Search Results (441)

Background/Objectives: Sjögren’s disease (SjD) flares are incompletely understood. The patient perspective is critical to closing this gap. This retrospective social media listening (SML) study characterized the flare lexicon within the online Reddit SjD community using novel machine learning and natural language processing. Methods: Documents (posts/comments) were analyzed from the subreddit group “r/Sjogrens” (October 2012 to August 2023). Outcomes were as follows: (1) Frequency of documents mentioning flare, and contexts in which flare was mentioned; (2) clinical concepts associated with flare (analyzed using co-occurrence and pointwise mutual information [PMI]); (3) proportion of flare vs. non-flare documents relevant to SYMPTOMS or TESTING (compared using a two-proportion z-test); and (4) primary emotions mentioned in flare documents. Results: Of 59,266 documents with 5025 authors, flare was mentioned 3330 times (4.4% of documents from 19.1% of authors). Flare was discussed as a symptom (1423 instances), disease (13), or with no clinical category (1890). Flare-associated clinical concepts (co-occurrence > 100 and PMI² > 3) included SYMPTOMS (pain, fatigue, dryness of eye, xerostomia, arthralgia, stress) and BODY PARTS (eye, mouth, joints, whole body). More flare vs. non-flare documents mentioned a SYMPTOM, whereas fewer mentioned a TEST (p < 0.001 for both). Within flare documents, 36.5% expressed emotions, primarily fear (40.5% of primary emotions), happiness (17.8%), sadness (15.7%), and anger (15.5%). Conclusions: The SjD community discusses flare frequently and in context with symptoms, specifically pain, eye and mouth dryness, and fatigue. Flare conversations frequently involve negative emotions. Additional research is required to clarify the patient experience of flare, its clinical parameters, and implications. Full article

In a world where artificial intelligence is constantly changing education, taking students’ feelings into account is a crucial framework for enhancing their engagement and academic performance. This article presents LearnerEmotions, an online application that employs machine vision technology to determine how learners are feeling in real time through their facial expressions. Teachers and institutions can access analytical dashboards and monitor students’ emotions with this tool, which is designed for use in both in-person and remote classes. The facial expression recognition model used in this application achieved an average accuracy of 0.91 and a loss of 0.3 in the real environment. More than 9 million emotional data points were gathered from an experiment involving 65 computer engineering students, and these insights were correlated with attendance and academic performance. While negative emotions like anger, sadness, and fear are associated with decreased performance and lower attendance, the statistical study shows a strong correlation between positive feelings like surprise and joy and successful academic performance. These results underline the necessity of technological tools that offer immediate pedagogical regulation and support the notion that emotions play an important role in the learning process. Thus, LearnerEmotions, which considers students’ emotional states, is a potential first step toward more adaptive learning. Full article

Music education offers well-documented benefits for student learning; however, generalist teachers often report low confidence in integrating music into their lessons. This study applies Universal Design for Learning (UDL) principles to develop teaching resources that address teacher barriers to music integration. Using framework analysis, data collected from semi-structured interviews with ten trainee primary teachers in United Arab Emirates (UAE) British curriculum schools were mapped against UDL’s three core principles: engagement, representation, and action and expression. Despite recognising music’s holistic educational value in cognitive enhancement, memory retention, and student expression, participants reported significant barriers to integrating the subject into their lessons. These barriers included performance anxiety, a perceived lack of subject knowledge, and fear of student judgement. The barriers were most pronounced when faced with the prospect of teaching upper-primary students. Framework analysis revealed how these challenges align with the UDL’s core principles. These findings led to the development of five-step music resources, categorised into beginner and intermediate levels. Each step of the resources is designed to systematically address these identified barriers through UDL’s proactive and intentional design criteria. This demonstrates how teacher education can move beyond identifying barriers to creating structured solutions that support inclusive music integration while maintaining pedagogical authenticity. Full article

Active learning promises richer engagement, yet transnational English-medium engineering classrooms can remain quiet even when students are motivated. This study aims to explain this silence by examining the factors that encourage students to participate, the barriers that discourage them, and how student characteristics and coping strategies influence their participation. We conducted a mixed-methods survey of 402 undergraduates (Years 2–4) in a China–United Kingdom (Sino-UK) joint engineering programme in China. We analysed the closed-ended responses using descriptive and inferential statistics (including effect sizes) and the open-ended responses using inductive thematic analysis. Quantitative results showed that interest in the subject (76.6%) and career relevance (72.8%) were the most potent motivators. In contrast, fear of making mistakes (56%) and low confidence in public speaking (51%) were the most common barriers to participation. Other constraints included language load, deference to instructors, and prior passive learning experiences. Gender and discipline differences were negligible (Cramér’s V ≤ 0.09; Cohen’s d < 0.20). A small year-of-study effect also emerged, with later-year students marginally more confident in English-medium interactions. Qualitative analysis revealed recurring themes of evaluation anxiety, demands for technical vocabulary, inconsistent participation expectations, and reliance on private coping strategies (e.g., pre-class preparation, peer support, and after-class queries). We propose a ‘motivated-but-silent’ learner profile and blocked-pathway model where cultural, linguistic, and psychological filters prevent motivation from becoming classroom voice, refining Self-Determination Theory/Expectancy–Value Theory (SDT/EVT) and Willingness to Communicate (WTC) theories for transnational engineering contexts. These findings inform practice by recommending psychological safety measures, discipline-specific language scaffolds, and culturally responsive pedagogy to unlock student voice in English-medium Instruction/Transnational Education (EMI/TNE) settings. Full article

Virtual reality (VR) has emerged as a transformative tool in the treatment of phobias and the cultivation of emotional resilience. This study aims to explore the potential of VR to create controlled, immersive environments that facilitate exposure therapy, enabling individuals to confront and desensitize themselves to their fears in a safe and personalized manner. The flexibility of VR systems allows therapists to tailor scenarios to the unique needs of patients, addressing specific phobias such as acrophobia, arachnophobia, and social anxiety disorders. Beyond phobia treatment, VR’s capacity to simulate challenging or stress-inducing scenarios presents opportunities for fostering emotional resilience by building adaptive coping mechanisms and reducing stress responses over time. The integration of biofeedback and machine learning further enhances VR applications, enabling real-time adjustments based on physiological and psychological responses. In this article, the current advancements, underlying mechanisms, and challenges in leveraging VR technology for therapeutic purposes are discussed with a focus on its implications for mental health care. By combining immersive technology with evidence-based practices, VR offers a promising pathway for improving mental health outcomes and expanding the accessibility of therapeutic interventions. Full article

Atypical and subtle facial expression patterns in individuals with autism spectrum disorder (ASD) pose a significant challenge for automated emotion recognition. This study evaluates and compares the performance of convolutional neural networks (CNNs) and transformer-based deep learning models for facial emotion recognition in this population. Using a labeled dataset of emotional facial images, we assessed eight models across four emotion categories: natural, anger, fear, and joy. Our results demonstrate that transformer models consistently outperformed CNNs in both overall and emotion-specific metrics. Notably, the Swin Transformer achieved the highest performance, with an accuracy of 0.8000 and an F1-score of 0.7889, significantly surpassing all CNN counterparts. While CNNs failed to detect the fear class, transformer models showed a measurable capability in identifying complex emotions such as anger and fear, suggesting an enhanced ability to capture subtle facial cues. Analysis of the confusion matrix further confirmed the transformers’ superior classification balance and generalization. Despite these promising results, the study has limitations, including class imbalance and its reliance solely on facial imagery. Future work should explore multimodal emotion recognition, model interpretability, and personalization for real-world applications. Research also demonstrates the potential of transformer architectures in advancing inclusive, emotion-aware AI systems tailored for autistic individuals. Full article

In 2023, the authors conducted a qualitative study in five bilingual educational institutions (two general education schools and three kindergartens) in Tartu, Estonia, undergoing a transition to Estonian-medium education. The empirical material for this qualitative research was collected during ten discussion evenings with Russian L1 parents, with around 300 attendees. Given the emotional and political sensitivity of the topic, the discussions were documented through researchers’ handwritten field notes and subsequently reconstructed from these notes for thematic analysis following the principles of qualitative content analysis. This study aimed to map the concerns and fears of Russian L1 parents and to collaboratively explore possible solutions. The broader objective was to understand and interpret Russian-speaking parents’ attitudes toward the shift to Estonian-medium instruction. A further aim was to raise language awareness among parents and to help lay a more positive foundation for the transition process. The theoretical framework draws on the notion that parents’ language attitudes significantly influence their children’s perceptions of the value of the language being learned. Our results show that many Russian L1 parents in Tartu consider it important for both Estonian- and Russian-speaking children to study in a shared, Estonian-medium learning environment. At the same time, parents identified several key challenges, including concerns about a decline in education quality, increased academic pressure and stress for children learning in a non-native language, a lack of suitable learning materials, and parents’ limited ability to assist with homework due to their own insufficient proficiency in Estonian. Full article

Background: Absence epilepsy is a common pediatric neurological disorder characterized by brief seizures and lapses in awareness. The relationship between anxiety and absence epilepsy is multifaceted. This study aims to investigate neurobehavioral signs directly and indirectly related to anxiety and potential sex differences in aged WAG/Rij rats, a well-established animal model of absence epilepsy. Methods: A battery of behavioral tests was conducted to assess various aspects of neurobehavior, including anxiety (elevated plus maze), anhedonia (sucrose preference), social function, and associative learning (fear conditioning). Multidimensional metrics assessed cognition, motor function, and exploration strategies, prioritizing anxiety as a key influencing factor. Results: Electroencephalogram (EEG) phenotyping was used to identify epileptic and non-epileptic rats. Traditional anxiety measures in the elevated plus maze did not reveal significant differences between groups. However, the Anxiety Composite Index revealed higher autonomic reactivity in non-epileptic females. Cognitive assessments showed no epilepsy- or sex-related differences in overall learning performance. Females exhibited superior avoidance learning compared males. Among epileptic males, those with poor learning performance also displayed higher anxiety-avoidance scores. Rats with high anxiety levels showed enhanced socio-affective reactivity and passive coping, with no effect on exploratory learning. Conclusions: Our findings highlight the importance of sex-specific analyses and physiological measures in epilepsy research. Neurobehavioral comorbidities in WAG/Rij rat model are significantly influenced by anxiety-like behavioral phenotype. Enhanced phenotyping of rat models of absence epilepsy can improve its translational value in understanding epilepsy-associated psychiatric disorders. Full article

Background: Genetic factors pertinent to Parkinson’s disease (PD) might predispose an individual to post-traumatic stress disorder (PTSD). Humans who are heterozygous for the glucocerebrosidase 1 (GBA) L444P Gaucher mutation have an increased PD risk and elevated levels of alpha synuclein (aSyn). Mice that are heterozygous for the GBA mutation and express aSyn with the A53T mutation show elevated anxiety levels at 20 months of age compared to those expressing only A53T. Objective: This study aims to assess whether A53T and A53T-L444P affect the risk of developing PTSD phenotypes and whether sex and age modulate this risk. Methods: Young (5.1 ± 0.2 months) and older (11.3 ± 0.2 months) A53T and GBA L444P female and male mice were tested for fear learning and memory extinction in the contextual fear conditioning and passive avoidance paradigms. Subsequently, the mice were tested for measures of activity and anxiety in the open field and for depressive-like behavior in the forced swim test. Results: In the contextual fear memory extinction paradigm, only young A53T female mice showed contextual fear memory extinction, while older A53T female mice showed increased activity levels over subsequent days. In the passive avoidance memory paradigm, no mice showed extinction of passive avoidance memory. When the frequency of entering the more anxiety-provoking center of the open field was analyzed, a test history x sex x age interaction was observed. In the forced swim test, test history affected the depressive-like behavior in mice trained; there was more depressive-like behavior in mice trained in the contextual fear memory extinction paradigm than in mice trained in the passive avoidance memory extinction paradigm. Moreover, there was an effect of age with more depressive-like behavior in older than in younger mice, and an effect of genotype with more depressive-like behavior in A53T-L444P compared to A53T mice. When cortical phosphorylated tau (pS 199) levels were analyzed, there was an effects of genotype, a sex x age interaction, and ant age x test history interaction. Conclusions: A53T and A53T-L444P affect the risk of developing PTSD phenotypes. Fear extinction test history, genotype, and age affect depressive-like behavior and tau pathology. Full article

Background: Ethanol intake leads to cognitive deficits. Recent research demonstrated that a dysregulation of synaptic vesicle glycoprotein 2A (SV2A) expression seems to be linked to anxiety and memory disorders. Levetiracetam and brivaracetam are two antiseizure drugs that affect the SV2A protein. This study aimed to assess the impact of these drugs on associative learning and anxiety-like behaviors in ethanol-treated rats. Methods: Adult male Wistar rats (n = 64) were given brivaracetam or levetiracetam via i.g. for three weeks at doses of 300 mg/kg or 6 mg/kg, respectively. Ethanol was administered as a 20% solution twice a day, via i.g., at a morning dose of 1.5 g/kg b.w. and an afternoon dose of 3.5 g/kg b.w. Additionally, 5% ethanol was available ad libitum between 4:00 p.m. and 8:00 a.m. Associative learning was evaluated using the passive avoidance test during the alcohol administration period, as well as the contextual fear conditioning and cued fear conditioning tests during the withdrawal period. The level of anxiety was determined using the elevated plus maze test in withdrawal rats. Results: Ethanol consumption resulted in impaired associative memory, and its withdrawal was linked to increased anxiety levels. Levetiracetam enhanced memory performance in the passive avoidance test, but brivaracetam disturbed memory associated with unpleasant stimuli in the contextual fear conditioning. Additionally, withdrawal-induced disturbance of locomotor activity persisted, particularly in animals receiving levetiracetam in the elevated plus maze. Conclusions: Levetiracetam appears to provide certain beneficial effects, whereas brivaracetam may worsen memory disturbances in rats. Full article

Attachment styles, rooted in Bowlby’s Attachment Theory, significantly influence our romantic relationships, workplace behavior, and decision-making processes. Traditional methods like self-report questionnaires often have biases, so we aimed to develop a predictive model using objective physiological data. In our study, participants engaged in the Secretary problem, a sequential decision-making task, while their brain activity was recorded with a 16-electrode EEG device. We transformed this data into coherence graphs and used Node2Vec and PCA to convert these graphs into feature vectors. These vectors were then used to train a machine learning model, XGBoost, to predict attachment styles. Using participant-level nested 5-fold cross-validation, our first model achieved 80% accuracy for Secure and 88% for Fearful-avoidant styles but had difficulty distinguishing between Avoidant and Anxious styles. Analysis of the first three principal components showed these two groups overlapped in coherence space, explaining the confusion. To address this, we created a second model that categorized participants as Secure, Insecure, or Extremely Insecure, improving the overall accuracy to about 92%. Together, the results highlight (i) large-scale EEG connectivity as a viable biomarker of attachment, and (ii) the empirical similarity between Anxious and Avoidant profiles when measured electrophysiologically. This method shows promise in using EEG data and machine learning to understand attachment styles. Our findings suggest that future research should include larger and more diverse samples to refine these models. If validated in multi-site cohorts, such graph-based EEG markers could guide personalised interventions by objectively assessing attachment-related vulnerabilities. This study demonstrates the potential for using EEG data to classify attachment styles, which could have important implications for both research and therapeutic practices. Full article

Background: Patient safety culture is critical for enhancing the quality and safety of healthcare. Studies in low- and middle-income countries have reported challenges in developing patient safety culture, especially in implementing nonpunitive responses to errors and event reporting. However, evidence from Laos remains limited. Objectives: This study aimed to assess patient safety culture in hospitals in southern Laos, using a validated survey tool to identify strengths and areas of improvement. Methods: A cross-sectional study using purposive sampling was conducted in four provincial and twenty-three district hospitals in southern Laos. Healthcare workers on patient safety committees responded to the Hospital Survey on Patient Safety Culture. The positive response rate was analyzed. Bivariate tests (chi-square/Fisher’s exact) were applied to compare positive response rates between hospital types and professions. Results: A total of 253 valid responses (75.5%) were analyzed. “Organizational Learning–Continuous Improvement” scored over 75% in both provincial and district hospitals. In contrast, “Nonpunitive Response to Error” and “Frequency of Events Reported” were scored <20% on average. Provincial hospitals scored significantly higher than district hospitals in supervisory support and handoffs. Conclusions: This study illustrated strengths in organizational learning while identifying nonpunitive responses and event reporting as critical areas of improvement for hospitals in Laos. To improve patient safety, hospitals in Laos must promote a culture in which errors can be reported without fear of blame. Strengthening leadership support and reporting systems is essential. These findings can inform strategies to enhance patient safety in other low-resource healthcare settings. Full article

Background/Objectives: Fear detection through EEG signals has gained increasing attention due to its applications in affective computing, mental health monitoring, and intelligent safety systems. This systematic review aimed to identify the most effective methods, algorithms, and configurations reported in the literature for detecting fear from EEG signals using artificial intelligence (AI). Methods: Following the PRISMA 2020 methodology, a structured search was conducted using the string (“fear detection” AND “artificial intelligence” OR “machine learning” AND NOT “fnirs OR mri OR ct OR pet OR image”). After applying inclusion and exclusion criteria, 11 relevant studies were selected. Results: The review examined key methodological aspects such as algorithms (e.g., SVM, CNN, Decision Trees), EEG devices (Emotiv, Biosemi), experimental paradigms (videos, interactive games), dominant brainwave bands (beta, gamma, alpha), and electrode placement. Non-linear models, particularly when combined with immersive stimulation, achieved the highest classification accuracy (up to 92%). Beta and gamma frequencies were consistently associated with fear states, while frontotemporal electrode positioning and proprietary datasets further enhanced model performance. Conclusions: EEG-based fear detection using AI demonstrates high potential and rapid growth, offering significant interdisciplinary applications in healthcare, safety systems, and affective computing. Full article

Objectives: Older adults who worry about not being able to stand up from the floor after a fall, reduce their physical activity, which leads to a higher risk of falling. The Backward Chaining Method (BCM) was developed specifically for this population to safely teach and practice the movement sequence required to stand up from the floor. Our aim is to evaluate the effectiveness of using the BCM to teach older adults how to stand up from the floor, and to determine whether this training has an impact on functional mobility, muscle strength, fear of falling, and life-space mobility. Methods: A total of 26 residents of a long-term care facility were randomly allocated to two groups. Residents in the intervention group (IG, n = 13) participated in a seven-week training program to learn how to stand up from the floor with BCM, in addition to the usual care generally offered in long-term care facilities. The participants in the control group (CG, n = 13) received the usual care alone. The primary outcome measure was functional mobility, assessed by the Timed Up and Go test. Secondary outcome measures included functional lower limb strength, grip strength, fear of falling, and life-space mobility. The outcomes were measured at baseline and after the seven-week intervention period. Results: We found no significant between-group differences in functional mobility, lower limb strength and grip strength; however, IG subjects demonstrated significantly lower fear of falling scores, and significantly higher life-space mobility and independent life-space mobility scores compared to CG subjects after the training program. Conclusions: This study demonstrates that the Backward Chaining Method is a feasible, well-tolerated intervention in a long-term care setting and it may have meaningful benefits, particularly in lessening fear of falling and improving life-space mobility and independent life-space mobility when incorporated into the usual physiotherapy interventions. Full article

Public attitudes toward immigration in Spain are influenced by media narratives, individual traits, and emotional responses. This study examines how portrayals of Arab and African immigrants may be associated with emotional and attitudinal variation. We address three questions: (1) How are different types of media coverage and social environments linked to emotional reactions? (2) What emotions are most frequently associated with these portrayals? and (3) How do political orientation and media exposure relate to changes in perception? A pre/post media exposure survey was conducted with 130 Spanish university students. Machine learning models (decision tree, random forest, and support vector machine) were used to classify attitudes and identify predictive features. Emotional variables such as fear and happiness, as well as perceptions of media clarity and bias, emerged as key features in classification models. Political orientation and prior media experience were also linked to variation in responses. These findings suggest that emotional and contextual factors may be relevant in understanding public perceptions of immigration. The use of interpretable models contributes to a nuanced analysis of media influence and highlights the value of transparent computational approaches in migration research. Full article