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Virtual reality for stress management and burnout reduction in nursing: A systematic review protocol
Background Burnout is a pervasive issue in the nursing profession, with detrimental consequences for nurses’ well-being, patient care, and healthcare systems. Virtual reality (VR) is a promising tool for delivering immersive and engaging interventions to manage stress and reduce burnout. This systematic review aims to synthesize the evidence on the effectiveness of VR interventions for stress and burnout in nursing, characterize the specific intervention approaches, and guide future research and practice. Methods We will search for published and unpublished studies in PubMed, Web of Science, Embase, CINAHL, MEDLINE, The Cochrane Library, PsycINFO, and Scopus from database inception to the present. Randomized controlled trials, quasi-experimental studies, and pre-post studies examining VR interventions for stress and/or burnout in licensed nurses will be included. Two reviewers will independently screen studies, extract data, and assess risk of bias using the Cochrane Risk of Bias 2 tool for randomized trials and the ROBINS-I tool for non-randomized studies. If appropriate, meta-analysis will be performed to estimate pooled effects on stress and burnout outcomes. Subgroup and sensitivity analyses will explore the influence of intervention characteristics and study quality. Narrative synthesis will be conducted if quantitative synthesis is not possible. The review protocol follows the PRISMA-P guidelines and is registered in PROSPERO. Discussion This systematic review will provide a comprehensive synthesis of the evidence on VR interventions for stress and burnout management in nurses. By critically appraising the research and identifying the most promising approaches, the review will guide the development and implementation of evidence-based VR programs to support nurses’ well-being and address the urgent problem of burnout. The findings will also identify gaps in the literature and directions for future research to optimize the design and delivery of VR interventions for this high-need population. Systematic review registration: PROSPERO CRD42024604179
Leiurus quinquestratus venom promotes β islets regeneration and restores glucose level in streptozotocin induced type 2 diabetes mellitus in rats
Abstract Diabetes mellitus type 2 (T2-DM) is one of the most prevalent chronic metabolic diseases, marked by insulin resistance and a relative lack of insulin production. T2-DM can be treated using various methods; however, these treatments are risky for several vital organs. Subsequently, novel T2-DM replacement therapies should be discovered. The goal of this study was to see how efficient Leiurus quinquestratus venom (LQV) was as a diabetic medicine for the treatment of T2-DM in rats. The median lethal dose (LD50) of LQV has been determined. Then, forty male Sprague Dawley rats were divided into four groups (n = 10) as follows, with group 1 (Gp1) separated as a negative control. Gp2, Gp3, and Gp4 were fed a high-fat diet (HFD) for 12 weeks before receiving an intraperitoneal (i.p) injection of streptozotocin (STZ) as 30 mg/kg b.wt. Gp3 received metformin (Met) as 150 mg/kg b.wt i.p. LQV as 1/40 LD50 was given i.p. to Gp4. Treatments with Met or LQV were once every day for eight weeks. Hematological, biochemical, histopathological, and immunohistochemical studies were determined, along with the percentages of changes in total body weight. Results: LD50 of LQV was 0.3 mg/kg b.wt. Met or LQV treatment reduced hyperglycemia and C-peptide levels and lessened the hepato-renal biomarkers disorders in T2-DM rats. Intriguingly, histological analysis revealed that LQV treatment outperformed Met in improving and restoring β-cells in pancreatic tissues of T2-DM mice. In conclusion, this study demonstrated a new and promising method for treating T2-DM with LQV. Further investigation is required to isolate the bioactive elements from LQV to treat T2-DM.
GDBM: A database of global drainage basin morphology
Rivers and their drainage basins are fundamental landscape units, and their morphology is a record of the cascade of geologic, tectonic, biological, and climatic processes acting upon them. Quantifying this cascade depends on morphometric measurements of rivers and drainage basins, and comparison of these measurements across diverse landscape settings. Here we present a new near-Global dataset of Drainage Basin Morphology, GDBM, which provides morphometric measurements of 254,966 basins and the longest river channel within them. This dataset is created by extracting channels from the 30-meter resolution Shuttle Radar Topography Mission (SRTM) topographic data which fall within Köppen-Geiger climate zones, to allow the influence of climate on river and basin morphology to be quantified. GDBM contains measurements of channel length, slope, relief, normalised concavity, basin area, basin shape and aridity. These data have been generated with minimal assumptions, focusing on identifying and classifying channels with high confidence, through the use of a conservative drainage area threshold. GDBM provides opportunities for rapid spatial analysis of channel morphology at a near-global scale and has the potential to yield continuing insight into landscape evolution across diverse climate regimes. This dataset also has potential applications across a range of Earth and environmental science domains, through the integration of additional data on, for example, forest canopy height, landcover, or soil properties to explore the spatial variability of channel and basin properties with climate.
Effect of steel fiber content on fatigue performance of high-strength concrete beams
CNN-LSTM based emotion recognition using Chebyshev moment and K-fold validation with multi-library SVM
Human emotions are not necessarily tends to produce right facial expressions as there is no well defined connection between them. Although, human emotions are spontaneous, their facial expressions depend a lot on their mental and psychological capacity to either hide it or show it explicitly. Over a decade, Machine Learning and Neural Networks methodologies are most widely used by the researchers to tackle these challenges, and to deliver an improved performance with accuracy. This paper focuses on analyzing the driver’s facial expressions to determine their mood or emotional state while driving to ensure their safety. we propose a hybrid CNN-LSTM model in which RESNET152 CNN is used along with Multi-Library Support Vector Machine for classification purposes. For the betterment of feature extraction, this study has considered Chebyshev moment which plays an important role as it has a repetition process to gain primary features and K-fold validation helps to evaluate the models performance in terms of both training, validation loss, training, and validation accuracy. This study performance was evaluated and compared with existing hybrid approaches like CNN-SVM and ANN-LSTM where the proposed model delivered better results than other models considered.
Application of peritumoral radiomics based on simulated positioning CT images in the prognosis of intermediate-advanced esophageal cancer
From lab to real life: Is there a link between lab-based and ecological assessment of Procedural Perceptual-Motor Learning tasks?
Procedural Perceptual-Motor Learning (PPML) refers to the process leading to the acquisition of new motor skills through repeated practice. It is crucial to (re-)acquire skills needed in daily life and rehabilitation. It can be divided in two processes: motor sequence learning (SL) and sensorimotor adaptation (SA). SL refers to the acquisition of a sequence of actions that follows a precise order, while SA involves continuously adjusting motor outputs to compensate for environmental or internal disturbances. These two processes are typically measured using different lab-based tasks and are presumed to play a role in ecological/ naturalistic tasks. However, to our knowledge, no study examined the relationship between performance on lab-based tasks and ecological/ naturalistic tasks. To address this gap, we designed two lab-based tasks and six ecological tasks assessing SL and SA in an original research including 42 participants (young adults). After ensuring with non-parametric repeated measures ANOVA that all the tasks presented features of learning (all 15.1 <χ² < 142; p < 0.5), Spearman’s rank correlation tests were performed between each lab-based task measuring SL and SA and the six ecological tasks. Our findings reveal low to moderate correlations between lab-based and ecological tasks measuring SL and SA (0.265 < rho < 0.395; p < 0.05). This suggests that the lab-based tasks partially reflect PPML as it occurs in everyday life. We believe that the partial ecological validity of these lab-based tasks is essential for their use, especially in the context of clinical evaluation prior to rehabilitation.
SAR evaluation of MIMO antennas with a wide tunable range power divider
Lightweight PCB defect detection method based on SCF-YOLO
Addressing the issues of large model size and slow detection speed in real-time defect detection in complex scenarios of printed circuit boards (PCBs), this study proposes a new lightweight defect detection model called SCF-YOLO. The aim of SCF-YOLO is to solve the problem of resource limitation in algorithm deployment. SCF-YOLO utilizes the more compact and lightweight MobileNet as the feature extraction network, which effectively reduces the number of model parameters and significantly improves the inference speed. Additionally, the model introduces a learnable weighted feature fusion module in the neck, which enhances the expression of features at multiple scales and different levels, thus improving the focus on key features. Furthermore, a novel SCF module (Synthesis C2f) is proposed to enhance the model’s ability to capture high-level semantic features. During the training process, a combined loss function that combines CIoU and GIoU is used to effectively balance the optimization of different objectives and ensure the precise location of defects. Experimental results demonstrate that compared to the YOLOv8 algorithm, SCF-YOLO reduces the number of parameters by 25% and improves the detection speed by up to 60%. This provides a fast, accurate, and efficient solution for defect detection of PCBs in industrial production.
An innovative modelling technique for bimodal soil water characteristic curve under wetting process
Monetary reward mechanism for promoting online knowledge sharing: A modeling study
Knowledge sharing is critical for an organization to acquire sustained competitive advantage. Bestowing monetary rewards may possibly the most direct method of stimulating online knowledge sharing. Under the monetary reward mechanism for promoting knowledge sharing, we intend to find a satisfactory knowledge-sharing promotion policy. First, based on a state evolutionary model for the knowledge-sharing community, we reduce the original problem to an optimal control model. Second, applying optimal control theory to the model, we give an algorithm for solving the model. Next, we validate the feasibility of the algorithm. Finally, we inspect the applicability of the algorithm. To our knowledge, this is the first time the optimal control modeling technique is applied to the research of knowledge sharing.
CRISPR-Cas9 genetic screens reveal regulation of TMPRSS2 by the Elongin BC-VHL complex
Abstract The TMPRSS2 cell surface protease is used by a broad range of respiratory viruses to facilitate entry into target cells. Together with ACE2, TMPRSS2 represents a key factor for SARS-CoV-2 infection, as TMPRSS2 mediates cleavage of viral spike protein, enabling direct fusion of the viral envelope with the host cell membrane. Since the start of the COVID-19 pandemic, TMPRSS2 has gained attention as a therapeutic target for protease inhibitors which would inhibit SARS-CoV-2 infection, but little is known about TMPRSS2 regulation, particularly in cell types physiologically relevant for SARS-CoV-2 infection. Here, we performed an unbiased genome-wide CRISPR-Cas9 library screen, together with a library targeted at epigenetic modifiers and transcriptional regulators, to identify cellular factors that modulate cell surface expression of TMPRSS2 in human colon epithelial cells. We find that endogenous TMPRSS2 is regulated by the Elongin BC-VHL complex and HIF transcription factors. Depletion of Elongin B or treatment of cells with PHD inhibitors resulted in downregulation of TMPRSS2 and inhibition of SARS-CoV-2 infection. We show that TMPRSS2 is still utilised by SARS-CoV-2 Omicron variants for entry into colonic epithelial cells. Our study enhances our understanding of the regulation of endogenous surface TMPRSS2 in cells physiologically relevant to SARS-CoV-2 infection.
A transformer-based structure-aware model for tackling the traveling salesman problem
Leveraging the Transformer architecture to develop end-to-end models for addressing combinatorial optimization problems (COPs) has shown significant potential due to its exceptional performance. Nevertheless, a multitude of COPs, including the Traveling Salesman Problem (TSP), displays typical graph structure characteristics that existing Transformer-based models have not effectively utilized. Hence, this study focuses on TSP and introduces two enhancements, namely closeness centrality encoding and spatial encoding, to strengthen the Transformer encoder’s capacity to capture the structural features of TSP graphs. Furthermore, by integrating a decoding mechanism that not only emphasizes the starting and most recently visited nodes, but also leverages all previously visited nodes to capture the dynamic evolution of tour generation, a Transformer-based structure-aware model is developed for solving TSP. Employing deep reinforcement learning for training, the proposed model achieves deviation rates of 0.03%, 0.16%, and 1.13% for 20-node, 50-node, and 100-node TSPs, respectively, in comparison with the Concorde solver. It consistently surpasses classic heuristics, OR Tools, and various comparative learning-based approaches in multiple scenarios while showcasing a remarkable balance between time efficiency and solution quality. Extensive tests validate the effectiveness of the improvement mechanisms, underscore the significant impact of graph structure information on solving TSP using deep neural networks, and also reveal the scalability and limitations.
Loneliness and repetitive negative thinking mediate the link between social health and cardiac distress in heart disease patients
Nature-based interventions for individual, collective and planetary wellbeing: A protocol for a scoping review
Nature-based interventions (NBIs) provide an opportunity to enhance individual wellbeing, improve community cohesion, and promote a culture of care for the environment. Several scoping reviews have attempted to catalogue the positive effects of NBIs on wellbeing, yet, these have typically focused on outcomes relating to individual wellbeing, thus restricting the assessment of the possible benefits of NBIs. Here we present a protocol for a scoping review that will synthesise the evidence relating to the impact of NBIs across a much broader range of domains with a focus on self (individual wellbeing), others (collective wellbeing) and nature (planetary wellbeing). This scoping review will also provide insight into the relative effectiveness of different types of NBIs at enhancing wellbeing across these domains and synthesise the underlying theory on which interventions have been developed and reported outcomes have been presented. A literature search for theses and peer-reviewed studies will be conducted on four databases (APAPsycINFO, Web of Science, Medline, and Scopus) and ProQuest Dissertations & Theses Global. Two independent reviewers will complete a two-stage screening process (title/abstract and full-text) using the Covidence platform. The protocol for this scoping review is registered with the Open Science Framework. Data extraction will focus on publication details, type of intervention, and wellbeing-related outcomes. Results will be reported in a scoping review following standardised guidelines relating to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. This research will inform the design and delivery of NBI’s across a wide range of sectors including health and social care, public policy, education, and community services, to ultimately promote human flourishing at scale.
18F-FDG PET/CT findings of intermediate bone tumors of the spine
Abstract Intermediate bone tumor has a certain risk of invasion and metastasis, but its invasion degree and metastasis probability are much lower than malignant bone tumor. The objective of this study was to retrospectively analyze 18F-FDG PET/CT findings in patients with spinal intermediate tumors. A total of 49 patients with spinal intermediate tumors consisting of 22 giant cell tumor of bone (GCTB), 11 osteoblastoma, 10 Langerhans cell histiocytosis (LCH) and 6 epithelioid hemangioma were subjected to evaluation using 18F-FDG PET/CT. Factors analyzed included lesion location, size, epicenter of the lesions (vertebral/posterior elements), eccentric expansile osteolysis, cortical integrity, residual bone trabeculae/spine/calcification, sclerotic rim, vertebral compression, soft tissue mass, and maximum standard uptake value (SUVmax) of lessions. GCTB, osteoblastoma, LCH, and epithelioid hemangioma showed statistically significant differences in the main body of the lesion, maximum diameter, eccentric expansile osteolysis, cortical integrity, residual bone trabeculae/spine/calcification, sclerosal margin, vertebral compression, and lesions’ SUVmax (p < 0.05). GCTB has a higher SUVmax, significantly higher than that of osteoblastoma and LCH (p < 0.05). Spinal GCTB, osteoblastoma, LCH, and epithelioid hemangioma have certain 18F-FDG PET/CT features, and 18F-FDG PET/CT may contribute to differentiate them from each other.
Correction: Online risk exposure and anxiety among college students in China: The chain mediating role of negative attribution and interpersonal security
Persistent neutrophilic inflammation is associated with delayed toxicity of phenylarsine oxide in lungs
Assessing microbial diversity in open-pit mining: Metabarcoding analysis of soil and pit microbiota across operational and restoration stages
Mine closure operations aim to restore the ecosystem to a near-original state. Microorganisms are indispensable for soil equilibrium and restoration. Metabarcoding was employed to characterize the bacterial and fungal composition in pristine soils, stockpiled soils (topsoils), enriched stockpiled soils (technosoils), enriched and revegetated soils (revegetated technosoils), and pit ecosystems in an open pit gold mine. Chao1 analysis revealed highest richness in pristine and topsoils, followed by technosoils (-17.5%) and pits (-63%). Bacterial diversity surpassed fungal diversity (-40%) in soil samples, but fungal OTUs were more abundant in pit samples (+73.4%). The findings identified the dominant microbial communities and conducted a comparative analysis of the shared microbiota. Dominant genera differed notably between pristine, topsoil, and technosoil samples for bacteria and fungi. The ecological indices’ results indicated that the pristine soil microbial communities were distinct from those in the topsoils, revealing significant alterations during the stockpiling process. The revegetated technosoil showed more similarity to the pristine and topsoil samples than to the freshly prepared technosoil, suggesting that microbial restoration is an ongoing phenomenon. Microbial restoration analysis revealed that Bacterial communities recover faster than fungal communities highlighting the potential of managing technosoil physicochemical parameters to enhance microbial recovery similar to those found in pristine soils. Runoff water contribute to this rebalancing by transporting microorganisms between ecosystem. All pit samples exhibited significant differences in their microbial composition, with moisture and rock composition representing the primary axes of dissimilarity. The greater community complexity observed in soils is related to the availability of nutrients, physicochemical variations, and the possibility of interaction with other microbes. Pits represent extreme ecosystems that limit the growth of most microorganisms. The presented research provides a scientific basis for future restoration strategies to improve microbial diversity and ecosystem resilience in altered landscapes.