Search Results (879)

Background: Burns are a common and severe medical emergency requiring immediate specialized care to minimize damage and prevent complications. Burn severity depends on depth, extent, and location, with more complex care needed for burns on critical areas or extensive burns. Nursing is essential in burn management, providing immediate care, adapting treatments, managing pain, preventing infections, and offering emotional support for recovery. The study aims to analyse the epidemiological and clinical characteristics of burns treated at the Hospital Emergency Department of the Hospital Complex of Cáceres (Spain) from January 2018 to December 2022. It looks at factors like gender, age, hospital stay duration, emergency type (paediatric or adult), main diagnosis, skin thickness, burn degree, affected body areas, percentage of body surface area burned, and treatment types. It also investigates how treatment varies by gender, age, skin thickness, and burn severity. The relevance of this research lies in the fact that periodic epidemiological studies are essential to monitor changes in diseases, evaluate the effectiveness of interventions, detect outbreaks quickly, update knowledge on risk factors, and guide health policy decisions. This ensures an adapted and effective response to the needs of the population. Methods: Retrospective, observational study that analysed burn cases treated at the Hospital Complex of Cáceres (Spain) 2018–2022. Inclusion criteria were based on ICD-10 codes for burns, excluding severe cases not treated in this service. Data were analysed using descriptive statistics, Student’s t-tests, Chi-square tests, and ANOVA. Results: 220 patients surveyed, with a mean age of 47 years and 60.9% male. Most burns (95.5%) affected the external body surface, with a mean hospital stay of 7.86 days. Medical treatment was provided to 75.5% of patients, and 24.5% required surgical intervention. Significant differences in treatment procedures were observed according to age, skin thickness, and burn degree. Older patients had more procedures and longer hospital stays. Excision and transfer procedures were more common in full-thickness and severe burns. Conclusions: The findings align with previous research on burn demographics and treatment approaches. Treatment differences by age and burn severity highlight the need for tailored interventions. The study underscores the importance of comprehensive burn management, including psychological support for improved long-term outcomes. Further research could explore the impact of socio-economic factors on burn incidence and treatment. This study was not registered. Full article

Facial expression recognition (FER) utilizes artificial intelligence for the detection and analysis of human faces, with significant applications across various scenarios. Our objective is to deploy the facial emotion recognition network on mobile devices and extend its application to diverse areas, including classroom effect monitoring, human–computer interaction, specialized training for athletes (such as in figure skating and rhythmic gymnastics), and actor emotion training. Recent studies have employed advanced deep learning models to address this task, though these models often encounter challenges like subpar performance and an excessive number of parameters that do not align with the requirements of FER for embedded devices. To tackle this issue, we have devised a lightweight network structure named RS-Xception, which is straightforward yet highly effective. Drawing on the strengths of ResNet and SENet, this network integrates elements from the Xception architecture. Our models have been trained on FER2013 datasets and demonstrate superior efficiency compared to conventional network models. Furthermore, we have assessed the model’s performance on the CK+, FER2013, and Bigfer2013 datasets, achieving accuracy rates of 97.13%, 69.02%, and 72.06%, respectively. Evaluation on the complex RAF-DB dataset yielded an accuracy rate of 82.98%. The incorporation of transfer learning notably enhanced the model’s accuracy, with a performance of 75.38% on the Bigfer2013 dataset, underscoring its significance in our research. In conclusion, our proposed model proves to be a viable solution for precise sentiment detection and estimation. In the future, our lightweight model may be deployed on embedded devices for research purposes. Full article

Postpartum depression (PPD) affects 174 million women worldwide and is characterized by profound sadness, anxiety, irritability, and debilitating fatigue, which disrupt maternal caregiving and the mother–infant relationship. Limited pharmacological interventions are currently available. Our understanding of the neurobiological pathophysiology of PPD remains incomplete, potentially hindering the development of novel treatment strategies. Recent hypotheses suggest that PPD is driven by a complex interplay of hormonal changes, neurotransmitter imbalances, inflammation, genetic factors, psychosocial stressors, and hypothalamic–pituitary–adrenal (HPA) axis dysregulation. This narrative review examines recent clinical studies on PPD within the past 15 years, emphasizing advancements in neuroimaging findings and blood biomarker detection. Additionally, we summarize recent laboratory work using animal models to mimic PPD, focusing on hormone withdrawal, HPA axis dysfunction, and perinatal stress theories. We also revisit neurobiological results from several brain regions associated with negative emotions, such as the amygdala, prefrontal cortex, hippocampus, and striatum. These insights aim to improve our understanding of PPD’s neurobiological mechanisms, guiding future research for better early detection, prevention, and personalized treatment strategies for women affected by PPD and their families. Full article

Introduction: The rapid spread of the COVID-19 pandemic has had a significant impact on the psychological well-being of millions of people around the world, and even more so among children. Contracting SARS-CoV-2, resulting in home confinement and restrictions on daily and school activities, led to negative effects on the mental health of the paediatric population. Although children suffering from COVID-19 had milder general symptoms compared to adults, impairments in cognitive, neuropsychological, and emotional-behavioural development were noted. Objective: The main aim of the present study was to detect possible changes in the neuropsychological and emotional-behavioural development of children after infection with the SARS-CoV-2 virus. The second aim was to investigate possible relationships between cognitive abilities and psychosocial characteristics. Methods: A total of 40 patients aged 8–9 years were recruited and divided into two groups: children who contracted (CG) and did not contract (NCG) SARS-CoV-2. The BVN 5–11 (Neuropsychological evaluation battery for developmental age from 5 to 11 years) instrument was administered to assess attention, memory, verbal recall, planning, phonemics, and categorical fluency domains in the paediatric population. Data on changes in emotional-behavioural profile and daily activities were collected through a questionnaire to parents. Results: The Wilcoxon signed-rank test revealed a significant change in mood after the COVID-19 period only in the CG participants (p = 0.019). However, the neuropsychological performance of the two identified groups on BVN 5–11 sub-items was below the cutoff of clinical significance. Correlations were found between sub-items of the BVN 5–11 battery, extracurricular activities, and children’s psycho-motor development. Significant positive correlations were observed between Naming on visual presentation and Reading time (p = 0.006), backward digit span and time of motor activity (p = 0.009), Visual attention and Reading time (p = 0.048), and Phonemic fluency and time observed using devices (p = 0.030). Positive statistically significant correlations were also found between Mood and Free behaviour (p = 0.000), between Mood and Structured behaviour (p = 0.005), and between Mood and peer Interaction (p = 0.013). Conclusions: SARS-CoV-2 infection negatively affected the emotional development of children contracting the virus. The neuropsychological functioning of the paediatric population was influenced by psychosocial variables and time spent on daily activities, which played a protective role in children’s cognitive development. Full article

Convolutional neural networks have made significant progress in human Facial Expression Recognition (FER). However, they still face challenges in effectively focusing on and extracting facial features. Recent research has turned to attention mechanisms to address this issue, focusing primarily on local feature details rather than overall facial features. Building upon the classical Convolutional Block Attention Module (CBAM), this paper introduces a novel Parallel Hybrid Attention Model, termed PH-CBAM. This model employs split-channel attention to enhance the extraction of key features while maintaining a minimal parameter count. The proposed model enables the network to emphasize relevant details during expression classification. Heatmap analysis demonstrates that PH-CBAM effectively highlights key facial information. By employing a multimodal extraction approach in the initial image feature extraction phase, the network structure captures various facial features. The algorithm integrates a residual network and the MISH activation function to create a multi-feature extraction network, addressing issues such as gradient vanishing and negative gradient zero point in residual transmission. This enhances the retention of valuable information and facilitates information flow between key image details and target images. Evaluation on benchmark datasets FER2013, CK+, and Bigfer2013 yielded accuracies of 68.82%, 97.13%, and 72.31%, respectively. Comparison with mainstream network models on FER2013 and CK+ datasets demonstrates the efficiency of the PH-CBAM model, with comparable accuracy to current advanced models, showcasing its effectiveness in emotion detection. Full article

Aggression is a fundamental behavior with essential roles in dominance assertion, resource acquisition, and self-defense across the animal kingdom. However, dysregulation of the aggression circuitry can have severe consequences in humans, leading to economic, emotional, and societal burdens. Previous inconsistencies in aggression research have been due to limitations in techniques for studying these neurons at a high spatial resolution, resulting in an incomplete understanding of the neural mechanisms underlying aggression. Recent advancements in optogenetics, pharmacogenetics, single-cell RNA sequencing, and in vivo electrophysiology have provided new insights into this complex circuitry. This review aims to explore the aggression-provoking stimuli and their detection in rodents, particularly through the olfactory systems. Additionally, we will examine the core regions associated with aggression, their interactions, and their connection with the prefrontal cortex. We will also discuss the significance of top-down cognitive control systems in regulating atypical expressions of aggressive behavior. While the focus will primarily be on rodent circuitry, we will briefly touch upon the modulation of aggression in humans through the prefrontal cortex and discuss emerging therapeutic interventions that may benefit individuals with aggression disorders. This comprehensive understanding of the neural substrates of aggression will pave the way for the development of novel therapeutic strategies and clinical interventions. This approach contrasts with the broader perspective on neural mechanisms of aggression across species, aiming for a more focused analysis of specific pathways and their implications for therapeutic interventions. Full article

Stress has various impacts on the health of human beings. Recent success in wearable sensor development, combined with advancements in deep learning to automatically detect features from raw data, opens several interesting applications related to detecting emotional states. Being able to accurately detect stress-related emotional arousal in an acute setting can positively impact the imminent health status of humans, i.e., through avoiding dangerous locations in an urban traffic setting. This work proposes an explainable deep learning methodology for the automatic detection of stress in physiological sensor data, recorded through a non-invasive wearable sensor device, the Empatica E4 wristband. We propose a Long-Short Term-Memory (LSTM) network, extended through a Deep Generative Ensemble of conditional GANs (LSTM DGE), to deal with the low data regime of sparsely labeled sensor measurements. As explainability is often a main concern of deep learning models, we leverage Integrated Gradients (IG) to highlight the most essential features used by the model for prediction and to compare the results to state-of-the-art expert-based stress-detection methodologies in terms of precision, recall, and interpretability. The results show that our LSTM DGE outperforms the state-of-the-art algorithm by 3 percentage points in terms of recall, and 7.18 percentage points in terms of precision. More importantly, through the use of Integrated Gradients as a layer of explainability, we show that there is a strong overlap between model-derived stress features for electrodermal activity and existing literature, which current state-of-the-art stress detection systems in medical research and psychology are based on. Full article

Detecting emotions is a growing field aiming to comprehend and interpret human emotions from various data sources, including text, voice, and physiological signals. Electroencephalogram (EEG) is a unique and promising approach among these sources. EEG is a non-invasive monitoring technique that records the brain’s electrical activity through electrodes placed on the scalp’s surface. It is used in clinical and research contexts to explore how the human brain responds to emotions and cognitive stimuli. Recently, its use has gained interest in real-time emotion detection, offering a direct approach independent of facial expressions or voice. This is particularly useful in resource-limited scenarios, such as brain–computer interfaces supporting mental health. The objective of this work is to evaluate the classification of emotions (positive, negative, and neutral) in EEG signals using machine learning and deep learning, focusing on Graph Convolutional Neural Networks (GCNN), based on the analysis of critical attributes of the EEG signal (Differential Entropy (DE), Power Spectral Density (PSD), Differential Asymmetry (DASM), Rational Asymmetry (RASM), Asymmetry (ASM), Differential Causality (DCAU)). The electroencephalography dataset used in the research was the public SEED dataset (SJTU Emotion EEG Dataset), obtained through auditory and visual stimuli in segments from Chinese emotional movies. The experiment employed to evaluate the model results was “subject-dependent”. In this method, the Deep Neural Network (DNN) achieved an accuracy of 86.08%, surpassing SVM, albeit with significant processing time due to the optimization characteristics inherent to the algorithm. The GCNN algorithm achieved an average accuracy of 89.97% in the subject-dependent experiment. This work contributes to emotion detection in EEG, emphasizing the effectiveness of different models and underscoring the importance of selecting appropriate features and the ethical use of these technologies in practical applications. The GCNN emerges as the most promising methodology for future research. Full article

We present the Group Cohesion and Emotion (GCE) dataset, which comprises 1029 segmented films sourced from YouTube. These videos encompass a range of interactions, including interviews, meetings, informal discussions, and other similar contexts. In the annotation process, graduate psychology students were tasked with assigning coherence levels, ranging from 1 to 7, and affective states as negative, neutral, or positive for each 30 s film. We introduce a foundational model that utilizes advanced visual and audio embedding techniques to investigate the concepts of group cohesion and group emotion prediction. The application of Multi-Head Attention (MHA) fusion is utilized to enhance the process of cross-representation learning. The scope of our investigation includes both unimodal and multimodal techniques, which provide insights into the prediction of group cohesion and the detection of group emotion. The results emphasize the effectiveness of the GCE dataset in examining the level of group unity and emotional conditions. Full article

(1) Background: This prospective study aimed to assess the impact on quality of life (QoL) from pretreatment to 3 years after treatment in oropharyngeal carcinoma (OPC) survivors. (2) Methods: QoL was measured with the EORTC QLQ-C30 and EORTC QLQ-H&N35 scales before treatment and in the first and third years. (3) Results: Of 72 patients, 51 completed all questionnaires over 3 years. A variable deterioration of QoL scores was detected before treatment. Most items worsened significantly after treatment and during the first year and improved in the third year. Advanced-stage cancer and definitive chemoradiotherapy treatment showed the worst scores. At 3 years, patients who underwent surgery with adjuvant radiation therapy/chemotherapy had significantly better scores on global QoL and emotional functioning compared to those treated with definitive chemoradiotherapy, who also reported problems with sticky salivation and dry mouth. Patients treated with an open surgical approach showed significantly greater deterioration in physical and role functioning compared to transoral surgery. (4) Conclusions: This long-term prospective study is the first in Spain to use EORCT scales in a homogeneous group of OPC survivors. QoL was generally good, although patients needed a long period of time to recover from both cancer and side effects of treatment. Advanced-stage cancer and definitive chemoradiotherapy showed the worst scores. Full article

This paper appraises convolutional neural network (CNN) models’ capabilities in emotion detection from facial expressions, seeking to aid the diagnosis of psychosomatic illnesses, typically made in clinical setups. Using the FER-2013 dataset, two CNN models were designed to detect six emotions with 64% accuracy—although not evenly distributed; they demonstrated higher effectiveness in identifying “happy” and “surprise.” The assessment was performed through several performance metrics—accuracy, precision, recall, and F1-scores—besides further validation with an additional simulated clinical environment for practicality checks. Despite showing promising levels for future use, this investigation highlights the need for extensive validation studies in clinical settings. This research underscores AI’s potential value as an adjunct to traditional diagnostic approaches while focusing on wider scope (broader datasets) plus focus (multimodal integration) areas to be considered among recommendations in forthcoming studies. This study underscores the importance of CNN models in developing psychosomatic diagnostics and promoting future development based on ethics and patient care. Full article

Background/Objectives: The increase in digital tools in early childhood education highlights the need for evidence-based assessments that support cognitive development and align with educational requirements and technological advances. This study contributes to the evaluation of the Bilingual English Language Learner Assessment (BELLA), designed to enhance early learning through curriculum-aligned tasks in preschool-aged children. Methods: Data were collected from 17 schools, including 506 preschool children, using a mixed-model approach to assess BELLA’s capacity to appraise early numeracy, literacy, science, and social/emotional development. Analyses included a three-way ANOVA to examine the effects of sex, age, and sub-domain on pass rates and mixed-effects models to evaluate interactions between age and domain. Results: The results indicated a significant effect of age on performance across all domains, with older children demonstrating higher pass rates (p < 0.0001). No significant gender bias was detected. The interaction between age and domain was also significant (p < 0.0001), suggesting domain-specific age-related performance trends, which aligns with internal validity requirements. Conclusion: These findings position BELLA within the growing body of literature on digital media use in early childhood assessment and education, highlighting its potential as a curriculum-compliant digital assessment tool that evaluates and supports cognitive development without a gender bias. This study contributes to the field by providing empirical evidence of BELLA’s effectiveness and suggesting future research directions, including the exploration of its bilingual (and potentially multilingual) applications and external validation against existing evidence-based assessments. Full article

Synchronous monitoring electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have received significant attention in brain science research for their provision of more information on neuro-loop interactions. There is a need for an integrated hybrid EEG-fNIRS patch to synchronously monitor surface EEG and deep brain fNIRS signals. Here, we developed a hybrid EEG-fNIRS patch capable of acquiring high-quality, co-located EEG and fNIRS signals. This patch is wearable and provides easy cognition and emotion detection, while reducing the spatial interference and signal crosstalk by integration, which leads to high spatial–temporal correspondence and signal quality. The modular design of the EEG-fNIRS acquisition unit and optimized mechanical design enables the patch to obtain EEG and fNIRS signals at the same location and eliminates spatial interference. The EEG pre-amplifier on the electrode side effectively improves the acquisition of weak EEG signals and significantly reduces input noise to 0.9 μV_rms, amplitude distortion to less than 2%, and frequency distortion to less than 1%. Detrending, motion correction algorithms, and band-pass filtering were used to remove physiological noise, baseline drift, and motion artifacts from the fNIRS signal. A high fNIRS source switching frequency configuration above 100 Hz improves crosstalk suppression between fNIRS and EEG signals. The Stroop task was carried out to verify its performance; the patch can acquire event-related potentials and hemodynamic information associated with cognition in the prefrontal area. Full article

Unaccompanied foreign minors (UFMs) face stigmatisation and social exclusion in Spanish territory. Given their growing presence in schools, it is crucial that trainee teachers have valid and real information about these students in order to provide equitable, personalised, and quality education to all their students in the near future and to mitigate any uninformed prejudices and stigma developed before they enter the classroom. This study seeks to validate a scale designed to assess the perceptions of pre-service teachers about UFMs (n = 169). The objective of this study was to validate a scale designed to assess the perceptions of pre-service teachers about UFMs (n = 169). All participants were studying primary education at the University of Granada (Spain) [♂ = 131 (77.5%); ♀ = 37 (21.9%)]. Methodology: A quantitative, descriptive, cross-sectional, ex post facto, and quantitative study was conducted. The data were analyzed with IBM SPSS^® 28.0 and IBM Amos Graphics^® 23.0 programs. Results: A multidimensional scale was developed with a Cronbach’s alpha of 0.858 and McDonald’s omega of 0.859, consisting of a total of 26 indicators divided into three factors: socio-educational characteristic (n = 13), social threat (n = 7), and physical and emotional well-being (n = 6). The general scale showed high reliability and acceptable fit (p < 0.001; KMO = 0.880; GFI = 0.832; IFI = 0.925; NFI = 0.816; CFI = 0.924; SMSR = 0.058). CFA reports that the items with the highest factor loadings are related to determining whether these minors respect cultural differences, are involved in drug trafficking, and arrive in Spanish territory with significant malnutrition. However, the items with the lowest factor loadings are linked to understanding the type of academic education these minors have, whether they consume alcohol, or if they require teachers with intercultural competencies to help them integrate socially. Conclusions: A reliable and robust scale was developed to assess the perceptions of pre-service primary school teachers about unaccompanied foreign minors. This instrument can be used to identify the knowledge of teachers in training, which allows training actions to be implemented in the context of higher education to raise awareness, detect biases, and make this vulnerable group visible. Full article

Lung cancer is the leading cause of cancer mortality worldwide. Fortunately, the advent of precision medicine, which includes targeted therapy and immunotherapy, offers hope. However, identifying specific mutations is imperative before initiating precise medications. Traditional methods, such as real-time PCR examination of individual mutations, are time-consuming. Contemporary techniques, such as tissue- and plasma-based next-generation sequencing (NGS), allow comprehensive genome analysis concurrently. Notably, plasma-based NGS has a shorter turnaround time (TAT) and thus a shorter time-to-treatment (TTT). In this case report, we demonstrate the benefits of plasma-based NGS before pathological diagnosis in a patient with image-suspected non-small cell lung cancer (NSCLC). An 82-year-old Taiwanese woman presented with lower back pain persisting for one month and left-sided weakness for two weeks. Whole-body computed tomography (CT) revealed lesions suspicious for brain and bone metastases, along with a mass consistent with a primary tumor in the left upper lobe, indicative of advanced NSCLC with T4N3M1c staging. The patient underwent a bronchoscopic biopsy on Day 0, and the preliminary report that came out on Day 1 was suggestive of metastatic NSCLC. Blood was also collected for plasma-based NGS on Day 0. The patient was Coronavirus disease 2019-positive and was treated with molnupiravir on Day 6. On Day 7, pathology confirmed pulmonary adenocarcinoma, and the results of plasma-based NGS included EGFR L858R mutation. The patient was started on targeted therapy (afatinib) on Day 9. Unfortunately, the patient died of hypoxic respiratory failure on Day 26, a complication of underlying viral infection. Plasma-based NGS offers a rapid and efficient means of mutation detection in NSCLC, streamlining treatment initiation and potentially improving the negative emotions of patients. Its utility, particularly in regions with a high prevalence of specific mutations, such as EGFR alterations in East Asian populations, highlights its relevance in guiding personalized therapy decisions. Full article