bluuebunny/crossref_metadata_2025 huggingface dataset:
Liver transplantation in a child with sclerosing cholangitis due to Langerhans cell histiocytosis: a case report
Authors: Wang Xue-Lian , Fang Chun-Xiao , Chen Min-Xia , Yang Hua-Mei , She Lan-Hui , Gong Yu , Xu Yi , Xiao Wei-Qiang , Tian Jin-Sheng , Ai Bin , Huang Li , Li Xu-Fang
Published: November 2024

Background

Langerhans cell histiocytosis (LCH) is a systemic neoplasia with diverse clinical manifestations, predominantly affecting bone and skin. However, in children, LCH presenting primarily with cholestasis is rare.

Case summary

We present the case of a 22-month-old boy who was admitted to our hospital with a history of intermittent fever and abdominal distension for over 2 months, and jaundice for over 1 month. Prior to admission, the child had been managed with anti-infective and anti-inflammatory drugs and supportive care at multiple hospitals without significant improvement. He was then referred to our facility for further treatment. Upon admission, a series of laboratory tests, imaging studies, and pathological examinations were conducted, revealing the presence of diabetes insipidus, sclerosing cholangitis (SC), and liver cirrhosis. These findings led to a clinical diagnosis of LCH. Given the absence of definitive pathological evidence, his progression to decompensated liver cirrhosis and his pronounced growth retardation, the child was deemed a candidate for living donor liver transplantation. Following the liver transplant, pathological examination of the explanted liver tissue confirmed the clinical diagnosis of LCH. The child received postoperative chemotherapy, which resolved his systemic symptoms and normalized liver function. There was no evidence of LCH recurrence. The symptoms of diabetes insipidus were controlled with desmopressin acetate, however, the existing destructive lesions in the pituitary gland persisted.

Conclusion

Sclerosing cholangitis is a late and severe form of liver involvement in LCH that may be accompanied by lesions in other tissues or organs. Along with pathological evidence, a diagnosis should be made based on characteristic rashes, osteolytic lesions, and thickening of the pituitary stalk. In children with LCH complicated by SC for whom pathological diagnostic evidence cannot be obtained, liver transplantation may be considered once signs of decompensated liver cirrhosis appear, followed by systemic chemotherapy to control active disease. This strategy represents a therapeutic approach with the potential to achieve a better prognosis in children with LCH.
The role of SIRT1-FXR signaling pathway in valproic acid induced liver injury: a quantitative targeted metabolomic evaluation in epileptic children
Authors: Zhao Mingming , Li Guofei , Zhao Limei
Published: November 2024

Aim

This study aimed to gain deeper insights into the hepatotoxicity mechanisms of valproic acid (VPA), as well as to identify potential risk markers for VPA-induced hepatotoxicity.

Methods

Twenty-two children with epilepsy treated with VPA monotherapy were divided into a normal liver function (NLF) group, a mild abnormal liver function (ANLF1) group, and a serious abnormal liver function (ANLF2) group based on their liver function indicator levels. The full quantitative targeted metabolomics technique was used to systematically investigate how the differential endogenous metabolic components change with the development of liver injury.

Results

A total of 195 metabolic components were quantitatively analyzed. Nineteen identified metabolites, including five organic acids, four short-chain fatty acids, four amino acids, three fatty acids, and three benzenoids, differed significantly among the three groups, showing a strong association with VPA-induced hepatotoxicity. Only three bile acid metabolites, taurodeoxycholic acid, taurochenodeoxycholic acid, and deoxycholic acid, were significantly different between the ANLF1 and ANLF2 groups, increasing at first and then decreasing with the aggravation of liver injury. The mechanistic evaluation showed that SRT1720 activation could alleviate the severity of liver function abnormalities induced by VPA. Immunocoprecipitation indicated that VPA significantly increased the acetylation level of FXR, and the application of agonist SRT1720 can antagonize the acetylation of FXR by VPA.

Conclusion

Nineteen identified metabolites showed a strong association with hepatotoxicity and three bile acid metabolites changed with the development of liver injury. The SIRT1–FXR pathway was firstly proposed to participate in VPA-induced hepatotoxicity.
Estrogen: the forgotten player in metaflammation
Authors: Zhu Bao-Ting , Liao Qing-Qing , Tian Hai-Ying , Yu Dao-Jiang , Xie Teng , Sun Xi-Lu , Zhou Xin-Meng , Han Ying-Xuan , Zhao Yu-Jie , El-Kassas Mohamed , Liu Xiu-Xiu , Sun Xiao-Dong , Zhang Yuan-Yuan
Published: November 2024

Metaflammation is low-grade inflammation triggered by chronic metabolic imbalance and caused by dysregulated metabolites in metabolic inflammatory syndrome (MIS), which includes four diseases: obesity, type 2 diabetes mellitus (T2DM), atherosclerosis (AS), and nonalcoholic fatty liver diseases (NAFLD, recently proposed to be replaced by metabolic dysfunction-associated steatotic liver disease, MASLD). These diseases exhibit apparent sex dimorphism as regards MIS. Estrogen not only plays a crucial role in gender differences in adults but also possesses an anti-inflammatory effect on many metabolic diseases. In this study, we present a prediction of the differential proteins and signal transduction of estrogen in MIS through network pharmacology and review the validated studies on obesity, T2DM, AS, and NAFLD. Subsequently, we compared them to obtain valuable targets, identify current gaps, and provide perspectives for future research on the mechanisms of estrogen in metaflammation.
A real-world study of adverse drug reactions of two isocitrate dehydrogenase inhibitor based on the US FDA adverse event reporting system and VigiAccess databases
Authors: Peng Mengmeng , Guo Qian , Dang Zihan , Zhang Baiquan , Li Manjuan , Wang Zixuan , Lu Xuemian , Lin Jie
Published: November 2024

Background and objectives

Isocitrate dehydrogenase (IDH) inhibitor drugs (Enasidenib, Ivosidenib) restore normal metabolism and epigenetic regulation in cells, offering a precision-targeted therapeutic option for acute myeloid leukemia (AML) patients with IDH mutations by specifically inhibiting mutated IDH enzymes. This research evaluates the relationship between adverse drug reactions (ADR) and the use of two isocitrate dehydrogenase inhibitors by using the database from the World Health Organization (WHO) VigiAccess and compares the characteristics of ADRs of the two drugs.

Methods

This study design used the retrospective descriptive analysis. We calculated the ratio of ADRs recorded in reports to compare the same points and different points in ADRs between two medications. Proportional reporting ratio (PRR) and reported odds ratio were used to evaluate the relationship between these two isocitrate dehydrogenase inhibitor medications and adverse events.

Results

Overall, during the search, 4,072 adverse events related to two types of isocitrate dehydrogenase inhibitors were reported in VigiAccess. The results revealed that the top 10 most common AEs were off label use, death, fatigue, nausea, diarrhea, acute myeloid leukemia, drug ineffective, differentiation syndrome, platelet count decreased and decreased appetite. Compared two drugs, enasidinib had the highest adverse reaction reporting rate in general disorders and administration site conditions while ivosidenib had the highest adverse drug reactions reporting rate in injury, poisoning and procedural complications.

Conclusion

Based on the current comparative observational studies, the ADR reports received by the World Health Organization, Food and Drug Administration for these drugs list common and specific adverse drug reactions. Clinical doctors should develop individualized treatment plans based on the adverse reactions of different drugs and the specific conditions of patients to promote the rational use of these expensive medications.
RNA-seq and metabolomic analyses of beneficial plant phenol biochemical pathways in red alder
Authors: Hixson Kim K. , Meng Qingyan , Moinuddin Syed G. A. , Kwon Mi , Costa Michael A. , Cort John R. , Davin Laurence B. , Bell Callum J. , Lewis Norman G.
Published: November 2024

Red alder (Alnus rubra) has highly desirable wood, dye pigment, and (traditional) medicinal properties which have been capitalized on for thousands of years, including by Pacific West Coast Native Americans. A rapidly growing tree species native to North American western coastal and riparian regions, it undergoes symbiosis with actinobacterium Frankia via their nitrogen-fixing root nodules. Red alder’s desirable properties are, however, largely attributed to its bioactive plant phenol metabolites, including for plant defense, for its attractive wood and bark coloration, and various beneficial medicinal properties. Integrated transcriptome and metabolome data analyses were carried out using buds, leaves, stems, roots, and root nodules from greenhouse grown red alder saplings with samples collected during different time-points (Spring, Summer, and Fall) of the growing season. Pollen and catkins were collected from field grown mature trees. Overall plant phenol biochemical pathways operative in red alder were determined, with a particular emphasis on potentially identifying candidates for the long unknown gateway entry points to the proanthocyanidin (PA) and ellagitannin metabolic classes, as well as in gaining better understanding of the biochemical basis of diarylheptanoid formation, i.e. that help define red alder’s varied medicinal uses, and its extensive wood and dye usage.
Evaluating the accuracy of nine canopy resistance models in estimating winter wheat evapotranspiration using the Penman–Monteith equation
Authors: Wu Yingnan , Li Qiaozhen , Zhong Xiuli , Liu Xiaoying
Published: November 2024

Accurate estimation of farmland evapotranspiration (ET) is crucial for agricultural production. The accuracy of the widely used Penman–Monteith (PM) equation for estimating crop ET depends on the quality of input data and their ability to accurately model the canopy resistance (rc). In this study, we evaluated the PM equation in estimating winter wheat ET using nine rc models, with both original and recalibrated parameters, including the Farias (FA), Monteith (MT), Garcίa-Santos (GA), Idso (IS), Jarvis (JA), Katerji-Perrier (KP), Stannard (ST), Todorovic (TD), and Coupled surface resistance (CO) models. We used long-term measurements (2018 to 2023) from the Bowen ratio energy balance method at both daily and seasonal scales. Parameterization was performed using data from the 2020–2021 growing season, while the remaining 4 years were used for verification. The results showed that the FA, KP, and ST models performed better in estimating daily ET with original parameters, achieving a root mean square error (RMSE) of 1.07–1.16 mm d−1 and a mean bias error (MBE) of −0.59–0.02 mm d−1. After parameterization, the performance of acceptable rc models based on RMSE (ranging from 1.07 to 1.22 mm d−1, averaged 1.16 mm d−1) ranked as follows on the daily scale: FA > CO > KP > ST > IS > GA > JA > MT. The rc models were more accurate in simulating ET on a seasonal scale than on the daily scale. Before calibration, the acceptable FA, KP, and MT models overestimated seasonal ET with the MBE ranging from 2.83 to 75.32 mm and RMSE from 29.79 to 82.38 mm. After correction, the suitable rc models based on RMSE values decreased by FA > CO > KP > IS > ST > GA > JA on the seasonal scale, which ranged from 29.79 to 76.35 mm. The performance of the revised rc models improved on both daily and seasonal scales, with RMSE reductions of 29.03% and 68.18%, respectively. Considering both the accuracy and calculation complexity, the FA and KP models were recommended to be used in the PM equation to estimate daily and seasonal ET in semiarid regions. The CO, GA, ST, IS, and JA models can also be used as alternatives, depending on the availability of meteorological parameters.
Effect of stand age on rhizosphere microbial community assembly of dominant shrubs during sandy desert vegetation restoration
Authors: Li Yunfei , Wang Bingyao , Wang Yanli , He Wenqiang , Wu Xudong , Zhang Xue , Teng Xiaorong , Liu Lichao , Yang Haotian
Published: November 2024

The rhizosphere microbial community helps govern biogeochemical cycling and facilitates complex plant-soil feedback. Understanding the evolutionary dynamics of microbial community structure and functional genes during vegetation succession is crucial for quantifying and understanding ecosystem processes and functions in restored sandy deserts. In this study, the rhizosphere microbial community structure of 11–66-year-old dominant shrubs in a desert revegetation area was examined using shotgun metagenomic sequencing. The interactions between the microbial community structure, functional gene abundances, soil properties, and plant characteristics of different stand ages were comprehensively investigated. The abundance of unique species first increased before subsequently decreasing with stand age, with shared species accounting for only 47.33%–59.42% of the total operational taxonomic units (OTUs). Copiotrophs such as Actinobacteria and Proteobacteria were found to dominate the rhizosphere soil microbial community, with their relative abundance accounting for 75.28%–81.41% of the total OTUs. There was a gradual shift in dominant microbial functional genes being involved in cellular processes towards those involved in environmental information processing and metabolism as stand age increased. Additionally, temporal partitioning was observed in both the microbial co-occurrence network complexity and topological parameters within the rhizosphere soil. Redundancy analysis revealed that dissolved organic carbon was the primary determinant influencing shifts in microbial community structure. Understanding the evolution of microbial community structure and function contributes to identifying potential mechanisms associating the soil microbiome with dominant sand-fixing shrubs as well as understanding the rhizosphere microbiome assembly process. These results shed light on the role of the rhizosphere microbiome in biogeochemical cycling and other ecosystem functions following revegetation of temperate sandy deserts.
Phytochemical profiling, antioxidant, enzymatic inhibitory, and antibacterial activities of Wigandia ecuadorensis
Authors: Viteri Rafael , Espinoza Fernando , Cornejo Xavier , Simirgiotis Mario J. , Manzano Patricia
Published: November 2024

Wigandia ecuadoriensis, a member of the Namaceae family, is a source of metabolites and has been traditionally used as an anti-inflammatory. This work aimed to determine the total phenolic content (TPC), total flavonoid content (TFC), antioxidant effect, inhibition of α-glucosidase and cholinesterase enzymes (AChE, BChE), and antibacterial activity of the methanolic extract (ME) and subfractions of Wigandia ecuadoriensis. The findings revealed that ME and its subfractions exhibited significant antioxidant capacity, with the ethyl acetate fraction being the most active, displaying an IC50 of 17.66 µg/mL against the 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical and 10.31 µg/mL against 2,2′-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid) diammonium salt (ABTS). This activity was attributed to its high total phenolic content (357.47 mg GAE/g). Furthermore, W. ecuadoriensis fractions showed marked antimicrobial properties against human pathogen strains with Minimum Bactericidal Concentration (MBC) values ​​of 1.56–6.25 mg/mL for S. aureus, E. faecalis and E. coli. Furthermore, aqueous fraction exhibited slight inhibition of acetylcholinesterase (IC50: 915.98 µg/mL) and butyrylcholinesterase (IC50: 380.42 µg/mL). Interestingly, EF showed the greatest inhibitory effect of α-glucosidase (IC50: 38.44 µg/mL) which is more potent than the control used, acarbose (IC50: 179.07 µg/mL). UHPLC-QTOF-MS analysis identified forty compounds, including phenolic acids, flavonoids, saponins, terpenes, and fatty acyls. As far as we know, this is the first study to evaluate the chemical composition and biological potential of W. ecuadoriensis. Our results provide the first evidence to the chemical knowledge of the species W. ecuadoriensis and demonstrate its bioactive potential as an interesting source of secondary metabolites with possible beneficial properties for health.
Research on the construction of a knowledge graph for tomato leaf pests and diseases based on the named entity recognition model
Authors: Wang Kun , Miao Yuyuan , Wang Xu , Li Yuze , Li Fuzhong , Song Haiyan
Published: November 2024

Introduction

Tomato leaf pests and diseases pose a significant threat to the yield and quality of Q6 tomatoes, highlighting the necessity for comprehensive studies on effective control methods.

Methods

Current control measures predominantly rely on experience and manual observation, hindering the integration of multi-source data. To address this, we integrated information resources related to tomato leaf pests and diseases from agricultural standards documents, knowledge websites, and relevant literature. Guided by domain experts, we preprocessed this data to construct a sample set.

Results

We utilized the Named Entity Recognition (NER) model ALBERT-BiLSTM-CRF to conduct end-to-end knowledge extraction experiments, which outperformed traditional models such as 1DCNN-CRF and BiLSTM-CRF, achieving a recall rate of 95.03%. The extracted knowledge was then stored in the Neo4j graph database, effectively visualizing the internal structure of the knowledge graph.

Discussion

We developed a digital diagnostic system for tomato leaf pests and diseases based on the knowledge graph, enabling graphical management and visualization of pest and disease knowledge. The constructed knowledge graph offers insights for controlling tomato leaf pests and diseases and provides new research directions for pest control in other crops.
A study on the mechanism of how sensory impairment affects depression in the elderly: the mediating roles of daily activity capability and social participation
Authors: Huang Chunjie , He Xiaoqing , Zhang Xin
Published: November 2024

Objectives

Through a longitudinal study, we explored the relationship between sensory impairments and depression in the elderly, and examined the mediating roles of daily activity capability and social participation within this relationship.

Methods

Based on data from the China Health and Retirement Longitudinal Study (CHARLS) collected in 2015 and 2018, a total of 4,419 individuals aged 60 and above were selected as research participants. Sensory impairments (predictor variables) were assessed in 2015 through self-rated visual and hearing capabilities. Daily activity capability and social participation (mediator variables) were also assessed in 2015, with daily activities assessed using the Basic Activities of Daily Living (BADL) and Instrumental Activities of Daily Living (IADL), and social participation assessed by the quantity of social activity participation. Depression status (outcome variable) was assessed in 2018 using the Center for Epidemiologic Studies Depression Scale (CESD-10). Statistical analysis was conducted using logistic regression and SPSS Macro PROCESS.

Results

First, there is a significant correlation between sensory impairments and an increased risk of depression among the elderly, including visual impairment (VI), hearing impairment (HI), and dual sensory impairment (DSI), all of which increase the likelihood of depression. Second, DSI indirectly affect depression through the cascading mediating effects of daily activity capability and social participation. Finally, in contrast to DSI, when there is only a single sensory impairment, either VI or HI, the cascading mediating effects of daily activity capability and social participation on depression are not statistically significant.

Conclusion

The elderly population with dual sensory impairments requires continued attention to help these individuals adopt preventive measures to halt the onset and worsening of depression.
The repercussions of cyberbullying on students on educational platforms
Authors: Alturif Ghada , Alsand Hessa
Published: November 2024

The study aimed to explore the repercussions of cyberbullying on students on educational platforms. This descriptive study depends on a social survey for a sample of male and female students in their high school. The sample is 824 people, and data was collected through a questionnaire. The paper studies the repercussions of cyberbullying on students and the educational atmosphere. The study’s outcome is that most of the sample’s participants agree on these repercussions on both, students and the educational atmosphere. The study recommended holding conferences, webinars, and workshops, handling the repercussions of cyberbullying and regulations for bullies on educational platforms to deter this cyberbullying, to raise awareness among students.
Investigation of the relationships between sports anxiety, positive thinking skills, and life satisfaction in male athletes
Authors: Tolukan Ersan , Yildiz Aydiner Birsin , Yenel Ibrahim Fatih , Yalcin Ilimdar , Stoica Leonard , Iordan Daniel-Andrei , Ilie Onu
Published: November 2024

Sports anxiety is an important obstacle for athletes’ performance, negatively affecting their life satisfaction levels. Positive thinking skills can contribute to overcoming such negative conditions. This study explored the relationships between sport anxiety, positive thinking skills, and life satisfaction in male athletes. A total of 338 male athletes participated voluntarily, using convenience sampling. The study employed a relational survey model, and data were collected through the Sports Anxiety Scale-2, Positive Thinking Skills Scale, and Life Satisfaction Scale. Analyses, including Pearson’s correlation, were performed using the JAMOVI program, with mediation analysis verified through bootstrapping. Results indicated a negative correlation between sport anxiety and life satisfaction, and a positive correlation between positive thinking skills and life satisfaction. Moreover, positive thinking skills were found to moderate the relationship between sport anxiety and life satisfaction. These insights underscore the value of developing positive thinking skills to help athletes reduce anxiety and enhance their life satisfaction. Therefore, incorporating strategies to foster these skills in training programs could be crucial for improving athletes’ overall wellbeing.
Mediating effects of artificial intelligence on the relationship between academic engagement and mental health among Chinese college students
Authors: Wang Yalin , Wang Hui
Published: November 2024

Introduction

Academic engagement of Chinese college students has received increasing research attention due to its impact on Students’ Mental health and wellbeing. The emergence of artificial intelligence (AI) technologies marked the beginning of a new era in education, offering innovative tools and approaches to enhance learning. Still, it can be viewed from positive and negative perspectives. This study utilizes the Theory of Planned Behavior (TPB) as a theoretical framework to analyze the mediating role of students’ attitudes toward AI, perceived social norms, perceived behavioral control, and their intention to use AI technologies in the relationships between Students’ academic engagement and Mental health.

Methods

The study involved a total of 2,423 Chinese college students with a mean age of approximately 20.53 ± 1.51 years. The survey was conducted through Questionnaire Star, using a secure website designed specifically for the study. The Hayes’ PROCESS Macro (Version 4.2) Model 80 with SPSS 29.0, a multivariate regression analysis with a chain mediation model that allows for multiple mediators to be tested sequentially, has been used. The statistical test explored the direct and indirect effects of students’ engagement (X) on mental health (Y) through a series of mediators: attitude toward AI (M1), subjective norm (M2), perceived behavioral control over AI use (M3), and AI use behavioral intention (M4).

Results

The direct positive relationship between engagement and mental health (β = 0.0575; p < 0.05), as well as identifying key mediating factors such as perceived behavioral control (β = 0.1039; p < 0.05) and AI use of behavioral intention (β = 0.0672; p < 0.05), highlights the potential of AI tools in enhancing students’ well-being. However, the non-significant mediating effects of attitude toward AI (β = 0.0135), and subjective norms (β = –0.0005), suggest that more research is needed to understand the nuances of these relationships fully.

Discussion

Overall, the study contributes to the growing body of literature on the role of AI in education and offers practical implications for improving mental health support in academic settings.
Implementation and evaluation of the three action teaching model with learning plan guidance in preventive medicine course
Authors: Chen Cai-Yun , Shi Ta-La , Wang Ruo-Yu , Li Ning , Hao Yi-Han , Zhang Jia-Le , Tang Ming , Liu Sha , Qin Guo-min , Mi Wei
Published: November 2024

Background

Toward the close of the 20th century, Chinese scholars introduced a novel pedagogical approach to education in China, distinguished by its divergence from conventional teaching methods. This instructional strategy assumes a pivotal role in imparting indispensable medical knowledge to students within a meticulously structured and all-encompassing framework.

Objective

The objective of this study is to assess the effectiveness of a novel teaching approach that integrates the three action teaching model with learning plan guidance within a preventive medicine course. Through this investigation, empirical evidence will be provided regarding the impact of utilizing learning guided by the three action teaching model with learning plan guidance as an innovative instructional method, thereby shedding light on its potential to enhance students’ autonomous learning in the field of preventive medicine.

Methods

The control group consisted of 48 students from Class 2 of clinical medicine in grade 2021, who were taught using the traditional classroom teaching mode. Meanwhile, Class 1 served as the experimental group comprising 47 individuals, who received instruction through the three-action teaching model with learning plan guidance. Evaluation was conducted using course tests and questionnaires, and data analysis was performed utilizing t-tests, analysis of variance, and rank sum tests in SPSS software.

Results

The average total score of the test group (79.44 ± 10.13) was significantly higher than that of the control group (70.00 ± 13.57) (t = 3.943, p < 0.001). Moreover, there were more experimental groups with total scores ranging from 80 to 89 and 90 to 100 compared to the control group (Z = 5.324, p = 0.002). The Subjective Evaluation System (SES) indicated that the experimental group (69.11 ± 8.39) outperformed the control group (61.23 ± 6.59) in terms of total scores (t = 5.095, p < 0.001), demonstrating superior performance in learning methods, emotions, engagement, and performance metrics (p < 0.05). Specifically, analysis using the Biggs study process questionnaire revealed that the experimental group exhibited higher levels of deep learning (t = 6.100, p < 0.001) and lower levels of superficial learning (t = −3.783, p < 0.001) when compared to the control group.

Conclusion

The implementation of a novel teaching approach that integrates the three-action teaching model with learning plan guidance significantly enhances students’ academic achievements and fosters their intrinsic motivation for learning. The success of this pedagogical method can be attributed to the enhanced classroom efficiency exhibited by teachers as well as the heightened enthusiasm for learning displayed by students.
Utilizing machine learning techniques to identify severe sleep disturbances in Chinese adolescents: an analysis of lifestyle, physical activity, and psychological factors
Authors: Zhang Lirong , Zhao Shaocong , Yang Wei , Yang Zhongbing , Wu Zhi’an , Zheng Hua , Lei Mingxing
Published: November 2024

Background

Adolescents often experience difficulties with sleep quality. The existing literature on predicting severe sleep disturbance is limited, primarily due to the absence of reliable tools.

Methods

This study analyzed 1966 university students. All participants were classified into a training set and a validation set at the ratio of 8:2 at random. Participants in the training set were utilized to establish models, and the logistic regression (LR) and five machine learning algorithms, including the eXtreme Gradient Boosting Machine (XGBM), Naïve Bayesian (NB), Support Vector Machine (SVM), Decision Tree (DT), CatBoosting Machine (CatBM), were utilized to develop models. Whereas, those in the validation set were used to validate the developed models.

Results

The incidence of severe sleep disturbance was 5.28% (104/1969). Among all developed models, the XGBM model performed best in AUC (0.872 [95%CI: 0.848-0.896]), followed by the CatBM model (0.853 [95% CI: 0.821-0.878]) and DT model (0.843 [95% CI: 0.801-0.870]), whereas the AUC of the logistic regression model was only 0.822 (95% CI: 0.777-0.856). Additionally, the XGBM model had the best accuracy (0.792), precision (0.780), F1 score (0.796), Brier score (0.143), and log loss (0.444).

Conclusions

The XGBM model may be a useful tool to estimate the risk of experiencing severe sleep disturbance among adolescents.
Partnership coordination for optimized COVID-19 vaccination: a case study of Benue and Niger states
Authors: Isiaka Saheed Dipo , Samuel Olugbemisola Wuraola , Jimoh Akolade Uthman , Atobatele Sunday , Sampson Sidney , Daniel Victor , David Joshua Cinwonsoko , Odira Okoye Irene , Adegoke Zubair
Published: November 2024

Background

Developing countries have partnered with non-governmental and development organizations to ensure universal health coverage and promote equity in accessing health services. This study was motivated by the timely and relevant collaborative partnership among the National Primary Health Care Development Agency (NPHCDA), Sydani Consulting, and other implementing partners at the sub-national level. There is also no evidence of a study on partnership coordination vis-à-vis COVID-19 vaccine optimization.

Objective

This study seeks to examine the influence of partnership coordination in Benue and Niger states for optimized COVID-19 vaccination.

Methods

This study adopted a qualitative approach to obtain data from purposively selected participants from Benue and Niger states. Fifty-sox In-depth and Key informant interviews were conducted. The recorded discussions were transcribed and coded (inductively and deductively) using Dedoose software (v9.0). Four themes and seven sub-themes were generated from the participants' responses.

Results

Findings from our study revealed that partners played significant roles in providing support to Benue and Niger states toward the optimization of COVID-19 vaccination in the two states. The provided support alleviated several challenges experienced by the states before the advent of partners in the two states. Partners' roles were assessed using the WHO Health Building Blocks Framework.

Conclusion

Our study concludes and finds it plausible that partner collaboration can effectively improve health outcomes for the populace, especially in resource-low settings.
Investigating a severe acute malnutrition outbreak in Dubti District, Awsiresu Zone, Afar Region, Northeast Ethiopia (2022)
Authors: Gashe Abiyie Demelash , Woldemichael Dawit Zenebe , Worku Fentahun Agegnehu , Mahmud Kedir Ali , Endries Aman Yesuf
Published: November 2024

Introduction

Ethiopia is a global hotspot for child malnutrition, with an estimated 1.2 million children under five affected by severe acute malnutrition (SAM) in 2022. In response, the country has integrated SAM into its broader disease surveillance system. In January 2022, the Dubti District Health Office in the Afar Region detected an unusual surge in SAM cases through its surveillance system. This study aimed to assess the extent of the outbreak and identify the associated risk factors.

Methods

We conducted an unmatched case–control study involving 258 mother–child dyads from five affected kebeles in the Dubti District of the Afar Region Ethiopia. The descriptive study included all 442 SAM cases from the line list, while 86 cases and 168 controls were selected using a simple random sampling method for the analytic study. The data were entered into EpiData software (version 3.1) and analyzed using SPSS software (version 25.0). Binary logistic regression (LR) analysis was performed to identify risk factors for SAM. Statistically, the results were summarized using an adjusted odds ratio (AOR), 95% confidence intervals (CIs), and a p-value of <0.05.

Results

The median age of the cases was 22 months, with an interquartile range of 12–34 months. A total of 39 deaths were reported, with a case fatality rate (CFR) of 8.82%. The identified SAM risk factors included households with more than five members (AOR = 3.341, 95% CI: 1.475–7.563), more than five under-five children (AOR = 4.442, 95% CI: 2.000–9.866), lack of vaccination (AOR = 3.641, 95% CI: 1.618–8.198), pneumonia (AOR = 5.61, 95% CI: 2.488–12.651), diarrhea (AOR = 4.68, 95% CI: 2.169–10.097), lack of access to sanitation and hygiene (AOR = 3.18, 95% CI: 1.462–6.934), and household food insecurity (AOR = 9.46, 95% CI: 2.095–42.712).

Conclusion

The study revealed a significant outbreak of SAM, with a CFR of 8.82%. The outbreak was associated with factors such as large family sizes, having multiple under-five children, a lack of vaccination, pneumonia, and diarrhea. These findings emphasize the urgent need to safeguard essential child health services, water supply, sanitation and hygiene, and household food security.
Evaluation of different robotic grippers for simultaneous multi-object grasping
Authors: Friedl Werner
Published: November 2024

For certain tasks in logistics, especially bin picking and packing, humans resort to a strategy of grasping multiple objects simultaneously, thus reducing picking and transport time. In contrast, robotic systems mainly grasp only one object per picking action, which leads to inefficiencies that could be solved with a smarter gripping hardware and strategies. Development of new manipulators, robotic hands, hybrid or specialized grippers, can already consider such challenges for multi-object grasping in the design stages. This paper introduces different hardware solutions and tests possible grasp strategies for the simultaneous grasping of multiple objects (SGMO). The four hardware solutions presented here are: an under-actuated Constriction Gripper, Linear Scoop Gripper suitable for deform-able object grasping, Hybrid Compliant Gripper equipped with mini vacuum gripper on each fingertip, and a Two-finger Palm Hand with fingers optimized by simulation in pybullet for maximum in-hand manipulation workspace. Most of these hardware solutions are based on the DLR CLASH end-effector and have variable stiffness actuation, high impact robustness, small contact forces, and low-cost design. For the comparison of the capability to simultaneously grasp multiple objects and the capability to grasp a single delicate object in a cluttered environment, the manipulators are tested with four different objects in an extra designed benchmark. The results serve as guideline for future commercial applications of these strategies.
Situating the discourse of recycled nutrient fertilizers in circular economy principles for sustainable agriculture
Authors: Cordeiro Cheryl Marie , Sindhøj Erik
Published: November 2024

This mini review explores the integration of recycled nutrient fertilizers (RNFs) into practices for sustainable agriculture within the circular economy framework. Regional nutrient imbalances challenge the efficiency of implementing nutrient recycling and concerns about contaminants such as potentially toxic elements, microplastics, and antibiotic resistance genes hinder the adoption of RNFs. This review examines the technological, environmental, economic, and policy dimensions of nutrient recycling, highlighting how these practices align with circular economy principles to promote resource efficiency, waste reduction and reduced environmental impact. The review underscores the importance of economic feasibility, supportive policies, and public perception in facilitating RNF adoption. Technological innovations like struvite precipitation and biochar production show promise but face challenges such as high costs and contamination risks. Effective policy frameworks and stakeholder engagement are crucial for broader acceptance. This interdisciplinary review draws insights from environmental science, economics, and agricultural engineering, contributing to a comprehensive understanding of how RNFs can enhance sustainable agricultural practices. The findings suggest that continued technological innovation, effective contamination management, supportive policies, and market incentives, combined with stakeholder education on the benefits and safety of RNFs, can significantly improve public perception and adoption. Together, these factors can contribute substantially to building a more sustainable and resilient agricultural system.
PIPET: A Pipeline to Generate PET Phantom Datasets for Reconstruction Based on Convolutional Neural Network Training
Authors: Sanz-Sanchez Alejandro , García Francisco B. , Mesas-Lafarga Pablo , Prats-Climent Joan , Rodríguez-Álvarez María José
Published: November 2024

There has been a strong interest in using neural networks to solve several tasks in PET medical imaging. One of the main problems faced when using neural networks is the quality, quantity, and availability of data to train the algorithms. In order to address this issue, we have developed a pipeline that enables the generation of voxelized synthetic PET phantoms, simulates the acquisition of a PET scan, and reconstructs the image from the simulated data. In order to achieve these results, several pieces of software are used in the different steps of the pipeline. This pipeline solves the problem of generating diverse PET datasets and images of high quality for different types of phantoms and configurations. The data obtained from this pipeline can be used to train convolutional neural networks for PET reconstruction.
Optimizing Parameters for Enhanced Iterative Image Reconstruction Using Extended Power Divergence
Authors: Kojima Takeshi , Yamaguchi Yusaku , Abou Al-Ola Omar M. , Yoshinaga Tetsuya
Published: November 2024

In this paper, we propose a method for optimizing the parameter values in iterative reconstruction algorithms that include adjustable parameters in order to optimize the reconstruction performance. Specifically, we focus on the power divergence-based expectation-maximization algorithm, which includes two power indices as adjustable parameters. Through numerical and physical experiments, we demonstrate that optimizing the evaluation function based on the extended power-divergence and weighted extended power-divergence measures yields high-quality image reconstruction. Notably, the optimal parameter values derived from the proposed method produce reconstruction results comparable to those obtained using the true image, even when using distance functions based on differences between forward projection data and measured projection data, as verified by numerical experiments. These results suggest that the proposed method effectively improves reconstruction quality without the need for machine-learning techniques in parameter selection. Our findings also indicate that this approach is useful for enhancing the performance of iterative reconstruction algorithms, especially in medical imaging, where high-accuracy reconstruction under noisy conditions is required.
Green Purchase Determinants in a Peripheral Region of Europe: How Can Green Marketing Influence Consumers’ Decisions? The Mediating Role of Green Awareness of Price
Authors: Lopes João M. , Silva Luis Filipe , Massano-Cardoso Ilda , Galhardo Ana
Published: November 2024

As environmental concerns continue to arise, companies are increasingly turning to green marketing strategies and promoting green purchasing decisions to meet consumer demand for sustainable products and services. Peripheral regions, often overlooked in discussions about sustainability, have a unique opportunity to position themselves as leaders in environmentally responsible practices through green marketing strategies. This can represent an opportunity to boost economic growth while protecting the environment for future generations. This paper studies the influencers of consumer purchasing decisions in a country located in the periphery of Europe. One thousand two hundred fifty-five responses were collected. A multiple linear regression analysis was conducted. It was found that the decision to purchase green products seems to be influenced by factors such as environmental awareness, the perceived quality of green products, the desire to purchase them, and the prospect of a greener future. This study’s key findings indicate that price awareness acts as a mediator, amplifying the influence of environmental concerns, perceived quality, willingness to purchase, future estimations, and perceived benefits on consumers’ green purchasing decisions. This underscores the importance of consumers’ awareness of the pricing for green products. Understanding the factors influencing green purchasing behavior in peripheral regions of Europe is crucial for businesses and policymakers seeking to promote sustainable consumption practices. The current study offers insights for promoting more sustainable consumption practices in these regions.
Association of Equine Squamous and Glandular Gastric Disease with Dental Status in 54 Horses
Authors: Lensing Rabea , Wirth Caroline , Thünker Franziska , Merle Roswitha , Barton Ann Kristin
Published: November 2024

Gastric pH is a key factor in the development of equine squamous gastric disease (ESGD). Although acid injury is unlikely the primary cause of equine glandular gastric disease (EGGD), low pH may promote mucosal damage. Gastric pH is influenced by roughage uptake, as chewing increases the alkaline saliva production. The proton pump inhibitor omeprazole is currently recommended by the American/European College of Veterinary/Equine Internal Medicine (ACVIM/ECEIM) for ESGD and EGGD. We hypothesized that dental disorders decrease saliva production and gastric pH, predisposing horses to EGUS. Gastroscopy, intragastric pH measurement and dental examinations were performed (n = 54). Omeprazole therapy was recommended (n = 32), dental disorders were corrected (n = 22) and gastroscopy was repeated 4 weeks later (n = 9). An influence of moderate to severe dental disorders on ESGD (grade ≥ 2/4) (p = 0.394) and EGGD (grade ≥ 2/3) (p = 0.857) could not be determined. Nevertheless, there was a trend that moderate to severe dental disorders were associated with a low gastric pH ≤ 4 (p = 0.100). Horses with no or mild dental problems had a mean pH of 5.4 versus 4.2 in moderate to severe dental disorders. In conclusion, our study results do not show a direct relationship between dental disorders and EGUS, but a gastric and oral cavity examination should still be considered in patients presented for weight loss or inappetence.
Factors Impacting the Adoption and Acceptance of ChatGPT in Educational Settings: A Narrative Review of Empirical Studies
Authors: Al-kfairy Mousa
Published: November 2024

This narrative review synthesizes and analyzes empirical studies on the adoption and acceptance of ChatGPT in higher education, addressing the need to understand the key factors influencing its use by students and educators. Anchored in theoretical frameworks such as the Technology Acceptance Model (TAM), Unified Theory of Acceptance and Use of Technology (UTAUT), Diffusion of Innovation (DoI) Theory, Technology–Organization–Environment (TOE) model, and Theory of Planned Behavior, this review highlights the central constructs shaping adoption behavior. The confirmed factors include hedonic motivation, usability, perceived benefits, system responsiveness, and relative advantage, whereas the effects of social influence, facilitating conditions, privacy, and security vary. Conversely, technology readiness and extrinsic motivation remain unconfirmed as consistent predictors. This study employs a qualitative synthesis of 40 peer-reviewed empirical studies, applying thematic analysis to uncover patterns in the factors driving ChatGPT adoption. The findings reveal that, while the traditional technology adoption models offer valuable insights, a deeper exploration of the contextual and psychological factors is necessary. The study’s implications inform future research directions and institutional strategies for integrating AI to support educational innovation.
An Improved Dung Beetle Optimizer for the Twin Stacker Cranes’ Scheduling Problem
Authors: Chen Yidong , Li Jinghua , Zhou Lei , Song Dening , Yang Boxin
Published: November 2024

In recent years, twin stacker crane units have been increasingly integrated into large automated storage and retrieval systems (AS/RSs) in shipyards to enhance operational efficiency. These common rail units often encounter conflicts, and the additional time costs incurred during collision avoidance significantly diminish AS/RS efficiency. Therefore, addressing the twin stacker cranes’ scheduling problem (TSSP) with a collision-free constraint is essential. This paper presents a novel approach to identifying and avoiding collisions by approximating the stacker crane’s trip trajectory as a triangular envelope. Utilizing the collision identification equation derived from this method, we express the collision-free constraint within the TSSP and formulate a mixed-integer programming model. Recognizing the multimodal characteristics of the TSSP objective function, we introduce the dung beetle optimizer (DBO), which excels in multimodal test functions, as the foundational framework for a heuristic optimizer aimed at large-scale TSSPs that are challenging for exact algorithms. To adapt the optimizer for bi-level programming problems like TSSPs, we propose a double-layer code mechanism and innovatively design a binary DBO for the binary layer. Additionally, we incorporate several components, including a hybrid initialization strategy, a Cauchy–Gaussian mixture distribution neighborhood search strategy, and a velocity revision strategy based on continuous space discretization, into the improved dung beetle optimizer (IDBO) to further enhance its performance. To validate the efficacy of the IDBO, we established a numerical experimental environment and generated a series of instances based on actual environmental parameters and operational conditions from an advanced AS/RS in southeastern China. Extensive comparative experiments on various scales and distributions demonstrate that the components of the IDBO significantly improve algorithm performance, yielding stable advantages over classical algorithms in solving TSSPs, with improvements exceeding 10%.