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Conceptualization of health literacy from the perspective of children and adolescents – a meta-ethnography
Risk factor analysis of adjacent vertebral compression fracture following the surgery of percutaneous kyphoplasty in postmenopausal women
Investigation of the mechanism of chenodeoxycholic acid in treating acute lung injury through network pharmacology and experimental validation
Abstract Network pharmacology and molecular simulation techniques were employed to predict the potential targets and signaling pathways of chenodeoxycholic acid in the treatment of acute lung injury. Subsequently, its therapeutic effects on acute lung injury were preliminarily validated using animal experiments. The target of Chenodeoxycholic acid in the treatment of acute lung injury was predicted using network pharmacology. Key active ingredients and core targets were further validated using molecular docking studies. Lipopolysaccharide was used to establish a mouse model of acute lung injury to study the effect of chenodeoxycholic acid on acute lung injury. A total of 73 potential targets of Chenodeoxycholic acid for the treatment of acute lung injury were identified, primarily HSP90AA1, STAT3, HSP90AB1, EP300, and NFKB1. These core targets influence pathways associated with bile secretion, prostate cancer, and receptor activation in chemical carcinogenesis. These targets modulate various processes, including steroid metabolism, steroid biosynthesis, and intracellular receptor signaling pathways, thus contributing to the treatment of acute lung injury. Molecular docking results indicated that Chenodeoxycholic acid exhibited strong binding affinity for the core targets, with docking energies ranging from −5.6729 to −7.4138 kcal/mol. The reliability of the results was further verified by molecular dynamics simulations. Results from animal experiments demonstrated that Chenodeoxycholic acid effectively ameliorated pathological injury to lung tissue in mice with acute lung injury, decreased levels of IL-6 and TNF-α (P < 0.01), and increased levels of IL-10 (P < 0.01). The mRNA expression levels of EP300, HSP90AB1, MTOR, and STAT3 were inhibited, while the mRNA expression level of NR1H4 was significantly increased (P < 0.01). Chenodeoxycholic acid can effectively improve acute lung injury.
Advanced wave dynamics in the STF-mBBM equation using fractional calculus
Stress management with HRV following AI, semantic ontology, genetic algorithm and tree explainer
Abstract Heart Rate Variability (HRV) serves as a vital marker of stress levels, with lower HRV indicating higher stress. It measures the variation in the time between heartbeats and offers insights into health. Artificial intelligence (AI) research aims to use HRV data for accurate stress level classification, aiding early detection and well-being approaches. This study’s objective is to create a semantic model of HRV features in a knowledge graph and develop an accurate, reliable, explainable, and ethical AI model for predictive HRV analysis. The SWELL-KW dataset, containing labeled HRV data for stress conditions, is examined. Various techniques like feature selection and dimensionality reduction are explored to improve classification accuracy while minimizing bias. Different machine learning (ML) algorithms, including traditional and ensemble methods, are employed for analyzing both imbalanced and balanced HRV datasets. To address imbalances, various data formats and oversampling techniques such as SMOTE and ADASYN are experimented with. Additionally, a Tree-Explainer, specifically SHAP, is used to interpret and explain the models’ classifications. The combination of genetic algorithm-based feature selection and classification using a Random Forest Classifier yields effective results for both imbalanced and balanced datasets, especially in analyzing non-linear HRV features. These optimized features play a crucial role in developing a stress management system within a Semantic framework. Introducing domain ontology enhances data representation and knowledge acquisition. The consistency and reliability of the Ontology model are assessed using Hermit reasoners, with reasoning time as a performance measure. HRV serves as a significant indicator of stress, offering insights into its correlation with mental well-being. While HRV is non-invasive, its interpretation must integrate other stress assessments for a holistic understanding of an individual’s stress response. Monitoring HRV can help evaluate stress management strategies and interventions, aiding individuals in maintaining well-being.
Revealing the distribution and change of abandoned cropland in Ukraine based on dual period change detection method
Whole genome sequencing of hepatitis B virus using tiled amplicon (HEPTILE) and probe based enrichment on Illumina and Nanopore platforms
Abstract Hepatitis B virus (HBV) whole genome sequencing (WGS) is currently limited as the DNA viral loads (VL) of many clinical samples are below the threshold required to generate full genomes using current sequencing methods. We developed two pan-genotypic viral enrichment methods, using probe-based capture and tiled amplicon PCR (HEP-TILE) for HBV WGS. We demonstrate using mock samples that both enrichment methods are pan-genotypic (genotypes A-J). Using clinical samples, we demonstrate that HEP-TILE amplification successfully amplifies full genomes at the lowest HBV VL tested (30 IU/ml), and the PCR products can be sequenced using both Nanopore and Illumina platforms. Probe-based capture with Illumina sequencing required VL > 300,000 IU/ml to generate full length HBV genomes. The capture-Illumina and HEP-TILE-Nanopore pipelines had consensus sequencing accuracy of 100% in mock samples with known DNA sequences. Together, these protocols will facilitate the generation of HBV sequence data, enabling a more accurate and representative picture of HBV molecular epidemiology, cast light on persistence and pathogenesis, and enhance understanding of the outcomes of infection and its treatment.
Optimizing blast design and bench geometry for stability and productivity in open pit limestone mines using experimental and numerical approaches
Author Correction: Changes in the potato rhizosphere microbiota richness and diversity occur in a growth stage-dependent manner
Baseline choroidal microvasculature dropout as a predictor of rapid global structural loss in open-angle glaucoma
User experience questionnaire in sign language for native users of Slovenian sign language
Job satisfaction, life satisfaction, and associated factors among hospital nurses: a cross-sectional study in Türkiye
Abstract Job satisfaction strongly affects nurses’ life satisfaction and is directly affected by life satisfaction. Our study aimed to determine nurses’ life and job satisfaction, show their relationship, and evaluate the factors affecting them. This cross-sectional study was conducted in 2022 at a university hospital in Türkiye. The study population included all nurses working at the hospital for at least one month, and 920 nurses participated. Data were collected using a structured questionnaire, which consisted of sections on sociodemographic characteristics, job and life satisfaction, and factors related to the nursing profession. Job satisfaction was measured using the Index of Job Satisfaction, while life satisfaction was assessed with the Life Satisfaction Scale, both validated tools. Data collection occurred during periodic health examinations through face-to-face interviews. Most participants chose the nursing profession willingly and found it suitable for themselves. However, many reported dissatisfaction with their earnings. Higher job satisfaction was associated with older age, having children, good perceived health, shorter weekly working hours, willingly choosing the nursing profession and unit, and favorable working conditions and income. Similarly, life satisfaction was higher among those with good perceived health, fewer weekly working hours, willingly chosen profession and unit, and no smoking or chronic diseases. Supportive working conditions and adequate income strongly influenced both job and life satisfaction. A significant, positive, and moderate correlation was found between job and life satisfaction, highlighting their interconnectedness. These findings suggest that improving nurses’ working conditions, ensuring adequate income, and supporting healthier lifestyles could enhance job and life satisfaction. Enhancing working conditions is essential to improving nurses’ job satisfaction, which, in turn, positively impacts their overall life satisfaction. Policymakers should prioritize initiatives that address workplace challenges and foster a supportive environment for nurses.
Determining human resource management key indicators and their impact on organizational performance using deep reinforcement learning
Impact of Covid-19 pandemic on trajectories of patients with severe alcohol use disorder treated with disulfiram
Abstract The manifestations and progression of alcohol use disorder (AUD) are influenced by a number of contextual factors, with the current coronavirus pandemic being a significant example. This pandemic has profoundly impacted nearly all aspects of human life and has, therefore, strongly influenced patients suffering from AUD. In some cases, the pandemic has led to a reduction in severity, while in others, it has had the opposite effect. In our own work we have been investigating the negative impact of the pandemic on 45 patients with AUD who were undergoing outpatient treatment, including supervised use of disulfiram (Antabuse), in a close-knit program. A linear trend analysis demonstrated significant alterations in the retention rate over a 3-year period, encompassing the pre-pandemic, pandemic, and post-pandemic periods. During the pandemic the number of treatment cancellations virtually increased. Following the pandemic, a tendency towards the normalization of patient numbers was observed. Our data indicate a high level of vulnerability among patients with severe AUD and highlight a need for the development of alternative, possibly telemedical, treatment methods.
STK11 genetic alterations in metastatic EGFR mutant lung cancer
Developing decision making framework on built-up site suitability assessment for urban regeneration in the industrial cities of Eastern India
Exploring rural middle school music teachers’ classroom interaction decision-making levels: A student’s perspective
Abstract This study explores students’ perceptions of rural middle school music teachers’ decision-making levels in the context of classroom interactions. Using a questionnaire survey of 246 students from a rural public middle school and follow-up interviews, we investigate students’ overall perceptions and examine differences among students of different genders, grades, and music study experiences. The results indicate that students generally perceive teachers’ decision-making levels to be slightly above average, with no significant differences between male and female students or among different grade levels in their perceptions of teacher decision-making levels. However, students with and without music study experience differ significantly in their perceptions of teacher decision-making levels. The qualitative insights gathered from the student interviews complement the quantitative findings, providing a deeper understanding of students’ perspectives on teacher decision-making in rural music education. Overall, this study provides valuable insights for enhancing the educational practices of teachers and explores effective teaching strategies in rural environments.