bluuebunny/crossref_metadata_2025_split huggingface dataset:
Malaria prevalence and associated risk factors among febrile patients in selected healthcare facilities of Jawi District, northwest Ethiopia
Authors: Dagnew Assaye , Yeshanew Solomon , Nibret Endalkachew , Atenafu Getnet , Adugna Fasil , Assefa Melese , Habitamu Asmamaw 
Published: June 2024

Abstract

Background Despite ongoing control and elimination efforts, malaria continues to impose a substantial burden on healthcare systems and communities in Ethiopia. Understanding the epidemiology of malaria and identifying associated risk factors are essential for guiding targeted interventions and improving health outcomes. This study thus aimed to assess malaria prevalence and associated risk factors among febrile patients in selected healthcare facilities of Jawi District, northwest Ethiopia.Methods A health facility-based cross-sectional study was conducted from April to November 2023 in Jawi District, northwest Ethiopia. The study involved 420 participants who visited government-owned health facilities seeking treatment for febrile illnesses. Socio-demographic data and malaria-related factors were collected using structured questionnaires. Additionally, blood samples were obtained from study participants and examined for malaria parasites using microscopy. Multivariate logistic regression was employed to identify the major explanatory risk factors associated with malaria prevalence.Results The study revealed a malaria prevalence rate of 41.2% (173/420), and Plasmodium falciparum being the predominant species (64.8%) detected. Socio-demographic factors such as lack of formal education (AOR = 2.222; 95% CI: 0.354–13.924, p = 0.041) and proximity of residence to mosquito breeding sites (AOR = 222.484; 95% CI: 5.085–973.440, p = 0.005) appeared as significant risk factors for malaria. Furthermore, awareness gaps regarding malaria transmission in the community (AOR = 1.744; 95% CI: 0.232–3.848, p = 0.039) were found to influence malaria vulnerability. Housing characteristics and inconsistent utilization of preventive measures were also identified as contributors to malaria prevalence among study participants.Conclusion The findings of the present study revealed that the rate of malaria transmission was high. The malaria transmission had a multifactorial nature including socio-demographic, environmental and behavioral factors. Integrated control strategies that address socio-demographic, environmental, and behavioral determinants are crucial for mitigating the burden of the disease. Strengthening education and awareness programs, improving housing conditions, and ensuring sustainable access to malaria diagnosis, treatment, and preventive services are essential components of effective malaria control in the study area.
Motivation and SRL in Home-Based Online Learning for University Students: A Second-Order Mediation Model
Authors: Xue Xiaoyang , Wang Xinyun , Li Dantong , Du Ke , Li Jianwei , Shi Gaojun , Lou Ligao 
Published: June 2024

Abstract

Large-scale online learning for university students is often plagued by challenges such as lack of learning motivation and autonomy. The achievement motivation theory emphasizes the individual’s motivation to pursue success and avoid failure. Based on the achievement motivation theory, this study aims to extend the research on understanding the relationship between learning motivation and self-regulated learning (SRL), including goal-setting, strategy use, self-regulation, and self-evaluation, by identifying possible mediators in this relationship. To achieve this, we survey 559 participants who are compelled to switch to home-based online learning (HBOL). Using structural equation modeling partial least squares (SEM-PLS) and Shapley value, we examine the effects of learning motivation, learning intention, and self-efficacy on SRL. Our findings reveal that learning motivation has both a direct and significant impact on SRL, as well as an indirect influence via self-efficacy and learning intention. These results underscore the importance of confidence and intention in successful online SRL for university students, and provide a foundation for further research in this area.
Stability Indicating and Green Solvent-assisted Chromatographic Analysis of an Antiviral Drug
Authors: Archana K. , Sumithra M. 
Published: June 2024

Abstract

This study presents a green chromatographic method for estimating the Ganciclovir, demonstrating validation parameters in alignment with ICH guidelines and green chemistry principles. The green analytical technique utilized the ethanol and acidic water at pH 3.0 as solvents, with an optimized mobile phase ratio of (80:20) and a flow rate of 1.0ml/min with a zorbax eclipse plus C18 (4.6 x 150mm, 5mm). Ethanol is listed as a green solvent in U.S Tri EPA with (CAS 64-17-5), as well as economical with manageable waste compared to traditional solvents. Green Evaluation tools such as AGREE, GAPI, and COMPLEX GAPI were employed to assess the greenness scores of the method. The AGREE tool provided an evaluation of the greenness of the method while the GAPI and COMPLEX GAPI offered details about the assessment of the method’s adherence to the green chemistry principles. These tools were developed in adherence to 12 green analytical chemistry principles that provide a holistic approach toward qualitative and quantitative evaluations. The importance of greenness in analytical methods lies in reducing environmental impact, improving analyst safety, lowering cost and waste, and fostering environmental responsibility.
The linearity range was 10-50μg/ml with a regression coefficient of less than or equal to 0.999 indicating a strong linearship. RSD values are consistently below 2% and recovery rates for ganciclovir ranged from 98% to 102% within acceptable ranges. Under forced degradation conditions the desired amount of degradation was exhibited 8.02% degradation in acid, 9.25% in alkaline hydrolysis, 4.02% degradation in peroxide, 5.42 % in thermal, and 6.38 % in photolytic studies which are within limits as per 5-20% as per ICH Q1B guidelines. Incorporating ICHQ1(R2) the method validation parameters are within the range and thoroughly addressed.
Risk of Drug Overdose Death among Residents of Urban Census Tracts with Low-Level Opportunity: How Granular Geographical Analyses Uncovers Socioenvironmental Correlates of Drug-related Mortality in Cuyahoga County, Ohio
Authors: McMaster Ryan , Masarweh-Zawahri Luma , Flynn Karen Coen , Deo Vaishali , Flannery Daniel 
Published: June 2024

Abstract

Geostatistical data aggregated at state, county, municipality, or ZIP code levels often are utilized for assessing drug overdose epidemic impact and planning resource distribution. However, data aggregated at these levels may obscure critical disparity among populations experiencing high rates of drug-related mortality (DRM), especially in densely populated urban areas. This study centered on Cuyahoga County (Cleveland), Ohio, which ranks as the 15th deadliest county in the United States for drug-related mortality. It builds on recent studies that adopted a finer geographical lens by examining DRM rates at the census tract level. Our investigation used Cuyahoga County census tracts with high and low DRM rates and compared them with Cuyahoga County census tracts with high and low levels of opportunity as developed by a statewide opportunity index available to the public. Analyzing DRM data from 2014 to 2022, we found that the odds of DRM in low-opportunity areas were quadruple that in high-opportunity zones. Our findings highlight the critical need for more granular geographic analysis in urban areas, where heterogenous socioeconomic and environmental conditions appear to correlate with significant heterogeneity in the ways in which residents experience the overdose epidemic. By focusing on smaller areas, this approach provides a clearer understanding of the DRM landscape that could facilitate the prioritization of targeted public health responses and interventions for communities most at risk.
Plyometric training increases thickness and volume of knee articular cartilage in mice
Authors: Chiaberge Marco , Thottappillil Neelima , Liphardt Anna-Maria , Furlanetto Anderson , Odell Dylan , Wang Christine , Hope Stephen , Smee Stephen , Rehfus Joseph , Niehoff Anja , Shelhamer Mark , Norman Colin , Philippon Marc J. , Huard Johnny , James Aaron W. , Fan Chen-Ming 
Published: June 2024

Abstract

Degeneration and thinning of articular cartilage lead to osteoarthritis and may result from reduced joint loading during e.g. bed rest or as a result of microgravity during space flight. Anabolic physical exercises for cartilage are not well studied to date. We built an experimental apparatus for plyometric training with mice to test potential benefits of jumping for articular cartilage. The exercise group (JUMP) performed jump training for 9 weeks and was compared with sedentary mice (control, CON) and hindlimb-suspended (HLS) mice (to simulate reduced loading) for the same duration. Knee cartilage was assessed via 3-dimensional reconstruction of micro-CT scans and histology. We observed significant thinning and volume reduction of articular cartilage at the medial tibial-femoral point of contact in the HLS group. Clustering of chondrocytes was present in HLS. By contrast, the JUMP group showed both cartilage thickening and volume increase. We observed a similar trend on trabecular bone thickness and volume. Our results show that plyometric training can stimulate cartilage thickness and volume in mice. This suggests further investigation of this mode of exercise as a countermeasure to prevent cartilage atrophy in disuse scenarios such as long duration spaceflight, and for patients at risk of developing osteoarthritis.
First case of Klebsiella pneumoniae invasion syndrome caused by trauma
Authors: Chen Hong-qiao , Wei Wu-xiao , Mo Zhen-hua , Meng Lian 
Published: June 2024

Abstract

Background
Infection is a common and serious medical problem, especially for patients with chronic diseases since it can lead to serious complications. Infection is a common and serious medical problem, especially for patients with chronic diseases since it can lead to serious complications. Klebsiella pneumoniae (Kp) is a common bacterial pathogen of hospital and community infections. In addition to causing liver abscess, it can also lead to lung abscess, endophthalmitis, meningitis and other extrahepatic infections through blood transmission, resulting in Klebsiella pneumoniae invasion syndrome (KPIS). As a rare and severe infectious disease, KPIS is characterized by aggressive infection of multiple organs, whose mortality is high. According to the literature, the Kp strains associated with invasive infection of multiple human organs are defined as the serotype hypervirulent Klebsiella pneumoniae (hvKp). In recent years, the morbidity of KPIS has gradually increased, especially in the Asia-Pacific region, with the detection rate of hvKp in China being 5.2%, which is among the top 3 in the world.
Case presentation
To our knowledge, this is the first case of KPIS caused by trauma. A 72-years-old male patient living in Liuzhou, Guangxi Zhuang Autonomous Region, China was admitted to the intensive care unit (ICU) with "right frontal pain caused by a fall for 12 days, and dyspnea and abnormal mental behavior for 11 hours". After his condition stabilized, he was transferred to the department of neurology for further treatment. After a thorough examination by a multidisciplinary team (MDT), the patient was finally diagnosed as trauma-induced KPIS with accompanying liver abscess, bacteremia and metastatic infections (lung abscess, left endophthalmitis, suppurative meningitis). We adopted treatments including a long course of meropenem, cefoperazone sulbactam sodium combined with antibiotic therapy, supportive nursing and rehabilitation, achieving excellent efficacy.
Conclusion
Although infection from injury is a common condition for the general public, especially those with diabetes, it is still necessary to increase awareness of managing and preventing complications from infection. We hope to supplement the existing literature on this topic, strive to increase clinicians' understanding of KPIS through relevant literature review, and organize MDT consultation as soon as possible, thereby enabling early diagnosis and treatment to improve the clinical outcome of patients.
Deep Learning-derived Optimal Aviation Strategies to Control Pandemics
Authors: Dogra Prashant , Rizvi Syed , Awasthi Akash , Peláez Maria J. , Wang Zhihui , Cristini Vittorio , Nguyen Hien Van 
Published: June 2024

Abstract

The COVID-19 pandemic affected countries across the globe, demanding drastic public health policies to mitigate the spread of infection, which led to economic crises as a collateral damage. In this work, we investigate the impact of human mobility, described via international commercial flights, on COVID-19 infection dynamics on a global scale. We developed a Graph Neural Network (GNN)-based framework called Dynamic Weighted GraphSAGE (DWSAGE), which operates over spatiotemporal graphs and is well-suited for dynamically changing flight information updated daily. This architecture is designed to be structurally sensitive, capable of learning the relationships between edge features and node features. To gain insights into the influence of air traffic on infection spread, we conducted local sensitivity analysis on our model through perturbation experiments. Our analyses identified Western Europe, the Middle East, and North America as leading regions in fueling the pandemic due to the high volume of air traffic originating or transiting through these areas. We used these observations to propose air traffic reduction strategies that can significantly impact controlling the pandemic with minimal disruption to human mobility. Our work provides a robust deep learning-based tool to study global pandemics and is of key relevance to policymakers for making informed decisions regarding air traffic restrictions during future outbreaks.
WITHDRAWN: The effects of community built environment and physical activity of varying intensity and purpose on depressive trajectories among middle-aged and elderly: a national longitudinal study based on latent growth curve modeling
Authors: Zhang Kaili , Huang Bowen , Divigalpitiya Prasanna 
Published: June 2024

Abstract

Background
The effects of physical activity (PA) with different purposes and intensities on depression are inconclusive, while it is critical to incorporate the contextual community built environment (CBE) into longitudinal reviews of the effects of PA on depression.
Methods
This longitudinal study included 5993 middle-aged and elderly people from the China Health and Retirement Longitudinal Study, a CBE evaluation indicator system was developed based on the community questionnaire to measure physical built environment attributes for each community. Using latent growth curve modeling to evaluate 3 waves of cohort data (2015–2020) to explore the differential effects of PA with different purposes and intensities on depression and the impact of CBE among them.
Results
The level of low-intensity PA for exercise and entertainment purposes showed a negative predictive effect on the initial level of depression among middle-aged and elderly people, while the level of moderate-to-vigorous-intensity PA for work purposes showed the opposite effect. In terms of depression trajectories, the initial level of PA for these two different purposes also has opposite effects on the initial level of depression. Among all CBE variables, only the infrastructure condition can significantly and negatively predict the initial level of depression (β=-0.11, p < 0.01). The infrastructure condition (β = 0.12, p < 0.001), diversity of elderly care facilities (β = 0.06, p < 0.05), and accessibility of public transport (β = 0.06, p < 0.01) can significantly and positively predict the initial level of low-intensity PA for exercise and entertainment purposes. The density of public facilities (β = 0.10, p < 0.05) significantly and positively predicts the rising changes in low-intensity PA for exercise and entertainment purposes.
Conclusions
The study identified the effects of different types of CBE on trajectories of depression and PA among middle-aged and elderly, expanding the insights into the "PA paradox" in the mental health field and emphasizing that the effect of PA on depression varies by purpose and intensity. The findings provide longitudinal evidence for health-promoting environmental planning and public health guidance.
Research on the prediction method of Apple firmness based on sound signal ridge regression analysis
Authors: Wang Ranran , Kong Xianglu , Zhang Jinxing , Yuan Chunhong , Wang Dong , Cui Dongdong 
Published: June 2024

Abstract

Firmness can be used as one of the important indexes to indicate the ripeness of apples, usually determined by acoustic signal detection nondestructively, for the high correlation between acoustic signal features and firmness. In this paper, acoustic signal features with high temporal correlation are selected as the independent variable of the model. The acoustic signal and quality of apples were measured, and then 32 time-dependent signal features were selected by MATLAB analysis data, and the firmness of the apple was predicted by a ridge regression model. Using this method, the accuracy of the training set and verification set is 84.7and 82.4% respectively. The actual change of apples is consistent with the prediction trend. Because of its more characteristics and more accurate model, this method has better fault tolerance for data, reduces the environmental requirements for acoustic signal detection, and paves the way for the firmness prediction of other apple varieties.
Non-hemolytic Peptide Classification Using a Quantum Support Vector Machine
Authors: Zhuang Shengxin , Tanner John , Wu Yusen , Huynh Du Q. , Liu Wei , Cadet Xavier F. , Fontaine Nicolas , Charton Philippe , Damour Cedric , Cadet Frederic , Wang Jingbo 
Published: June 2024

Abstract

Quantum machine learning (QML) is one of the most promising applications of quantum computation. However, it is still unclear whether quantum advantages exist when the data is of a classical nature and the search for practical, real-world applications of QML remains active. In this work, we apply the well-studied quantum support vector machine (QSVM), a powerful QML model, to a binary classification task which classifies peptides as either hemolytic or non-hemolytic. Using three peptide datasets,we apply and contrast the performance of the QSVM, numerous classical SVMs, and the best published results on the same peptide classification task, out of which the QSVM performs best. The contributions of this work include (i) the first application of the QSVM to this specific peptide classification task, (ii) an explicit demonstration of QSVMs outperform-ing the best published results attained with classical machine learning models on this classification task and (iii) empirical results showing that the QSVM is capable of outperforming many (and possibly all) classical SVMs on this classification 1 task. This foundational work paves the way to verifiable quantum advantages in the field of computational biology and facilitates safer therapeutic development.
Screening and identification of Bacillus velezensis FLU-1 from the intestinal tract of largemouth bass and its use as a feed additive
Authors: Yang Mingqi , Wang Sunan , Yun Lili , Liu Zhikun , Chang Xulu 
Published: June 2024

Abstract

Probiotics have been widely used in aquaculture and may serve as a potential alternative to antibiotics. Host-derived probiotics are widely used in aquaculture because they are able to adapt to the host intestinal environment more easily than other probiotics. This study was conducted to evaluate the probiotic potential of the bacteria isolated from the gut of largemouth bass. The actions of dietary supplementation with B. velezensis FLU-1 were investigated in the largemouth bass with respect to growth, the morphology of the intestine, digestive and immune functions, and antioxidant capacity, as well as intestinal microbiota. The results revealed that B. velezensis FLU-1 exhibited promising probiotic traits, including extracellular enzyme production, ability to withstand acidic conditions, high bile salt concentration, and elevated temperatures. Furthermore, it demonstrated high hydrophobicity and auto-aggregation cability, alongside being free from antibiotic resistance and displaying a non-hemolytic nature. A diet with host–derived B. velezensis FLU-1 supplementation improved the growth performance of the fish. It also increased the length of the intestinal villi and tight junction gene expression levels, including claudin-2, occludin, and ZO-1. Host–derived B. velezensis FLU-1 supplementation enhanced the activities of protease, α-amylase, lipase, alkaline phosphatase, acid phosphatase, lysozyme, catalase, glutathione peroxidase, decreased the level of MDA, increased the level of the anti-inflammatory cytokine TGF-β, and decreased the level of the pro-inflammatory cytokine TNF-α. Furthermore, B. velezensis FLU-1 increased the levels of several probiotics, including Lactobacillus and Lactococcus, and bacteria that produce short-chain fatty acids, including Faecalibacterium, Bacteroides, and Clostridium. The results in vivo show that adding B. velezensis FLU-1 to the feed could reduce the mortality of largemouth bass after infection with A. hydrophila, as well as reduce the bacterial load in the spleen.The results indicated that further study is warranted concerning the use of B. velezensis FLU-1 combined with sodium gluconate as a diet supplement in other economically viable fish.
Antimicrobial Activity of Colletotrichum gloeosporioides Isolates from Mango (Mangifera indica) and Its Implications for Anthracnose Disease Management
Authors: Dangwal Pranshu , Juyal Saransh 
Published: June 2024

Abstract

Anthracnose disease, caused by the fungal pathogen Colletotrichum gloeosporioides, severely affects mango (Mangifera indica) production. This study aimed to evaluate the antimicrobial activity of various microbes, phytoextract and bioenzymes against C. gloeosporioides. The pathogen was isolated from leaves and fruits of mango trees in Amsaur village, Pauri, Garhwal region of Uttarakhand. Cultivation of C. gloeosporioides on different media allowed us to observe distinct cultural characteristics, including colony colour, elevation type, and growth pattern. The ideal growth conditions were found to be a temperature of 25°C and a pH of approximately 6. Our study evaluated the inhibitory effects of various microbial agents for biocontrol against C. gloeosporioides. Among these, Pseudomonas fluorescens exhibited a substantial inhibitory effect, with an impressive zone of inhibition measuring approximately 25 mm. Additionally, the extract derived from nettle grass demonstrated significant inhibitory activity, with a zone measuring approximately 20 mm. Notably, bioenzymes prepared from lemon peel exhibited the highest antimicrobial activity against the pathogenic fungus. These bioenzymes, produced from fruit waste, offer a sustainable and eco-friendly approach to managing anthracnose disease in mango plants. the biology of C. gloeosporioides and exploring effective biocontrol agents can contribute to mitigating the impact of anthracnose disease in mango production.