bluuebunny/crossref_metadata_2025_split huggingface dataset:
Endomyocardial biopsy in patients with myocarditis—still justified in the CMR era? A single-centre experience
Authors: Seuthe Katharina , Pfister Roman , Pennig Lenhard , Mons Ute , Klingel Karin , Freyhaus Henrik ten 
Published: November 2024

Abstract
Background
In the past decades, cardiovascular magnetic resonance (CMR) was established as a non-invasive tool supporting the diagnosis of myocarditis and there is often reluctance in performing EMB due to potentially severe complications. We sought to identify patient subgroups that could still benefit from EMB in the CMR era.

Methods
Data of patients presenting with myocarditis between 01/2016 and 06/2023 were analysed according to patient risks. Prespecified risk factors were (i) left ventricular ejection fraction (LVEF) ≤ 30%; (ii) severe arrhythmias; or (iii) pre-existing autoimmune disease. Furthermore, the subgroup of recurrent myocarditis cases was analysed separately.

Results
A total of 137 patients (35.5 ± 14.8 years, 80.3% male) were included. 26/137 patients had a documented LVEF ≤ 30%, 13/137 a LVEF > 30% with at least one other risk factor and 98/137 a LVEF > 30% without risk factors. EMB was performed in 21/26 patients with LVEF ≤ 30% (80.8%), in 7/13 patients with LVEF > 30% and risk factors (53.8%) and in 16/98 (16%) patients without risk factors. EMB led to the initiation of immunosuppressive therapy in 11/28 patients with risk factors (39.3%) and in none of the patients without risk factors (0/16, 0%, p = 0.003). With respect to the subgroup of patients presenting with recurrent myocarditis (n = 10), no specific therapy was initiated.

Conclusions
Due to a high therapeutic yield for initiation of immunosuppressive therapy in non-infectious myocarditis, performing EMB should be considered in all high-risk patients. In patients without clinical risk factors including cases of recurrent or relapsing myocarditis no specific therapy was initiated.
Microvascular disease and severe COVID-19 outcomes in UKBiobank participants with diabetes
Authors: Tochel Claire , Engelmann Justin , Giarratano Ylenia , Dhillon Baljean , Megaw Roly , Bernabeu Miguel O. 
Published: November 2024

Abstract

Aims
Early in the COVID-19 pandemic, evidence emerged suggesting that people with diabetic retinopathy (DR) or other microvascular diseases had greater risk of severe short-term outcomes. This study evaluated longer-term outcomes, providing more generalisable evidence.

Methods
We identified a cohort of UKBiobank participants with diabetes and retrieved their diagnostic codes for a variety of microvascular diseases, complications of diabetes and systemic comorbidities. We investigated relationships between diagnoses and the study outcome: admission to Critical Care or death from COVID-19, taking age, sex and diabetes duration into account. We tested relationships, adding baseline covariates and weighting diagnostic codes according to their recency prior to COVID-19 diagnosis.

Results
In univariate analyses, DR (OR: 1·519, p = 0·016) and microvascular disease (OR: 2·001, p = 0·000) were associated with greater risk of the outcome. In multivariate analyses, as expected, respiratory disease was most strongly associated with the study outcome, microvascular disease second. Adjusting analyses by number of admissions (general health proxy) and weighted diagnostic coding (comorbidity severity at COVID-19 diagnosis indicator), did not improve predictive power of the model.

Conclusions
The presence of microvascular disease in routinely-collected healthcare data predicts risk of COVID-19 severe outcomes, independently of general health, in a cohort of people with diabetes.
AI-driven segmentation of the pulp cavity system in mandibular molars on CBCT images using convolutional neural networks
Authors: Slim Marie Louise , Jacobs Reinhilde , de Souza Leal Renata Maíra , Fontenele Rocharles Cavalcante 
Published: November 2024

Abstract
Objective
To develop and validate an artificial intelligence (AI)-driven tool for automated segmentation of the pulp cavity system of mandibular molars on cone-beam computed tomography (CBCT) images.

Materials and methods
After ethical approval, 66 CBCT scans were retrieved from a hospital database and divided into training (n = 26, 86 molars), validation (n = 7, 20 molars), and testing (n = 33, 60 molars) sets. After automated segmentation, an expert evaluated the quality of the AI-driven segmentations. The expert then refined any under- or over-segmentation to produce refined-AI (R-AI) segmentations. The AI and R-AI 3D models were compared to assess the accuracy. 30% of the testing sample was randomly selected to assess accuracy metrics and conduct time analysis.

Results
The AI-driven tool achieved high accuracy, with a Dice similarity coefficient (DSC) of 88% ± 7% for first molars and 90% ± 6% for second molars (p > .05). The 95% Hausdorff distance (HD) was lower for AI-driven segmentation (0.13 ± 0.07) compared to manual segmentation (0.21 ± 0.08) (p < .05). Regarding time efficiency, AI-driven (4.3 ± 2 s) and R-AI segmentation (139 ± 93 s) methods were the fastest, compared to manual segmentation (2349 ± 444 s) (p < .05).

Conclusion
The AI-driven segmentation proved to be accurate and time-efficient in segmenting the pulp cavity system in mandibular molars.

Clinical Relevance
Automated segmentation of the pulp cavity system may result in a fast and accurate 3D model, facilitating minimal-invasive endodontics and leading to higher efficiency of the endodontic workflow, enabling anticipation of complications.
Prevalence of physical health complaints and analgesic medication use in children and the role of psychological and lifestyle factors
Authors: Konijnenberg C. 
Published: November 2024

Abstract
Aim
Given the rising trend of physical health problems reported by children and an increasing reliance on analgesic medications, this study aimed to describe the prevalence of physical health complaints and analgesic medication use in school-aged children aged 10–12 years. Additionally, associations with psychological and lifestyle factors were investigated.

Subjects and methods
A total of 99,806 children from Norway completed the Ungdata Junior survey between 2021 and 2022. This national standardized questionnaire-based survey contains questions measuring physical health, analgesic medication use, physical exercise, bullying, friendship, digital media use, and mental health. Multiple logistic regression analyses were conducted to investigate factors associated with daily physical health complaints and analgesic medication use.

Results
Of the children surveyed, 11.6% reported experiencing headaches, neck or shoulder pain, abdominal pain, and/or nausea on a daily basis during the past month. The prevalence of analgesic medication use during the past week was 23.6% for the entire sample and 25.9% for girls. Among the psychological and lifestyle factors investigated, mental health problems and being bullied were the strongest predictors of both physical health problems and analgesic medication use. Additionally, friendship, digital media use, and physical exercise also predicted physical health problems and analgesic medication use.

Conclusion
The study revealed a high prevalence of physical health complaints and analgesic medication use among children aged 9–12 years, highlighting significant associations with psychological and lifestyle factors. The findings suggest that children may rely on analgesic medications to manage not only physical pain but also emotional distress.
Enhancing keratoconus detection with transformer technology and multi-source integration
Authors: Ismael Osama 
Published: November 2024

AbstractKeratoconus is a progressive eye disease characterized by the thinning and conical distortion of the cornea, leading to visual impairment. Early and accurate detection is essential for effective management and treatment. Traditional diagnostic methods, relying primarily on corneal topography, often fail to detect early-stage keratoconus due to their subjective nature and limited scope. In this research, we present a novel multi-source detection approach utilizing transformer technology to predict keratoconus progression more accurately. By integrating and analyzing diverse data sources, including corneal topography, aberrometry, pachymetry, and biomechanical properties, our method captures subtle changes indicative of disease progression. Transformer networks, known for their capability to model complex dependencies in data, are employed to handle the multimodal datasets effectively. Experimental results demonstrate that our approach significantly outperforms existing methods, such as SVM-based, Random Forests-based, and CNN-based models, in terms of accuracy, precision, recall, and F-score. Moreover, the proposed system exhibits lower execution times, highlighting its efficiency in clinical settings. This innovative methodology holds the potential to revolutionize keratoconus management by enabling earlier and more precise interventions, ultimately enhancing patient outcomes and contributing significantly to both the medical and machine learning communities.
Neural Network for Valuing Bitcoin Options Under Jump-Diffusion and Market Sentiment Model
Authors: Pindza Edson , Clement Jules , Mwambi Sutene , Umeorah Nneka 
Published: November 2024

AbstractCryptocurrencies and Bitcoin, in particular, are prone to wild swings resulting in frequent jumps in prices, making them historically popular for traders to speculate. It is claimed in recent literature that Bitcoin price is influenced by sentiment about the Bitcoin system. Transaction, as well as the popularity, have shown positive evidence as potential drivers of Bitcoin price. This study introduces a bivariate jump-diffusion model to capture the dynamics of Bitcoin prices and the Bitcoin sentiment indicator, integrating trading volumes or Google search trends with Bitcoin price movements. We derive a closed-form solution for the Bitcoin price and the associated Black–Scholes equation for Bitcoin option valuation. The resulting partial differential equation for Bitcoin options is solved using an artificial neural network, and the model is validated with data from highly volatile stocks. We further test the model’s robustness across a broad spectrum of parameters, comparing the results to those obtained through Monte Carlo simulations. Our findings demonstrate the model’s practical significance in accurately predicting Bitcoin price movements and option values, providing a reliable tool for traders, analysts, and risk managers in the cryptocurrency market.
High Azimuthal Mode Selectivity of a Cavity with Mode-Joining Corrugations for High-Harmonic Gyrotrons
Authors: Shcherbinin Vitalii I. , Tkachova Tetiana I. , Hlushchenko Anton V. , Tatematsu Yoshinori , Thumm Manfred , Jelonnek John 
Published: November 2024

Abstract
Mode-joining longitudinal corrugations are studied as a means of high azimuthal mode selectivity for cavities of high-harmonic terahertz gyrotrons. Their number dictates the choice of the jointed operating mode, which has a form of strongly coupled co- and counter-rotating azimuthal harmonics. It is found that the distinctive feature of this mode is a weak dependence of eigenvalue and ohmic losses on corrugation size. First, this favors the use of mode-joining corrugations with variable depth for efficient suppression of all competing modes by both diffractive and ohmic losses in the gyrotron cavity. Second, this provides a good robustness of gyrotron performance against manufacturing errors in the size of corrugations and only a minor conversion of the operating mode to spurious modes at junctions of the corrugated cavity with smooth-walled waveguides. The beneficial properties of mode-joining corrugations are demonstrated by a cavity design for a gyrotron operated in the second-harmonic TE±9,4 and third-harmonic TE±18,4 modes at 398 GHz and 593 GHz, respectively.
Risk Factors of At-Risk/Problem Gambling Among Young Adult Spanish Students
Authors: Krotter A. , Secades-Villa R. , Iza-Fernández C. , González-Roz A. 
Published: November 2024

AbstractThe gambling industry in Spain has experienced sustained growth in recent years. This has raised concerns about its potential impact, particularly during emerging adulthood, which is a high-risk period for the development of problematic gambling behaviors. The aim of this study was to estimate the prevalence of problem gambling in young Spanish adults and to examine individual factors associated with at-risk or problem gambling. The participants were 2762 young adults studying at university and vocational schools (64.16% women, Mage = 19.47; SD = 1.64) recruited in three Spanish autonomous communities. Individuals completed an e-assessment including sociodemographic variables, gambling-related characteristics, and the Problematic Gambling Severity Index. A total of 3.51%, 2.14%, and 0.51% were low risk, moderate risk, and problem gamblers, respectively. Predictors of at-risk/problem gambling were early gambling onset (OR 0.790; 95% CI 0.672, .929), mixed versus offline modality gambling access (OR 2.216; 95% CI 1.396, 3.516), and having gambled in the previous year in a casino (OR 1.910; 95% CI 1.194, 3.056), sports betting (OR 1.597; 95% CI 1.003, 2.542), or electronic gaming machines (OR 2.111; 95% CI 1.064, 4.189). Gambling is ubiquitous in the general population and a high proportion of young adults present low and moderate risk of problem gambling. Addressing gambling is a public health challenge and policies to regulate it should be targeted at delaying the age of onset and restricting access to online gambling.
Emotion Regulation Versus Emotion Care as a Mechanism of Mindfulness in Predicting Well-Being
Authors: Lam Shui-fong , Tsang Kitty Ka Yee , Shum Kathy Kar-man , Wong Gloria Hoi Yan , Wong Savio Wai-Ho , Wu Ka Chun , Kwan Hong Wang , Su Michael R 
Published: November 2024

Abstract
Objectives
Researchers predominantly understand mindfulness in terms of cognitivist or top-down models. However, the applicability of emotion-regulation strategies from such models in mindfulness is questionable. The aim of the current study was to determine whether emotion-regulation strategies (distraction, suppression, and cognitive reappraisal) or alternative emotion-care strategies (anchoring, acceptance, and decentering) were mechanisms that explained the effects of mindfulness on well-being.

Methods
A randomized controlled trial was conducted with 283 educators assigned to two groups (8-week mindfulness training vs. waitlist control). At three time points, T0 (baseline), T1 (post-intervention), and T2 (2-month follow-up), they completed questionnaires that measured their levels of mindfulness, well-being (i.e., general health, stress, positive and negative affect, life satisfaction), and frequency in using strategies of emotion regulation and emotion care.

Results
Participants in the mindfulness training group reported higher levels of mindfulness and well-being. They also reported more frequent use of all three emotion-care strategies but only one emotion-regulation strategy (cognitive reappraisal) than their counterparts in the control group at T1 and T2. Mediation analyses indicated that none of the three emotion-regulation strategies mediated the effects of mindfulness training on well-being. Instead, anchoring and decentering were significant mediators (p < 0.05) while acceptance was a near significant mediator (p < 0.06).

Conclusions
Emotion-care strategies instead of emotion-regulation strategies were found to be a mechanism that explained the effectiveness of mindfulness. The results offer evidence for an alternative to cognitivist or top-down models in understanding the mechanisms of mindfulness.
Deep Q-learning Network-based Imbalanced Classification for Fatality Prediction of Single-Vehicle Motorcycle Crashes
Authors: Zulherman Dodi , Yang Jia , Shimizu Kosuke , Yokota Yasunari 
Published: November 2024

AbstractEven though the number of motorcycle crash accidents in Japan has trended downward over the past decade, there persists a necessity to propose effective safety measures targeting factors associated with fatal crash accidents. Previous studies have demonstrated the utility of various machine-learning models in classifying and investigating crash accidents involving motorcycles. However, the comprehensive examination of fatal cases in single-vehicle motorcycle crashes remains limited due to imbalanced crash data between fatal and non-fatal cases. Despite the ability of several conventional machine learning (ML) models to predict motorcycle crash severity, the imbalanced single-vehicle crash data poses a challenge in accurately classifying fatal crashes using ML models. To address this challenge, the deep q-learning network-based imbalanced classification (DQNIC), a modification of Lin’s DQNimb, was employed to classify single-vehicle motorcycle crashes (fatal and non-fatal). Moreover, SHAP value analysis was utilized to interpret the DQNIC model and explore the factors associated with fatal crashes. The 4-year crashes in Japan nationwide are used for empirical study. The significant findings indicate 1) that the DQNIC model demonstrates effective classification of fatal crashes and 2) that factors such as the crash type, road width, terrain, road alignment, and collision point exert significant influence on fatal crashes. Traffic safety professionals can use the findings of this study to implement effective measures to reduce the fatality rate of single-vehicle motorcycle crashes.
Vigilant trust in scientific expertise
Authors: Metzen Hanna 
Published: November 2024

AbstractThis paper investigates the value of trust and the proper attitude lay people ought to have towards scientific experts. Trust in expertise is usually considered to be valuable, while distrust is often analyzed in cases where it is harmful. I will draw on accounts from political philosophy and argue that it is not only public trust that is valuable when it comes to scientific expertise – but also public vigilance. Expertise may be distorted in different ways, which cannot be remedied by internal control mechanisms alone. This reveals the importance of some forms of democratic oversight. The proper attitude is vigilant trust in expertise. However, vigilant trust seems to be a contradictory notion: How can one be trusting and watchful at the same time? I will show that it is not, and that trust and vigilance can be compatible to a certain extent. I will do so by distinguishing between different levels of both trust and vigilance. Interestingly, this argument requires acknowledging the value of some forms of distrust in scientific expertise, even if that distrust targets trustworthy experts.
Strategies for Operationalizing Xenotransplantation
Authors: Anderson Douglas J. , Locke Jayme E. 
Published: November 2024

Abstract
Purpose of Review
Xenotransplantation represents the most readily available solution to the shortage of donor organs for transplantation. This review seeks to address progress made in xenotransplantation thus far, and forecast barriers and opportunities for eventual clinical translation.

Recent Findings
Over the past several years, significant progress has been made to advance xenotransplantation to the clinic. Increasingly complex genetic manipulation of the donor source pigs has allowed for prevention of hyperacute rejection and graft function with clinically-available, FDA-approved immunosuppression. The Parsons Model utilizing recently deceased humans as recipients has allowed for the study of xenografts in a pre-clinical human model. These studies have confirmed function of kidney xenografts and maintenance of renal physiology. Two cases of human heart xenotransplantation have been reported, with limited early success. Two human recipients have received porcine kidney grafts. Moving forward, careful recipient selection will be needed for the first clinical trials. Diligent screening of the donor animals will reduce the risk of zoonotic infection, and post-transplant surveillance of recipients will remain important.

Summary
Studies to date have demonstrated the feasibility of xenotransplantation with promising early results. With additional studies designed to address the remaining unanswered questions, xenotra
nplantation may finally realize its potential and become a clinical reality.
Decadal Variability of Dry Days in Central Chile
Authors: Latoja Daniela , Lillo-Saavedra Mario , Gonzalo-Martin Consuelo , Godoy-Faúndez Alex , Somos-Valenzuela Marcelo , Rivera Diego 
Published: November 2024

AbstractDry days are crucial in precipitation variability and water scarcity, particularly in Mediterranean regions facing increasing aridity. Despite their importance, most research focuses on precipitation amounts and temporal dynamics. This study addresses this gap by analyzing dry days’ temporal and spatial variability in central Chile (32–40 S), a region experiencing prolonged drought. We examined dry day patterns from 1960 to 2021 using high-resolution gridded precipitation data, defining dry days with five precipitation thresholds (0.10, 1, 2.5, 5, and 10 mm/day). Principal component and trend analyses were employed to characterize spatial and temporal variability. Results reveal a spatial pattern of dry days closely following precipitation patterns, with more dry days in northern and coastal areas. The first principal component explains 70–80% of the variance, and clustering methods allowed the definition of five homogeneous regions with distinct monthly dry-day characteristics. Long-term trends show a significant increase in annual dry days south of 38°S, while trends are weaker and non-significant further north. Notably, trend direction is highly sensitive to the analysis period, with some regions showing opposing trends before and after 1982. The 2010–2019 megadrought is detectable in decadal anomalies. We found links between dry day anomalies and large-scale climate patterns, suggesting modulation by changes in subtropical and extratropical atmospheric circulation. This comprehensive characterization of dry day climatology and variability provides crucial insights for water resource management and climate change adaptation in central Chile and similar Mediterranean regions worldwide. Our findings highlight the importance of considering dry day frequency in drought assessment and water planning, contributing to a more nuanced understanding of precipitation patterns in Mediterranean climates.
Household changes in post-Apartheid South Africa
Authors: Garenne Michel , Stiegler Nancy 
Published: November 2024

AbstractSouth Africa underwent dramatic political and social changes since 1991. This study documents changes in household size and structure from 1991 to 2022 using population census data. The average household size declined from 4.9 to 3.5 persons; the proportion of female-headed households increased from 30 to 50%; the proportion of single person households increased from 10 to 25%. These changes were found in all social strata, by residence status, by population group (race), and by province. They were associated with major changes: the dismantling of Apartheid, economic development and restructuration, rising unemployment, fertility decline, decline in marriage rates, new housing arrangements, and the HIV/AIDS crisis. In addition, a new wave of freedom from traditional structures seems to explain the outstanding development of people living alone, especially among young men.