mdpi-ai/citelens huggingface dataset:
Advanced Hybrid Machine Learning Model for Accurate Detection of Cardiovascular Disease
Field: AI

Cardiovascular disease (CVD) is one of the foremost reasons behind the death of people worldwide. Prevention and early diagnosis are the only ways to control its progression and onset. Thus, there is an urgent need for a detection model comprising intelligent technologies, including Machine Learning (ML) and deep learning, to predict the future state of an individual suffering from cardiovascular disease by effectively analyzing patient data. This study aims to propose a hybrid model that provides a deep insight into the data under consideration to enhance model accuracy for effectively detecting cardiovascular disease. This current research proposes a hybrid model comprising four stages. In the first stage of the proposed hybrid model, the data imbalance problem is solved using a hybrid sampling technique named Synthetic Minority Oversampling Technique-Edited Nearest Neighbors Rule. In the second stage, the Chi-square is applied as a feature selection method to select the highly relevant features from the records of 1190 with 11 clinical features, curated by combining the 5 most popular datasets, including Long Beach VA, Hungarian, Switzerland, and Statlog (Heart). In the third stage, the preprocessed dataset is passed to a stacking ensemble model comprising three base learners: Random Forest Tree (RFT), K-Nearest Neighbor (K-NN), and AdaBoost classifier and one meta-learner: Logistic Regression (LR), optimized with Grid Search Cross-Validation (GSCV) optimization approach, whose performance is evaluated against individual classifier. In the fourth stage, the performance is evaluated in terms of accuracy, sensitivity, specificity, F1 score, and ROC_AUC score.. The comparative results prove that the proposed hybrid model scored the highest accuracy of 97.8%, 96.15% sensitivity, and 96.75% specificity and 98.6% ROC_AUC score when compared with the existing techniques and models after applying the SMOTE–ENN (for data balancing) and Chi-square (for feature selection) methods for the efficient detection of cardiovascular disease. The implementation results demonstrate that the suggested hybrid model may accurately identify cardiovascular disease among patients. It facilitates the application of robust clinical treatment strategies.
Deep learning analysis of exercise stress electrocardiography for identification of significant coronary artery disease
Field: AI

Background: The diagnostic power of exercise stress electrocardiography (ExECG) remains limited. We aimed to construct an artificial intelligence (AI)-based method to enhance ExECG performance to identify patients with significant coronary artery disease (CAD).Methods: We retrospectively collected 818 patients who underwent both ExECG and coronary angiography (CAG) within 6 months. The mean age was 57.0 ± 10.1 years, and 614 (75%) were male patients. Significant coronary artery disease was seen in 369 (43.8%) CAG reports. We also included 197 individuals with normal ExECG and low risk of CAD. A convolutional recurrent neural network algorithm, integrating electrocardiographic (ECG) signals and features from ExECG reports, was developed to predict the risk of significant CAD. We also investigated the optimal number of inputted ECG signal slices and features and the weighting of features for model performance.Results: Using the data of patients undergoing CAG for training and test sets, our algorithm had an area under the curve, sensitivity, and specificity of 0.74, 0.86, and 0.47, respectively, which increased to 0.83, 0.89, and 0.60, respectively, after enrolling 197 subjects with low risk of CAD. Three ECG signal slices and 12 features yielded optimal performance metrics. The principal predictive feature variables were sex, maximum heart rate, and ST/HR index. Our model generated results within one minute after completing ExECG.Conclusion: The multimodal AI algorithm, leveraging deep learning techniques, efficiently and accurately identifies patients with significant CAD using ExECG data, aiding clinical screening in both symptomatic and asymptomatic patients. Nevertheless, the specificity remains moderate (0.60), suggesting a potential for false positives and highlighting the need for further investigation.
Relations Between Medical Students’ Motivational Persistence Skills and Their Acceptance of Specific Blended Learning Tools
Field: PSYCHOLOGY

The concept of blended education, which refers to the intensive integration of digital resources into the teaching process and its mixed online and on-site delivery, combining as much as possible the advantages of both methods in an optimal way, is becoming increasingly popular among teaching tools. There is no universal recipe for designing a successful blended course; the success of such courses is measured entirely through their degree of acceptance among students, defined by their emotional motivation to learn and the obtained practical results. Our study aimed to evaluate the motivational persistence degree (MPS) of medical students in connection with the students’ acceptance of different didactic tools involved in blended-learning approaches. Materials and Method: We investigated a sample comprising 523 students in Dental Medicine or General Medicine, belonging to all years of study, from four main Universities in Romania; we classified them according to their motivational persistence profile (using k-means data clustering) and we comparatively investigated the main relevant features of students in each cluster—gender, age group, opinions about the general usefulness of multimedia resources in the learning process, and their degree of acceptance of specific types of instructional materials involved in blended learning. Results: We found that the students who mostly enjoy the traditional learning style have average motivational persistence skills; they are perseverant and competitive, but they are not so good at planning their daily tasks. They enjoy external directions, set by teachers. The students who most enjoy PowerPoint presentations and those who enjoy instructional videos show similar behavior, both belonging to the cluster with the highest MPS score. They have the best motivational persistence skills amongst all categories; they are particularly excellent at planning and fulfilling daily tasks, as well as following their goals in the long term. The students who mostly enjoy online documentary sources belong also to a cluster with above average MPS score; they excel in fulfilling daily tasks, but exhibit weaker performance in recalling unachieved goals. These results are similar to those already reported in the literature; the strength of our study is in that it provides much more specific evaluations oriented to the motivational persistence degree, which is highly significant in the case of medical students, because it is a measure of their commitment in fulfilling certain tasks. Conclusions: Our results have the potential to highlight reasons for academic success or failure according to a student’ s profile, and will prove helpful in selecting the most appropriate didactic tools.
The relationship of depression and quality of life with mediating role of death anxiety, silver lining and religious coping among women cancer patients in Pakistan
Field: ONCOLOGY

Objective: Pakistani women are among those most likely to be diagnosed with cancer. Cancer patients experience significant changes that impact their mental and physical health, primarily due to the increased burden of the disease. This study aims to explore the relationship between depression and quality of life (QOL) in cancer patients, as well as how religious coping (RC), silver lining (SL), and death anxiety (DA) influence this connection.Materials and methods: A total of 450 individuals diagnosed with cancer were recruited from outpatient departments of various hospitals in Islamabad. Out of these, 421 patients who met the inclusion criteria were included in the study. Three types of cancer were considered for data collection there was 132 (31.4%) breast cancer, 154 (36.6%) blood cancer and 135 (32.1%) lung cancer patients Participants were assessed using the following measurement tools: the Demographic Form, The Short Muslim Religious Practice and Religious Belief, the Patient Health Questionnaire (PHQ-9, 2011), the Death Anxiety Scale, the Silver Lining Questionnaire, and the WHOQOL-BREF Questionnaire.Results: The findings of the current study revealed a negative association between depression and quality of life (QOL). Additionally, death anxiety (DA) was positively correlated with both depression and QOL. Conversely, silver lining (SL) and religious coping (RC) were negatively associated with depression and positively associated with QOL. Path analysis indicated that DA, SL, and RC served as mediators in the relationship between depression and QOL among cancer patients.Conclusion: The study concluded that cancer patients can better manage their depression and enhance their quality of life by strengthening their silver lining (SL) and religious coping (RC). These findings should be considered when developing strategies to manage depression and other psychological issues in cancer patients, thereby providing more effective treatments for this population
Boosting Active Learning Through a Gamified Flipped Classroom: A Retrospective Case Study in Higher Engineering Education
Field: EDUCATION

Active learning and associated techniques such as flipped classes have been demonstrated to have positive impacts on student learning and performance. Active learning faces several challenges when learners apply weak learning styles. Weak learning might happen when a student is not motivated to carry out any pre-class content activity, actively participate in the class activity, or reflect and reinforce the learned content during and after the class. This study explores how a gamified flipped classroom affects active learning performance and learning outcomes. The case is related to a technical course in the Maintenance Engineering Field, which is well known for a high rate of misunderstanding and low learning outcomes. It is found that sequential game-boosting activities in the flipped classroom have managed to level up students’ learning outcomes by explaining almost all concepts with low levels of misconceptions.
Investigating the emotion regulation of STEM teachers: a scoping review
Field: EDUCATION

A significant tension exists between the necessity for teachers to regulate their emotions and the tendency to overlook these emotions in STEM education. Teachers’ emotion regulation is inherently context-sensitive and discipline-specific. Therefore, it is crucial for researchers to explore the particularities of teachers’ emotion regulation in the context of STEM education. This study presents a scoping review of existing research on STEM teachers’ emotion regulation, focusing on theoretical underpinnings, methodological approaches, and research foci. Following scoping review guidelines, a corpus of 23 studies published between 2004 and 2023 was collected and analyzed. Emotional intelligence emerged as the most frequently employed theoretical underpinning for conceptualizing STEM teachers’ emotion regulation, followed by emotional labor and emotion regulation. Among the four research approaches—quantitative, qualitative, mixed, and conceptual—the majority of studies adopted quantitative and qualitative methods to investigate the nature and the relational model of teachers’ emotion regulation in STEM education. The findings indicate that research on STEM teachers’ emotion regulation exhibits imbalances in theoretical and methodological approaches. Although various contexts, antecedents, and outcomes of STEM teachers’ emotion regulation have been identified, the scope and depth of these investigations remain limited. Research on STEM teachers’ emotion regulation is still in its early stages for several reasons: the paucity of studies in this area, a reliance on broad emotional constructs rather than specific emotion regulation perspectives, and the lack of tailored theoretical frameworks addressing STEM teachers’ emotion regulation. This scoping review maps existing knowledge on teachers’ emotion regulation in STEM education, elucidates the underlying philosophical standpoints of prior research, and offers recommendations for future research directions.
Safety and efficacy of apatinib in combination treatment versus apatinib as second-line treatment for advanced gastric cancer
Field: ONCOLOGY

Background: Apatinib is a systemic therapeutic agent for advanced gastric adenocarcinoma (GAC) and gastroesophageal junction adenocarcinoma (GEJA). Its efficacy can be enhanced by applying it as a combination therapy, but the evidence supporting its combination application as a second-line treatment is not well documented. In the current study, we aimed to assess the efficacy and safety profile of apatinib, both as a monotherapy and in combination regimens, for second-line treatment of GAC and GEJA in real-world settings.Methods: In this retrospective cohort analysis, we analyzed clinical data from 96 patients with advanced GAC or GEJA who received second-line apatinib monotherapy or combination therapy. Cox regression analysis was performed to identify prognostic factors influencing clinical outcomes of different treatment approaches (apatinib combination with other drugs).Results: The results indicated that the overall objective response rate (ORR) and disease control rate (DCR) for second-line apatinib therapy were 19.8% and 31.3%, respectively. The median progression-free survival (mPFS) was 4.8 months (95% CI: 4.3-6.2m), while the median overall survival (mOS) was 10.3 months (95% CI: 8.9-12.4m). Multivariable Cox regression analysis identified gender, liver metastasis, and peritoneal metastasis as independent predictors of inferior PFS and OS outcomes. In terms of safety, the primary adverse reactions included myelosuppression, elevated AST and ALT levels, hypertension, hand-foot syndrome, hyperbilirubinemia, proteinuria, fatigue, and vomiting, with a low incidence of grade 3–4 toxicities.Conclusions: Apatinib-based combination therapy significantly enhances both progression-free survival and overall survival in patients with advanced gastric cancer when compared to monotherapy, while also demonstrating a safe and reliable profile.
Increased expression of DNAJC7 promotes the progression of hepatocellular carcinoma by influencing the cell cycle and immune microenvironment
Field: ONCOLOGY

Background Hepatocellular carcinoma (HCC) is the leading cause of cancer-related mortality worldwide owing to the lack of effective and early diagnostic tools and therapeutic approaches. DNAJC7, a member of the DnaJ heat shock family, is crucial in protein folding and stability; however, its specific functions and mechanisms in HCC remain unclear. Objective This study aimed to explore the role of DNAJC7 in HCC progression and evaluate its potential clinical significance as a prognostic marker. Methods Public databases (TCGA, ICGC, GEO, and TIMER) were used to assess DNAJC7 expression, correlations with clinical parameters, and related signaling pathways. Proliferation, migration, invasion, and cell cycle assays were performed to evaluate the function of DNAJC7 in HCC. Immune infiltration and associations with checkpoint proteins were analyzed using TIMER, and a Gene Set Enrichment Analysis (GSEA) was used to explore enriched pathways. Results DNAJC7 expression was higher in HCC tissues than in adjacent normal tissues and was associated with advanced malignancy and poor prognosis, including a lower overall survival, progression-free survival, and disease-free survival. DNAJC7 knockdown resulted in reduced malignant behavior of HCC cells, leading to S-phase cell cycle arrest. Increased DNAJC7 expression was associated with immune cell infiltration and the presence of immunological checkpoint molecules, including CTLA4 and PD-1. GSEA highlighted the activation of key pathways, including WNT signaling and cell cycle regulation. Conclusion DNAJC7 regulates tumor cell proliferation, migration, invasion, and immune evasion by acting as an oncogene in HCC. It can serve as a diagnostic and prognostic biomarker and potential treatment target for HCC.
FASTSeg3D: A Fast, Efficient, and Adaptive Ground Filtering Algorithm for 3D Point Clouds in Mobile Sensing Applications
Field: AI

Background: Accurate ground segmentation in 3D point clouds is critical for robotic perception, enabling robust navigation, object detection, and environmental mapping. However, existing methods struggle with over-segmentation, under-segmentation, and computational inefficiency, particularly in dynamic or complex environments. Methods: This study proposes FASTSeg3D, a novel two-stage algorithm for real-time ground filtering. First, Range Elevation Estimation (REE) organizes point clouds efficiently while filtering outliers. Second, adaptive Window-Based Model Fitting (WBMF) addresses over-segmentation by dynamically adjusting to local geometric features. The method was rigorously evaluated in four challenging scenarios: large objects (vehicles), pedestrians, small debris/vegetation, and rainy conditions across day/night cycles. Results: FASTSeg3D achieved state-of-the-art performance, with a mean error of <7%, error sensitivity < 10%, and IoU scores > 90% in all scenarios except extreme cases (rainy/night small-object conditions). It maintained a processing speed 10× faster than comparable methods, enabling real-time operation. The algorithm also outperformed benchmarks in F1 score (avg. 94.2%) and kappa coefficient (avg. 0.91), demonstrating superior robustness. Conclusions: FASTSeg3D addresses critical limitations in ground segmentation by balancing speed and accuracy, making it ideal for real-time robotic applications in diverse environments. Its computational efficiency and adaptability to edge cases represent a significant advancement for autonomous systems.
Exploring teachers’ pedagogical reasoning in mathematics education using the TPACK framework
Field: EDUCATION

Effective integration of technology in mathematics education requires teachers to blend content knowledge, pedagogical strategies, and digital tools. The Technological Pedagogical Content Knowledge (TPACK) framework offers a lens for understanding teachers’ pedagogical reasoning when designing technology-enhanced lessons. However, the ways in which TPACK informs instructional planning and the challenges educators face remain under - synthesized. A systematic review following PRISMA guidelines was conducted using Scopus (2015–2024). Search terms included “pedagogical reasoning,” “instructional reasoning,” “TPACK,” and “math*.” From 118 records retrieved, title/abstract screening and full - text eligibility assessments yielded eight empirical studies examining TPACK and pedagogical reasoning in mathematics contexts. The included studies employed predominantly qualitative case studies and mixed - methods designs to capture teachers’ decision - making processes. Findings indicate that educators leverage TPACK to enhance conceptual understanding and student engagement via dynamic visualizations, interactive simulations, and scaffolded digital tasks. Common obstacles include limited subject - specific professional development, resource constraints, and heterogeneity in teachers’ TPACK proficiency. Evidence also highlights TPACK’s capacity to foster inquiry - based learning and develop teachers’ adaptive expertise. Sustained, targeted professional development and equitable access to technology are essential for deepening TPACK enactment. Implications for practice include designing PD programs that integrate subject - specific technology applications and creating institutional support structures. Future research should investigate longitudinal impacts of TPACK on teachers’ reflective practices and student outcomes, and develop standardized assessment tools tailored to mathematics instruction.
Co-Learning: code learning for multi-agent reinforcement collaborative framework with conversational natural language interfaces
Field: AI

Online question-and-answer (Q&A) systems based on the Large Language Model (LLM) have progressively diverged from recreational to professional use. However, beginners in programming often struggle to correct code errors independently, limiting their learning efficiency. This paper proposed a Multi-Agent framework with environmentally reinforcement learning (E-RL) for code correction called Code Learning (Co-Learning) community, assisting beginners to correct code errors independently. It evaluates the performance of multiple LLMs from an original dataset with 702 error codes, uses it as a reward or punishment criterion for E-RL; Analyzes input error codes by the current agent; selects the appropriate LLM-based agent to achieve optimal error correction accuracy and reduce correction time. Experiment results showed that 3% improvement in Precision score and 15% improvement in time cost as compared with no E-RL method respectively. The results indicate that integrating E-RL with a multi-agent selection strategy can effectively enhance both the accuracy and efficiency of LLM-based code correction systems, making them more practical for educational and professional programming support scenarios.
Problem-solving: development and validation of a short instrument for higher education
Field: EDUCATION

Problem-solving is becoming more and more seen as an important skill for college students to learn to build metacognitive skills, critical thought, and the ability to learn on their own. Even though this skill is very important, there aren’t many approved tools that can be used to test it in schools, especially in Peru. The goal of this study is to fill in that gap by creating and testing a short problem-solving scale based on the Rational Problem-Solving Style, which stresses taking a planned and organized approach to problems. 733 Peruvian college students (mean age: 21.56 years, standard deviation: 4.15 years; 59.89% female) took part. A 15-item Problem-Solving Questionnaire and used experimental (EFA) and confirmatory factor analysis (CFA) to test it. The scale’s validity and reliability were checked, along with its link to academic self-efficacy. There were four parts to the Problem-Solving Questionnaire: Solution Analysis and Planning, Critical Evaluation of Solutions, Generation and Evaluation of Alternatives, and Prioritization and Review of Alternatives. Fit scores from CFA (like CFI = 0.98 and RMSEA = 0.062) and reliability coefficients (ω = 0.73–0.90) showed that it was a reliable educational tool. There was proof of concept validity in the form of correlations with academic self-efficacy (r = 0.36–0.80). The scale is a validity and effective way to test the problem-solving skills of university students in Peru. Due to its brevity and emphasis on logical methods, it is suitable for use in both education and research, aligning with global goals for quality education.