bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adapting and testing an eLearning resource for professionals to support families when a significant caregiver for children is dying with cancer
Authors: O’Neill Carla , Hanna Jeffrey R. , Sheehan Sarah , McCance Tanya , Drury Amanda , Semple Cherith J. 
Published: November 2024

Abstract
Purpose
Health and social care professionals (professionals) often lack knowledge, skills and confidence to support adults at end of life with significant caregiving responsibilities for children, < 18. A recent systematic review highlighted a dearth of educational interventions (n = 2) to equip professionals to provide supportive care to families when a parent has cancer. Addressing an evident gap in education, this paper details the adaption and optimisation of a face-to-face educational intervention to an accessible eLearning resource.

Methods
Guided by the ‘Person-based Approach’, a theory-driven and evidence-based face-to-face educational intervention was adapted and optimised as an eLearning resource. This incorporated current evidence, alongside insights from an expert group, learning technologists and research team, leading to the design of an eLearning prototype. This was optimised for acceptability and usability using think-aloud interviews with end-users (n = 13) and patient and public involvement (n = 4).

Results
An iterative adaption and optimisation process enabled implementation of navigational improvements, changes to enhance clarity on language and appropriateness of images and interactive components. During optimisation, positive feedback was reported; especially regarding the ‘look and feel’ and on the educational videos and reflective exercises embedded throughout the eLearning resource.

Conclusion
The systematic adaption and optimisation of this novel eLearning resource has aimed to promote relevance, appropriateness, and applicability of an accessible evidence-based and theory-driven training resource for professionals. It has the potential to promote family-centred supportive end of life cancer care, which ultimately can promote better bereavement outcomes. An evaluation of the intervention is required to explore impact on practice.
Wolbachia incompatible insect technique program optimization over large spatial scales using a process-based model of mosquito metapopulation dynamics
Authors: Lim Preston LJ , Cook Alex R , Bansal Somya , Chow Jo Yi , Lim Jue Tao 
Published: November 2024

Abstract
Background
Wolbachia incompatible insect technique (IIT) programs have been shown in field trials to be highly effective in suppressing populations of mosquitoes that carry diseases such as dengue, chikungunya, and Zika. However, the frequent and repeated release of Wolbachia-infected male mosquitoes makes such programs resource-intensive. While the need for optimization is recognized, potential strategies to optimize releases and reduce resource utilization have not been fully explored.

Results
We developed a process-based model to study the spatio-temporal metapopulation dynamics of mosquitoes in a Wolbachia IIT program, which explicitly incorporates climatic influence in mosquito life-history traits. We then used the model to simulate various scale-down and redistribution strategies to optimize the existing program in Singapore. Specifically, the model was used to study the trade-offs between the intervention efficacy outcomes and resource requirements of various release program strategies, such as the total number of release events and the number of mosquitoes released. We found that scaling down releases in existing sites from twice a week to only once a week yielded small changes in suppression efficacy (from 87 to 80%), while requiring 44% fewer mosquitoes and release events. Additionally, redistributing mosquitoes from already suppressed areas and releasing them in new areas once a week led to a greater total suppressive efficacy (83% compared to 61%) while also yielding a 16% and 14% reduction in the number of mosquitoes and release events required, respectively.

Conclusions
Both scale-down and redistribution strategies can be implemented to significantly reduce program resource requirements without compromising the suppressive efficacy of IIT. These findings will inform planners on ways to optimize existing and future IIT programs, potentially allowing for the wider adoption of this method for mosquito-borne disease control.
Combined impact of prediabetes and hepatic steatosis on cardiometabolic outcomes in young adults
Authors: Choi Wonsuk , Park Minae , Park Sojeong , Park Ji Yong , Hong A Ram , Yoon Jee Hee , Ha Kyoung Hwa , Kim Dae Jung , Kim Hee Kyung , Kang Ho-Cheol 
Published: November 2024

Abstract
Objectives
This study aimed to investigate the impact of hepatic steatosis on cardiometabolic outcomes in young adults with prediabetes.

Methods
A nationwide cohort study was conducted with 896,585 young adults under 40 years old without diabetes or previous history of cardiovascular disease. Hepatic steatosis was identified using a fatty liver index of ≥ 60. The outcomes of this study were incident diabetes (DM) and composite major adverse cardiovascular events (MACE), including myocardial infarction, stroke, or cardiovascular death.

Results
During a median follow-up of 11.8 years, 27,437 (3.1%) incident DM cases and 6,584 (0.7%) MACE cases were recorded. Young adults with prediabetes had a significantly higher risk of incident DM (hazard ratio [HR]: 2.81; 95% confidence interval [CI]: 2.74–2.88; P-value: <0.001) and composite MACE risk (HR: 1.10; 95% CI: 1.03–1.17; P-value: 0.003) compared to individuals with normoglycemia, after adjusting for relevant covariates. Stratification based on hepatic steatosis showed that the combination of prediabetes and hepatic steatosis posed the highest risk for these outcomes, after adjusting for relevant covariates. For incident DM, the HRs (95% CI; P-value) were: 3.15 (3.05–3.26; <0.001) for prediabetes without hepatic steatosis, 2.89 (2.78–3.01; <0.001) for normoglycemia with hepatic steatosis, and 6.60 (6.33–6.87; <0.001) for prediabetes with hepatic steatosis. For composite MACE, the HRs (95% CI; P-value) were 1.05 (0.97–1.13; 0.235) for prediabetes without hepatic steatosis, 1.39 (1.27–1.51; <0.001) for normoglycemia with hepatic steatosis, and 1.60 (1.44–1.78; <0.001) for prediabetes with hepatic steatosis.

Conclusions
Prediabetes and hepatic steatosis additively increased the risk of cardiometabolic outcomes in young adults. These findings hold significance for physicians as they provide insights into assessing high-risk individuals among young adults with prediabetes.
Fostering college students’ mental well-being: the impact of social networking site utilization on emotion management and regulation
Authors: Han Yuehua , Xu Zhifen 
Published: November 2024

AbstractWith the widespread proliferation of the Internet, social networking sites have increasingly become integrated into the daily lives of university students, leading to a growing reliance on these platforms. Several studies have suggested that this emotional dependence on social networking sites stems from unmet psychological needs. Meanwhile, social rejection has been identified as a prevalent phenomenon that exacerbates the deficiency of individual psychological needs. However, existing research on aspect-level sentiment analysis among college students within social networking sites faces challenges such as inadequate feature extraction, ineffective handling of data noise, and the neglect of complex interactions in multimodal data. To address these issues, this paper introduces a novel approach, the Multi-Granular View Dynamic Fusion Model (MVDFM), developed from both coarse-grained and fine-grained perspectives. MVDFM extracts multi-granular view features from textual and visual content, incorporating a dynamic gating self-attention mechanism. Additionally, it proposes a three-view decomposition higher-order pooling mechanism for a two-stage dynamic fusion of these features. Experimental results demonstrate the model’s effectiveness, achieving accuracy and F1 values of 78.78% and 74.48% on the Twitter-2015 dataset, and 73.89% and 72.47% on the Twitter-2017 dataset, respectively. This efficient supervision enables the extraction of deep semantic information from multimodal data generated by college students on social networking sites. The model adeptly mines pertinent information related to target aspect-based words, enhancing the efficacy of aspect-level emotion prediction. Furthermore, it facilitates an effective exploration of the intricate interplay between social rejection, monitoring on social networking sites, the fear of missing out, and dependence on social networking sites, ultimately aiding university students in regulating their emotional management.
Big data processing using hybrid Gaussian mixture model with salp swarm algorithm
Authors: Saravanakumar R. , TamilSelvi T. , Pandey Digvijay , Pandey Binay Kumar , Mahajan Darshan A. , Lelisho Mesfin Esayas 
Published: November 2024

AbstractThe traditional methods used in big data, like cluster creation and query-based data extraction, fail to yield accurate results on massive networks. To address such issues, the proposed approach involves using the Hadoop Distributed File System (HDFS) for data processing, the map-reduce programming paradigm for data processing, and query optimization techniques to quickly and effectively extract accurate outcomes from a variety of options with a high processing capacity. The methodology proposed in this work makes use of Gaussian Mixture Model (GMM) for data clustering and the Salp Swarm Algorithm (SSA) for optimization. The security of preprocessed data stored on networked clusters with interconnections has been ensured by SHA algorithms. Finally, incorporating into consideration the important parameters, evaluation findings for the experimental performance of the model in the indicated methodology are produced. For this work, the estimated range of input file sizes is 60–100 MB. The processing of 100 MB of input files yielded an accuracy of 96% and results for specificity and sensitivity of 90% and 93%, respectively. The outcomes have been compared with well-known methods like fuzzy C-means and K-means approaches, and the results show that the proposed method effectively distributes accurate data processing to cluster nodes with low latency. Moreover, it uses the least amount of memory resources possible when operating on functional CPUs. As a result, the proposed approach outperforms existing techniques.
Relationship between undergraduate students search skills and usage of electronic information resources in Ambrose Alli University (AAU) Nigerian
Authors: Ikenwe Iguehi Joy , Solomon Anthony Oboite 
Published: November 2024

This research work investigated the relationship between undergraduate search skills and usage of electronic information resources (EIRs) in Ambrose Ali University (AAU), Edo State, Nigeria. The population of the study was 1,798 final year undergraduate students of AAU for the 2023/2024 academic session, with a sample of 113 from three Faculties. Accidental sampling and throwing of a dice were the adopted sampling techniques. The research instrument for data collection was a structured questionnaire, and data were analyzed using descriptive statistics of percentages, mean scores (x̄), and standard deviation (SD) for the research questions while Pearson Product Moment Correlation Co-efficient statistical method was used to test the hypothesis; and the data were presented in Tables. Findings showed that AAU undergraduate students can define their information needs and select articles with ease (x̄ = 3.46). They can use basic ICTs and possess basic search skills (x̄ = 3.08). While they mainly use OPAC (x̄ = 3.8), e-books (x̄ = 3.8), e-journals (x̄ = 3.6), online databases (x̄ = 3.8), e-thesis (x̄ = 3.0), and e-magazines (x̄ = 3.0), they use them for leisure (x̄ = 3.1), searching for internship opportunities (x̄ = 3.6), and class assignment (x̄ = 3.3). They encounter challenges like unavailability of relevant EIRs (x̄ = 3.3), lack of awareness of EIRs (x̄ = 3.4), inadequate searching skills (x̄ = 3.3), and high cost of internet access in their quest to use EIRs (x̄ = 3.1). Thus, the paper concluded that the undergraduate students of AAU possess the search skills and showed that here is a significant relationship between the search skills and the use of electronic information resources. The study recommended updates on the search skills and use, as well as provision of facilities to ensure equitable quality learning of the students.
On the Origin of Entrepreneurial Theories: How Entrepreneurs Craft Complex Causal Models with Theorizing and Data
Authors: Ott Timothy E. , Hannah Douglas P. 
Published: November 2024

The emerging theory-based view depicts entrepreneurs as sophisticated thinkers who form, update, and act on rich causal theories. In support of this view, recent empirical work has demonstrated both (a) the value of theories as well as (b) the importance of experimentation for testing and refining theories. Yet, the process by which entrepreneurs initially form these theories remains largely unobserved. To address this gap, we conduct a longitudinal study of nine consumer platform ventures over five years. Our primary contribution is to illuminate how entrepreneurs move from the spark of an initial idea to an actionable theory. This process begins with a thesis, which is an initial, poorly formed idea that guides subsequent learning and theorizing. To elaborate this thesis, entrepreneurs then knit together multiple distinct mental theorizing practices, as well as different approaches to generating new data. Extending prior work, we articulate the distinct roles these practices play in terms of adding novelty, identifying new subproblems, and explicating beliefs. We also add insight into the role of data generation in spurring further theorizing. Overall, we contribute empirical evidence and a rich processual understanding of how entrepreneurs articulate complex causal theories. Funding: We appreciate the generous support of the Strategy Research Foundation Dissertation Research Grant Program.
Impact of climate and land use on the temporal variability of sand fly density in Sri Lanka: A 2-year longitudinal study
Authors: Senanayake Sanath C. , Liyanage Prasad , Pathirage Dulani R. K. , Siraj M. F. Raushan , De Silva B. G. D. Nissanka Kolitha , Karunaweera Nadira D. 
Published: November 2024

Background
Leishmaniasis has emerged as an escalating public health problem in Sri Lanka, with reported cases increasing nearly three folds over past decade, from 1,367 in 2014 to 3714 cases in 2023. Phlebotominae sand flies are the vectors of leishmaniasis. Their density is known to be influenced by context-specific climatic and land use patterns. Thus, we aimed to investigate how these factors drive sand fly density across Sri Lanka.

Methodology/Principal findings
We analysed monthly collections of sand flies (n = 38,594) and weather data from ten sentinel sites representing three main geo-climatic zones across Sri Lanka, over 24 months. Site-specific land use data was also recorded. The influence of climate and land use patterns on sand fly density across the sentinel sites were estimated using distributed lag non-linear models and machine learning. We found that climate played a major role on sand fly density compared to land use structure. Increase in rainfall and relative humidity at real time, and ambient temperature and soil temperature with a 2-month lag were associated with a statistically significant increase in sand fly density. The maximum relative risk (RR) observed was 3.76 (95% CI: 1.58–8.96) for rainfall at 120 mm/month, 2.14 (95% CI: 1.04–4.38) for relative humidity at 82% (both at real time). The maximum RR was 2.81 (95% CI: 1.09–7.35) for ambient temperature at 34.5°C, and 11.6 (95% CI, 4.38–30.76) for soil temperature (both at a 2-month lag). The real-time increase in ambient temperature, sunshine hours, and evaporation rate, however, reduced sand fly density homogeneously in all study settings. The high density of chena and coconut plantations, together with low density of dense forests, homesteads, and low human footprint values, positively influenced sand fly density.

Conclusions/Significance
The findings improve our understanding of the dynamic influence of environment on sand fly densities and spread of leishmaniasis. This knowledge lays a foundation for forecasting of sand fly densities and designing targeted interventions for mitigating the growing burden of leishmaniasis among the most vulnerable populations, particularly in an era of changing climate.
Drug-related problems among type 2 diabetic patients in Sunwal Municipality of Western Nepal
Authors: Chapagain Sushma , Pathak Nabin , Subedi Kushal , Ghimire Prakash , Adhikari Bijay , Shrestha Niranjan , Dangi Nim Bahadur 
Published: November 2024

Background
Several diseases co-exist with diabetes such as hypertension, and dyslipidemia, leading to cases of non-adherence, several drug interactions, and an increased risk of adverse drug reactions among patients, which are often termed as drug-related problems (DRPs). The role of pharmacists in high-income countries is well-defined in identifying DRPs among type 2 diabetes patients. However, these roles still need to be explored within low- and middle-income countries. The study aimed to identify DRPs in Type 2 diabetic patients.

Methods
A community-based cross-sectional study was conducted in the Sunwal Municipality, Lumbini Province, Nepal from April to November 2021 where a stratified random sampling technique was employed to collect the data. The study included patients aged ≥ 18 years of either gender with type 2 diabetes who were prescribed at least one anti-diabetic medication. Patients were visited at their homes once identified through the community pharmacies, and a prescription review was conducted to identify the DRPs by using the Pharmaceutical Care Network Europe (PCNE) V8.02 tool and pertinent guidelines.

Results
Among 182 patients, 97 (53.3%) had DRPs. Most of the patients were 50–60 years (n = 46; 25.3%), with a mean ± SD age of 55.43±14.46, as most were female (n = 94; 51.6%). Biguanides and sulfonylureas were the common classes of drugs prescribed. The major class of drug associated with DRPs were biguanides (n = 85; 49.7%), followed by sulfonylureas (n = 42; 24.6%). Metformin was the major drug associated with DRPs (n = 85; 49.4%). The major type of DRP identified was treatment effectiveness (n = 82; 79.61%), while patients not adhering to drug therapy (n = 97; 71.85%) was the leading cause of DRPs. DRPs were significantly associated with the duration of diabetes (p = .007) and the number of fruit servings (p = .007).

Conclusion
The majority of the patients were found to have DRPs. The visiting patients at home by the pharmacists helped in identifying the DRPs and associated factors among type 2 diabetes patients, which may aid in the prevention and management of the disease.
Integrated global and unique metabolic characteristics to reveal the intervention effect of Yiyi decoction on acute pancreatitis
Authors: Gong Guanwen , Wu Yongping , Jiang Yanwen , Cao Yuan 
Published: November 2024

Yiyi decoction is a Chinese herbal formula for the treatment of acute pancreatitis that has been used in clinical practice for decades. A previous study has suggested that resveratrol, emodin, rhein and their derivatives might be the potential pharmacodynamic components in Yiyi decoction, and researchers have proposed that resveratrol, emodin and rhein are candidate markers for quality control. The present study investigated the intervention effect of Yiyi decoction and its effective components on murine acute pancreatitis using metabolomic approach that integrated global and unique metabolic characteristics. First, serum metabolomics based on the platform of ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry was performed to assess metabolic changes in experimental acute pancreatitis. Second, an in-depth analysis of bile acid metabolism was performed based on an in-house database. Finally, an integrated analysis of the intervention effect of Yiyi decoction and its effective components in response to these metabolic perturbations was performed. As a result, 39 potential biomarkers for the pathogenesis of acute pancreatitis, mainly phospholipids, fatty acids, bile acids and lipoylcarnitines, were screened and annotated. Integrated analysis revealed that the metabolic disorders in acute pancreatitis mice were reversed by Yiyi decoction primarily via regulating glycerophospholipid metabolism, bile acid biosynthesis, carnitine synthesis and fatty acid metabolism. Yiyi decoction components may effectively target the migratory metabolome. Histopathological and biochemical analyses suggested that Yiyi decoction maintained the gut barrier function and inhibited inflammatory cytokines, thus exert anti-acute pancreatitis effects. The present study utilized an approach that integrated global and unique metabolic characteristics to elucidate the underlying mechanisms of Chinese herbal formulas from a metabolomics perspective.
Diagnostic value of arterial spin labeling for Alzheimer’s disease: A systematic review and meta-analysis
Authors: Zhang Xin-Yue , Zhang Hong , Bao Qiong-Nan , Yin Zi-Han , Li Ya-Qin , Xia Man-Ze , Chen Zheng-Hong , Zhong Wan-Qi , Wu Ke-Xin , Yao Jin , Liang Fan-Rong 
Published: November 2024

Background
Arterial spin labeling (ASL) is a magnetic resonance imaging (MRI) technique that offers a non-invasive approach for measuring cerebral blood perfusion (CBF). CBF serves as a marker of neuronal activity, and ASL has demonstrated the potential to detect reductions in CBF associated with early-stage neurodegenerative diseases like Alzheimer’s disease (AD). Consequently, ASL has garnered growing interest as a potential diagnostic tool for AD. Despite the promise of ASL for diagnosing AD, there is a paucity of data regarding the pooled specificity and sensitivity of this technique in this context. The purpose of this systematic review and meta-analysis is to identify the accuracy of ASL in the diagnosis of AD with international clinical diagnosis as the gold standard.

Methods
Four English databases and four Chinese databases were searched from their inception to 30 November 2023. Two independent reviewers extracted relevant information from the eligible articles, while the quality assessment of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). The meta-analysis was carried out using the area under the Receiver Operator Characteristic (ROC) curves (AUC) and sensitivity and specificity values. Meta-DiSc 1.4 was used to perform the statistical analysis. STATA 16.0 was used to perform publication bias and sensitivity analysis.

Results
Of 844 relevant articles retrieved, 10 studies involving 494 participants (AD patients = 262, healthy controls = 232) met the inclusion criteria and were included in the meta-analysis. However, the quality of studies was low based on QUADAS-2. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio of ASL for diagnosing AD was 0.83 (95% CI: 0.78–0.87), 0.81 (95% CI: 0.76–0.86), 4.52 (95% CI: 3.40–6.00), 0.22 (95% CI: 0.17–0.28), and 19.31(95% CI: 12.30–30.31), respectively. The pooled AUC = 0.8932. There was low heterogeneity across the included studies. Finally, sensitivity analysis suggested that the results were reliable.

Conclusion
ASL is an effective and accurate method for the diagnosis of AD. However, due to the limited quantity and quality of the included studies, the above conclusions need to be verified by more studies.

PROSPERO registration
PROSPERO registration number: CRD42023484059.