Search Results (25,715)

Introduction: With the emergence of the expert patient and the expansion of health literacy, the importance of planning and building health technologies aimed at teaching and training health professionals, especially nurses, due to their activities with patients in Primary Health Care, with the aim of meeting the real and constant demands of the expert patient, is evident. Methods: Methodological study with a quantitative approach. The course was constructed based on a scope review, scientific reference, and observational visits during the months of September 2021 and August 2022. For validation, an organized electronic form was used with general information about the research and items of the course constructed for later evaluation by the judges with the three-point Likert scale and with the application of the Delphi Technique between the months of September and October 2022; for the agreement of the judges, the Content Validation Coefficient > 0.8 was considered. Results: Based on the content selected in the scope review, the reference contribution, and the observational visits, the course was constructed. Nine judges participated in the validation stage in Delphi I with a total Content Validation Coefficient above 0.90 and with some suggestions for modifications and improvements pointed out by them. In Delphi II, six judges evaluated the course, resulting in a total Content Validation Coefficient of 0.99. Conclusions: The course developed was considered valid to support the training of Primary Health Care nurses in the formation of the expert patient, with a view to promoting patient autonomy in self-care management, optimizing Primary Health Care, and reducing unnecessary hospital admissions. Full article

The widespread adoption of heartbeat monitoring sensors has increased the demand for secure and trustworthy multimodal cardiac monitoring systems capable of accurate heartbeat pattern recognition. While existing systems offer convenience, they often suffer from critical limitations, such as variability in the number of available modalities and missing or noisy data during multimodal fusion, which may compromise both performance and data security. To address these challenges, we propose MultiHeart, which is a robust and secure multimodal interactive cardiac monitoring system designed to provide reliable heartbeat pattern recognition through the integration of diverse and trustworthy cardiac signals. MultiHeart features a novel multi-task learning architecture that includes a reconstruction module to handle missing or noisy modalities and a classification module dedicated to heartbeat pattern recognition. At its core, the system employs a multimodal autoencoder for feature extraction with shared latent representations used by lightweight decoders in the reconstruction module and by a classifier in the classification module. This design enables resilient multimodal fusion while supporting both data reconstruction and heartbeat pattern classification tasks. We implement MultiHeart and conduct comprehensive experiments to evaluate its performance. The system achieves 99.80% accuracy in heartbeat recognition, surpassing single-modal methods by 10% and outperforming existing multimodal approaches by 4%. Even under conditions of partial data input, MultiHeart maintains 94.64% accuracy, demonstrating strong robustness, high reliability, and its effectiveness as a secure solution for next-generation health-monitoring applications. Full article

Recent advances in computational biology have provided powerful tools for analyzing, modeling, and optimizing probiotic microorganisms, thereby supporting their development as promising agents for improving human health. The essential role of the microbiota in regulating physiological processes and preventing disease has driven interest in the rational design of next-generation probiotics. This review highlights progress in in silico approaches for enhancing the functionality of probiotic strains. Particular attention is given to genome-scale metabolic models, advanced simulation algorithms, and AI-driven tools that provide deeper insight into microbial metabolism and enable precise probiotic optimization. The integration of these methods with multi-omics data has greatly improved our ability to predict strain behavior and design probiotics with specific health benefits. Special focus is placed on modeling probiotic–prebiotic interactions and host–microbiome dynamics, which are essential for the development of functional food products. Despite these achievements, key challenges remain, including limited model accuracy, difficulties in simulating complex host–microbe systems, and the absence of unified standards for validating in silico-optimized strains. Addressing these gaps requires the development of integrative modeling platforms and clear regulatory frameworks. This review provides a critical overview of current advances, identifies existing barriers, and outlines future directions for the application of computational strategies in probiotic research. Full article

Background: Exposure-based cognitive behavioral therapy (Ex-CBT) is widely seen as the gold-standard treatment for anxiety and obsessive-compulsive disorder (OCD). Yet, minoritized youth are underrepresented in efficacy studies, raising questions about the applicability of Ex-CBT to minoritized youth. Effectiveness data suggest systematic adaptation of Ex-CBT to address youth culture and context is likely needed, and many clinicians make adaptations and augmentations in practice. However, research on the specific strategies clinicians use to address their youth clients’ culture and context within anxiety and OCD treatment is lacking. In the current study, we assess practice-based adaptations, augmentations, and process-based approaches utilized when delivering treatment to youth for OCD and anxiety in public mental health clinics. Methods: We conducted qualitative interviews with 16 clinicians from both specialty anxiety and general mental health clinics serving youth with anxiety or OCD in the public mental health system. Participating clinicians had a mean age of 32.19 (SD = 5.87) and 69% of therapists identified as female; 69% identified as White, 25% identified as Asian, and 6% as Black or African American. In qualitative interviews, clinicians shared how they addressed clients’ culture and context (e.g., social identities, stressors and strengths related to social identities and lived environment). Thematic analysis identified the strategies clinicians employed to address culture and context. Results: Clinicians reported incorporating culture and context through process-based approaches (e.g., building trust gradually, considering clients’ social identity stressors, engaging in self-awareness to facilitate cultural responsiveness) and through culturally adapting and augmenting treatment to promote person-centered care. Core strategies included proactive and ongoing assessment of clients’ cultural and contextual factors, adapting exposures and augmenting Ex-CBT with strategies such as case management and discussion of cultural context, and taking a systems-informed approach to care. Conclusions: Examining practice-based adaptations, augmentations, and process-based approaches to treatment for minoritized youth with OCD or anxiety can inform efforts to understand what comprises person-centered culturally responsive Ex-CBT. Empirical testing of identified strategies is a needed area of future research. Full article

Background/Objectives: Cancer represents a significant health challenge, with high mortality and morbidity rates. Its diagnosis often triggers chronic stress, adversely affecting patient outcomes. Exercise has emerged as complementary therapy, enhancing treatment adherence and mitigating the side effects of chemotherapy. This study examines the effects of mild exercise during chemotherapy on patient anxiety. Methods: This prospective paired cohort study was conducted in the General Oncology Hospital of Kifisia “Agioi Anargyroi” in Athens, Greece. Adult cancer patients undergoing chemotherapy participated, excluding those with cognitive, hearing, or motor impairments, those who experienced side effects, or those who declined consent. Anxiety was measured before and after a 20-minute exercise routine performed during chemotherapy, using the Greek-translated State–Trait Anxiety Inventory (STAI). The exercise regimen included warm-up, full-body stretching, and cool-down exercises. Pre- and post-exercise scores were analyzed using the Wilcoxon signed-rank test. Results: Forty-five patients (20 women, 25 men; mean age 69.02 ± 10.62 years) with various cancer backgrounds participated. Pre-intervention anxiety levels were in the borderline “moderate” range, dropping post-exercise to the “low” range. Mean STAI scores decreased from 37.73 ± 13.33 to 32.00 ± 14.22 (p < 0.0001), with a medium-large effect size (Cohen’s d for paired samples = −0.646). No significant correlation was found between age and anxiety scores. Discussion: This study found a significant short-term reduction in anxiety, suggesting that incorporating mild exercise during chemotherapy may help in alleviating patient stress. The medium-to-large effect size supports the potential for meaningful short-term benefits. Conclusions: Incorporating mild exercise during chemotherapy may help reduce anxiety and psychological burden. These findings underscore the need for more comprehensive research in larger, more diverse populations to better understand the benefits of incorporating mild exercise during chemotherapy. Full article

This study analyses the effectiveness of a contextualized teaching and learning sequence (TLS) based on forensic entomology (FE) to disprove the idea of spontaneous generation (SG) among students enrolled in the Higher Vocational Education and Training (VET) Cycle in Pathological Anatomy and Cytodiagnosis. Through an inquiry- and project-based learning approach, students replicate a version of Francesco Redi’s historical experiments, enabling them to engage with core scientific concepts such as the metamorphic cycle of insects and the role of entomology in forensic science. The research adopts a semiquantitative and exploratory design. It investigates: (1) whether students’ prior knowledge about FE and related biological processes is sufficient to refute SG; (2) to what extent this knowledge is influenced by their previous academic background and gender; and (3) whether a contextualized TLS can significantly enhance their conceptual understanding. The results reveal that most students begin with limited initial knowledge of FE and multiple misconceptions related to SG, irrespective of their previous study. Gender differences were observed at baseline, with women showing lower prior knowledge, but these differences disappeared after the intervention. The post-intervention data demonstrate a significant improvement in student’s ability to reject SG and explain biological processes coherently. The study highlights the importance of integrating entomology into health-related VET programs, both as a means to promote scientific literacy and correct misconceptions and as a pedagogical tool to foster critical thinking. It also highlights the potential and historically grounded methodologies to equalize learning outcomes and strengthen the scientific preparation of future healthcare professionals. Full article

With the growth of the global population and the increasing scarcity of arable land, traditional agricultural production is confronted with multiple challenges, such as efficiency improvement, precision operation, and sustainable development. The progressive advancement of artificial intelligence (AI) technology has created a transformative opportunity for the intelligent upgrade of agricultural equipment. This article systematically presents recent progress in computer vision, machine learning (ML), and intelligent sensing. The key innovations are highlighted in areas such as object detection and recognition (e.g., a K-nearest neighbor (KNN) achieved 98% accuracy in distinguishing vibration signals across operation stages); autonomous navigation and path planning (e.g., a deep reinforcement learning (DRL)-optimized task planner for multi-arm harvesting robots reduced execution time by 10.7%); state perception (e.g., a multilayer perceptron (MLP) yielded 96.9% accuracy in plug seedling health classification); and precision control (e.g., an intelligent multi-module coordinated control system achieved a transplanting efficiency of 5000 plants/h). The findings reveal a deep integration of AI models with multimodal perception technologies, significantly improving the operational efficiency, resource utilization, and environmental adaptability of agricultural equipment. This integration is catalyzing the transition toward intelligent, automated, and sustainable agricultural systems. Nevertheless, intelligent agricultural equipment still faces technical challenges regarding data sample acquisition, adaptation to complex field environments, and the coordination between algorithms and hardware. Looking ahead, the convergence of digital twin (DT) technology, edge computing, and big data-driven collaborative optimization is expected to become the core of next-generation intelligent agricultural systems. These technologies have the potential to overcome current limitations in perception and decision-making, ultimately enabling intelligent management and autonomous decision-making across the entire agricultural production chain. This article aims to provide a comprehensive foundation for advancing agricultural modernization and supporting green, sustainable development. Full article

Background: Empowerment during pregnancy is linked to improved maternal and infant health outcomes and greater maternal well-being. Group Antenatal Care (GANC), a participant-centered model of care, promotes empowerment, active engagement, and the deconstruction of hierarchy between participants and care providers. It combines health assessment, interactive learning, and community building. While empowerment is a core concept of GANC, the ways it manifests and the elements that facilitate it remain unclear. Method: We conducted a generic qualitative study across four organizations in Brussels, using multiple data collection methods. This included interviews with 13 participants and 21 observations of GANC sessions, combining both the insider and outsider perspective. An adapted version of the Pregnancy-Related Empowerment Scale (PRES) guided the interviews guide and thematic analysis. Results: We identified seven themes that capture how empowerment occurs in GANC: peer connectedness, provider connectedness, skillful decision-making, responsibility, sense of control, taking action, and gaining voice. Several aspects of GANC contribute to empowerment, particularly the role of facilitators. Conclusions: This study highlights how GANC enhances empowerment during pregnancy through interpersonal, internal, and external processes. Important components within GANC that support this process include the group-based format and the interactive nature of the discussions. The presence of skillful GANC facilitators is an essential prerequisite. In a diverse and often vulnerable context like Brussels, strengthening empowerment through GANC presents challenges but is especially crucial. Full article

Background: Maxillary sinus puncture (MSP), once a cornerstone for diagnosing and treating acute rhinosinusitis (ARS), has declined with the rise in less invasive techniques. This study explores MSP trends, its association with age, and long-term effects on quality of life using data from the Study of Health in Pomerania (SHIP). Methods: Data from SHIP-START-2 (n = 2332), SHIP-START-3 (n = 1717), and SHIP-TREND-0 (n = 4420) cohorts were analyzed to assess MSP prevalence, demographic correlations, and quality- of-life impacts using SNOT-20-D, EQ-5D-3L, and SF-12. Results: MSP prevalence was higher in older SHIP-START cohorts (11.2% in START-2) compared to SHIP-TREND-0 (9.5%), reflecting its historical decline. The procedure was more frequently reported by participants aged > 60 years (e.g., 14.0% in START-2) than by younger groups (<40 years: 3.5% in START-2). MSP was associated with increased SNOT-20-D scores across cohorts (e.g., +0.28 in START-2, p < 0.001) and minor reductions in EQ-5D-3L and SF-12 mental health scores, indicating greater symptom burden but limited general health impact. The age- and time-related decline in MSP highlights its diminishing role in modern practice. Conclusions: While MSP offers diagnostic insights and serves as an indicator for ARS, its modest impact on long-term quality-of-life underscores the need for alternative, minimally invasive techniques for sinonasal conditions. Full article

Background: China is confronted with the dual challenges of deeply interwoven population aging and the digitalization process. The digital integration and mental health issues of the elderly group are becoming increasingly prominent. Objectives: The present study aimed to analyze the pathways through which individual, family, and social factors influence Internet use in the elderly through a multi-level analysis framework, to examine the association between Internet use and mental health with a view to providing empirical evidence for digital technology-based mental health intervention programs for the elderly, and to promote the scientific practice of the goal of healthy aging. Methods: Based on the data of the 2021 China General Social Survey (CGSS) and provincial Internet development indicators, a mixed cross-sectional dataset was constructed. Logistic hierarchical regression and OLS regression methods were adopted to systematically investigate the multi-level factors associated with Internet use among the elderly group and its association with mental health. Results: The results indicate that individual resources (younger age, higher education level, and good health status) and family technical support (family members’ Internet access) are strongly associated with Internet usage among the elderly, while regional Internet penetration rate appears to operate indirectly through micro-mechanisms. Analysis of the association with mental health showed that Internet use was related to a lower score of depressive tendency (p < 0.05), and this association remained robust after controlling for variables at the individual, family, and social levels. Conclusions: The research results provide empirical evidence for the health promotion policies for the elderly, advocating the construction of a collaborative intervention framework of “individual ability improvement–intergenerational family support–social adaptation for the elderly” to bridge the digital divide and promote the digital integration of the elderly population in China. Full article

The ship waterjet propulsion system is a crucial power unit for high-performance vessels, and the operational state of its core component, the waterjet pump, is directly related to navigation safety and mission reliability. To enhance the intelligence and accuracy of pump fault diagnosis, this paper proposes a novel diagnostic framework that integrates a supervised autoencoder (SAE) with a large language model (LLM). This framework first employs an SAE to perform task-oriented feature learning on raw vibration signals collected from the pump’s guide vane casing. By jointly optimizing reconstruction and classification losses, the SAE extracts deep features that both represent the original signal information and exhibit high discriminability for different fault classes. Subsequently, the extracted feature vectors are converted into text sequences and fed into an LLM. Leveraging the powerful sequential information processing and generalization capabilities of LLM, end-to-end fault classification is achieved through parameter-efficient fine-tuning. This approach aims to avoid the traditional dependence on manually extracted time-domain and frequency-domain features, instead guiding the feature extraction process via supervised learning to make it more task-specific. To validate the effectiveness of the proposed method, we compare it with a baseline approach that uses manually extracted features. In two experimental scenarios, direct diagnosis with full data and transfer diagnosis under limited-data, cross-condition settings, the proposed method significantly outperforms the baseline in diagnostic accuracy. It demonstrates excellent performance in automated feature extraction, diagnostic precision, and small-sample data adaptability, offering new insights for the application of large-model techniques in critical equipment health management. Full article

This paper aims to leverage artificial intelligence (AI) to assist physicians in utilizing advanced deep learning techniques integrated into developed models within electronic health records (EHRs) in medical information systems (MISes), which have been in use for over 15 years in health centers across the Republic of Serbia. This paper presents a human-centered AI approach that emphasizes physician decision-making supported by AI models. This study presents two developed and implemented deep neural network (DNN) models in the EHR. Both models were based on data that were collected during the COVID-19 outbreak. The models were evaluated using five-fold cross-validation. The convolutional neural network (CNN), based on the pre-trained VGG19 architecture for classifying chest X-ray images, was trained on a publicly available smaller dataset containing 196 entries, and achieved an average classification accuracy of 91.83 ± 2.82%. The DNN model for optimizing patient appointment scheduling was trained on a large dataset (341,569 entries) and a rich feature design extracted from the MIS, which is daily used in Serbia, achieving an average classification accuracy of 77.51 ± 0.70%. Both models have consistent performance and good generalization. The architecture of a realized MIS, incorporating the positioning of developed AI tools that encompass both developed models, is also presented in this study. Full article

Objectives: The aim of the study was to investigate caries experience in correlation with self-reported oral health-related habits in a sample of blind and low-vision individuals in Croatia. Methods: The study is a part of the research in the “Project for Oral Health Promotion in Blind and Visually Impaired Persons” conducted at the Zagreb University School of Dental Medicine from 2014 to 2018. The final sample consisted of 85 adults: 42 females and 43 males; 50 blind and 35 low-vision individuals, age range 18–98. The assessment included dental examination and a questionnaire. Results: The median DMFT (Decayed, Missing, and Filled Teeth) index score was 17.0 (IQR = 12.5–22.0), with no significant difference between sexes or between blind and low-vision individuals. The occurrence of untreated caries was low (median D-component = 1.0), while the median F-component was 6.0. There was a significant increase in M-component and DMFT in older age groups. The number of untreated caries (D-component) was significantly correlated with consummation of soft drinks and smoking. Total DMFT did not correlate with frequency of tooth brushing, time since last dental visit, smoking, or level of education. Conclusions: This study revealed high caries experience among blind and visually impaired individuals that did not correlate with factors that normally influence oral health. Similar results were found in the control group, reflecting a 30-year post-war period without organized preventive care. The low number of decayed teeth reflects the availability of public dental care in Croatia; however, preventive care should be provided for both this vulnerable group and the general population. Full article

Biomass and other solid wastes create potential environmental and health hazards in our modern society. Conversion of the wastes into energy presents a promising avenue for sustainable energy generation. However, the feasibility of the approach is limited by the challenges in material handling because of the special properties of the materials. Despite their critical importance, the complexities of material handling often evade scrutiny until operational implementation. This paper highlights the challenges inherent in standard solid material-handling processes, preceded by a concise review of common solid waste typologies and their physical properties, particularly those related to biomass and biowastes. It delves into the complexities of material flow, storage, compaction, agglomeration, separation, transport, and hazard management. Specialised characterisation techniques essential for informed process design are also discussed to mitigate operational risks. In conclusion, this paper emphasises the necessity of a tailored framework before the establishment of any further conversion processes. Given the heterogeneous nature of biomaterials, material-handling equipment must demonstrate adaptability to accommodate the substantial variability in material properties in large-scale production. This approach aims to enhance feasibility and efficacy of any energy conversion initiatives by using biomass or other solid wastes, thereby advancing sustainable resource utilisation and environmental stewardship. Full article

The global rise in antimicrobial resistance poses a major threat to public health, with multidrug-resistant bacterial infections expected to surpass cancer in mortality by 2050. As traditional antibiotic pipelines stagnate, novel therapeutic alternatives are critically needed. Antimicrobial peptides (AMPs), particularly those derived from marine organisms, have emerged as promising antimicrobial candidates due to their broad-spectrum activity, structural diversity, and distinctive mechanisms of action. Unlike conventional antibiotics, AMPs can disrupt microbial membranes, inhibit biofilm formation, and even modulate immune responses, making them highly effective against resistant bacteria. This review highlights the potential of marine AMPs as next-generation therapeutics, emphasizing their efficacy against multidrug-resistant pathogens and biofilm-associated infections. Furthermore, marine AMPs show promise in combating persister cells and disrupting quorum sensing pathways, offering new strategies for tackling chronic infections. Despite their potential, challenges such as production scalability and limited clinical validation remain; nevertheless, the use of new technologies and bioinformatic tools is accelerating the discovery and optimization of these peptides, paving the way for bypassing these challenges. This review consolidates current findings on marine AMPs, advocating for their continued exploration as viable tools in the fight against antimicrobial resistance. Full article