bluuebunny/crossref_metadata_2025_split huggingface dataset:
Autophagy and exosomes; inter-connected maestros in Alzheimer’s disease
Authors: Atya Hanaa B. , Sharaf Nadia Mohamed , Abdelghany Ragwa Mansour , El-Helaly Sara Nageeb , Taha Heba 
Published: April 2024

AbstractAutophagy is a crucial process involved in the degradation and recycling of cytoplasmic components which are transported to the lysosomal compartment by autophagosomes. Exosomes are an important means of communication and signaling in both normal and diseased states, and they have a significant role in the transmission and propagation of proteins, especially proteins implicated in neurodegenerative disorders. Autophagy may affect exosomal processing, but whether autophagy controls the release of aggregated β-amyloid and tau proteins in exosomes of Alzheimer disease (AD) is unclear. Therefore, our study aimed to investigate how modulating autophagy affects the exosomal release of these proteins in animal models of AD. Isolated exosomes from brain tissues of 48 male albino mice were divided into four groups (Negative control, LPS, rapamycin (RAPA), and chloroquine (CQ). LC3 I and LC3 II as well as Aβ and Tau proteins levels were determined. All mice undergone Neuro-behavioral tests (Morris Water maze test, Y-maze test, and Novel Object Recognition). Both LPS and CQ groups showed reduced expression levels of LC3 II and LC3 II/LC3 I ratio. In contrast, RAPA group showed a significant increase in both LC3-II expression and LC3-II/LC3-I ratio. The levels of both Aβ & Tau in exosomes of CQ & LPS groups were higher. While RAPA group showed a significant diminished levels of tau & Aβ proteins. In conclusion, our findings suggest that autophagy alterations in AD can influence the release of Aβ and tau proteins through exosomes, which may impact the spread of misfolded proteins in AD. These results highlight a potential innovative therapeutic approach for combating AD.
Love of Work or Love and Work: Does a Business Owner’s Compulsion to Work Pay Off?
Authors: Wiatt Renee D. , Marshall Maria I. , Lee Yoon G. 
Published: March 2024

AbstractA business owner’s compulsion to work is a condition that can have jarring effects on business-owning families. A compulsion to work has been defined as a component of workaholism. A random sample of 478 small business owners in the United States were classified as “compulsive” and “non-compulsive” owners using cluster analysis. A probit regression was used to determine the characteristics associated with being a compulsive owner. The probability of being a compulsive owner was lower for female owners, was higher as the number of children in the house increased, and was inversely related to family-business functioning. Further analysis found that compulsive owners did not have higher business incomes than non-compulsive owners. Thus, a compulsion to work did not appear to pay off for small business owners. We contribute to the literature by identifying factors associated with compulsive owner tendencies and if those tendencies lead to higher business income.
Incorporation of Magnetic Filler (Co0.2Zn0.2Cd0.6Fe2O4) in PVA Matrix and Studying the Structural, Optical and Mechanical Properties of Nanocomposite Films
Authors: Mostafa M. , Abdelhakim Nermin A. , Hemeda O. M. , Shalaby Rizk M. , Salem B. I. 
Published: March 2024

AbstractThe goal of this study is to synthesize nanocomposite PVA/ Co0.2Zn0.2Cd0.6Fe2O4 films with varying contents of Co0.2Zn0.2Cd0.6Fe2O4 nanoparticles (x = 0, 0.2, 0.4, 0.6, 0.8) to improve the structural, optical, and mechanical characteristics of the examined materials. Casting technique is used to prepare PVA/ Co0.2Zn0.2Cd0.6Fe2O4 films. X-ray diffraction, FTIR, UV–Vis spectroscopy, and Vickers Hardness were used to examine the effect of magnetic nanoparticle content on the structural, optical, and mechanical characteristics of PVA nanocomposite films. The refractive index rises from 1.4967 for pure PVA to 7.6511 for PVA + 0.8Co0.2Zn0.2Cd0.6Fe2O4, allowing it to be employed in a variety of applications such as solar cell construction. Because of the presence of ferrite in the glass matrix, the average hardness values enhanced and increased from 90.7 to 114.2 MPa. The stress exponent (n) varies from 1.12 to 3.33, with grain boundary sliding as the deformation mechanism for x = 0, viscous glid for x = 0.2, 0.4, and 0.6, and dislocation climb for x = 0.8 nanocomposite thin films.
Authorship conflicts in academia: an international cross-discipline survey
Authors: Savchenko Elizaveta , Rosenfeld Ariel 
Published: March 2024

AbstractCollaboration among scholars has emerged as a significant characteristic of contemporary science. As a result, the number of authors listed in publications continues to rise steadily. Unfortunately, determining the authors to be included in the byline and their respective order entails multiple difficulties which often lead to conflicts. Despite the large volume of literature about conflicts in academia, it remains unclear how exactly these are distributed over the main socio-demographic properties, as well as the different types of interactions academics experience. To address this gap, we conducted an international and cross-disciplinary survey answered by 752 academics from 41 fields of research and 93 countries that statistically well-represent the overall academic workforce. Our findings are concerning and suggest that conflicts over authorship credit arise very early in one’s academic career, even at the level of Master and Ph.D., and become increasingly common over time.
One-year morbidity and mortality in patients treated with standard-dose and low-dose apixaban after acute large vessel occlusion stroke
Authors: Murakami Yasutaka , Todo Kenichi , Uchida Kazutaka , Yamagami Hiroshi , Sakai Nobuyuki , Gon Yasufumi , Okazaki Shuhei , Sasaki Tsutomu , Yoshimura Shinichi , Morimoto Takeshi , Mochizuki Hideki 
Published: March 2024

AbstractAlthough low-dose direct oral anticoagulants (DOACs) are recommended for patients at high risk of bleeding complications, it remains unclear whether the dose reduction in real-world setting is also appropriate in patients after large-vessel occlusion (LVO) stroke. This study hypothesized that patients with atrial fibrillation (AF) and LVO receiving low-dose DOACs have an increased risk of ischemic and hemorrhagic events. The study aimed to assess 1 year morbidity and mortality in patients treated with standard-dose and low-dose apixaban after LVO stroke. A post hoc analysis was performed using the acute LVO registry data, which enrolled patients with AF and LVO who received apixaban within 14 days of stroke onset. The incidences of ischemic events (ischemic stroke, acute coronary syndrome, acute myocardial infarction, and systemic embolism), major bleeding events, and death from any cause were compared between patients receiving standard- and low-dose apixaban. Of 643 patients diagnosed with LVO, 307 (47.7%) received low-dose apixaban. After adjustment for clinically relevant variables, no significant differences were observed in the incidence of ischemic events (adjusted hazard ratio [aHR]: 2.12, 95% confidence interval [CI] 0.75–6.02), major bleeding events (aHR: 1.17, 95% CI 0.50–2.73), and death from any cause (aHR: 1.95, 95% CI 0.78–4.89) between patients receiving standard- and low-dose apixaban. No significant differences were observed in the incidence of ischemic events, major bleeding events, or death from any cause between patients with AF and LVO receiving standard- and low-dose apixaban.
Enhanced removal of rare earth elements from aqueous media: exploring the potential of AM-3 and AM-4 titanosilicates
Authors: Almeida Joana C. , Sousa Cátia , Tavares Daniela S. , Pinto João , Henriques Bruno , Lin Zhi , Rocha João , Pereira Eduarda 
Published: April 2024

AbstractThis study investigates the impact of three key variables on the performance of nanoporous AM-3 and layered AM-4 titanosilicates in removing nine REEs (Y, La, Ce, Pr, Nd, Eu, Gd, Tb, and Dy) from natural mineral water and identifies optimal operational conditions using Response Surface Methodology (RSM). The experimental conditions were determined by a Box-Behnken Design of 3 factors-3 levels (pH 4, 6, and 8; sorbent dose 20, 100, and 180 mg/L; and element concentration 1, 3, and 5 μmol/L). Three-dimensional response surfaces were used to assess the linear, quadratic, and interaction influences of each factor on the REEs’ removal percentage. The pH was the most significant factor in the removal process using AM-3, while the sorbent dose was more important for AM-4. The results highlighted the sorbents’ strong capacity for REE removal. The optimal operating conditions obtained by RSM were applied to aqueous solutions with salinity 10 (common in coastal and transitional systems) and 30 (average seawater salinity). The results showed that AM-3 has a strong potential for removing REEs in solutions with salinity 10 and 30, while AM-4 was less efficient due to competition between REEs and other ions present in the solution.
An evaluation of a hepatotoxicity risk induced by the microplastic polymethyl methacrylate (PMMA) using HepG2/THP-1 co-culture model
Authors: Boran Tugce , Zengin Ozge Sultan , Seker Zehra , Akyildiz Aysenur Gunaydin , Kara Mehtap , Oztas Ezgi , Özhan Gül 
Published: April 2024

AbstractInappropriate disposal of plastic wastes and their durability in nature cause uncontrolled accumulation of plastic in land/marine ecosystems, also causing destructive effects by bioaccumulating along the food chain. Microplastics may cause chronic inflammation in relation to their permanent structures, especially through oxidative stress and cytotoxic cellular damage, which could increase the risk of cancer development. The accumulation of microplastics in the liver is a major concern, and therefore, the identification of the mechanisms of their hepatotoxic effects is of great importance. Polymethyl methacrylate (PMMA) is a widely used thermoplastic. It has been determined that PMMA disrupts lipid metabolism in the liver in various aquatic organisms and causes reproductive and developmental toxicity. PMMA-induced hepatotoxic effects in humans have not yet been clarified. In our study, the toxic effects of PMMA (in the range of 3–10 μm) on the human liver were investigated using the HepG2/THP-1 macrophage co-culture model, which is a sensitive immune-mediated liver injury model. Cellular uptake of micro-sized PMMA in the cells was done by transmission electron microscopy. Determination of its effects on cell viability and inflammatory response, oxidative stress, along with gene and protein expression levels that play a role in the mechanism pathways underlying the effects were investigated. The results concluded that inflammation, oxidative stress, and disruptions in lipid metabolism should be the focus of attention as important underlying causes of PMMA-induced hepatotoxicity. Our study, which points out the potential adverse effects of microplastics on human health, supports the literature information on the subject.
Accounting for nutrition-related health impacts in food life cycle assessment: insights from an expert workshop
Authors: Scherer Laura , Blackstone Nicole Tichenor , Conrad Zach , Fulgoni, III Victor L. , Mathers John C. , van der Pols Jolieke C. , Willett Walter , Fantke Peter , Pfister Stephan , Stylianou Katerina S. , Weidema Bo P. , Milà i Canals Llorenç , Jolliet Olivier 
Published: April 2024

AbstractSub-optimal dietary patterns make major contributions to the Global Burden of Disease and are among the most pressing issues affecting human health. Consequently, they are key to consider when assessing the human health and other environmental impacts of foods and diets within life cycle assessments. The UN Environment Life Cycle Initiative convened a task force on nutrition-related human health impacts as part of the Global Life Cycle Impact Assessment Method (GLAM) project. The health impacts of dietary patterns can be expressed in disability-adjusted life years (DALYs), in line with reporting human health impacts of other impact categories within the life cycle impact assessment (LCIA) framework. The task force held a workshop with nutrition experts to receive guidance in its process to develop a consensus-based impact assessment framework for addressing nutrition-related health impacts in LCIA. The workshop aimed to (1) evaluate the general assessment framework, (2) discuss scientific questions for quantifying human health impacts from nutrition for food items and diets, and (3) provide initial guidance for further development. The proposed framework based on the Global Burden of Disease (GBD) risk ratios was regarded as a good starting point to assess the relative health risks of the general population, provided that the dietary context is considered and several limitations, such as incomplete disease coverage, are acknowledged. The experts advised against a potentially misleading use of adult-derived dietary risk factors for children. To improve global coverage of the GLAM framework, it is important to consider a wider range of dietary patterns. The experts also recommended using a metric complementary to DALYs, such as nutrient adequacy, also considering, e.g., vitamin A and iron, to complement the assessment.
Estimation of Potato Water Footprint Using Machine Learning Algorithm Models in Arid Regions
Authors: Abdel-Hameed Amal Mohamed , Abuarab Mohamed , Al-Ansari Nadhir , Sayed Hazem , Kassem Mohamed A. , Elbeltagi Ahmed , Mokhtar Ali 
Published: April 2024

AbstractPrecise assessment of water footprint to improve the water consumption and crop yield for irrigated agricultural efficiency is required in order to achieve water management sustainability. Although Penman-Monteith is more successful than other methods and it is the most frequently used technique to calculate water footprint, however, it requires a significant number of meteorological parameters at different spatio-temporal scales, which are sometimes inaccessible in many of the developing countries such as Egypt. Machine learning models are widely used to represent complicated phenomena because of their high performance in the non-linear relations of inputs and outputs. Therefore, the objectives of this research were to (1) develop and compare four machine learning models: support vector regression (SVR), random forest (RF), extreme gradient boost (XGB), and artificial neural network (ANN) over three potato governorates (Al-Gharbia, Al-Dakahlia, and Al-Beheira) in the Nile Delta of Egypt and (2) select the best model in the best combination of climate input variables. The available variables used for this study were maximum temperature (Tmax), minimum temperature (Tmin), average temperature (Tave), wind speed (WS), relative humidity (RH), precipitation (P), vapor pressure deficit (VPD), solar radiation (SR), sown area (SA), and crop coefficient (Kc) to predict the potato blue water footprint (BWF) during 1990–2016. Six scenarios (Sc1–Sc6) of input variables were used to test the weight of each variable in four applied models. The results demonstrated that Sc5 with the XGB and ANN model gave the most promising results to predict BWF in this arid region based on vapor pressure deficit, precipitation, solar radiation, crop coefficient data, followed by Sc1. The created models produced comparatively superior outcomes and can contribute to the decision-making process for water management and development planners.
From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies
Authors: Mukherjee Arnab , Abraham Suzanna , Singh Akshita , Balaji S. , Mukunthan K. S. 
Published: April 2024

AbstractIn the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.
The predictive value of NLR, PLR and MLR in the differential diagnosis of benign uterine diseases and endometrial malignant tumors
Authors: Qin Lin 
Published: March 2024

Abstract
Objective
To explore the application of neutrophils to lymphocytes ratio (NLR), platelet to lymphocyte ratio (PLR) and monocyte to lymphocyte ratio (MLR) in the differential diagnosis of benign uterine diseases and endometrial malignant tumors.

Methods
80 patients with endometrial malignant tumor diagnosed in our hospital from January 2019 to December 2022 were selected as the study group, and 74 patients with benign uterine diseases confirmed by pathology in our hospital during the same period were randomly selected as the control group. The differences of NLR, PLR and MLR in the peripheral blood of patients in each group were compared, and the value of individual indicators in the diagnosis of endometrial malignant tumor was evaluated using the Receiver Operating Characteristic (ROC) curve.

Results
In peripheral blood, the NLR, PLR and MLR value in patients with endometrial cancer (EC) were significantly higher than those in patients with benign uterine diseases (P < 0.05). The area under the curve (AUC) of NLR, PLR, MLR in peripheral blood were 0.777, 0. 675 and 0.698. The best cutoff values were 2.02, 132.06 and 0.166. The sensitivity and specificity were 62.5% and 79.7%, 62.5% and 63.5%, 81 3% and 47.3%. The combination of these three indicators can significantly improved the diagnostic efficiency in endometrial cancer (AUC = 0.780), and the sensitivity and specificity were 60% and 83.8%.

Conclusions
In peripheral blood, NLR, PLR and MLR have certain diagnostic value in the differential diagnosis of endometrial cancer. When NLR, PLR and MLR are elevated, we should be alert to the occurrence of endometrial malignant tumors, and the combined diagnostic efficiency is high.
Center-injected Polishing for Efficient Slurry Utilization
Authors: Kim Dong-Ryul , Yoon Hae-Sung , Kim Ji-Hwan , Ahn Semin , Kim Jisoo , Ahn Sung-Hoon 
Published: April 2024

AbstractPolishing is one of the most crucial finishing processes and usually consumes a sufficient slurry to achieve an ultra-fine surface. However, excess slurry consumption is environmentally costly, as it generates a large amount of wastewater. Given the growing environmental concerns, it is essential to improve the process efficiency and minimize the environmental burdens. Considering this, a novel polishing system, herein referred to as center-injected polishing, is proposed by injecting slurry into the center of the polishing pad. Here, it is aimed to utilize the centrifugal force of the rotating pad, with the aim of efficient slurry utilization. The slurry is directly introduced between the pad and the workpiece, then dispersed across the pad by centrifugal force. A simple experiment was conducted with computational analysis using the specially designed polishing tool to prove the concept; slurry was distributed more uniformly in center-injected polishing when compared to the conventional process. The polishing system was then constructed to evaluate polishing performances. Based on sets of experiments in the polishing of silicon carbide (SiC), slurry efficiencies and productivity were analyzed with respect to different rotational speeds and slurry supply rates. The material removal rate (MRR) was more than twice the rate achieved by conventional polishing at the same processing conditions; whereas the slurry consumption was approximately 60% less at the same MRR. The extended Preston equation was used to predict the MRR of the new process. It is expected that efficient slurry utilization will reduce the environmental footprint of abrasive processes.
Predicting popularity trend in social media networks with multi-layer temporal graph neural networks
Authors: Jin Ruidong , Liu Xin , Murata Tsuyoshi 
Published: April 2024

AbstractPredicting what becomes popular on social media is crucial because it helps us understand future topics and public interests based on massive social data. Previous studies mainly focused on picking specific features and checking past statistic numbers, ignoring the hidden impact of messages passing along the complex relationships among different entities. People talk and connect with others on social media; thus, it is essential to consider how information spreads when studying social media networks. This work proposes a multi-layer temporal graph neural network (GNN) framework for predicting what will be popular on social media networks. This framework takes into account the way information spreads among different entities. The proposed method involves multi-layer relations and temporal information within a sequence of social media network snapshots. It learns the temporal representations of target entities in each snapshot and predicts how the popularity of a particular entity will change in future snapshots. The proposed method is evaluated with real-world data across four popularity trend prediction tasks. The experimental results prove that the proposed method performs better than various baselines, including traditional machine learning regression approaches, prior methods for popularity trend prediction, and other GNN models.