementation of religious moderation values in schools as an effort to build students' critical attitudes towards extremist ideologies. The results of the study show that inclusive, interactive, and collaborative teaching approaches in religious education, both in the classroom and by involving the external community, play an important role in fostering tolerant characters and being able to reject radical narratives. The study highlights the importance of collaboration between schools and families, as well as the need for digital literacy education to strengthen students' resilience to exposure to extremist content on social media. The implications of this study include the need to integrate materials relevant to actual issues in the religious education curriculum, as well as the development of community-based educational programs that involve families and communities as additional bulwarks of protection for students.

Enhanced GRU-based regression analysis via a diverse strategies whale optimization algorithm

Taking into account the whale optimization algorithm’s tendency to get trapped in local optima easily and its slow convergence rate, this paper proposes a diverse strategies whale optimization algorithm (DSWOA) and uses it to optimize the parameters of GRU, thereby achieving better regression prediction effects. First, an innovative t-distribution perturbation is used to perturb the optimal whale to expand the optimization space of the optimal whale. Secondly, in the random search stage, we perform a Cauchy walk on the whale’s position and then use reverse learning to enable the algorithm to effectively navigate away from the local optimum. Finally, we adopt a horizontal learning strategy for all whales and use two random whales to determine the current whale’s position. Updated, the results suggest that DSWOA is highly effective in global optimization. By utilizing DSWOA, the parameters of GRU were fine-tuned. The experimental findings reveal that GRU produces promising outcomes on multiple datasets, making it a more effective tool for regression prediction tasks.

Ontology, Epistimologi dan Axiologi dalam Filsafat Ilmu Untuk Pengembangan Teori Manajemen Pendidikan Islam

This research aims to determine the role of Ontology, Epistomology and Axiology in the philosophy of science, as well as the basic values in Islamic Education Management. This type of research uses literature study with the stages of Literature Search, Source Evaluation and Selection, Critical Analysis, Theme or Category Grouping, Synthesis of Findings, Identification of Research Gaps, and Writing of Literature Review. The main sources of this research are scientific articles from the results of the research, books, and other sources that are relevant to the main topic of the research. Data analysis is carried out qualitatively consisting of data reduction, data presentation, and conclusion drawing. The findings of this research are the construction of Islamic Education Management Theory based on the integration of Islamic values with modern management concepts. Principles such as monotheism, shura, trust, and justice are important foundations, followed by the application of managerial principles such as planning, organizing, mobilizing, and controlling which are all oriented towards the formation of kamil people. These values of Islamic education management not only focus on technical and administrative aspects, but also uphold moral, spiritual, and social aspects. The implementation of Islamic education management faces a variety of complex challenges, ranging from the difficulty of balancing Islamic values with modern demands, to a lack of understanding and commitment to Islamic principles.

Syndromic obesity in children (using the example of clinical cases)

Obesity is a heterogeneous group of hereditary and acquired diseases associated with excessive accumulation of adipose tissue in the body. One example of syndromic obesity in children is Bardet-Biedl syndrome. This is a rare autosomal recessive disease from the group of ciliopathies, characterized by retinal dystrophy, obesity, polydactyly, mental retardation, hypogonadism, and renal dysfunction. The article presents two clinical cases of Bardet-Biedl syndrome. Diagnostic criteria for the disease are given, and the need for molecular genetic research methods in the early stages of the diagnostic search is shown. Promising directions in the treatment of the syndrome are considered.

Can calf massage prevent deep vein thrombosis? A systematic review

Background: Deep vein thrombosis (DVT) is a condition in which blood clots form in deep veins, usually in the legs. The causes of thrombosis are divided into two, namely those related to immobilization and those related to hypercoagulation, both of which are related to genetic factors and acquired risk factors. DVT can be prevented by preventing recurrent DVT, patients must continue to exercise, and provide compression therapy and anticoagulation therapy with the right duration. Purpose: To evaluate the efficacy of calf massage techniques in lowering the risk of deep vein thrombosis (DVT). Method: The research design used is a systematic review method with article selection referring to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The article search stage is carried out systematically using keywords written manually or using the search facility available in each database. The keywords used in the article search are (massage OR therapy OR pressure) AND (calf OR foot) AND (Deep Vein Thrombosis (DVT)). Article searches are carried out electronically to filter relevant articles. Articles are sourced from the Google Scholar, PubMed, Science Direct, and ProQuest database search engines. The inclusion criteria for this literature review were articles published in the last ten years, namely 2013-2023, articles in Indonesian and English, interventions given to patients at risk of deep vein thrombosis (DVT), interventions using calf massage either with or without tools, randomized controlled trials, experimental/quasi-experimental, and full manuscripts available. Results: Calf massage has been shown to be effective in reducing the risk of deep vein thrombosis (DVT). This is shown based on five journals that have been analyzed that there is a significant decrease in the risk of DVT in the results of the experimental group with the control group and there is only one journal that states that the use of foot pumps does not significantly reduce the incidence of DVT in patients undergoing total knee arthroplasty (TKA) with edoxaban thromboprophylaxis. Conclusion: Massage interventions carried out using tools or without tools with various versions and methods have a positive impact on reducing the risk of DVT.

NASPrecision: Neural Architecture Search-Driven Multi-Stage Learning for surface roughness prediction in ultra-precision machining

Accurate surface roughness prediction is critical for ensuring high product quality, especially in sectors such as manufacturing, aerospace, and medical devices, where the smallest imperfections can compromise performance or safety. However, this is very challenging due to complex, non-linear interactions among variables, which is further exacerbated when working with limited and imbalanced datasets. Existing methods leveraging traditional machine learning algorithms require extensive domain knowledge for feature engineering and substantial human intervention for model selection. To address these issues, we propose a Neural Architecture Search (NAS)-Driven Multi-Stage Learning framework, named NASPrecision. This innovative approach autonomously identifies the most suitable features and models for various surface roughness prediction tasks and significantly enhances the performance by multi-stage learning. Our framework operates in three stages: (1) architecture search stage, employing NAS to automatically identify the most effective model architecture; (2) initial training stage, where we train the neural network for initial predictions; (3) refinement stage, where a subsequent model is appended to refine and capture subtle variations overlooked by the initial training stage. In light of limited and imbalanced datasets, we adopt a generative data augmentation technique to balance and generate new data by learning the underlying data distribution. We perform extensive experiments on three distinct real-world datasets, each associated with a different machining technique, and compare with various machine learning algorithms. The experimental results underscore the superiority of our framework, which achieves an average improvement of 25.7%, 22.7%, and 39.1% in terms of Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Standard Deviation of Residuals (STDR), respectively. This significant performance enhancement not only confirms the robustness of our framework but also establishes it as a generic solution for accurate surface roughness prediction. The success of this approach can lead to improved production efficiency and product quality in critical industries while also reducing the need for extensive domain knowledge and human intervention.

Effectiveness, Challenges, and Environmental Impacts of New Food Strategies with Plant and Animal Protein Products.

Sustainable food practices are intrinsically linked to human nutrition in the preservation of the ecosystem. This study, therefore, evaluates the effectiveness, challenges, environmental impacts, and new food strategies related to plant and animal products, with a view to promoting more sustainable and healthy eating practices. The search stages were conducted using the following databases: PubMed, Science Direct, and SciElo. The studies selected included those published from 2018 to 2024 and government documents, available in English, Portuguese, and Spanish. The 34 articles analyzed in this study showed the environmental impacts related to the production of plant and animal proteins, highlighting the urgency of implementing changes in this sector. However, factors such as land use, carbon footprint, and water footprint show remarkable differences depending on the type of crop cultivated, agricultural practices adopted, and stages involved in the supply chain. As final considerations, the analysis suggests that achieving sustainability in food systems requires an integrate approach that combines the optimization of plant protein production with a reduction in environmental impacts and the development of technologies that that support the efficiency and resilience of the industry. Meeting the nutritional needs of the population in a sustainable way will only be possible through regional actions and a deep understanding of the challenges and opportunities.

Sudden cardiac death due to adrenal insufficiency masquerading as Brugada syndrome. Case report

Sudden cardiac death in young people in 20% of cases is caused by cardiomyopathies and channelopathies. One of the forms of channelopathies is Brugada syndrome, a hereditary disease characterized by ST segment elevation in the right precordial leads (V1-V3) and an increased risk of sudden cardiac death in the absence of structural heart disease. Brugada phenocopies are also known – clinical situations that are manifested by electrocardiogram (ECG) patterns identical to true Brugada syndrome. They are caused by different clinical circumstances and form a group of heterogeneous conditions that are often difficult to distinguish from true congenital Brugada syndrome due to identical ECG patterns. The formation of Brugada phenocopy due to hyperkalemia is presented in the literature in various conditions: with renal failure, after extensive trauma, application of medications. The article presents a case report demonstrating a rare cause of sudden cardiac arrest in a young patient without a history of cardiovascular pathology: the occurrence of the Brugada pattern on the ECG due to severe hyperkalemia in adrenal insufficiency. The stages of the differential diagnostic search are described, which made it possible to verify the final diagnosis and prescribe effective hormone replacement therapy. Performing a provocative test with novocanamide allowed us to confirm that the patient had a phenocopy, and not Brugada syndrome. Differential diagnosis of phenocopies of Brugada syndrome – a series of often life-threatening cardiac and non-cardiac diseases and conditions, manifested by similar ECG changes in the form of a peculiar ST segment elevation in leads V1-V3, is often a difficult task. This case represents a phenocopy of Brugada in the setting of severe hyperkalemia with development of cardiac arrest due to adrenal insufficiency, which resolved with correction of electrolyte abnormalities and treatment of the underlying disease. Typical ECGs are presented: a graph of the Brugada phenomenon, hyperkalemia, a sinusoidal curve during cardiac arrest, recorded over time in a patient, and the pathogenetic mechanisms causing the formation of the Brugada pattern in adrenal insufficiency are explained.

A Two-Stage Target Search and Tracking Method for UAV Based on Deep Reinforcement Learning

To deal with the complexities of decision-making for unmanned aerial vehicles (UAVs) in denial environments, this paper applies deep reinforcement learning algorithms to search and rescue (SAR) tasks. It proposes a two-stage target search and tracking method for UAVs based on deep reinforcement learning, which divides SAR tasks into search and tracking stages, and the controllers for each stage are trained based on the proposed deep deterministic policy gradient with three critic networks (DDPG-3C) algorithm. Simulation experiments are carried out to evaluate the performance of each stage in a two-dimensional rectangular SAR scenario, including search, tracking, and the integrated whole stage. The experimental results show that the proposed DDPG-3C model can effectively alleviate the overestimation problem, and hence results in a faster convergence and improved performance during both the search and tracking stages. Additionally, the two-stage target search and tracking method outperforms the traditional single-stage approach, leading to a more efficient and effective decision-making ability in SAR tasks.

A Highly Efficient Compressive Sensing Algorithm Based on Root-Sparse Bayesian Learning for RFPA Radar

Off-grid issues and high computational complexity are two major challenges faced by sparse Bayesian learning (SBL)-based compressive sensing (CS) algorithms used for random frequency pulse interval agile (RFPA) radar. Therefore, this paper proposes an off-grid CS algorithm for RFPA radar based on Root-SBL to address these issues. To effectively cope with off-grid issues, this paper derives a root-solving formula inspired by the Root-SBL algorithm for velocity parameters applicable to RFPA radar, thus enabling the proposed algorithm to directly solve the velocity parameters of targets during the fine search stage. Meanwhile, to ensure computational feasibility, the proposed algorithm utilizes a simple single-level hierarchical prior distribution model and employs the derived root-solving formula to avoid the refinement of velocity grids. Moreover, during the fine search stage, the proposed algorithm combines the fixed-point strategy with the Expectation-Maximization algorithm to update the hyperparameters, further reducing computational complexity. In terms of implementation, the proposed algorithm updates hyperparameters based on the single-level prior distribution to approximate values for the range and velocity parameters during the coarse search stage. Subsequently, in the fine search stage, the proposed algorithm performs a grid search only in the range dimension and uses the derived root-solving formula to directly solve for the target velocity parameters. Simulation results demonstrate that the proposed algorithm maintains low computational complexity while exhibiting stable performance for parameter estimation in various multi-target off-grid scenarios.

A Literature Review Study on the Effectiveness of Pre-operative Education for Post-operative Pain Management

Context: Post-operative pain (POP) is one of the essential consequences of various surgeries in surgical patients, and various pharmacological and non-pharmacological methods should reduce it. Due to the side effects of medicinal methods, the use of non-medicinal methods has attracted the attention of nurses and health care workers nowadays. Considering this issue, the main goal of this study was to evaluate pre-operative education (POE) on the reduction of POP in surgical patients. Evidence Acquisition: This narrative review evaluated previous research from 1990 to 2024. Accordingly, 184 studies were obtained in the first search stage through advanced search in national and international databases, including Web of Science, Islamic World Science Citation Center (ISC), Scopus, PubMed, Magiran, and Google Scholar, Iran-Doc. Then, considering the inclusion and exclusion criteria, 22 studies were considered for the present review study. Results: Based on the present review, most evaluated studies (approximately 64%) showed that POE positively and significantly affects POP. Previous studies have reported that various parameters play a role in the impact of POE on POP, including the type of surgery, the length of follow-up, the continuity of care, the method of providing training, the quality of the intervention, the patient's awareness of the pain measurement method, with or without the educator's interaction and empathy during education, and the presence or absence of patient anxiety during training. Conclusions: Considering the positive and significant effect of POE on POP, nurses and caregivers of patients who are candidates for surgery should use the appropriate educational content and educational methods to reduce POP in patients according to the type of surgery and other influencing variables. However, before that, the nurses themselves should get enough information about the different methods of POE so that they can convey the educational content to the patients with proper quality and efficiency.

Clinical and epidemiological aspects of primary immunodeficiencies in the Republic of Karakalpakstan

Studying the prevalence of primary immunodeficiency disorders (PID) is an important aspect of epidemiological research, as it allows for assessing the frequency of these diseases occurring in different regions. The distribution of PID may vary depending on ethnic background and geographical location. Information on the prevalence of PID at regional, national, and international levels enables a more accurate determination of the scale of the problem and the development of effective prevention and treatment strategies. Studying the epidemiological and clinical profile of congenital immune disorders (PID) in the Republic of Karakalpakstan is crucial for understanding the prevalence, age and gender structure, as well as the spectrum of nosological forms of these rare diseases. The aim of this study was to investigate the epidemiological and clinical characteristics of congenital immune disorders, primary immunodeficiencies in the Republic of Karakalpakstan. The study material consisted of data on patients receiving inpatient treatment at a multidisciplinary children’s hospital in the Republic of Karakalpakstan and those under the care of an allergist-immunologist in the outpatient clinic at district medical centers in the Republic of Karakalpakstan. The average delay in diagnosis from the onset of clinical manifestation of PID in the region was 2.7 years. It was found that the mortality rate was 2.8%. Screening tests for verifying PID in the diagnostic search stage included clinical and biochemical blood analyses, and determination of serum immunoglobulins. At the time of diagnosis, patients exhibited a significant decrease in IgG and IgA immunoglobulin levels. In addition to the disorder in the humoral branch of adaptive immunity, children with this disease showed a decrease in the absolute number of T lymphocytes. The data obtained from the study indicate insufficient diagnosis of primary immunodeficiencies in the Republic of Karakalpakstan, which may serve as a basis for the development of educational programs aimed at increasing awareness of primary immunodeficiencies. Further research and systematization of information on patients with primary immunodeficiencies are necessary for the development of regional programs for the implementation of screening diagnostics.

Surrogate-assisted push and pull search for expensive constrained multi-objective optimization problems

In many real-world engineering optimizations, a large number of objective and constraint function values often need to be obtained through simulation software or physical experiments, which incurs significant computational costs and/or time expenses. These problems are known as expensive constraint multi-objective optimization problems (ECMOPs). This paper combines the push and pull search (PPS) framework and proposes a surrogate-assisted evolutionary algorithm to solve ECMOPs through Bayesian active learning, naming it the surrogate-assisted PPS (SA-PPS). Specifically, during the push search stage, candidate solutions are selected based on two indicators: hypervolume improvement and objective uncertainty. These aim to quickly guide the population towards the unconstrained Pareto front while ensuring diversity. During the pull search stage, the population is partitioned into many subregions through reference vectors, and different selection strategies are assigned to each subregion based on its state, aiming to guide the population towards the constrained Pareto front while ensuring diversity. Furthermore, we introduce a batch data selection strategy that utilizes Bayesian active learning to enable the surrogate model to focus on regions of interest in the pull search stage. Extensive experimental results have shown that the proposed SA-PPS algorithm exhibits superior convergence and diversity compared to 9 state-of-the-art algorithms across a variety of benchmark problems and a real-world optimization problem.

Applications of GANs to Aid Target Detection in SAR Operations: A Systematic Literature Review

Research on unmanned autonomous vehicles (UAVs) for search and rescue (SAR) missions is widespread due to its cost-effectiveness and enhancement of security and flexibility in operations. However, a significant challenge arises from the quality of sensors, terrain variability, noise, and the sizes of targets in the images and videos taken by them. Generative adversarial networks (GANs), introduced by Ian Goodfellow, among their variations, can offer excellent solutions for improving the quality of sensors, regarding super-resolution, noise removal, and other image processing issues. To identify new insights and guidance on how to apply GANs to detect living beings in SAR operations, a PRISMA-oriented systematic literature review was conducted to analyze primary studies that explore the usage of GANs for edge or object detection in images captured by drones. The results demonstrate the utilization of GAN algorithms in the realm of image enhancement for object detection, along with the metrics employed for tool validation. These findings provide insights on how to apply or modify them to aid in target identification during search stages.

GTalign: spatial index-driven protein structure alignment, superposition, and search

With protein databases growing rapidly due to advances in structural and computational biology, the ability to accurately align and rapidly search protein structures has become essential for biological research. In response to the challenge posed by vast protein structure repositories, GTalign offers an innovative solution to protein structure alignment and search—an algorithm that achieves optimal superposition at high speeds. Through the design and implementation of spatial structure indexing, GTalign parallelizes all stages of superposition search across residues and protein structure pairs, yielding rapid identification of optimal superpositions. Rigorous evaluation across diverse datasets reveals GTalign as the most accurate among structure aligners while presenting orders of magnitude in speedup at state-of-the-art accuracy. GTalign’s high speed and accuracy make it useful for numerous applications, including functional inference, evolutionary analyses, protein design, and drug discovery, contributing to advancing understanding of protein structure and function.

Innovative Exercise for Nursing PhD Students: Addressing Biases During Integrative Literature Reviews.

Nursing PhD students face the difficulty of conducting integrative literature reviews that may lead to bias and lack of rigor particularly in the literature search, data evaluation, and data analysis stages. This article describes the development and use of an arts-based learning exercise to identify potential biases and improve on the critical and writing skills of students during the integrative literature review process. The exercise consisted of visits to the university's museum, moderated discussions with nursing faculty and the museum educator, visual analyses of artwork, building art portfolios, and writing assignments. The exercise was modified over 3 years based on student feedback. Students reported the exercise helped in critical and writing skills, in identifying areas of potential bias, and in enhancing the methodological rigor of the review process. Conducting literature reviews is integral in doctoral nursing education and this innovative exercise can facilitate this process. [J Nurs Educ. 2024;63(X):XXX-XXX.].