bluuebunny/crossref_metadata_2025_split huggingface dataset:
Exploring the Educational Journey: Perspectives of Ethnic Minority GP- Trainees in Dutch GP-Specialty Training - A Qualitative Interview Study
Authors: Moppes N.M. van , Nasori M. , Jorissen A.C. , Es J.M. van , Bont J. , Visser M.R.M. , Muijsenbergh M.E.T.C. van den 
Published: May 2024

Abstract

Background:
Previous research highlights persistent differential attainment by ethnicity in medical education, wherein the perceived inclusiveness significantly influences ethnic minority students’ and trainees’ outcomes. Biased organizational practices and microaggressions exacerbate the challenges faced by ethnic minorities, leading to lower academic performance and higher dropout rates. Consequently, understanding ethnic minority GP-trainees’ experiences and perspectives regarding relevant educational aspects is crucial for addressing these disparities and cultivating a more inclusive environment within medical education.
Research question:
We aimed to investigate the experiences of minority GP-trainees throughout their educational journey in Dutch GP-specialty training, emphasizing their challenges, sources of support, and suggestions for enhancing their learning environment.
Method:
We conducted semi-structured, in-depth interviews with minority GP trainees, employing purposive convenience sampling to ensure diversity across multiple dimensions. These included gender, age, ethnicity, social background, migration generation, educational stage, encountered challenges, sources of support, and the GP training institute attended. The analysis involved iterative, open coding, axial coding, and thematic analysis.
Results:
All fourteen ethnic minority interviewees had faced educational barriers stemming from misunderstandings and stereotyping in a predominantly 'white' organization. These barriers impacted various aspects of their education, including professional identity formation, application, admission, assessment procedures, social networks, course content, and expert guidance. Microaggressions permeated throughout their educational journey, hindering their full expression and potential. Their ideal GP-specialty training emphasized uniqueness of all trainees, comprehensive staff engagement in inclusivity, robust diversity, equity, and inclusion (DEI)-policies, individual mentorship, transparent standards, concise language usage in test questions, and bias elimination through mandatory DEI staff training.
Conclusion:
Ethnic minority GP-trainees in the Netherlands face significant challenges like biased assessment and admission, stereotyped course content, inadequate support networks, and microaggressions, putting them at risk for underperformance outcomes. They emphasize the need for inclusive training with robust DEI-policies to eliminate bias.
Effects of BMI on prognosis , disease-free survival and overall survival of breast cancer
Authors: Zangouri Vahid , Balaneji Souzan Soufizadeh , Golmoradi Roya , Kafili Ehsan , Bologhi Saleh , Mousavi Seyed Amin , Hesar Ataollah Ahmadi , Amestejani Morteza 
Published: May 2024

Abstract

Background
Obese breast cancer patients have worse prognosis than normal weight patients, but the level at which obesity is prognostically unfavorable is unclear. This study aimed to investigate different effects of Body Mass Index (BMI) on prognosis disease-free survival and overall survivor of breast cancer patients.
Method
This retrospective cohort study analyzed the medical records of breast cancer patients who sought treatment at Namazi hospital in Shiraz, Iran between 2014 and 2019. Three groups of patients were divided according to BMI. Menopausal status, BMI status, clinicopathological characteristics, treatment, and overall survival (OS), and disease free survival (DFS) were comprehensively evaluated.
Results
Of the 7134 breast cancer patients, the majority (42.6%) were in 25–30 kg/m2. Menopausal status significantly were associated with obesity (P <0 .001). The majority of patients were categorized as grade 2 and stage 2 according to the BMI categories (P = 0.12, P = 0.08, respectively). BMI categories regardless of menopausal status displayed increased 1, 3, and 5-year DFS and 5- year OS in stage 1 and increased 1, 3, and 5-year OS and 1 and 3-year DFS in stage 2. In stage 3, the risks of relapse and death were significantly decreased in all three groups of BMI patients with post-menopausal period.
Conclusion
Obesity leads to worse DFS and OS in patients with BC and the effects of obesity on the breast cancer prognosis seem to be clinically related to menopausal status. Once validated, these results should be considered in the development of prevention programs.
Individual empowerment and community norm effects of engaging young husbands in reproductive health in rural India: findings from a pilot study
Authors: Diamond-Smith Nadia , Vaishnav Yogesh , Choudhary Usha , Sharma Payal , Kachhwaha Ankur , Panjalingam Tamera , Vallin Janelli , Das Debangana , Gopalakrishnan Lakshmi 
Published: May 2024

Abstract

Background: Despite decades of a call to action to engage men in reproductive health, men are often left out of programs and interventions. In India, where half of pregnancies are reported as unintended, patriarchal gender norms and still dominant patterns of arranged marriages make engaging men in family planning and strengthening couples communication critical in increasing reproductive autonomy and helping young couples meet their reproductive goals. This study explores the feasibility and acceptability from the men’s perspective of the pilot of a gender transformative intervention for newly married couples in India.
Methods: A pilot study was conducted of TARANG, a 4-month intervention for newly married women, with light touch engagement of husbands (4 sessions). A total of 41 husbands participated in the pilot, and we collected baseline knowledge and endline feasibility and acceptability data from them, along with in depth qualitative interviews with 13 men. The study was conducted in June-January, 2023.
Results: Men had low levels of knowledge about biology, family planning, with the majority of men reporting that no one had provided them information about these topics. Most men wanted to delay the first birth by at least 2 years, yet less than a quarter had discussed childbearing plans with their partner or engaged in family planning methods. While all men reported high acceptability (satisfaction and usefulness), feasibility (participation) was low, with only 43% attending 2 or more sessions. Main barriers to participation included commitments due to work and migration. Men reported that the intervention led to improvements in their relationships with their wives, gave them a sense of empowerment, and led them to become resources for other men in their community.
Conclusions: Men in these rural communities are not receiving the information that they need to meet their reproductive goals, however, they greatly desire this information and ways to improve relationships with their new wives. Such an intervention appears to have the potential to help change norms and spread information in the community and provide men with positive, life affirming feelings. Providing information through technology could address barriers to in-person engagement.
Trial registration: clinicaltrials.gov (NCT06320964), 03/13/24
Radiological signs of stone impaction add no value in predicting spontaneous stone passage
Authors: Popiolek Marcin , Lidén Mats , Georgouleas Petros , Sahlén Klara , Sundqvist Pernilla , Jendeberg Johan 
Published: May 2024

Abstract

Stone size and location are key factors in predicting spontaneous stone passage (SSP), but little attention has been paid to the influence of radiological signs of stone impaction (RSSI). This research aims to determine whether RSSI, alongside stone size, can predict SSP and to evaluate the consistency of ureteral wall thickness (UWT) measurements among observers. In this retrospective study, 160 patients with a single upper or middle ureteral stone on acute non-enhanced computed tomography (NECT) were analysed. Patient data were collected from medical records. Measurements of RSSI, including UWT, ureteral diameters, and average attenuation above and below the stone, were taken on NECT by four independent readers blind to the outcomes. The cohort consisted of 70% males with an average age of 51 ± 15. SSP occurred in 61% of patients over 20 weeks. The median stone length was 5.7 mm (IQR: 4.5–7.3) and was significantly shorter in patients who passed their stones at short- (4.6 vs. 7.1, p < 0.001) and long-term (4.8 vs. 7.1, p < 0.001) follow-up. For stone length, the area under the receiver operating characteristic curve (AUC) for predicting SSP was 0.90 (CI 0.84–0.96) and only increased to 0.91 (CI 0.85–0.95) when adding ureteral diameters and UWT. Ureteral attenuation did not predict SSP (AUC < 0.5). Interobserver variability for UWT was moderate, with ± 2.0-mm multi-reader limits of agreement (LOA). The results suggest that RSSI do not enhance the predictive value of stone size for SSP. UWT measurements exhibit moderate reliability with significant interobserver variability.
Community: component based differential cell communication analysis in large multi-sample case-control scRNAseq datasets
Authors: Solovey Maria , Celik Muhammet , Salcher Felix , Abdelfattah Mohamed , Ismail Mostafa , Scialdone Antonio , Ziemann Frank , Colomé-Tatché Maria 
Published: May 2024

Abstract

Cell-cell communication is essential for physiological tissue function. In disease, communication often gets disbalanced by changes in the tissue cell type composition, fraction of cells engaged in communication, and changes in expression levels of ligands, receptors and adhesion molecules. Single cell RNAseq analyses allow to measure these parameters in healthy and diseased tissues. Here we present community, an R-based tool that is designed to perform differential communication analysis using scRNAseq data between large cohorts of cases and controls. Community performs differential analysis to identify communication channels affected in disease by reconstructing the communication between different cell types using three components: cell type abundance, fraction of active cells, and ligand/receptor expression levels, both in cases and controls. This approach allows to not only identify up- or down-regulated interactions, but also detect cases of compensation, where a shift in one component gets compensated by a counter-shift in another component, keeping the levels of communication stable. The component analysis enables us to better understand the underlying biological processes leading to changes in communication. We demonstrate the performance of community by using two disease entities, ulcerative colitis and acute myeloid leukemia. We compared the performance of our tool to other differential communication pipelines, which community outperformed in robust identification of up- and down-regulated interactions, as well as its unique feature of identifying compensated communication shifts. Overall, community is a fast, well-scalable, user-friendly R tool to assess differential cell-cell communication using large case-control scRNAseq datasets, and disentangle the driving mechanisms of communication shifts in disease.
Deep Learning Model to Differentiate Intracranial Germ Cell Tumors Subtypes and Predict Survival
Authors: Li Yanong , Zhuo Zhizheng , Weng Jinyuan , Haller Sven , Bai Harrison X. , Li Bo , Liu Xing , Zhu Mingwang , Wang Zheng , Li Jane , Qiu Xiaoguang , Liu Yaou 
Published: May 2024

Abstract

Background Pretherapeutic differentiating subtypes of primary intracranial germ cell tumors (iGCTs), including germinomas (GEs) and non-germinomatous germ cell tumors (NGGCTs) is essential for clinics because of their distinct treatment strategies and prognosis profiles. This study aimed to develop a deep learning model, iGNet, to assist in differentiation of iGCT subtypes by employing pretherapeutic MR images.Methods The iGNet model was developed using a retrospective dataset of 280 pathology-confirmed iGCT patients, including 83 GE and 117 NGGCTs in train dataset, and 31 GEs and 49 NGGCTs in the retrospective internal test dataset. The model's diagnostic performance was then tested through area under the receiver operator characteristics curve (AUC) in a prospective internal dataset (n = 22) and two external datasets (n = 22 and 20). Next, we compared the diagnostic performance in six neuroradiologists with or without the assistance of iGNet. Lastly, the predictive ability of the iGNet outputs for progression-free and overall survival was assessed in comparation with pathological diagnosis.Results iGNet achieved high diagnostic performance with AUCs between 0.869 and 0.950 across the four test datasets. With the assistance of iGNet, neuroradiologists' diagnostic AUCs (average of the four test datasets) were increased by 9.22–17.90% across six neuroradiologists. The iGNet output can predicting the progression-free and overall survival, comparable to that based on pathological diagnosis (P = .889).Conclusions The iGNet, leveraging pretherapeutic MR imaging, accurately differentiates iGCT subtypes, thereby facilitating clinical stratified treatment and prognostic evaluation.
The development of a duplex RT-qPCR assay for the simultaneous detection of cycas necrotic stunt virus and lychnis mottle virus in Paeonia lactiflora
Authors: Soeng Chandara , Kean Chanchota , Yoon Ju-Yeon , Ju Ho-Jong 
Published: May 2024

Abstract

In this study, a duplex probe based reverse transcription quantitative polymerase chain reaction (RT-qPCR) assay was utilized to simultaneously detect cycas necrotic stunt virus (CNSV) and lychnis mottle virus (LycMoV) in Paeonia lactiflora collected from various locations in South Korea. CNSV and LycMOV infections were verified by using conventional reverse transcription polymerase chain reaction (RT-PCR) using gene-specific primers. Due to peony’s high secondary metabolites, multiple standard templates in the form of both DNA and RNA were evaluated. The circular plasmid was observed to produce the finest results and was used in dye-based qPCR to select the best-performing primers characterized by their ability to yield a low threshold cycle (Ct) and high fluorescence. The high precision quantification duplex probe-based qPCR assay was conducted and then optimized. The combination of primer concentration of 5 pmol/µl coupled with probe concentration of 4 pmol/µl at the annealing temperature of 57 ℃ produced stable and consistent amplification plots and standard curves. This combination demonstrated the capability to simultaneously detect plasmid DNA of both CNSV and LycMoV at concentrations as low as 10-6 ng/µl. These primer sets and optimum conditions were applied in RT-qPCR to detect total RNA of peony leaves co-infected with CNSV and LycMoV. Successful detection occurred with a slightly weaker sensitivity, having a detection limit of 10-3 ng/µl. The use of duplex probe-based RT-qPCR assay demonstrated in this study should improve the virus screening process of CNSV and LycMoV, leading to a reduction of the spread of these two plant viruses.
Machine learning-based hazard assessment of landslides within the rainband of typhoon Lekima
Authors: Feng Qiangqiang , DING Mingtao , He Yufeng , Ming Yicheng , Ren Heming 
Published: May 2024

Abstract

Amid escalating global climate change, the frequency of extreme weather events, such as powerful typhoons, has been on the rise. Notably, Typhoon Lekima in 2019 severely impacted Zhejiang Province, triggering a series of severe landslides. This study employs six different machine learning approaches to propose a landslide hazard assessment zoning model that takes into consideration the typhoon track and rainbands. For this purpose, Zhejiang Province was selected as a case study area based on the path and internal rainbands of Typhoon Lekima. The cumulative rainfall 48 hours before and after the typhoon's landfall was recorded, and various factors such as topography, geology, vegetation, and human engineering activities were integrated. Landslide hazard was evaluated using six machine learning techniques: Random Forests (RF), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LR), Artificial Neural Networks (ANN), and Gradient Boosting Decision Tree (GBDT). The evaluation results indicate that all models effectively identified landslide-prone areas, with exceptional performance from the GBDT and RF models, achieving AUC values of 0.95 and 0.94, respectively. Furthermore, the distribution of landslide hazards was closely linked to typhoon intensity and trajectory. Landslide risks were high in areas experiencing category 12 typhoons, centrally symmetric along the typhoon path and decreasing towards the borders of the inner rainbands; areas affected by category 10–11 typhoons exhibited asymmetric hazards concentrated on the coastal side, whereas regions under category 9 typhoons showed lower risk levels. Lastly, the local interpretability of models through the LIME (Local Interpretable Model-agnostic Explanations) algorithm enhanced understanding of model predictive behaviors, offering increased accuracy and interpretability in landslide hazard assessment compared to traditional methods. This research provides a scientific basis for developing targeted landslide disaster prevention and control strategies and improving emergency management of such disasters.
Experimental study on the adhesion force of ground film in sandy soil in South Xinjiang
Authors: Guo Pengfei , Bai Yunhui , Wang Xufeng , Guo Wensong , Liu Yang , Liu Xuefang 
Published: May 2024

Abstract

Xinjiang is the most important cotton producing area in China. In the cotton planting process, mulching drip irrigation technology is usually used, which has the functions of moisturizing and inhibiting weed growth, etc. After one planting cycle, residual mulching film in the soil will adhere to the soil, resulting in difficult mechanical recovery of mulching film. At present, the influence of various factors on the adhesion between soil and mulching film is unclear. In order to solve this problem, single-factor experiment and multi-factor experiment were used to verify the variation rule of soil adhesion. The results of single factor experiment showed that the effect of soil particle size on its adhesion increased first and then decreased, and the maximum adhesion appeared in the particle size of 8.47um to 33.87um. The influence of soil contact time on the adhesion of mulch film increased first and then stabilized, and reached the peak value at 5 minutes. When soil is affected by pressure, soil adhesion increases first and then decreases, and reaches the maximum at 1500N. Soil moisture content also affected the adhesion, reaching the maximum value at 22.87%. Multi-factor analysis showed that contact time, moisture content and particle size had significant effects together. The regression equation was established to predict the adhesion between film and soil, which provided theoretical support for the treatment of residual film and mechanized recovery.
A NER Method Based on Location-Aware Multi-Feature Fusion
Authors: ZHOU Wen 
Published: May 2024

Abstract

Named Entity Recognition (NER) is a fundamental and critical task in natural language processing, which mainly includes entity boundary recognition and entity classification. Previous studies of NER have made a lot of achievements, but most of them ignore the important feature of location information, and the extraction of text span feature is not enough. For the above shortcomings, we propose a location-aware multi-feature fusion entity recognition method, which enhances the sensitivity of the model to the span length and span by explicitly adding rotation position coding. Then the feature extraction and fusion of span are carried out by conditional layer normalization (CLN) fusion dilated convolution and multiplicative attention respectively, which strengthens the richness of span features. Thanks to the inclusion of the above methods, our model outperforms several SOTA baseline models on several widely used public datasets. Our model is also applicable to both flat NER and nested NER.
Latent Classes of Adolescent Health Behaviour, Social Covariates and Mental Wellbeing: A Longitudinal Birth Cohort Study
Authors: Knowles Christopher , Thornton Emma , Petersen Kimberly , Lereya Suzet Tanya , Humphrey Neil 
Published: May 2024

Abstract

Background: Adolescent wellbeing has been declining in the United Kingdom for over a decade. The expansion of services to support the mental health and wellbeing of young people is a public health priority and a core component of the National Health Service’s Long-Term Plan. In this paper, we contribute to knowledge regarding the epidemiology of adolescent mental wellbeing by leveraging secondary analysis of a very large longitudinal dataset (#BeeWell) to generate insights regarding different patterns of health behaviour, their covariates, and consequences for mental wellbeing one year later.
Methods: A Latent Class Analysis was conducted using data on physical activity, sleep, and eating habits collected in 2021 from 18,478 Year 8 pupils from Greater Manchester (United Kingdom) to (1) identify distinct latent classes of adolescent health behaviour; (2) establish factors likely to be associated with latent class membership; and (3) determine whether latent class membership contributes to variance in self-reported mental wellbeing one year later.
Results: A three-class solution was identified as an excellent fit to the data, discriminating between: the Wellness Weary (n = 2,717; 15%); the Balanced Bunch (n = 7,377; 40%); and the Green and Dream Team (n = 8,384; 45%). Several factors significantly influenced class membership. Most notably, socio-economic disadvantage and social media use were linked with less favourable health behaviour patterns, whilst cisgender heterosexual girls were likely to endorse healthier patterns. After adjusting for covariates, the Green and Dream Team reported significantly greater mental wellbeing than the Balanced Bunch one year later, signalling that health behaviours endorsed in adolescence may have a long-term impact on mental health.
Conclusions: Beyond advancements in fundamental understanding, findings yield significant translation opportunities through their use and application in health, education, and allied professional settings designed to support young people.