bluuebunny/crossref_metadata_2025_split huggingface dataset:
Is private insurance enough to address barriers to accessing dental care? Findings from a Canadian population-based study
Authors: Abdelrehim Mona , Singhal Sonica 
Published: February 2024

Abstract

Background
In Canada, as in many other countries, insurance plays a crucial role in facilitating access to dental care. Private dental insurance, though it greatly reduces financial barriers to dental care, does not guarantee affordability, as there are issues with the quality and level of coverage of insurance plans. Individuals experiencing barriers to dental care are expected to have poorer oral health. It is important to examine access to dental care and the oral health status of individuals with private insurance.
Methods
Our study is a secondary data analysis of the most recent available cycle (2017-18) of the Canadian Community Health Survey (CCHS), a national cross-sectional survey. Univariate analysis was conducted to determine the characteristics of Ontarians with private insurance (n = 17,678 representing 6919,814 Ontarians)—bivariate analysis to explore their financial barriers to dental care, and how they perceive their oral health. Additionally, logistic regressions were conducted to identify relationships between covariates and each outcome variable in our study (cost barriers to dental care, visiting the dentist only for emergency, perceived their oral health as “fair to poor” and “dissatisfied and very dissatisfied” with their teeth/denture appearance).
Results
We found that the majority of those with private insurance did not experience cost barriers to dental care and perceived their oral health as good to excellent. However, specific populations, including those aged 20–39 years, and those earning less than $40,000, despite having private dental insurance, faced significantly more cost barriers to access to care. Additionally, those with the lowest income (earning less than $20,000) perceived their oral health as “fair to poor” more than their counterparts. Adjusted estimates revealed that respondents aged 20–39 were six times more likely to report cost barriers to dental care and ten times more likely to visit the dentist only for emergencies than those aged 12–19. Additionally, those aged 40–59 were two times more likely to report poorer oral health status compared to those aged 12–19.
Conclusion
Given the upcoming implementation of the Canadian Dental Care Plan, the results of this study have implications for identifying vulnerable populations who currently are ineligible for insurance coverage.
Neuroinflammation Protective Efficacy of Montelukast in a Mouse Model of Parkinson’s Disease Revealed by [18F]GSK1482160 PET Quantification Targeting P2X7R
Authors: Ye Peizhen , Bi Lei , Qiu Yifan , Yang Min , Huang Guolong , Zhao Xianxian , Liu Yongshan , Hou Yuyi , Sun Rui , Zheng Pengcheng , Tong Henry Hoi Yee , Jin Hongjun 
Published: February 2024

Abstract
Purpose
In vivo neuroinflammation was quantified via [18F]GSK1482160 positron emission tomography (PET) imaging targeting P2X7 receptor (P2X7R) to evaluate the therapeutic effect of Montelukast (MK) treatment in transgenic A53T mice model of Parkinson's disease (PD).
Methods
MK treatment (10 mg/kg, i.g.) was performed in a mice model of PD for 5 weeks. Movement disorder was measured by motor functional tests (grip tests, pole tests, and inverted screen tests), the expression of microglial P2X7R was quantified by [18F]GSK1482160 positron emission tomography-Magnetic Resonance Imaging (PET-MRI) as well as ex vivo autoradiography, and the damage of dopaminergic neurons (DA neurons) was quantified by [18F]L-DOPA PET-MRI in PD mice to monitor the therapeutic effect of MK. The movement abnormality was exploited by performing a correlation analysis between the PET quantification and motor functions. Immunofluorescence staining was performed to validate the results of PET-MRI.
Results
PD mice suffered from severe motor deficits accompanied by increased neuroinflammation throughout the brain regions, as well as partially damaged DA neurons in striatum. The MK treatment significantly improved the motor deficits (grip test: PD + NS: 0.630 ± 0.020 N vs. PD + MK: 0.736 ± 0.011 N, p < 0.0001). PET measurement indicated that MK treatment significantly reduced the brain uptakes of [18F]GSK1482160 (SUVRLV in striatum: PD + NS: 1.701 ± 0.047 vs. PD + MK: 1.313 ± 0.075, p < 0.0001). Correlation analyses showed that motor function was positively correlated with [18F]L-DOPA PET in stratum (p = 0.0482, r = 0.5565), and was negatively correlated with [18F]GSK1482160 PET in brain globally (p = 0.0008, r = -0.7342).
Conclusions
[18F]GSK1482160 is a promising PET tracer for assessing neuroinflammation in PD. In vivo monitoring of inflammation in PD is beneficial to explore the association between movement disorders and the neurological damage in the brain regions, and to detect potential damage to non-DA neurons, which facilitates the assessment of the disease and contributes to the early diagnosis and treatment of PD.
High Prevalence of Work-Related Musculoskeletal Disorders Among Gastrointestinal Endoscopy Nurses in China: A Cross-Sectional Survey Study
Authors: WANG Maosha , Li Wen , Wang Jing , CHEN Ting , Liu Feng , ZHANG ying 
Published: February 2024

Abstract
Background and objectives:
Gastrointestinal endoscopy has assumed a paramount role in modern gastroenterology, characterized by notable advancements in techniques. Consequently, considerable attention has been directed towards investigating the prevalence of work-related musculoskeletal disorders among gastrointestinal endoscopists. However, an equally important aspect that warrants investigation is the prevalence of WMSD experienced by gastrointestinal endoscopy nurses, who play a vital role in the procedure. While previous studies in the United States have reported varying rates of WMSD (ranging from 19.6% to 52.2%) among these nurses, the data pertaining to the Chinese gastrointestinal endoscopy nurse population is notably lacking. Hence, this study endeavors to assess the prevalence, affected sites, prevalence rate, and potential risk factors of WMSD among Chinese gastrointestinal endoscopy nurses.
Methods:
To achieve this objective, an anonymous electronic survey comprising 62 questions was administered to 528 gastrointestinal endoscopy nurses.
Results:
The survey was completed by 396 respondents, revealing that 376 nurses (94.9%) reported experiencing WMSD in at least one location. The most frequently reported pain symptoms encompassed low back pain (reported by 316 nurses, 79.8%), neck pain (reported by 311 nurses, 78.5%), and shoulder pain (reported by 296 nurses, 74.7%). Notably, through multivariate logistic regression analysis, the study identified frequent forward/backward neck tilting at work and a confined workspace as significant associated risk factors for shoulder WMSD, while frequent pushing and pulling of heavy objects and a tight workspace emerged as significant risk factors for low back WMSD.
Conclusions:
In conclusion, the study found that WMSD is highly prevalent among registered nurses engaged in gastrointestinal endoscopy care, with the lower back, neck, and shoulder being the most commonly affected areas. To address this issue effectively, it is imperative to focus on understanding the occurrence and mechanisms underlying WMSD, imparting preventive knowledge, and implementing ergonomic protective measures. Incorporating these aspects into the professional training of gastrointestinal endoscopy nurses is of paramount importance.
A Novel Approach to Detect Driver Drowsiness Using Transfer Learning and Hybrid Features
Authors: S Priyanka , S Shanthi 
Published: February 2024

Abstract
In worldwide, drowsiness is one of the prevalent reasons to cause accident. Statistics show that fatigued drivers are a major factor in causing many accidents. According to studies by the National Sleep Foundation, 20% of drivers feel sleepy to some extent while driving. Deep learning-based methods are the most recent ones that researchers have used to analyse videos and detect tiredness. Convolution neural networks utilizes extracted face features like yawning, eye flashing and head movements to detect exhaustion and sleepiness. Incorporating modified InceptionV3, VGG16, ResNet50, DenseNet201 and MobileNetV2 architecture over Driver Drowsiness Dataset to propose an ensemble deep learning model. Feature extraction was done using these models. The global max pooling layer is used to improve spatial robustness and dropout approach was included in these models to avoid overfitting on training data. Finally, Sigmoid classifier is used to classify positive (drowsy) or a negative (nondrowsy) result. These models outputs are given to a proposed ensemble algorithm. This model outperforms the alternative strategy with respect to performance metrics. The suggested ensemble framework performs better in identifying driver drowsiness than existing state-of-the-art techniques on basis of accuracy.
Unveiling Pandemic Patterns: A Detailed Analysis of Transmissibility and Severity Parameters Across Four COVID-19 Waves in Bogotá, Colombia
Authors: Cucunuba Zulma , Quevedo David Santiago , Domigues Nicolas , Perez Diego , Cabrera Maria Alejandra , Serrano Juan David , Abril Felipe Segundo , Moyano Diane , Rios Diana Sofía , Gonzalez Manuel , Whittaker Charles 
Published: February 2024

Abstract
This retrospective study on COVID-19's four waves in Bogotá, Colombia, scrutinises 1.77 million cases from March 2020 to April 2022, revealing significant shifts in both transmissibility and severity. The study highlights dynamic changes in the instantaneous reproduction number (Rt), with the highest values (> 2.5) corresponding to the ancestral and Omicron variants. There was a notable 88% decrease in the Case Fatality Ratio (CFR) from the first to the fourth wave, emphasising changing severity levels. The third wave, marked by the Mu variant, saw the highest case and death counts, yet paradoxically showed a decrease in CFR and an increase in the hospitalisation fatality ratio. Conversely, the fourth wave, dominated by Omicron, had the lowest severity despite higher hospitalisation rates in children. Additionally, the study records a consistent reduction in average hospital and ICU stay durations, from 10.84 days to 7.85 days and from 16.2 days to 12.4 days respectively, across the waves. These findings underscore the importance of ongoing epidemiological surveillance and adaptable public health strategies in lower-middle-income regions like Bogotá, deepening our understanding of COVID-19's impact in Latin America.
Novel stochastic multi breather type, a-periodic, hybrid periodic and other type of waves of the Shrödinger-Hirota model with Wiener process
Authors: AL-Essa Laila A. , Rahman Mati ur 
Published: February 2024

Abstract

The current manuscript elaborate a novel characteristic of stochastic multi breather type, periodic, hybrid periodic and different type of waves solutions for the Shrödinger-Hirota (SH) equation. The mentioned equation is studied by a Wiener process and white noise which is used for the sudden and large-scale fluctuation. By applying the wave transformation technique the considered equation is transformed into ordinary differential equations, gives meaningful analysis. From the solution of the SH equation various type of results, including soliton, dark, bright, hybrid and periodic solitons. To visually capture these intricate behaviors, we employ MATLAB tools to generate 2D and 3D graphs of the analytical stochastic solutions. This combination of mathematical rigor and numerical simulations provides a comprehensive understanding of the phenomena under study, making a valuable contribution to the field of nonlinear wave equations and stochastic processes.
Harnessing the optimization of enzyme catalytic rates in engineering of metabolic phenotypes
Authors: Razaghi-Moghadam Zahra , Babadi Fayaz Soleymani , Nikoloski Zoran 
Published: February 2024

Abstract
The increasing availability of enzyme turnover number measurements from experiments and of turnover number predictions from deep learning models prompts the use of these enzyme parameters in precise metabolic engineering. Yet, there is no computational approach that allows the prediction of metabolic engineering strategies that rely on modification of turnover numbers. It is also unclear if modifications of turnover numbers without alterations in the host’s regulatory machinery suffice to increase the production of chemicals of interest. Here, we present a constraint-based modelling approach, overcoming kinetic obstacles (OKO), that uses enzyme-constrained metabolic models to predict in silico strategies to increase the production of a given chemical, while ensuring specified cell growth. We demonstrate that the application of OKO to enzyme-constrained metabolic models of Escherichia coli and Saccharomyces cerevisiae results in strategies that can at least double the production of over 40 compounds with little penalty to growth. Interestingly, we show that the overproduction of compounds of interest does not entail only an increase in the values of turnover numbers. Lastly, we demonstrate that a refinement of OKO, allowing also for manipulation of enzyme abundance, facilitates the usage of the available compendia of turnover numbers in the design of precise metabolic engineering strategies.
Construction of a five-disulfidptosis-related-lncRNA signature for predicting prognosis and immune activity in kidney renal clear cell carcinoma
Authors: Xu Kangjie , Li Dongling , Liu Hui , Qian Jinke , Ji Kangkang , Zhang Yanhua , Zhang Minglei , Zhou Hai , Hou Xuefeng , Jiang Jian , Zhang Zihang , Mao Weipu , Dai Hua , Li Li 
Published: February 2024

Abstract
Background: Kidney renal clear cell carcinoma (KIRC) is a highly aggressive cancer. Disulfidptosis is a novel mechanism of programmed cell death. However, the role of disulfidptosis-related lncRNAs (DRlncRNAs) in KIRC remains unknown. This study aimed to develop a prognostic model based on DRlncRNAs and examine their prognostic value in KIRC.
Methods: RNA sequencing and relevant clinical data were obtained from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analyses and the lasso algorithm were used to identify prognostic DRlncRNAs and establish a prognostic model. Multiple methods were used to assess the reliability of the model. Gene set enrichment analysis (GSEA), immune infiltration analysis and somatic mutation analysis were performed to evaluate the predictive performance of the model, and anticancer drugs were predicted.
Results: The prognostic model was established based on five DRlncRNAs and was identified as a good predictor of the survival and prognosis of patients with KIRC. GSEA revealed that DRlncRNAs were associated with apoptosis and immune-related pathways. Immune analysis suggested that low-risk patients had better immunotherapeutic outcomes. Somatic mutation analysis revealed that low-risk patients had a lower somatic mutation rate and TMB score and a better prognosis. In addition, axitinib, ibrutinib, osimertinib and ruxolitinib were found to be more effective in low-risk patients, whereas crizotinib, lapatinib, linsitinib and nilotinib were found to be more effective in high-risk patients. Finally, qRT-PCR was performed to determine the expression of DRlncRNAs in normal kidney cells and KIRC cell lines.
Conclusion: We constructed a risk model and proposed a novel strategy for diagnosing and treating KIRC.
Linking Sustainability Reporting and Energy Consumption Through Global Reporting Initiative Standards and Sustainable Development Goals
Authors: Molnár Péter , Tóth Árpád , Lukács Bence 
Published: February 2024

Abstract

In response to the urgent need for a comprehensive approach to reporting that integrates real business impacts and sustainability dimensions, this study introduces a novel methodology. Building on the synergy between Global Reporting Initiative (GRI) standards, Sustainable Development Goals (SDG) targets and environmental, social, and governance (ESG) considerations, the theoretical framework provides a structured methodology for organizations to articulate their contributions to global sustainability goals. By linking the analysis of GRI standards to the Affordable and Clean Energy SDG, this approach not only analyses reporting processes but also lays the groundwork for advanced analytics by linking corporate environmental impact and sustainability reporting. Empirical findings from a homogeneous sample of companies highlight the need to improve the accuracy, efficiency, and relevance of reporting. In addition, the study underscores the broader societal benefits of sustainability reporting practices, enhancing stakeholder trust, and enabling more informed decision-making. As such, this research advances the discourse on sustainable practices and reporting, provides practical implications for practitioners, and contributes to a more sustainable future.
Molecular Docking Simulation of Antidiabetic Molecules of Libas (Spondias pinnata) Fruit and Prediction of their Pharmacokinetic Properties
Authors: Diacos Joy Elaine 
Published: February 2024

Abstract

Diabetes mellitus is one of the chronic metabolic disorders which affects more than 16 million Filipinos. Proper education, medical intervention, and a good lifestyle can help control and manage this disease. Spondias pinnata is one of underutilized crops in the Philippines which is well-known for its satisfactory flavor and medicinal properties including its antidiabetic activity. A quest for natural and effective drug to manage diseases is a continuous work in progress. Drug discovery and design is a tedious and expensive process. Computer-aided drug design guides the design and makes the process more efficient and less costly. Molecular docking simulation was used to determine the potential antidiabetic compounds from the 48 reported compounds found in S. pinnata fruit. Seven compounds namely squalene (-9.1kcal/mol), rutin (-9kcal/mol), catechin(-8.7kcal/mol), quercetin (-8.5kcal/mol), tocopherol (-8.4kcal/mol), myricetin (-8.4kcal/mol), and ellagic acid (-8.3kcal/mol) showed comparable binding affinity with peroxisome proliferator-activated receptor gamma (PPARγ). Tocopherol and catechin showed good ADMET properties. Between the two compounds, catechin passed the four filters for drug-likeness. Thus, catechin can be a potential compound that can be used to develop antidiabetic drugs.
The Hazards of Fine Particulate Matter on Mice Kidney: Effects from Different Emission Sources and Potential Toxic Components
Authors: Cheng Fu-Jen , Chang-Chien Guo-Ping , Chen Pei-Shih , Tseng Yu-Lun , Yuan Chung-Shin , Lai Ching-Shu 
Published: February 2024

Abstract

Associations exist between fine particulate matter (PM2.5) exposure and impaired kidney function. However, the specific mechanisms and components causing renal damage remain unclear. PM2.5 was collected from an industrial and a rural area. Mice were categorized according to exposure, and biochemical, western blotting, histological, and immunohistochemical analyses were performed to evaluate the impact of PM2.5 constituents on their kidneys. The impact of different PM2.5 components on inflammatory responses was assessed by exposing the murine macrophage cell line (RAW 264.7); a chelating resin, which removed heavy metals from the water extract, allowed for evaluating the inflammatory effects caused by various PM2.5 constituents. The major metallic elements at the industrial site were Fe, Mg, Zn, and Ca, whereas those at site Rural were Ca, K, and Mg. PM2.5 water extracts from both sites induced inflammatory cytokine upregulation in the lungs and kidneys, and inflammatory cell infiltration, antioxidant activity downregulation, and elevated levels of kidney injury molecule 1 in the kidneys. Compared to industrial-site PM2.5, rural-site PM2.5 significantly upregulated inflammatory cytokine infiltration in the lungs, increased inflammatory cell levels in the kidneys, and significantly increased kidney Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL)-positive apoptotic cells. Exposure to PM2.5 water extract increased the mRNA levels of tumor necrosis factor-α and interleukin-6, and nitrite production in RAW264.7 macrophages. The inflammatory response and nitrite production induced by the industrial-site PM2.5 water extract were significantly suppressed after treatment with a chelating resin, whereas that in the rural area were suppressed by the Toll-like receptor 4 (TLR4) antagonist. At equivalent dosages, renal toxicity induced by the rural-site PM2.5 water-soluble components might exceed that from industrial areas. Metal components may be crucial factors in PM2.5-induced cellular inflammatory responses in industrial areas, while TLR4-mediated inflammatory pathways were a major factor responsible for this response in the rural area.
The Triple Attention Transformer: Advancing Contextual Coherence in Transformer Models
Authors: Ghaith Shadi 
Published: February 2024

Abstract

This paper introduces the Triple Attention Transformer (TAT), a transformative approach in transformer models, tailored for enhancing long-term contextual coherence in dialogue systems. TAT innovates by representing dialogues as chunks of sequences, coupled with a triple attention mechanism. This novel architecture enables TAT to effectively manage extended sequences, addressing the coherence challenges inherent in traditional transformer models. Empirical evaluations using the Schema-Guided Dialogue Dataset from DSTC8 demonstrate TAT's enhanced performance, with significant improvements in Character Error Rate, Word Error Rate, and BLEU score. Importantly, TAT excels in generating coherent, extended dialogues, showcasing its advanced contextual comprehension. The integration of Conv1D networks, dual-level positional encoding, and decayed attention weighting are pivotal to TAT's robust context management. The paper also highlights the BERT variant of TAT, which leverages pre-trained language models to further enrich dialogue understanding and generation capabilities. Future developments include refining attention mechanisms, improving role distinction, and architectural optimizations. TAT's applicability extends to various complex NLP tasks, affirming its potential as a pioneering advancement in natural language processing.