bluuebunny/crossref_metadata_2025_split huggingface dataset:
WITHDRAWN: Clinical Characteristics of Diabetic Patients with COVID-19 During the Full Liberalization of the Pandemic
Authors: Zhuang Zejian , Li Jingyuan , Sun Lingfeng , Tang Haocheng , He Honghui , Wang Fang 
Published: May 2024

Abstract

Diabetes is an important comorbidity of COVID-19. This study aimed to compare the clinical course and outcomes of COVID-19 patients with and without diabetes. We enrolled 321 hospitalized patients with COVID-19 from the Third Xiangya Hospital (Hunan, China) between December 20, 2021, and March 1, 2021, including 158 patients with diabetes and 163 patients without diabetes. Data from medical records, including clinical characteristics, coexisting conditions, complications, laboratory tests, radiographic studies, treatments and outcomes, were extracted and analyzed. Among COVID-19 patients with type 2 diabetes, several biochemical parameters, such as systolic blood pressure [137.00 (IQR: 122.00-148.50) vs. 126.00 (IQR: 116.00-137.00), P<0.001], CRP [64.25 (IQR: 28.16-129.83) vs. 44.13 (IQR: 17.56-87.26), P = 0.005], PCT [0.11 (IQR: 0.06-0.44) vs. 0.08 (IQR : 0.05-0.14), P = 0.002], urea nitrogen [15.40 (IQR 10.62-24.44) vs. 10.34 (IQR:7.56-15.14), P < 0.001), In terms of clinical symptoms, patients with diabetes had pleural effusion [17.3% vs. 7.5%, P=0.010], fever [39.0% vs. 60.7%, P<0.001], and nausea and vomiting [10.4% vs. 1.2%, P<0.001], and fatigue symptoms [22.7% vs 12.9%, P=0.022]. There were significant differences in comorbidities, including hypertension (71.2% vs. 37.0%, P<0.001) and chronic kidney disease (6.4% vs. 18.5%, P=0.001). The statistically significant difference in treatment between COVID-19 patients with diabetes and those without diabetes was mainly reflected in the use of azvudine (15.9% vs. 40.7%, P<0.001). Regression analysis revealed that the lymphocyte ratio (0R=0.925, 95% CI 0.885-0.966, P<0.001) and age (0R=1.025, 95% CI 1.003-1.048, P=0.027) had a statistically significant effect on the severity of respiratory failure. Some clinical symptoms and medication differences had statistically significant effects on survival time, including calcification (HR=0.410, 95% CI 0.193-0.872, P=0.021), pleural effusion (HR=1.935, 95% CI 1.259-2.975, P=0.003), diabetes (HR=1.416, 95% CI 1.042-1.926, P=0.026), chronic kidney disease (HR=1.557, 95% CI 1.032-2.349, P=0.035), use of antibiotics (HR=1.623, 95% CI 1.139-2.315, P=0.007) and glucocorticoids (HR=1.623, 95% CI 1.139-2.315, P=0.005), and use of mechanical ventilation (HR=4.201, 95% CI 1.883-9.377, P<0.001). Our study suggested that COVID-19 patients with diabetes tend to have more severe disease and poorer clinical outcomes, and this relationship requires further exploration.Retrospectively registered.
Toxicity of botanical insecticides on the stingless bee jataí (Tetragonisca angustula)
Authors: TOMAZ ADRIANO , Silva Francisco , Takeuchi Katiuchia , Campos Mateus , Silva Geraldo , Santos Joice 
Published: May 2024

Abstract

This study aimed to investigate the toxicity of botanical insecticides on the essential pollinator stingless bee Jataí (Tetragonisca angustula). At first, two bioassays were carried out to assess the effect of the botanical insecticides: garlic, marigold, castor bean, Mexican tea, chili pepper, black pepper, clove, eucalyptus, and neem on bees' survival after exposure by ingestion or contact. Then, additional bioassays were carried out to investigate the effects of extraction solvent, concentrations, and use of adjuvants on the toxicity of botanical insecticides on Jataís bees. Results revealed toxicity of Eucalyptus oil to Jataí bees through contact and ingestion exposure even in low concentrations (0.1% v/v). The Neem oil in concentrations above 2.5% v/v and alcoholic extracts of cloves and black pepper in concentrations above 5% v/v are toxic to Jataí. Addition of detergent increases the toxicity of oil insecticides. Garlic extract prepared under the highest concentration of mineral oil and longest exposure time is toxic to jataí bees. Therefore, we can conclude that the use of botanical insecticides should be performed carefully to preserve this vital insect.
Engrailed-1 (EN-1) inactivation leads to scarless skin wound healing through extracellular matrix (ECM) remodeling
Authors: Hao Ailing , Dong Xiangyu , Gou Yannian , Li Aohua , Li Jiajia , Xiang Han , Rahaman Saidur , Zhu Yi , Zhang Hui , You Wulin , Shen Guowei , Luo Changqi , Mei Ou , Wu Xingye , Shi Lewis L. , Reid Russell R. , He Tong-Chuan , Fan Jiaming 
Published: May 2024

Abstract

Hypertrophic scarring (HS) is a major source of morbidity after extensive cutaneous injury, and yet underlying mechanisms of hypertrophic scars and keloid lesions are not well understood. It has been shown that prevention of Engrailed-1 (EN-1) activation in fibroblasts was beneficial for wound healing without scarring. However, the role of EN-1 activation in wound healing and scarring needs to be further evaluated. In this study, we found EN-1 was upregulated in mouse wound dermis and in transforming growth factor-β1 (TGF-β1)-stimulated mouse dermal fibroblasts (mDFs). When mDFs were treated with Verteporfin or transduced with adenoviral vector expressing three siRNAs (Ad-simEn1), the expression of scar formation and hyaluronic acid (HA) catabolic related genes was down-regulated, the F-actin of cytoskeleton also decreased, the ultrastructure of the extracellular matrix (ECM) revealed a remarkable shift from dense to sparse with a marked reduction of component proteins fibronectin (FN-1) and fibrinogen (FIB), and a significant increase in HA content in ECM. Furthermore, we demonstrated that silencing EN-1 expression effectively suppressed keloid formation and promoted scarless healing in a rabbit model of ear skin injury, as further evidenced by the reduced ECM production and decreased expression of scar formation related gene products. These results demonstrated that inhibition of EN-1 function in dermal fibroblasts can effectively promote scarless healing of skin wounds through ECM remodeling.
Effects of Ca2+ and Mg2+ in the water column on the growth and moult of Macrobrachium rosenbergii
Authors: Li Xuenan , Lan Hairong , Dai Xilin 
Published: May 2024

Abstract

In order to investigate the effect of different Ca2+ and Mg2+ concentrations in the water column on the growth and moulting of Macrobrachium rosenbergii. In this study, three concentrations of Ca2+ and three concentrations of Mg2+ were designed to conduct a culture experiment for a period of four complete moult cycles. The results showed that with increasing Ca2+ concentration in water, the expression level of MIH showed a decreasing trend, and the expression level of EcR and chitinase showed a gradual increasing trend, which accelerated the moult frequency and shortened the moult cycle. However, high Ca2+ concentration significantly reduced the survival rate of M. rosenbergii. With increasing Mg2+ concentration in the water column, the heartbeat count increased significantly, the MDH activity during postmolt A and intermolt C tended to increase, and the energy metabolism was accelerated. An increase in Ca2+ concentration in water promotes an increase in Ca2+ content in muscle and shell. However, as the Mg2+ concentration in water increased, the Mg2+ content in muscle and shell showed different patterns of change. Too low or too high Ca2+ and Mg2+ in the external environment will have different degrees of negative effects on the gill tissue, especially the damage of Mg2+ to the gill tissue is more serious. In summary, low levels of Ca2+ and Mg2+ in the external environment reduce ion levels in the muscle and shell, growth, moult and survival are inhibited, while having a stressful effect on gill tissue. High concentrations of Ca2+ and Mg2+ may accelerate moulting rate and energy metabolism, but will cause more serious damage to gill tissue and reduce the survival rate.
Evaluation of Bayesian Linear Regression Derived Gene Set Test Methods
Authors: Bai Zhonghao , Gholipourshahraki Tahereh , Shrestha Merina , Hjelholt Astrid , Hu Sile , Kjølby Mads , Rohde Palle Duun , Sørensen Peter 
Published: May 2024

Abstract

Background
Gene set tests can pinpoint genes and biological pathways that exert small to moderate effects on complex diseases like Type 2 Diabetes (T2D). By aggregating genetic markers based on biological information, these tests can enhance the statistical power needed to detect genetic associations.
Results
Our goal was to develop a gene set test utilizing Bayesian Linear Regression (BLR) models, which account for both linkage disequilibrium (LD) and the complex genetic architectures intrinsic to diseases, thereby increasing the detection power of genetic associations. Through a series of simulation studies, we demonstrated how the efficacy of BLR derived gene set tests is influenced by several factors, including the proportion of causal markers, the size of gene sets, the percentage of genetic variance explained by the gene set, and the genetic architecture of the traits. By using KEGG pathways, eQTLs, and regulatory elements as different kinds of gene sets with T2D results, we also assessed the performance of gene set tests in explaining more about real phenotypes.
Conclusions
Comparing our method with other approaches, such as the gold standard MAGMA (Multi-marker Analysis of Genomic Annotation) approach, our BLR gene set test showed superior performance. Combining performance of our method in simulated and real phenotypes, this suggests that our BLR-based approach could more accurately identify genes and biological pathways underlying complex diseases.
Rapid Online Detection of Dissolved Acetylene in Transformer Oil by Photoacoustic Spectroscopy and Membrane Degassing
Authors: Shi Yunxing , Gu Pengcheng , Zhao Meixuan , Han Yuwang 
Published: May 2024

Abstract

Acetylene is one of the main fault gases for oil transformers. The rapid and highly sensitive detection of dissolved acetylene in the insulating oil plays an extremely important role in the diagnosis of transformer faults, as it is produced by the decomposition of hydrocarbons due to discharge and overheating. This work describes a rapid real-time online monitoring system for dissolved acetylene in oil, which integrates a highly efficient Teflon-AF2400/ceramic composite degassing module and a high-sensitive laser photoacoustic detector. Real-time online monitoring is feasible as the detection period of the device is as short as 1.5 min, and the equilibrium concentration of acetylene in the oil can be accurately determined from test data at a degassing time of 15 min. When the concentration of acetylene in the oil changes suddenly, the device can report more than 90% of the change within 30 min. The detection accuracy is improved from 0.9 µL·L− 1 to 0.3 µL·L− 1 after corrections are made to account for the influence of temperature on the oil-gas separation membrane.
Quantifying Customer Sentiments: A Machine Learning Approach to Analyzing Automobile Brand Perception on Twitter
Authors: Mathew Sujith Samuel , Hayawi Kadhim , Venugopal Neethu , Barachi May El 
Published: May 2024

Abstract

Social networking sites provide a platform for individuals to express their opinions publicly. Brand managers actively use these platforms to gain insights into brand perceptions, as users often share their views on products and services. In this study, we use sentiment analysis to assess customer sentiment towards five leading automobile brands, analyzing text content shared on Twitter. The research models the 'Brand Polarity Score', which indicates whether customers perceive the brand positively or negatively. This score is further weighted based on the tweet's influence, characterized by the engagement metrics of the tweet and the author's follower count. We also demonstrate how this brand polarity score can effectively communicate near real-time brand positioning, providing a valuable tool for monitoring brand sentiment over time. The proposed Brand Polarity Score (BPS) not only gauges brand perception but also serves as a reliable tool for progressive and competitive analyses, contributing to a comprehensive understanding of brand dynamics. Qualitative and quantitative analyses are performed to validate the robustness of the proposed BPS system.
Frog-Snake prey-predation Relationship Optimization (FSRO) : A novel nature-inspired metaheuristic algorithm for feature selection
Authors: SAITOU Hayata , HARAGUCHI Harumi , KIUCHI Yuuto 
Published: May 2024

Abstract

Swarm intelligence algorithms have traditionally been designed for continuous optimization problems, and these algorithms have been modified and extended for application to discrete optimization problems. Notably, their application in feature selection for machine learning has demonstrated improvements in model accuracy, reduction of unnecessary data, and decreased computational time. This study proposes the Frog-Snake prey-predation Relationship Optimization (FSRO) algorithm, inspired by the prey-predation relationship between frogs and snakes for application to discrete optimization problems. The algorithm models three stages of a snake's foraging behavior “search”, “approach”, and “capture” as well as the frog's characteristic behavior of staying still to attract and then escaping. Furthermore, the introduction of the concept of evolutionary game theory enables dynamic control of the search process. The proposed algorithm conducts computational experiments on feature selection using 26 types of machine learning datasets to analyze its performance and identify improvements. In computer experiments, the proposed algorithm showed better performance than the comparison algorithms in terms of the best and standard deviation of fitness value and Accuracy. It was also proved that dynamic search control by evolutionary game theory is an effective method, and the proposed algorithm has the ability of a well-balanced search, achieving the two objectives of improving accuracy and reducing data.
Integrated Framework for Real-time Sea State Estimation of Stationary Marine Units Using Wave Buoy Analogy
Authors: Majidiyan Hamed , Enshaei Hossein , Howe Damon , Wang Yiting 
Published: May 2024

Abstract

Understanding the impact of environmental factors, particularly seaway, on marine units is critical for developing efficient control and decision support systems. Despite extensive research on the wave buoy analogy (WBA), real-time sea state estimation (SSE) has remained challenging due to the need for adequate data to construct a consistent probability density function of waves. The current study builds on previous work, aiming to propose an AI framework to reduce the estimation time lag between exciting waves and respective estimation by transforming temporal/spectral features into a manipulated scalogram form for subsequent data analysis. To achieve this, an adaptive ship response predictor and deep learning approach have been incorporated to classify seaway data while minimizing network complexity through feature engineering. Specifically, a Convolution Neural Network (CNN) and deep transfer learning have been employed for training purposes. The system's performance was evaluated using data obtained from an experimental test on a semi-submersible platform, and the results demonstrate the promising functionality of the proposed approach for a fully automated system. Furthermore, the deficits with the feature transformation of the existing data-driven SSE models have been discussed. This study provides a foundation for improving online sea state estimation and promoting the seaway acquisition for stationary marine units.
Stochastic Optimization of Surface Roughness Using Monte Carlo Algorithms
Authors: Čajić Elvir 
Published: May 2024

Abstract

In this paper, we investigate the application of Monte Carlo algorithms for the optimization of surface roughness in production processes. Using stochastic methods, a mathematical model was developed that accurately predicts surface roughness based on key processing parameters. Simulations were performed on samples of different materials, where the effects of changes in input parameters on the final roughness were analyzed. The results show that Monte Carlo algorithms can significantly improve the accuracy of process prediction and optimization, enabling a better control of the quality of the final processing. Algorithms are implemented using MATLAB and Python, which enables flexibility and efficiency in data analysis. The results show that Monte Carlo algorithms can significantly improve the accuracy of process prediction and optimization, enabling better control of the quality of finishing. In addition, this approach reduces the need for an experimental approach, resulting in reduced costs and processing time.
Radiolytic degradation of 4-hydroxybenzoate in aerated and deoxygenated aqueous solutions
Authors: Albarrán Guadalupe , Mendoza Edith 
Published: May 2024

ABSTRACT

The radiolytic degradation of 4-hydroxybenzoate (4-HBA–) in aerated, oxygen-free and N2O-saturated aqueous solutions at concentrations of 0.10 and 0.25 mmol/dm3 were gamma irradiated at different doses in a source of Co-60. The results show that ·OH adds predominantly to the 3 position of the aromatic ring, and elimination of the acid group leads to the degradation of 4-HBA–. With an N2O-saturated 0.10 mmol/dm3 4-HBA– solution, total degradation occurred at 1.6 kGy, and with a 0.25 mmol/dm3 solution, total degradation occurred at 3.5 kGy. In the aerated and oxygen-free 0.25 mmol/dm3 4-HBA– solutions, the behavior was similar, degradation occurring at a dose of 13.1 kGy. At the concentration of 0.10 mmol/dm3, total degradation occurred at 7.0 kGy, with small amounts of radiolytic products and byproducts. We propose a mechanism for the degradation of 4-HBA– caused by water radicals produced in the three environments, leading to formation of the identified stable products. Oxidation was followed by chemical oxygen demand (COD), which decreased as the 4-HBA− concentration increased. The kinetics showed a pseudo-first-order behavior. The rate constant of degradation was similar for the solutions with and without oxygen.
Current research hotspots and frontier trends on carbon budget of coastal wetlands: A bibliometric analysis
Authors: Zhang Xinyi , Liu Wenhao , Lu Jiaxing , Tanveer Muhammad , Qi Zhen , Fu Chengkai , Xie Huijun , Zhuang Linlan , Hu Zhen 
Published: May 2024

ABSTRACT

Coastal wetlands are the main distribution of blue carbon in coastal zones and well known for their high carbon sequestration capacity. Investigating the variation of carbon budget is crucial for understanding the functionality of coastal wetlands and effectively addressing climate change. In this study, a bibliometric analysis of 4,509 articles was conducted to reveal research progress, hot issues, and emerging trends in the coastal wetland carbon budget field. The number of publications and citations in this field increased exponentially from 1991 to 2022. The leading subject category was Environmental Sciences with 1,844 articles (40.9%). At present, studies have been focused on blue carbon, the effects of climate change and man-made disturbances on carbon cycle, and the restoration of coastal wetlands. Based on the hotspots and trends in this field, the future researches should include (1) exploring the functional mechanisms of various factors affecting carbon cycle and establishing a methodological system for the estimation of blue carbon in coastal wetlands; (2) researching restoration techniques of coastal wetland and constructing wetland restoration evaluation index system; and (3) formulating enforceable carbon trading policy and strengthening international cooperation.
Research on the Impact of Smart City Construction on the Development of Green Economy
Authors: Wu Hongshuang 
Published: May 2024

The development of green economy is one of the core essences to realize Chinese path to modernization. Based on China’s provincial panel data from 2011-2021, this paper uses the entropy weight method to measure the smart city construction index, which includes three dimensions: digitalization, intelligence and informatization; Secondly, a benchmark regression model was used to explore the effect of smart city construction on the development of green economy. Furthermore, a mediation effect model and a threshold regression model were constructed to explore the indirect and nonlinear effects of smart city construction on the development of green economy under the constraint variables of energy utilization efficiency and manufacturing intelligence. Finally, research has shown that the construction of smart cities has shown a positive and direct role in the development of green economy, with a gradient decreasing effect of “western>central>eastern”; The construction of smart cities can indirectly enhance the development of green economy in the region by improving energy utilization efficiency; Based on the characteristics of regional heterogeneity, the construction of smart cities has different threshold effects on the development of regional green economy. When the level of manufacturing intelligence is lower than the threshold value, the construction of smart cities can have a more favorable and positive impact. During the strategic intersection period between smart city construction and the “dual carbon” goal, this article provides scientific basis and theoretical reference for achieving Chinese style green modernization.
Spectrum of Chronic Gastritis Based on Morphological Examination of Gastric Biopsies
Authors: Tertychnyy А. S. , Lapina Т. L. , Nagornaya D. Р. , Pachuashvili N. V. , Kiryukhin А. Р. , Fedorenko А. А. , Pavlov Р. V. , Protsenko D. D. , Ivashkin V. Т. 
Published: May 2024

Aim: in the retrospective study to assess the frequency of various gastritis variants based on the results of morphological examination of gastric biopsies over a 5-year observation period.Methods. The study included 3162 individuals who underwent esophagogastroduodenoscopy with biopsy collection between 2017 and 2022. Pathological examination of biopsies was conducted using the updated Sydney system, determining the histological variant and, when possible, the etiology of gastritis. In some cases, chronic gastritis was assessed using the OLGA/OLGIM system.Results. The most frequently diagnosed type was active H. pylori-associated gastritis (36.7 %), followed by chronic atrophic gastritis with intestinal metaplasia in cases where H. pylori infection was not detected (28.4 %), including immunohistochemical examination, classified as gastritis after successful H. pylori eradication. Atrophic forms of chronic gastritis constituted 34.8 %. Minimal or mild changes close to normal histological characteristics were оbserved in 19.2 % of patients. Reactive gastropathy was present in 7.6 % of cases. Autoimmune gastritis ranked fourth in frequency, with a relatively high percentage of observations (8.6 %).Conclusion. According to the analysis of gastric biopsies in the Russian population, a high frequency of H. pylori-associated gastritis was observed, and autoimmune gastritis is not uncommon. The high prevalence of atrophic gastritis emphasizes the importance of dynamic patient monitoring within cancer prevention programs.