bluuebunny/crossref_metadata_2025_split huggingface dataset:
Association between Job burnout and Productivity among Iranian operating room Nurses: A cross-sectional study in northwest Iran
Authors: Rahmani Vahid , Alizadeh Amirali , Arabsorkhi Mohammad , Gheibi Mohaddeseh , Aghazadeh Nasrin 
Published: October 2024

Abstract

Background
Job burnout is a widespread issue among medical staff and operating room nurses due to the complex and stressful conditions they face in their work environment. This phenomenon can have several negative effects, such as reducing job satisfaction, increasing absenteeism, and lowering the quality of patient care. Therefore, this study aims to investigate the association between job burnout and productivity among Iranian operating room nurses.
Methods
This cross-sectional study was conducted on 294 operating room nurses in Iran. The study made use of a demographic information questionnaire, Maslach Burnout Inventory (MBI), and Hersey and Goldsmith Productivity Questionnaire for data collection. The collected data were analyzed using descriptive and analytical tests with SPSS V.27.
Results
The study's findings indicate that operating room nurses experience a moderate level of job burnout (62.18 ± 14.17) and productivity (55.61 ± 12.37). Furthermore, the study discovered a significant negative correlation between burnout and productivity in these nurses (P < 0.001, r=-0.278).
Conclusion
The study shows a negative association between job burnout and productivity, suggesting actions like workload reduction, enhanced social support, better work-life balance, process improvements, and increased appreciation could mitigate burnout and boost productivity, thereby improving patient care quality.
Activating Wnt1/β-Catenin signaling pathway to restore Otx2 expression in the dopaminergic neurons of ventral midbrain
Authors: Lin Xian , Li Zhao , Duan Jin , Cao An , Gong Zhuo , Liu Hao , Shen Dan , Ye Tong , Zhu Shun , Cen Qi , He Shuai , He Yong , Zheng Can 
Published: October 2024

Abstract

Parkinson’s disease (PD) is the world’s second most prevalent neurodegenerative disease. Currently, aside from levodopa, there are no other effective drugs clinically available to slow its progression. Otx2 plays a critical role in the differentiation of midbrain dopaminergic neurons (mDANs) during midbrain development. However, in adulthood, Otx2 is primarily expressed in the ventral tegmental area (VTA)-ventral part, and mDANs in the dorsal part of the VTA and the substantia nigra pars compacta (SNc) show no Otx2 expression. Research indicates that Otx2 is essential not only for the development of mDANs but also for their protection against the toxicity of MPTP and rotenone. Consequently, Otx2 is a potential clinical target for mDANs protection. Identifying the upstream mechanism that regulates Otx2 expression is crucial to restoring its expression in the SNc and enhancing its levels in the entire ventral midbrain mDANs. In this study, we have demonstrated the safety of Otx2 overexpression in vitro using adeno-associate virus (AAV) and explored the feasibility of promoting Otx2 expression through the Wnt/β-Catenin signaling pathway using various drugs, a miR-34 mimic, and an inhibitor. Our results showed that Otx2 overexpression via AAV in the SNc is relatively safe, and CHIR99021 can induce Otx2 expression in mouse mDANs, thereby, alleviating PD-liked motor symptoms induced by MPTP. These findings suggest that modulating Otx2 expression through the Wnt/β-Catenin signaling pathway holds a therapeutic approach for Parkinson’s disease.
The BrainHealth Project Study Protocol: A Longitudinal Study in Generally Healthy Adults
Authors: Cook Lori G. , Spence Jeffrey S. , Venza Erin E. , Tate Aaron , Robertson Ian H. , D'Esposito Mark , Ling Geoffrey S.F. , Wigginton Jane , Chapman Sandra Bond 
Published: October 2024

Abstract

Background: Optimization of brain health is a focal point in medical science, yet data regarding measuring, preserving, and improving lifelong brain health are lacking. This void demands an objective, change-sensitive measure of brain health and proven strategies to strengthen brain performance. The BrainHealth Project addresses these key issues, drawing upon neuroplasticity evidence of persistent modifiability of brain function across the lifespan. This landmark study aims to definitively (i) evaluate and refine holistic change indices of brain health, (ii) evaluate the impact of evidenced-based cognitive strategies and lifestyle interventions on improving/maintaining brain health, and (iii) elucidate the mechanisms associated with brain health gains/losses.
Methods: This prospective, longitudinal, interventional, open-label, single-arm clinical trial aims to recruit 100,000 generally healthy adults over 10 years. Assessments, coaching, and training are conducted online through the secure BrainHealth Platform, allowing utilization tracking. The BrainHealth Index (BHI)—a multidimensional assessment—is offered at baseline and every six months. Participants have access to coaching every three months and continual access to self-paced trainings and resources.
The primary outcome is the further validation of a novel composite BHI score and its sub-scores: Clarity (cognitive health), Connectedness (social health), and Emotional Balance (well-being). The BHI includes measures sensitive to change, including gains from behavior change and integration of cognitive strategies into daily life. These contrast with traditional assessments focused on detecting cognitive decline or diagnosing pathological conditions.
The primary online training, SMART (Strategic Memory Advanced Reasoning Tactics), is a strategy-based program validated with 25+ years of research. SMART promotes improvements in neural health, cognition, well-being, connectedness, and real-life function, previously reported in randomized controlled trials (RCTs). Statistical approaches focus on individual prediction using nonlinear models trained with large samples and on assessing mechanisms influencing gains or losses on brain health metrics.
Discussion: This research extends RCT evidence to a longitudinal, epidemiologic approach, leveraging digital health and machine learning tools to deliver a generalizable measure of longitudinal brain health and to achieve precision brain health. By integrating advanced statistical methods and large-scale data, the BrainHealth Project should provide medicine and society with accurate and actionable ways to optimize brain health practices across the lifespan.
ClinicalTrials.gov Identifier: NCT04869111 (registered April 27, 2021)
Rare constituents of the nasal microbiome contribute to the acute exacerbation of chronic rhinosinusitis
Authors: Zhang Yunfan , Yuan Fan , Liu Zheng , Huang Xiaoxi , Hong Junsheng , Chang Feifan , Wu Dawei 
Published: October 2024

Abstract

Background
Dysbiosis of the nasal microbiome is considered to be related to the acute exacerbation of chronic rhinosinusitis (AECRS). The microbiota in the nasal cavity of AECRS patients and its association with disease severity has rarely been studied. This study aimed to characterize nasal dysbiosis in a prospective cohort of patients with AECRS.
Methods
We performed a cross-sectional study of 28 patients with AECRS, 20 patients with chronic rhinosinusitis (CRS) without acute exacerbation (AE), and 29 healthy controls using 16S rRNA gene sequencing. Subjective and objective assessments of CRS disease severity during AE were also collected.
Results
Compared to healthy controls and patients with CRS without AE, AECRS presented with a substantial decrease of the Corynebacterium_1 and a significant increase of Ralstonia and Acinetobacter at the genus level (LDA score > 2.0 [P < 0.05]). Furthermore, 29 genera with a substantial alteration in AECRS were rare constituents of the microbiome, of which 18 rare genera were highly associated with subjective and objective disease severity. Moreover, a combination of 15 genera could differentiate patients with AECRS with an area under the curve of 0.870 (95% CI = 0.784–0.955). Prediction of microbial functional pathways involved significantly enhanced lipopolysaccharide biosynthesis pathways and significantly decreased folate biosynthesis, sulfur relay system, and cysteine and methionine metabolism pathways in patients with AECRS.
Conclusions
The rare nasal microbiota correlated with disease status and disease severity in patients with AECRS. The knowledge about the pattern of the nasal microbiome and its metabolomic pathway may contribute to the fundamental understanding of AECRS pathophysiology.
Updating the impact of mRNA COVID-19 vaccine exposure during pregnancy on obstetric and neonatal outcomes.
Authors: Adusei-Mensah Frank , Olubamwo Olubunmi , Olaleye Sunday , Akter Laboni , Balogun Oluwafemi Samson , Moshoeshoe Rethabile Joyce , Awoniyi Luqman , Olawuni Adedayo , Kauhanen Jussi 
Published: October 2024

Abstract

Background
The safety of mRNA COVID-19 vaccines in pregnant women remains a critical concern. This systematic review and meta-analysis evaluate the maternal and neonatal outcomes associated with mRNA COVID-19 vaccination during pregnancy.
Methods
We conducted a systematic search of PubMed, Embase, Cochrane Library, and clinical trial registries for studies published between December 2020 and July 2024. Studies were included if they assessed obstetric and neonatal outcomes following mRNA COVID-19 vaccination in pregnant women. Data were extracted and analyzed using a random-effects model to calculate pooled odds ratios (ORs) and 95% confidence intervals (CIs).
Results
Twelve studies met the inclusion criteria, encompassing 42,944 vaccinated and 183,733 unvaccinated pregnant women. mRNA vaccination was associated with a significant reduction in preterm delivery (OR 0.743, 95% CI 0.607–0.911), fetal distress (OR 0.699, 95% CI 0.546–0.893), neonatal congenital abnormalities (OR 0.712, 95% CI 0.570–0.889), and NICU admissions (OR 0.718, 95% CI 0.617–0.836). However, a slight increase in gestational diabetes risk was observed (OR 1.107, 95% CI 1.054–1.162).
Conclusion
mRNA COVID-19 vaccines are safe during pregnancy and associated with reduced risks of adverse obstetric and neonatal outcomes. The observed increase in gestational diabetes risk underscores the need for vigilant monitoring. These findings support the inclusion of pregnant women in vaccination campaigns and inform public health policies and clinical practices to improve maternal and neonatal health outcomes.
Identification of hub genes in osteoarthritis via integrating bioinformatics analysis and machine learning
Authors: Wang Yanqing , Xu Yizhou , Deng Gang , Xu Shuyi , Li Xican , Zhou Xianxi , Guo Jiasong , Chen Dongfeng 
Published: October 2024

Abstract

Osteoarthritis (OA) represents a significant burden on global healthcare systems that causes pain and functional impairment by affecting joint tissue. Ubiquitin-specific peptidase 53 (USP53), a member of the ubiquitin-specific protease (USP) family, is involved in the progression of various disease states, but its role in OA has not been investigated. This study employed bioinformatics analysis to identify 92 common genes were identified in the positivity related modules of OA. Further studies demonstrated that the DEGs enriched in 20 pathways, mainly including the Focal adhesion, PI3K-Akt signaling pathway, ECM-receptor interaction, Human papillomavirus infection and Pyrimidine metabolism in KEGG enrichment analysis. Furthermore, machine learning results show that USP53, CA12 and PON could be effective biomarkers for OA diagnosis. Preliminary laboratory investigations showed that compared with the control group, the expression of USP53 in the OA group showed significant changes in vivo. The clinical specimen test results showed the protein expression of USP53 in cartilage tissue of OA patients. All in all, this study indicated the hub genes USP53 could be a potential biomarker for OA, thus providing a novel insight into the modulation of ubiquitin in OA clinical diagnosis and treatment.
Cockayne syndrome mice reflect human kidney disease and are defective in de novo NAD biosynthesis
Authors: Bohr Vilhelm , Pekhale Komal , Tiwari Vinod , Kulikowicz Thomas , Bridges Christy , Croteau Deborah , Levy Moshe , Wang Xiaoxin , Lee Jong-Hyuk 
Published: October 2024

Abstract

Cockayne Syndrome (CS) is a premature aging disorder caused by mutations in the CSA and CSB genes involved in DNA metabolism and other cellular processes. CS patients display many features including premature aging, neurodegeneration, and kidney abnormalities. Nicotinamide dinucleotide (NAD+) deprivation has been oberserved in CS patient-derived cells. NAD+ has essential roles in regulating cellular health, stress responses, and renal homeostasis. While kidney dysfunction is a common featurein CS patients, its molecular pathogenesis is not understood. Here, we report that severe kidney pathology is present in CS A and B mice. We find that the NAD+ biosynthetic pathways are impaired in kidneys from these mice. Using human renal tubular epithelial cells, we show that CSA/B downregulation causes persistent activation of the ATF3 transcription factor on the quinolinate phosphoribosyl transferase gene locus, a rate-limiting enzyme in de novo NAD+ biosynthesis in the kidney, causing impaired transcription and deficient NAD+ homeostasis.
Autodisplay made easy: a plasmid toolbox for the surface display of recombinant proteins and its optimization.
Authors: Jose Joachim , Röhe Philip , Furtmann Christoph 
Published: October 2024

Abstract

A plasmid library is provided for the surface display of proteins, to be used by experts and non experts, including a simple option for optimization. Based on a classical autotransporter protein, variants were constructed with different signal peptides, linkers and adjacent regions, set under control of a rhaBAD promoter, resulting in a library of 24 plasmids named Autodisplay ToolBox (ATB). For proof of concept, a β-glucosidase as model passenger was subjected to combinatorial permutation using the plasmids of the ATB (pATB). The best combination based on enzymatic activity was pATB_45-β-Gluc with an OprF signal peptide, a C terminal spacer (GGGDDNAAPA), and a short linker (ΔepitopeΔβ1). Activity was 4.9-fold increased compared to the initial construct.
ATB can be extended to 72 variants by using two additional promoters (PRox306, ParaBAD) and to 81 variants by using an inverse autotransporter protein (YeeJ). Besides using the pATB library, individual plasmids can be picked for custom-made surface display.
ML-test: a machine learning-based test for integer ambiguity validation
Authors: Hou Yanqing , Shi Yining , Duan Xiaojun , Chen Xuan 
Published: October 2024

Abstract

The accurate resolution of integer ambiguity is crucial for Real-Time Kinematic (RTK) and Precise Point Positioning (PPP), with ambiguity validation serving as a pivotal stage. The ratio test based on a fixed threshold (RT) is currently the most widely used test for ambiguity validation. However, due to its inflexible characteristics of remaining a fixed threshold, it is challenging to guarantee availability and reliability simultaneously. The fixed failure rate ratio test (FFRT) addresses this by adjusting the threshold dynamically according to model strength. However, its reliability is compromised due to the detachment from real scenarios in Monte Carlo simulations and theoretical limitations. This paper contributes by proposing the ML-test, a machine learning (ML)-based test for ambiguity validation. ML-test aims to discover patterns influencing the correctness of ambiguity validation by utilizing a substantial amount of real data for ML. To verify the performance of ML-test, comparative experiments between ML-test, RT, and FFRT are conducted over multiple days and baselines. The results indicate that the total average fix rate of RT (with a fixed threshold of 3) is between 74% and 78%, FFRT (with a fixed failure rate of 0.001) is between 98% and 99%, and ML-test is between 89% and 96%. The total average conditional success rate of RT is nearly 100%, FFRT is between 96% and 98%, and ML-test is between 98% and 99%, generally higher than FFRT. It is evident that ML-test demonstrates advantages in balancing availability and reliability, with stable performance that ensures a high fix rate while maintaining a sufficiently high conditional success rate.
Towards Understanding the Firm Specific Determinants of Corporate Financial Flexibility
Authors: Al-ATHAMNEH MUSTAFA , OBEIDAT MOHAMMED , ALMOMANI MOHAMMAD , ALMOMANI TAREQ , Darkel Nadeen 
Published: October 2024

Abstract

The study objects for determining the most important firm specific factors affecting the corporate financial flexibility of the listed manufacturing firms at Amman Stock Exchange. Firm specific factors including, profitability, assets tangibility, cash holdings, and retained earnings, are taken into consideration, as possible internal determinants of corporate financial flexibility. To achieve the objectives of the study, secondary data covering the period 2013-2021, of 40 listed manufacturing listed firms at Amman Stock Exchange, had been collected and used in the analysis and hypotheses testing. Employing both of the simple and linear regression methods in hypotheses testing, and at the individual level of independent variables, the result reveals a significant impact of profitability, assets tangibility, cash holdings, and capital structure, and insignificant impact of retained earnings, on corporate financial flexibility. Moreover, a combined grouping significant impact, the result shows for the different firm specific factors, as a single group, on financial flexibility. The study recommends more investigations regarding financial flexibility and its internal and external macroeconomic determinants.
A Unified Multi-Objective Adaptive Task Scheduling for VM Optimization in Heterogeneous Cloud Data Center
Authors: MP Abdul Razaak , Masood Mohamed 
Published: October 2024

Abstract

In light of the swift progress of cloud technology, substantial cloud computing centers have surfaced to cater to the escalating demand in the business realm. These centers are designed to cater to the increasing demands of data processing and machine learning, with their vast number of computing resources, efficient resource and energy management becomes crucial in order to reduce costs and minimize environmental impact. Energy efficiency in cloud computing is a critical concern as the demand for cloud services continues to grow. Energy efficiency in cloud computing is not only a technological imperative but also a pivotal step towards a more sustainable and environmentally responsible future. With the increasing environmental awareness and the need to reduce operational costs, optimizing energy consumption in data centres and cloud infrastructure is essential. The Virtual Machine Consolidation (VMMC) is a strategy to optimize power utilization in the Data Center of Cloud by optimizing hardware and software, consolidating virtual machines, scheduling workloads, and integrating renewable energy. In this paper, we proposed the ARMS (Adaptive Resource Management Systems) framework to a mixture of the Autoregressive Integrated Moving Average (ARIMA) model with Random Forest Algorithm (RFA). The experiment results indicate that the benchmark methods are outperformed in terms of Energy Efficiency, Cost, Makespan, SLAV, and Number of Iteration etc.
The role of modelling in stakeholder discussions for nature-based solutions
Authors: Bogatinoska Borjana , Lansu Angelique , Hugé Jean , Dekker Stefan C. , Stoorvogel Jetse 
Published: October 2024

Abstract

Nature-based solutions have gained recognition for their potential in addressing multiple challenges such as flooding, droughts but also carbon sequestration. NbS often require interventions and changes of land management in rural areas that require the involvement of multiple stakeholders. This study explores the role of environmental modelling to support stakeholder discussions aimed at planning nature-based solutions. The research focuses on the Aa of Weerijs catchment, a transboundary area in Belgium and the Netherlands facing challenges like droughts and flooding. Four potential model contributions are identified to support stakeholder discussions around interventions including: sensitivity analysis, impact assessment, scenario evaluation – catchment level and scenario evaluation – local level. The study makes use of a stakeholder-oriented approach, emphasizing an iterative process for aligning the decision space of stakeholders to the opportunity space. Opportunity space refers to options that work and help reach the goals and decision space includes the options that are considered by the stakeholders. Results demonstrate the sensitivity of the catchment to extrememe land use land cover changes, identify impactful locations for nature-based solutions’ implementation, evaluate diverse nature-based scenarios, and assess location-specific feasibility. The findings underscore the significance of integrating advanced visualization tools together for a model understanding specifically of water-carbon interaction and iterative stakeholder engagement for effective nature-based solutions’ design, offering a pathway for informed decision-making.
Dragonfly Visual Attention-Merged Evolutionary Neural Network: Biomimetic Optimizer Solving LSGO problems
Authors: Zhang Zhuhong , Wang Heng 
Published: October 2024

Abstract

The biological visual system includes a variety of motion-sensitive neurons that intrinsically perceive changes in visual motion. Although some of their neurophysiological findings have been successfully used to construct computational models for engineering applications targeting specific problems, it remains unclear how the relevant response mechanisms can be used to optimize the subject. Hereby, related to grayscale intensity difference, the dragonfly visual information-processing and attention mechanisms are borrowed to develop a feedforward dragonfly visual attention-merged neural network with presynaptic and postsynaptic neural subnetworks, which can characterize changes of visual motion in terms of the on-line output activities named learning rates. Afterwards, integrated such learning rates into a new-type and metaheuristics-inspired state transition strategy, a dragonfly attention-merged visual evolutionary neural network with the unique parameter of input resolution, which enables current states to discover the global optimum quickly, is developed to solve large-scale global optimization (LSGO) and even hyperdimensional global optimization (HDGO) problems. The theoretical analysis implicates that the evolutionary neural network’s complexity is mainly decided by the optimization problem itself. Comparative experiments have validated that it, as a highly competitive optimizer, can significantly outperform the compared approaches and successfully solve the design optimization problem of a complex sixth-order analog active filter.