mdpi-ai/citelens huggingface dataset:
Case study of a neuroendocrine tumor of uncertain origin: single-cell transcriptomics unravels potential primary location
Field: ONCOLOGY

Purpose Determining the primary origin of non-organ-confined neuroendocrine tumors (NETs) for accurate diagnosis and management. Neuroendocrine tumors are rare neoplasms with diverse clinical behaviors. Determining their primary origin remains challenging in cases of non-organ-confined NETs. This study explores the histogenesis of a retroperitoneal, non-functional NET localized between the duodenum and pancreatic head, utilizing advanced molecular diagnostics to elucidate its probable primary source. Methods Initial diagnostic methods, including imaging and histopathology, failed to resolve the tumor’s origin. The tumor was subjected to single-cell RNA sequencing (scRNA-seq) and whole exome sequencing (WES). Publicly available transcriptomic datasets from pancreatic and small intestine NETs were used to develop and validate a molecular gene signature for tissue-of-origin identification. Results The gene signature distinguished pancreatic and small intestine NETs with high accuracy. The tumor cells presented a molecular profile consistent with a pancreatic origin, likely derived from ectopic pancreatic tissue. Conclusions This case demonstrates the value of integrating scRNA-seq and WES for the molecular characterization of complex NETs. Identifying the tumor’s pancreatic origin informed a targeted management approach, avoiding unnecessary systemic treatment and underscoring the potential of single-cell approaches in personalized oncology.
Investigating the effect of mindfulness training for stress management in military training: the relationship between the autonomic nervous system and emotional regulation
Field: PSYCHOLOGY

Military personnel face an increased risk of developing mental disorders owing to the stressful environments they encounter. Effective stress management strategies are crucial to mitigate this risk. Mindfulness training (MT) is promising as a stress management approach in such demanding settings. This study uses a quantitative approach to investigate the impact of MT on the relationship between the autonomic nervous system (ANS) and emotional regulation. The study evaluated the effectiveness of MT in reducing stress among 86 military personnel. Participants were divided into two groups: MT (n = 42) and non-MT (n = 38). The study compared the two groups using measures of heart rate variability (HRV), a reliable indicator of ANS activity. The MT group exhibited a significant increase in HRV (14.4%, p = 0.001) and alpha asymmetry (AA) in the frontal lobe (45.7%, p < 0.001) compared to the non-MT group. Notably, the MT group achieved significantly higher scores on the parachute landing fall (PLF) training performance (p < 0.001). These improvements in HRV, AA, and PLF performance were strongly correlated. Furthermore, AA fully mediated the relationship between HRV and PLF training performance. The findings suggest that MT has a positive impact on stress resilience, potentially by mitigating anxiety and attention deficits induced by extreme stressors. These positive effects are facilitated by concurrent modulation of the frontal cortex and autonomic nervous system. Our findings provide insight into the neural mechanisms behind MT-induced stress reduction from the perspective of neuromodulation.
Psychological impact and coping mechanisms among sudanese medical students: a study on anxiety, depression, behavioral, and cognitive changes post COVID-19 lockdown and ongoing conflict
Field: PSYCHOLOGY

Mental health is crucial for overcoming obstacles, completing tasks, and contributing to society. Mental, social, and cognitive healths are included. In demanding fields like medicine, academic pressure can cause exhaustion, poor performance, and behavioral changes. Mental health must be addressed to improve student success and well-being. Medical students’ coping strategies, anxiety, depression, and behavioral changes in uncontrollable situations will be studied. A cross-sectional study involved 393 medical students from various universities in Khartoum. Data was collected using an online questionnaire to assess mental health responses during both controllable and uncontrollable situations across all academic years. Data analysis using SPSS 27 indicated minimal missing data (0.25%) among the 393 participants. PHQ-4 scores assessed psychological distress, anxiety, and depression. The study found that 74.2% of participants experienced behavioral, cognitive, and emotional changes. Significant associations were observed between PHQ-4 scores and these changes (p < .05) using Chi-Square testing. Most participants were females aged 20 to 22, primarily from the Medicine and Pharmacy departments. The study revealed that most individuals utilized pharmacological coping strategies following significant life changes due to uncontrollable situations. The study highlights that women experienced stress, dissatisfaction, concern, and anger more frequently than men during ongoing war and the post-COVID-19 lockdown. Medical students faced substantial challenges in behavior, emotions, and cognition during societal unrest, including fatigue, feelings of failure, and sleep disturbances. Over 74% reported multiple changes in their emotions and behaviors. Coping strategies included nicotine, sleeping aids, socializing, exercise, venting, meditation, and journaling.
A culturally relevant, imbued, and sustaining pedagogy framework for culturally connected math curriculum
Field: EDUCATION

This article introduces the CRISP (Culturally Relevant, Imbued, and Sustaining Pedagogy) framework in the context of a three-course sequence, “Indigenous Math I, II, and III,” taught at Turtle Mountain College. These three courses seek to revitalize mathematical ways of knowing embedded within the Turtle Mountain language(s) and culture(s). The Indigenous Math framework and Indigenous Math Education framework guide these three courses, as well as the Secondary Math Education bachelor’s degree program that spurred development of these courses. Discussing the relationship (i.e., connections, similarities, differences) between Western math and Indigenous math is central to these courses. The CRISP framework extends this discussion by describing four significant components of revitalizing and teaching Indigenous math. Multiple Indigenous math examples are shared as evidence for the value of the CRISP framework.
Drawing the future: gender and future occupational aspirations of young children in Sweden
Field: EDUCATION

Introduction: Research on young children’s occupational aspirations and the factors shaping them is still limited, especially in early interventions addressing gender disparities in high-status fields like STEM.Methods: This is the first study in Sweden utilizing the Drawing the Future method, surveyed 1,832 children (aged 5–13) from 28 schools in Skåne region of southern, asking them to draw their dream jobs. This exercise was conducted in a classroom setting and facilitated by their class teacher.Results: Significant gender differences emerged, revealing distinct stereotypical patterns in children’s future occupational aspirations and influencing factors. Only three occupations—footballer, doctor, and police officer—were popular among both genders. Girls preferred people- or animal-centered roles, while boys leaned toward jobs involving “things” (p < 0.001). Girls felt they could pursue similar careers as boys, but boys showed more skepticism (p < 0.001). Influence patterns also varied by gender: 25% of girls were inspired by mothers, while 45% of boys were inspired by fathers (p = 0.02). Beyond immediate family, girls often sought career information from acquaintances, while boys turned to media (p < 0.001). STEM interest was limited, with “game developer” being the only STEM job on boys’ lists. Additionally, a larger proportion of boys ranked STEM subjects among their top 10 favorite school subjects, while girls preferred crafts, art, and English (p < 0.001).Discussion: These findings highlight the need for early, unbiased, evidence-based career interventions and policies to broaden children’s awareness of diverse job options and opportunities in the labor market.
How gamification boosts learning in STEM higher education: a mixed methods study
Field: EDUCATION

The demand for professionals with expertise in Science, Technology, Engineering, and Mathematics (STEM) continues to grow. To meet this demand, universities are actively seeking strategies to engage more students in STEM disciplines and improve their learning outcomes. One promising approach is gamification, specifically using leaderboards. This study investigates the impact of leaderboard-based gamification on the learning performance of 175 students in a calculus course, with a focus on the mediating roles of autonomous motivation and self-efficacy, as well as potential moderating factors such as gender and gaming experience. A mixed-method research approach was employed, combining a pretest–posttest quasi-experimental design with nine qualitative interviews. A significant improvement in learning performance for students in the gamified condition was observed. However, no significant effects were found related to the mediating variables. Qualitative analysis supported these findings, revealing that students did not perceive an increase in autonomy within the gamified condition, and instead, themes of controlled motivation were prevalent. While the leaderboard provided a sense of achievement for most participants, the quantitative analysis did not show a strong correlation between self-efficacy and learning performance. This study suggests that leaderboard-based gamification can enhance learning performance in calculus courses at the university level. However, the findings highlight the importance of careful gamification design, particularly in how different game elements influence students' motivational aspects.
Characterizing microbial communities and their correlation with genetic mutations in early-stage lung adenocarcinoma: implications for disease progression and therapeutic targets
Field: ONCOLOGY

Background: Lung adenocarcinoma (LUAD), the most prevalent form of lung cancer. The transition from adenocarcinoma in situ (AIS), and minimally invasive adenocarcinoma (MIA) to invasive adenocarcinoma (IAC) is not fully understood. Intratumoral microbiota may play a role in LUAD progression, but comprehensive stage-wise analysis is lacking.Methods: Tumor and bronchoalveolar lavage fluid (BALF) samples from patients with AIS/MIA or IAC were collected for next-generation sequencing to characterize microbial diversity and composition. DNA extraction involved lysing samples with nuclease and protease, followed by homogenization and elution. Sequencing libraries were prepared and sequenced on the Illumina platform. Whole exome sequencing was performed to identify somatic mutations and genetic variants. Bioinformatics analysis, including taxonomic annotation with Kraken2 and de novo assembly with MEGAHIT, was conducted to process metagenomic data. Correlation analysis was performed to link microbial species with mutated genes using custom R scripts.Results: Metagenomic analysis revealed a distinct microbial profile in IAC compared to AIS/MIA, with increased abundance of Bacteroidetes and Firmicutes in the IAC group. Bosea sp. and Microbacterium paludicola, were less abundant in IAC, suggesting a potential protective role in early-stage disease. Conversely, Mycolicibacterium species were more prevalent in IAC, indicating a possible contribution to disease progression. Genetic sequencing identified PTPRZ1 strongly correlating with microbial composition, suggesting a mechanistic link between microbiota and genetic alterations in LUAD.Conclusion: This study characterizes microbial communities in various stages of LUAD, revealing links between microbiota and genetic mutations. The unique microbiota suggests its role in LUAD progression and as a therapeutic target.
The sound of science: a sonification learning experience in an Italian secondary school
Field: EDUCATION

Introduction: The present article reports on a case study aimed at improving STEAM education in secondary schools. It discusses the use of sonification as a teaching strategy to integrate music into science learning, using different approaches from data audification to parameter mapping into aural models and to the rewriting of song lyrics based on STEM topics.Methods: A qualitative research study has been performed in a secondary school in the school district of Bari (South of Italy). More specifically, students’ and experts’ perceptions of experienced sonification activities have been collected through six rounds of focus group interviews.Results: While there was a good improvement in student achievement in science, it is worth noting how musical activities also led to some benefits for students involved in the sonification workshops. The integration of music with STEM disciplines has promoted more cooperation and empathy among the students. Additionally, musical inputs can help students discover and regain interest in music. However, the study also highlighted the differences in teacher training and content knowledge, suggesting the need for future research to consider broader samples and experimental designs.Discussion: Results and implications for educational research and practice are discussed considering the recent literature on STEAM. Finally, this study demonstrates the importance of a robust instructional design for sonification activities, so that they can be more effective, aligned with the school curriculum, and integrated into the classroom teaching-learning process.
Corrigendum: Person-based design and evaluation of MIA, a digital medical interview assistant for radiology
Field: AI

Corrigendum on: Denecke K, Reichenpfader D, Willi D, Kennel K, Bonel H, Nairz K, Cihoric N, Papaux D and von Tengg-Kobligk H (2024) Person-based design and evaluation of MIA, a digital medical interview assistant for radiology. Front. Artif. Intell. 7:1431156. doi: 10.3389/frai.2024.1431156 In the published article, there was an error in the Data Availability statement. We were missing to add the links to the repositories mentioned in the paper. The correct Data Availability statement appears below.
Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets
Field: AI

Introduction: Diabetes prediction using clinical datasets is crucial for medical data analysis. However, class imbalances, where non-diabetic cases dominate, can significantly affect machine learning model performance, leading to biased predictions and reduced generalization.Methods: A novel predictive framework employing cutting-edge machine learning algorithms and advanced imbalance handling techniques was developed. The framework integrates feature engineering and resampling strategies to enhance predictive accuracy.Results: Rigorous testing was conducted on three datasets—PIMA, Diabetes Dataset 2019, and BIT_2019—demonstrating the robustness and adaptability of the methodology across varying data environments.Discussion: The experimental results highlight the critical role of model selection and imbalance mitigation in achieving reliable and generalizable diabetes predictions. This study offers significant contributions to medical informatics by proposing a robust data-driven framework that addresses class imbalance challenges, thereby advancing diabetes prediction accuracy.
Comprehensibility of gender-fair language in German-language video lectures
Field: PSYCHOLOGY

In many languages, it is common to use masculine-only forms when all genders are meant or gender is irrelevant to the actual statement. This practice is criticized for making women and members of other genders, their achievements and interests, less visible. Gender-fair language is intended to represent all genders equally. Recently introduced forms such as the glottal stop and the gender star are intended to also represent people outside the male–female dichotomy on the linguistic surface. However, it is often argued that gender-fair language would make texts less comprehensible and less aesthetically appealing. The critics’ assumptions were tested in an experiment with 272 participants. Subjects watched a screencast on self-regulated learning and were randomly assigned to either a version using masculine-only forms or a version using the glottal stop and the gender star. Subsequently, participants rated the comprehensibility and aesthetic appeal of the video they had watched. Structural equation models show no statistically significant influence of the use of gender-fair language on the comprehensibility (β = −0.13) or the aesthetic appeal (β = −0.16) of the videos. The critics’ assumptions are therefore not supported. But further studies are needed, especially regarding the corresponding singular forms and with non-academic participants.
Assessing self-determined motivation for drinking alcohol via the Comprehensive Relative Autonomy Index for Drinking
Field: PSYCHOLOGY

Introduction: Self-Determination Theory (SDT) examines human motivation in multiple domains; however, the only existing measure assessing SDT-informed behavioral regulations for drinking focuses on responsible drinker behaviors, rather than drinking per se, which is important given the alignment between SDT and harm reduction approaches to alcohol use. The aim of this study was to test the structural validity of the SDT-informed Comprehensive Relative Autonomy Index for Drinking (CRAI-Drinking) among college students.Methods: Participants included two convenience samples with a total of 630 adult drinkers (Mage = 21.5, 55% female, 88% undergraduates). Participants rated drinking behavioral regulations on the 24 original CRAI-Drinking items on a 5-point Likert Scale. Multi-dimensional scaling analyses and factor analyses were used to investigate the underlying autonomy continuum and factor structure of the CRAI-Drinking.Results: In Sample 1 (n = 274), multi-dimensional scaling analyses confirmed that CRAI-Drinking item and subscale order aligned with SDT's autonomy continuum. Confirmatory factor analyses supported a five factor, 19-item model of the CRAI-Drinking with factors for intrinsic, identified, positive introjected, external, and amotivation regulations (Cronbach's α: 0.68–0.85). In Sample 2 (n = 356), a confirmatory factor analysis confirmed that the 19-item model fit was comparable to Sample 1.Discussion: This study provides evidence for the structural validity of CRAI-Drinking scores for assessing SDT-based behavioral regulations for drinking in adults.
SPEMix: a lightweight method via superclass pseudo-label and efficient mixup for echocardiogram view classification
Field: AI

Introduction: In clinical, the echocardiogram is the most widely used for diagnosing heart diseases. Different heart diseases are diagnosed based on different views of the echocardiogram images, so efficient echocardiogram view classification can help cardiologists diagnose heart disease rapidly. Echocardiogram view classification is mainly divided into supervised and semi-supervised methods. The supervised echocardiogram view classification methods have worse generalization performance due to the difficulty of labeling echocardiographic images, while the semi-supervised echocardiogram view classification can achieve acceptable results via a little labeled data. However, the current semi-supervised echocardiogram view classification faces challenges of declining accuracy due to out-of-distribution data and is constrained by complex model structures in clinical application.Methods: To deal with the above challenges, we proposed a novel open-set semi-supervised method for echocardiogram view classification, SPEMix, which can improve performance and generalization by leveraging out-of-distribution unlabeled data. Our SPEMix consists of two core blocks, DAMix Block and SP Block. DAMix Block can generate a mixed mask that focuses on the valuable regions of echocardiograms at the pixel level to generate high-quality augmented echocardiograms for unlabeled data, improving classification accuracy. SP Block can generate a superclass pseudo-label of unlabeled data from the perspective of the superclass probability distribution, improving the classification generalization by leveraging the superclass pseudolabel.Results: We also evaluate the generalization of our method on the Unity dataset and the CAMUS dataset. The lightweight model trained with SPEMix can achieve the best classification performance on the publicly available TMED2 dataset.Discussion: For the first time, we applied the lightweight model to the echocardiogram view classification, which can solve the limits of the clinical application due to the complex model architecture and help cardiologists diagnose heart diseases more efficiently.
Play, reflect, cultivate social and emotional learning: a pathway to pre-service teacher SEL through playful pedagogies
Field: EDUCATION

Playful pedagogies, rooted in experiential learning, integrate play, humor, spontaneity, and levity to create engaging educational experiences. Playful pedagogies have been shown to support adults' emotional resilience and sense of belonging while reducing stress and anxiety. Despite these benefits, their use in education preparation programs (EPPs) remains underexplored. Given the increasing focus on teacher social and emotional learning (SEL), playful pedagogies hold significant potential for equipping future educators with the skills needed to foster both their own and their students' SEL growth. This paper advocates for a shift in teacher education from predominantly lecture-based instruction to a model that incorporates joy, humor, and experiential learning. We propose integrating playful pedagogies with a reflective learning cycle to enhance SEL competencies among pre-service teachers. Specifically, we introduce a conceptual model that combines a four-level pyramid of playful learning with an iterative reflection process. By integrating playful pedagogy into EPPs, we aim to foster resilience, creativity, and collaboration among future teachers, empowering them to create inclusive learning environments that nurture their students' holistic development.
Image Registration Using the Arithmetic Optimization Algorithm for Robotic Visual Servoing
Field: AI

Visual servoing using image registration is a method employed in robotics to control the movement of a system using visual information. In this context, we propose a new intensity-based image registration algorithm (IBIR) that uses information derived from images acquired at different times or from different views to determine the parameters of the geometric transformations needed to align these images. The Arithmetic Optimization Algorithm (AOA) is used to optimize these parameters, minimizing the difference between the images to be aligned. The proposed algorithm, Intensity-Based Image Registration via Arithmetic Optimisation Algorithm (IBIRAOA), is robust to image data fluctuations and perturbations and can avoid local optima. Simulation results prove the importance and efficiency of the proposed algorithm in terms of computation time and similarity of aligned images compared to other methods based on various metaheuristics. In addition, our results confirm a significant improvement in the trajectory of the wheeled mobile robot, thus reinforcing the overall effectiveness of our method in practical navigation and robotic control applications.