bluuebunny/crossref_metadata_2025_split huggingface dataset:
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 objectivesIsocitrate 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.MethodsThis 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.ResultsOverall, 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.ConclusionBased 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.
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

IntroductionTomato 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.MethodsCurrent 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.ResultsWe 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.DiscussionWe 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

ObjectivesThrough 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.MethodsBased 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.ResultsFirst, 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.ConclusionThe elderly population with dual sensory impairments requires continued attention to help these individuals adopt preventive measures to halt the onset and worsening of depression.
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

IntroductionAcademic 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.MethodsThe 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).ResultsThe direct positive relationship between engagement and mental health (β = 0.0575; p &lt; 0.05), as well as identifying key mediating factors such as perceived behavioral control (β = 0.1039; p &lt; 0.05) and AI use of behavioral intention (β = 0.0672; p &lt; 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.DiscussionOverall, 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

BackgroundToward 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.ObjectiveThe 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.MethodsThe 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.ResultsThe 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 &lt; 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 &lt; 0.001), demonstrating superior performance in learning methods, emotions, engagement, and performance metrics (p &lt; 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 &lt; 0.001) and lower levels of superficial learning (t = −3.783, p &lt; 0.001) when compared to the control group.ConclusionThe 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

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

BackgroundDeveloping 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.ObjectiveThis study seeks to examine the influence of partnership coordination in Benue and Niger states for optimized COVID-19 vaccination.MethodsThis 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.ResultsFindings 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.ConclusionOur 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

IntroductionEthiopia 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.MethodsWe 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 &lt;0.05.ResultsThe 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).ConclusionThe 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.
Abordagens de preservação alveolar após a exodontia - revisão bibliográfica
Authors: Tessarolo Giulia Donadello , Gonçalves Vanessa , Langa Weslley Alexsander de Oliveira , Peçanha Marcelo Massaroni , Almeida Luiz Guilherme Ribeiro de 
Published: November 2024

A inclusão de biomateriais na regeneração óssea visa mitigar a reabsorção da tábua óssea alveolar pós-exodontia. Enxertos ósseos, com ou sem membranas, apresentam resultados positivos, mas enfrentam desvantagens como tempo prolongado para substituição por osso maduro, morbidade cirúrgica e custos elevados. A preservação alveolar imediata após a remoção dentária promove cicatrização natural e mantém o tamanho do alvéolo até a colocação do implante, reduzindo a reabsorção do processo alveolar e auxiliando na reabilitação dental. A escolha do biomaterial adequado requer avaliação criteriosa dos benefícios e riscos, justificando a necessidade de explorar técnicas de preservação alveolar e uso de biomateriais para aprimorar os resultados clínicos. Objetiva-se apresentar os desafios encontrados para a preservação do alvéolo após remoção de elementos dentários, métodos disponíveis eficazes para minimizar a perda óssea e áreas que necessitam de mais pesquisa para otimizar as práticas de preservação do rebordo alveolar. Esta revisão bibliográfica de cunho qualitativo e caráter exploratório baseia-se em artigos publicados entre os anos de 2014 a 2024, utilizando bases de dados como PubMed, Cochrane, LILACS, MEDLINE, Scielo e BVS.Os dados foram comparados para identificar vantagens, limitações e implicações clínicas. Desse modo, observa-se que é válido e importante o uso dos biomateriais na regeneração óssea pós-exodontia. O que permite concluir que a preservação alveolar após exodontia é essencial para manter o volume ósseo e garantir o sucesso de reabilitações estéticas e funcionais futuras, sendo necessária uma avaliação criteriosa para cada caso.
Avanços na detecção precoce de doenças cardiovasculares através de biomarcadores
Authors: Bittencourt Amicia Jacira Moura Nunes , Penna Ana Clara Della , Ribeiro Bruna Martins , Bianco Débora , Lima Fernanda de Sousa , Deitos Gabriela Eduarda Palauro , Souza Guilherme Batista Teixeira , Oliveira Maria Laura de Paula Angelo , Menasche Nathalie Dockhorn , Lopes Rafael Motta da Costa , Vargas Rafaella Vitória Neres 
Published: November 2024

Introdução: As doenças cardiovasculares (DCV) são definidas como um conjunto de afecções que prejudicam a fisiologia cardíaca. Elas abrangem em seu total dez afecções principais, sendo a doença cardiovascular aterosclerótica uma representante substancial da carga global de doenças cardiovasculares. Embora medidas reguladoras de fatores de risco tradicionais de DCV estão bem definidas na literatura, a implementação de biomarcadores na prática clínica capazes de prever e sugerir danos cardíacos existentes estão sendo objetos de estudo. Objetivo: revisar na literatura novos biomarcadores inflamatórios, oxidativos, de disfunção endotelial e cardíaca, averiguando a sua eficácia na previsão de eventos cardiovasculares, de modo a viabilizar intervenções mais precoces e eficazes. Metodologia: revisão integrativa da literatura nas bases de dados PubMed, Scielo e Lilacs, mediante a utilização dos descritores: "Cardiovascular Diseases", "Biomarkers", "Diagnosis" e "Recent". Foram utilizados artigos publicados entre 2019 a 2024, excluindo periódicos de baixos fatores de impacto e metodologias inconclusivas, portanto após análise foram selecionadas 8 referências. Discussão: A aterosclerose subclínica, a síndrome coronariana aguda, o aneurisma de aorta abdominal, o tromboembolismo venoso, a doença arterial periférica e a insuficiência cardíaca são condições cardiovasculares que demandam atenção à detecção precoce. A aterosclerose, principal causa de morbimortalidade, ainda se apresenta indetectável até eventos agudos, tornando urgente a identificação por meio de novos biomarcadores. Os microRNAs têm demonstrado alta sensibilidade e especificidade, destacando-se no diagnóstico precoce da aterosclerose carotídea e do infarto agudo do miocárdio. A lipoproteína A surge como indicador crítico para a detecção de aneurismas, enquanto o perfil metabólico é promissor na análise do tromboembolismo venoso. A análise da microcirculação retiniana é uma abordagem inovadora para o rastreamento da doença arterial periférica. No contexto da insuficiência cardíaca, biomarcadores como Neopterina, galectina-3 e ST2 têm demonstrado potencial diagnóstico e prognóstico. A integração desses biomarcadores na prática clínica pode aprimorar significativamente a detecção e manejo das doenças cardiovasculares, promovendo melhores desfechos para os pacientes. Conclusão: A incorporação de biomarcadores na prática clínica tem o potencial de transformar o panorama da saúde cardiovascular, proporcionando rastreamento mais eficiente e resposta terapêutica mais adequada, instaurando métodos cada vez menos invasivos que influenciam no curso das doenças, viabilizando o diagnóstico precoce e estratificação de grupos de risco.