bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effectiveness and cost‐effectiveness of an app and rewards‐based intervention in type 2 diabetes: A randomised controlled trial
Authors: Finkelstein Eric Andrew , Gardner Daphne Su‐Lyn , Tham Kwang Wei , Gandhi Mihir , Cheung Yin Bun , Bairavi Joann , Lee Chun Fan , Tan Ngiap Chuan , Yeoh Ester , Lee Phong Ching , Ho Emily Tse Lin , Adamjee Thofique , Bee Yong Mong , Goh Su‐Yen 
Published: December 2024

AbstractAimDigital health interventions and economic incentives have shown promise in facilitating diabetes self‐management, though evidence is limited. Therefore, this study aimed to evaluate the effectiveness and cost‐effectiveness of a comprehensive app‐based diabetes self‐management programme with rewards for healthy behaviours and health outcomes.Materials and MethodsThe TRIal to slow the Progression Of Diabetes (TRIPOD) study was an open‐label, parallel‐group, randomised controlled trial conducted at Duke‐NUS Medical School, Singapore. Adults with Type 2 Diabetes (diabetes), HbA1c of 7.5%‐11.0% (inclusive) and taking at least one oral diabetes medication were eligible. In total, 269 participants were randomised across three arms [Usual care (UC): 117, diabetes management programme (DMP) (intervention without rewards): 36, DMP+ (intervention with rewards): 116]. Data were analysed using intention‐to‐treat analysis with change in HbA1c at month 12 between DMP+ and UC as the primary outcome. Cost‐effectiveness of DMP+ relative to UC was also calculated.ResultsMean HbA1c improved by 0.1% in UC and by 0.5% in DMP+ at 12 months, revealing a mean difference of 0.4% (95% confidence interval (CI): −0.70, −0.08, p = 0.015). The odds ratio of HbA1c improvements of >0.5% was 2.12 (95% CI: 1.17, 3.85, p = 0.013) for DMP+ relative to UC. The incremental cost‐effectiveness ratio of DMP+ relative to UC was SGD8,516 (USD6,531) per quality‐adjusted life year gained if effectiveness could be maintained with a single year of intervention.ConclusionsA comprehensive app‐based diabetes self‐management programme with rewards for healthy behaviours and health outcomes (DMP+) cost‐effectively improved glycaemic control in Type 2 diabetes patients. Organizations focusing on value‐based healthcare should consider subsidising similar interventions.
The fibrosis investigating navigator in diabetes (FIND): A tool to predict liver fibrosis risk in subjects with diabetes
Authors: Li Mingkai , Yu Hongsheng , Wan Sizhe , Hu Fulan , Luo Qingtian , Gong Wei 
Published: December 2024

AbstractBackgroundType 2 diabetes increases the risk of cirrhosis and liver cancer. Noninvasive and early assessment of liver fibrosis is essential. We aimed to develop a score to aid in the initial assessment of liver fibrosis in the diabetic population.MethodsA fibrosis investigating navigator in diabetes (FIND) score was developed and validated in the NHANES dataset (2017–2020). Fibrosis was defined as a liver stiffness measurement (LSM) ≥8.0 kPa. The diagnostic accuracies of FIB‐4, NFS, LiverRisk, steatosis‐associated fibrosis estimator (SAFE) and metabolic dysfunction–associated fibrosis (MAF‐5) were compared. FIND was also externally validated in various liver diseases via biopsy as a reference in an Asian centre between 2016 and 2020. Finally, we examined the prognostic implications of the FIND index utilizing data from the UK Biobank cohort (2006–2010).ResultsThe FIND score model yielded an AUROC of 0.781 for the prediction of an LSM ≥8 kPa in the validation set, which was consistently greater than that of other available models (all p < 0.05). In the whole NHANES dataset, the 85% sensitivity cut‐off of 0.16 corresponded to a NPV of 91.9%, whereas the 85% specificity cut‐off of 0.31 corresponded to a PPV of 50.6%. FIND displayed overall accuracies similar to those of the other models in staging fibrosis stages, with biopsy used as a reference. In the UK Biobank cohort, FIND >0.31 was associated with an increased risk of all‐cause and liver‐related mortality in the diabetic population in adjusted models (HR, 1.75; 95% CI, 1.62–1.89; HR, 23.59; 95% CI, 13.67–40.69).ConclusionsIn diabetes patients, the novel FIND score performs well in identifying subjects at risk of liver fibrosis and predicting all‐cause and liver‐related mortality.
Advances in Microbiome‐Based Therapeutics for Dermatological Disorders: Current Insights and Future Directions
Authors: Madaan Tushar , Doan Kyla , Hartman Alexandra , Gherardini Dominick , Ventrola Alec , Zhang Yuhang , Kotagiri Nalinikanth 
Published: December 2024

ABSTRACTThe human skin hosts an estimated 1000 bacterial species that are essential for maintaining skin health. Extensive clinical and preclinical studies have established the significant role of the skin microbiome in dermatological disorders such as atopic dermatitis, psoriasis, diabetic foot ulcers, hidradenitis suppurativa and skin cancers. In these conditions, the skin microbiome is not only altered but, in some cases, implicated in disease pathophysiology. Microbiome‐based therapies (MBTs) represent an emerging category of live biotherapeutic products with tremendous potential as a novel intervention platform for skin diseases. Beyond using established wild‐type strains native to the skin, these therapies can be enhanced to express targeted therapeutic molecules, offering more tailored treatment approaches. This review explores the role of the skin microbiome in various common skin disorders, with a particular focus on the development and therapeutic potential of MBTs for treating these conditions.
Drivers and Annual Totals of Methane Emissions From Dutch Peatlands
Authors: Buzacott Alexander J. V. , Kruijt Bart , Bataille Laurent , van Giersbergen Quint , Heuts Tom S. , Fritz Christian , Nouta Reinder , Erkens Gilles , Boonman Jim , van den Berg Merit , van Huissteden Jacobus , van der Velde Ype 
Published: December 2024

ABSTRACTRewetting peatlands is required to limit carbon dioxide (CO2) emissions, however, raising the groundwater level (GWL) will strongly increase the chance of methane (CH4) emissions which has a higher radiative forcing than CO2. Data sets of CH4 from different rewetting strategies and natural systems are scarce, and quantification and an understanding of the main drivers of CH4 emissions are needed to make effective peatland rewetting decisions. We present a large data set of CH4 fluxes (FCH4) measured across 16 sites with eddy covariance on Dutch peatlands. Sites were classified into six land uses, which also determined their vegetation and GWL range. We investigated the principal drivers of emissions and gapfilled the data using machine learning (ML) to derive annual totals. In addition, Shapley values were used to understand the importance of drivers to ML model predictions. The data showed the typical controls of FCH4 where temperature and the GWL were the dominant factors, however, some relationships were dependent on land use and the vegetation present. There was a clear average increase in FCH4 with increasing GWLs, with the highest emissions occurring at GWLs near the surface. Soil temperature was the single most important predictor for ML gapfilling but the Shapley values revealed the multi‐driver dependency of FCH4. Mean annual FCH4 totals across all land uses ranged from 90 ± 11 to 632 ± 65 kg CH4 ha−1 year−1 and were on average highest for semi‐natural land uses, followed by paludiculture, lake, wet grassland and pasture with water infiltration system. The mean annual flux was strongly correlated with the mean annual GWL (R2 = 0.80). The greenhouse gas balance of our sites still needs to be estimated to determine the net climate impact, however, our results indicate that considerable rates of CO2 uptake and long‐term storage are required to fully offset the emissions of CH4 from land uses with high GWLs.
Resilience Indicators for Tropical Rainforests in a Dynamic Vegetation Model
Authors: Bathiany Sebastian , Nian Da , Drüke Markus , Boers Niklas 
Published: December 2024

ABSTRACTTropical forests and particularly the Amazon rainforest have been identified as potential tipping elements in the Earth system. According to a dynamical systems theory, a decline in forest resilience preceding a potential shift to a savanna‐like biome could manifest as increasing autocorrelation of biomass time series. Recent satellite records indeed exhibit such a trend and also show larger autocorrelation, indicative of reduced resilience, in drier forest regions. However, it is unclear which processes underlie these observational findings and on which scales they operate. Here, we investigate which processes determine tropical forest resilience in the stand‐alone, state‐of‐the‐art dynamic global vegetation model LPJmL. We find that autocorrelation is higher in dry climates than wet climates (approx. 0.75 vs. 0.2, for a lag of 10 years), which qualitatively agrees with observations. By constructing a reduced version of LPJmL and by disabling and enabling certain processes in the model, we show that (i) this pattern is associated with population dynamics operating on different time scales in different climates and (ii) that the pattern is sensitive to the allocation of carbon to different pools, especially in years of stress. Both processes are highly uncertain, oversimplified or even lacking in most Earth system models. Our results indicate that the observed spatial variations and trends in vegetation resilience indicators may be explained by local physiological and ecological mechanisms alone, without climate–vegetation feedbacks. In principle, this is consistent with the view that the Amazon rainforest is responding to climate change locally and does not necessarily need to approach one large‐scale tipping point, although the latter cannot be ruled out based on our findings.
The Influence of Submission Devices on User‐Generated Content—A Systematic Literature Review and Meta‐Analysis
Authors: Wolf Lukas , Madlberger Maria 
Published: December 2024

ABSTRACTIn today's ever‐changing and modern world, user‐generated content (UGC) on the internet exerts a significant influence on consumers. Therefore, understanding UGC and its design is of great interest to researchers and practitioners alike. One examined factor influencing UGC is the submission device, such as the smartphone, tablet, or laptop. Despite digital devices offering similar capabilities for content creation and submission, they differ substantially in their characteristics, including screen size, user interface, and usage context. This study conducts a framework‐based systematic literature review on the influence of submission devices on UGC. Through a comprehensive descriptive analysis, the authors examine the theories, contexts, and methods used, offering a structured overview of current research. In a subsequent weight analysis and meta‐analysis, the strength and combined effect sizes of the relationships studied are illustrated and an insight into moderators that explain result variations is provided. The findings reveal that the choice of submission device (mobile vs. nonmobile) exerts a multi‐faceted influence on the creation of UGC. Mobile devices, as opposed to nonmobile devices, demonstrate a significant and moderate negative impact on temporal distance, text length, and diversity of UGC across studies. This analysis clarifies previous inconsistencies and establishes the robustness of specific relationships, providing practical recommendations for managers to adapt marketing strategies for different digital devices.
Prediction Model of the Temporal Dynamics of Severe Pest Cashew Anacampsis phytomiella Using Artificial Neural Networks
Authors: Pancieri Guilherme Pratissoli , Mota Maria do Socorro Cavalcante de Souza , Paes Jhersyka da Silva , Sant'Ana Letícia Caroline da Silva , Soares Juliana Magalhães , das Graças do Carmo Daiane , da Silva Ricardo Siqueira , Picanço Marcelo Coutinho 
Published: December 2024

ABSTRACTThe cashew tree (Anacardium occidentale) is a tropical plant that yields the world's most consumed nut. Anacampsis phytomiella (Lepidoptera: Gelechiidae) is the primary pest that infests cashew nuts, leading to losses of up to 80%. The comprehension of the spatiotemporal dynamics of pests enables enhanced planning of sampling and control methods. Artificial neural networks (ANNs) are computational models in machine learning with high predictive capability. Thus, the objective of this study was to determine a seasonal dynamic model of A. phytomiella using ANNs. Over 3 years, the pest attack intensity and climatic elements were monitored in two cashew orchards. A total of 1716 ANN models were determined. The model predictors included the time of plant fruiting, average air temperature, dew point, atmospheric pressure and rainfall. The temperature, atmospheric pressure and time of plant fruiting had a positive effect on pest infestation, while the opposite occurred with rainfall. The pest infestation curve in relation to the dew point exhibited a point of maximum. The model successfully predicted the intensity of A. phytomiella infestation across different years, plant fruiting stages and pest densities. Therefore, the ANN model determined in this study is promising for predicting the intensity of A. phytomiella infestation in cashew orchards.
18S rRNA Metagenomic Analysis of Nodular Gill Disease in Swiss Rainbow Trout (Oncorhynchus mykiss)
Authors: Wynne James W. , English Chloe J. , Vannetti Stefania M. , Rigby Megan , Quezada‐Rodriguez Petra R. , Knüsel Ralph , Huynh Christine , Schmidt‐Posthaus Heike 
Published: December 2024

ABSTRACTNodular gill disease (NGD) is a serious proliferative gill condition that affects farmed salmonids, particularly in Europe. While the cause of NGD remains unknown (and maybe multifactorial), various amoebae are often isolated from the gills of affected fish and can in some cases be seen associated with lesions by histopathology. The present study aimed to quantify the abundance of different amoeba species directly from the gills of rainbow trout affected by NGD and healthy controls. An 18S rRNA amplicon metagenomic approach was employed to profile the diversity and abundance of micro‐eukaryotes (including amoebae) while suppressing the amplification of host DNA using a salmonid‐specific C3 spacer blocking primer. The 18S rRNA metagenomics approach identified a diversity of micro‐eukaryotes on the gills of rainbow trout, including the phylum's Amoebozoa, Diatomea, Platyhelminthes and Ciliophora. Rainbow trout clinically affected by NGD had a significantly higher abundance of a specific sequence (zOTU2) classified as Vannella sp. compared to healthy controls. A quantitative PCR assay was then developed and validated which accurately quantified the abundance of this Vannella sp. sequence from a NGD outbreak in a Swiss rainbow trout farm. Additional PCR and Sanger sequencing analysis of the zOTU2 sequence demonstrated that this sequence is most likely derived from Vannella mustalahtiana. Our study highlights the potential role of Vannella mustalahtiana in NGD in Switzerland and further describes a specific and validated diagnostic PCR assay for accurate detection of this Vannella species.
Comparison of three different satellite data on 2D flood modeling using HEC‐RAS (5.0.7) software and investigating the improvement ability of the RAS Mapper tool
Authors: Kaya Yunus Ziya , Üneş Fatih 
Published: December 2024

AbstractFlood modeling is essential to determine and protect vulnerable areas. However, due to complexity of flooding, it is challenging to model floods with a high level of sensitivity. While many factors affect flood models' accuracy, topography is among the most critical. With developing technologies, designing high‐accuracy topographical data is becoming more feasible, especially for small catchments. In this study, the authors focus on macro‐scale modeling using different types of satellite data across the Amik Plain; a large plain with a complex stream network. SRTM, Aster, and Alos Palsar satellite data were used to create digital terrain models (DTMs). The pre‐evaluation of the results showed that even the main streams in the Amik Plain were not visible. So, the geometry of the streams was created and added to the digital elevation models using the HEC‐RAS software RAS Mapper tool. A flood in 2012 was simulated using all three improved DTMs. As a result, it is seen that an enhanced version of the DTM created from SRTM data provides the best performance for use in macro‐scale flood modeling. The usage of the RAS Mapper tool as a GIS tool also performed well in the case of DTM improvements. The DTM improvements on the satellite data for the large plains can give a fairly reasonable output instead of using high‐cost sensitive data.
The ubiquitination degradation of KLF15 mediated by WSB2 promotes lipogenesis and progression of hepatocellular carcinoma via inhibiting PDLIM2 expression
Authors: Chen Jing , Chen Xuemin , Cai Huihua , Yang Yong , Zhu Qinqin , Sun Donglin , Gao Cao 
Published: December 2024

AbstractBackground and AimKrüppel‐like factors15 (KLF15) is a cancer suppressor in many cancers. However, its precise function in the development of hepatocellular carcinoma (HCC) remains unclear. Lipogenesis is necessary for the development of HCC. This research aims to investigate the role of KLF15 in the regulation of hepatic lipid production and HCC progression.MethodsThe binding relationships among genes were confirmed by ChIP, dual luciferase assays, and Co‐IP. Lipogenesis was examined by oil red O staining. Triglyceride and cholesterol levels were measured through commercial kits. The effect of treatment on HCC cell viability, proliferation, migration, and invasion were assessed using CCK‐8, clone formation, or transwell assays. A subcutaneous tumorigenic model was utilized to explore the effects of PDLIM2 in HCC in vivo.ResultsKLF15 were downregulated in human HCC tissues. KLF15 overexpression reduced lipid droplet production, suppressed the expression of genes associated with lipogenesis, and promoted cell proliferation, migration, and invasion. KLF15 suppressed the NF‐κB pathway through transcriptional activation of PDLIM2. PDLIM2 knockdown attenuated the effect of KLF15 overexpression on HCC. WSB2 degraded KLF15 through ubiquitination to promote HCC lipogenesis and development.ConclusionThe ubiquitination degradation of KLF15 was mediated by WSB2, which led to transcriptional repression of PDLIM2 and further activation of the NF‐κB pathway, ultimately promoting HCC lipogenesis and development.
Plasma brain‐derived tau correlates with cerebral infarct volume
Authors: Gonzalez‐Ortiz Fernando , Holmegaard Lukas , Andersson Björn , Brännmark Cecilia , Blomstrand Christian , Zetterberg Henrik , Jood Katarina , Blennow Kaj , Jern Christina , Stanne Tara M. 
Published: December 2024

AbstractBackgroundA blood‐based biomarker that accurately reflects neuronal injury in acute ischemic stroke could be an easily accessible and cost‐effective complement to clinical and radiological evaluation. Here, we investigate whether plasma levels of the novel biomarker brain‐derived tau (BD‐tau) reflect cerebral infarct volumes and whether BD‐tau can improve clinical outcome prediction.MethodsThe present study included 713 consecutive cases from two different hospital‐based cohorts, the Sahlgrenska Academy Study on Ischemic Stroke (SAHLSIS) and SAHLSIS phase 2 (SAHLSIS2). Acute stroke severity was determined by the Scandinavian Stroke Scale converted to the National Institutes of Health stroke scale (NIHSS) in SAHLSIS and by the NIHSS in SAHLSIS2. All participants were assessed for functional outcome 3 months after stroke by the modified Rankin Scale, and 254 participants in SAHLSIS had quantitative neuroimaging available.FindingsPlasma BD‐tau concentrations and cerebral infarct volumes were highly correlated (ρ 0.72, p < 0.001). BD‐tau improved the prognostic accuracy of suffering an unfavorable outcome over age and stroke severity in the whole cohort. However, the gain in predictive power was dependent on stroke severity and infarct location. The largest improvement was observed for mild ischemic strokes (NIHSS <5; area under the curve [AUC] = 0.73 for age + NIHSS versus AUC = 0.84 with addition of BD‐tau; DeLong p 0.02), posterior circulation stroke (AUC = 0.75 vs. AUC = 0.84; DeLong p 0.06) and more specifically for infarcts in the brainstem/cerebellum (AUC = 0.74 vs. 0.87; DeLong p 0.009).ConclusionPlasma BD‐tau can provide information on the extent of acute neuronal damage in ischemic stroke and adds prognostic value for outcome, especially for mild and posterior circulation strokes.
Impact of internet addiction and gaming disorder on body weight in children and adolescents: A systematic review
Authors: Che Mokhtar Mushira , McGee Richard 
Published: December 2024

AimInternet use and gaming have increased in daily life. Internet addiction has not been recognised in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition or 11th Revision of International Classification of Diseases despite gaming addiction having been listed since 2013. Previous studies have shown an association between internet use and weight gain or disordered eating patterns in the general population, however none have addressed this association in a paediatric population. The aim of this study is to investigate the relationship between internet addiction or gaming addiction and weight in children and adolescents under 18 years of age.MethodsA systematic search of electronic databases was conducted, and articles were screened for relevance. The Joanna Briggs Institute (JBI) 3 critical appraisal tool was used to assess quality of studies. Due to heterogeneity of the studies, a meta‐analysis could not be performed, thus a narrative summary was performed with the results summarised and tabulated.ResultsOf 8114 initial articles identified, we included 14 studies. The review produced inconsistent results, with three of the 14 studies demonstrating no statistically significant relationship between internet and/or gaming addiction and obesity. The remaining 11 studies showed a statistically significant positive association. One study demonstrated that those who are underweight as well as overweight and obese were more likely to have problematic internet use. Four articles demonstrated increased rates of poor nutritional habits, poor eating patterns and weight control behaviour in children and adolescents with internet addiction or gaming disorder. Another study showed no statistically significant association between internet addiction and disordered eating or eating disorder.ConclusionMost of the available studies revealed increased rates of obesity in children and adolescents with internet addiction or gaming disorder, with poor patterns of eating. Clearer definitions and consistent use of terminology are required to enable more robust research in this field.