Search Results (1,205)

Background: Older adults in residential aged care facilities (RACFs) experience disproportionate levels of poor oral health relative to other groups in the general population, affecting their physical and mental wellbeing. The Oral Health Assessment Tool (OHAT) is a validated and widely used dental assessment tool; however, recent systematic reviews have identified shortcomings with respect to its measurement properties. Objective: The objective of this protocol is to provide a detailed overview of a multidisciplinary qualitative study that aims to (a) co-design and develop a modified OHAT for RACFs and (b) inform the development of an OHAT training package and implementation strategies. Methods: This study will utilize a co-design methodology with aged care residents, caregivers, staff members, and health professionals. The co-design workshops will: (1) investigate the barriers to and enablers of optimal oral healthcare in RACFs; and (2) co-design a modified version of the Oral Health Assessment Tool and a referral to treatment pathway that is appropriate for use in RACFs. The co-design workshops will facilitate group discussion and involve interactive activities using, for example, mind mapping and Sticky Notes. Qualitative data (transcripts and artefacts from co-design activities) will be analyzed in NVivo using an inductive codebook thematic analysis, specifically a template analysis. Conclusion: The findings of this study will inform a modified OHAT (M-OHAT), as well as future study phases regarding training and implementation strategies. It is expected that the M-OHAT will have enhanced usability and relevance to RACFs, facilitating the identification of poor oral health and timely referral to dental treatment. Full article

Introduction: Optical Coherence Tomography (OCT) has emerged as an important imaging modality in non-invasive diagnosis for oral cancer and can provide real-time visualisation of tissue morphology with the required high resolution. This systematic review aims to assess the diagnostic accuracy of OCT in the detection of oral cancers, and to explore the potential integration of OCT with artificial intelligence (AI) and other imaging techniques to enhance diagnostic precision and clinical outcomes in oral healthcare. Methods: A systematic literature search was conducted across PubMed, Embase, Scopus, Google Scholar, Cochrane Central Register, and Web of Science from inception until August 2024. Studies were included if they employed OCT for oral cancer detection, reported diagnostic outcomes, such as sensitivity and specificity, and were conducted on human subjects. Data extraction and quality assessment were performed independently by two reviewers. The synthesis highlights advancements in OCT technology, including AI-enhanced interpretations. Results: A total of 9 studies met the inclusion criteria, encompassing a total of 860 events (cancer detections). The studies spanned from 2008 to 2022 and utilised various OCT techniques, including clinician-based, algorithm-based, and AI-driven interpretations. The findings indicate OCT’s high diagnostic accuracy, with sensitivity ranging from 75% to 100% and specificity from 71% to 100%. AI-augmented OCT interpretations demonstrated the highest accuracy, emphasising OCT’s potential in early cancer detection and precision in guiding surgical interventions. Conclusions: OCT could play a very prominent role as a new diagnostic tool for oral cancer, with very high sensitivity and specificity. Future research pointed towards integrating OCT with other imaging methods and AI systems in providing better accuracy of diagnoses, plus more clinical usability. Further development and validation with large-scale multicentre trials is imperative for the realisation of this potential in changing the way we practice oral healthcare. Full article

Background/Objectives: In perioperative nursing practice, nursing students can engage in direct, in-person clinical experiences in perioperative environments; however, they face limitations due to infection and contamination risks. This study aimed to develop and evaluate educational content for perioperative clinical practice for nursing students using virtual reality (VR) and haptic technology. Methods: The program, based on the Unity Engine, was created through programming and followed the system development lifecycle (SDLC) phases of analysis, design, implementation, and evaluation. This program allows nursing students to engage in perioperative practice using VR and haptic technology, overcoming previous environmental limitations and enhancing practical and immersive experiences through multi-sensory stimuli. Results: Expert evaluations indicated that the developed content was deemed suitable for educational use. Additionally, a usability assessment with 29 nursing students revealed high levels of presence, usability, and satisfaction among the participants. Conclusions: This program can serve as a foundation for future research on VR-based perioperative nursing education. Full article

N6-methyladenosine (m⁶A) plays a crucial regulatory role in the control of cellular functions and gene expression. Recent advances in sequencing techniques for transcriptome-wide m⁶A mapping have accelerated the accumulation of m⁶A site information at a single-nucleotide level, providing more high-confidence training data to develop computational approaches for m⁶A site prediction. However, it is still a major challenge to precisely predict m⁶A sites using in silico approaches. To advance the computational support for m⁶A site identification, here, we curated 13 up-to-date benchmark datasets from nine different species (i.e., H. sapiens, M. musculus, Rat, S. cerevisiae, Zebrafish, A. thaliana, Pig, Rhesus, and Chimpanzee). This will assist the research community in conducting an unbiased evaluation of alternative approaches and support future research on m⁶A modification. We revisited 52 computational approaches published since 2015 for m⁶A site identification, including 30 traditional machine learning-based, 14 deep learning-based, and 8 ensemble learning-based methods. We comprehensively reviewed these computational approaches in terms of their training datasets, calculated features, computational methodologies, performance evaluation strategy, and webserver/software usability. Using these benchmark datasets, we benchmarked nine predictors with available online websites or stand-alone software and assessed their prediction performance. We found that deep learning and traditional machine learning approaches generally outperformed scoring function-based approaches. In summary, the curated benchmark dataset repository and the systematic assessment in this study serve to inform the design and implementation of state-of-the-art computational approaches for m⁶A identification and facilitate more rigorous comparisons of new methods in the future. Full article

The prevalence of dementia, a condition associated with high social costs, is rising alongside the aging population. Early diagnosis of mild cognitive impairment (MCI), a precursor to dementia, is essential for effective intervention. Recent research has focused on diagnosing cognitive function in the elderly by analyzing behavioral data, such as gait and hand movements. Compared to traditional neuropsychological assessment methods, behavioral data-based assessments offer advantages, including reduced fatigue for patients and examiners, faster testing procedures, and more objective evaluation of results. This study reviews 15 research projects from the past five years (2018–2023) that have utilized behavioral data to assess cognitive function. It examines the specific gait and hand movement variables used, the technologies implemented, and user experiences reported in these studies. As these types of assessments require new technologies or environments, we analyzed usability issues that should be considered for accurate cognitive assessment. Based on this analysis, the paper proposes future directions for research in the field of behavioral data-based cognitive function assessment. Full article

The integration of digital technologies in managing technical and design information is transforming architecture, engineering, and construction (AEC) processes within educational institutions. Despite this, construction education lacks practical, interactive learning tools, and there is insufficient collaboration between academia and the construction industry. To address these challenges, the CAMPUS 2.0 project at Pontificia Universidad Javeriana developed a web-based platform that integrates building information modeling (BIM) and gamification elements. This platform improves project coordination, facilitates interdisciplinary learning, and enhances the management of technical and design information for campus buildings. CAMPUS 2.0 also promotes collaboration and active user engagement, filling a critical gap in the practical tools in construction education. This study assesses the usability of CAMPUS 2.0 among 235 students, teachers, and staff members, demonstrating a positive impact on the university community. The findings provide insights into how digital tools can improve project management, interdisciplinary collaboration, and knowledge sharing within educational settings, offering broader implications for other institutions. Full article

Case-based learning (CBL) is an educational method widely used in health professional training, emphasizing theoretical knowledge’s practical application. However, traditional CBL has many challenges related to the complexity and accessibility of case scenarios and its demands on educators to effectively facilitate and evaluate student discussions. Despite its popularity and proven benefits, the comparative effectiveness and efficiency of CBL against newer educational technologies still need to be explored. In this quasi-experimental study, third-year physiotherapy students at the University of Lleida used a computer program for procedural content generation (PCG) and interactive clinical cases (ICs), and assessed them using the Spanish physiotherapy self-efficacy scale and the System Usability Scale, and a satisfaction scale. The study showed a significant improvement in self-efficacy among participants after using PCG and ICs. The usability of the PCG was moderate, while the ICs scored higher and had greater user satisfaction. Satisfaction metrics revealed a strong preference among students for incorporating clinical cases into other subjects, with higher satisfaction levels observed in the IC group compared to the PCG group. The study demonstrated that PCG and ICs significantly enhance clinical skills acquisition in physiotherapy education by improving student performance and engagement. Full article

Integrating technological tools in teaching statistics can revolutionize educational methods, making learning more engaging, interactive, and effective. This study aims to assess the effectiveness of a math application for Android in enhancing the teaching and learning of statistics for eighth-grade students in Alcalá, Valle del Cauca, Colombia. The project involved coordinating with local mathematics teachers to implement the app in the classroom, focusing on interactive digital content to improve student engagement. The study employed a mixed-methods approach, gathering data from 63 students through surveys that measured user satisfaction, topic relevance, interface usability, and the likelihood of recommending the app. The evaluation revealed a high likelihood of recommendation, with 74.6% of the students indicating they would “Almost always” recommend the app. Positive feedback was also noted in terms of user satisfaction and topic relevance. However, technical performance and content engagement were identified as needing improvement. The study also highlighted limitations, including a small sample size, reliance on self-reported data, and a short-term evaluation period. The Math app shows considerable promise as a tool for teaching statistics, supporting the Sustainable Development Goals (SDGs), particularly SDGs 4 and 9, involving quality education, industry, innovation, and infrastructure. Future work should address the identified limitations and focus on continuous improvement to enhance the app’s effectiveness and reach. Full article

Over the past few years, numerous bacterial strains have become resistant to selected drugs from various therapeutic groups. A potential tool in the fight against these strains is antimicrobial photodynamic therapy (APDT). APDT acts in a non-specific manner by generating reactive oxygen species and radicals, thereby inducing multidimensional intracellular effects. Importantly, the chance that bacteria will develop defense mechanisms against APDT is considered to be low. In our research, we performed the synthesis and physicochemical characterization of curcumin derivatives enriched with morpholine motifs. The obtained compounds were assessed regarding photostability, singlet oxygen generation, aggregation, and acute toxicity toward prokaryotic Aliivibrio fischeri cells in the Microtox^® test. The impact of the compounds on the survival of eukaryotic cells in the MTT assay was also tested (WM266-4, WM115—melanoma, MRC-5—lung fibroblasts, and PHDF—primary human dermal fibroblasts). Initial studies determining the photocytotoxicity, and thus the potential APDT usability, were conducted with the following microbial strains: Candida albicans, Escherichia coli, Staphylococcus aureus, Streptococcus pneumoniae, and Pseudomonas aeruginosa. It was noted that the exposure of bacteria to LED light at 470 nm (fluence: 30 J/cm²) in the presence of quaternized curcumin derivatives at the conc. of 10 µM led to a reduction in Staphylococcus aureus survival of over 5.4 log. Full article

With the outbreak and ongoing spread of the COVID-19 pandemic, a wave of post-COVID-19 zeitgeist has emerged worldwide. From the perspective of fashion trends, an increasing number of consumers are demanding higher standards of health, environmental friendliness, and sustainability. Over the past 10 years, the integration of technology in clothing has facilitated continuous updates in both functionality and aesthetics. This article aims to explore the relationship between clothing technology and sustainable fashion in the post-COVID-19 era, seeking to enhance the development and innovation of sustainable fashion through the empowerment of clothing technology. This study conducts extensive research on the literature and online case studies, categorizing empowered sustainable fashion technology design cases into six major categories and four directional themes. It introduces an innovative sustainable fashion design assessment model, PCBS (Product, Customer, Business, Society). Subsequently, the knowledge framework is applied in design practice alongside consumer research to validate the system’s usability. Furthermore, this research synthesizes and summarizes the methods through which fashion technology can support sustainable fashion in the post-pandemic era. Designers and practitioners can reference these insights for more advanced sustainable design initiatives, thereby promoting a lower-carbon and environmentally friendly system that drives global innovation and development in a sustainable society. Full article

This work reports on an in vivo Raman-based endoscopy system, invaScope, enabling Raman measurements of healthy and tumor bladder tissue during an endoscopic procedure in the operating theatre. The presented study outlines the progression from the initial concept (validated through previously performed ex vivo studies) to the approval and implementation of a clinical investigational device according to the requirement within the framework of the European Medical Device Regulation (MDR2017/745). The study’s primary objective was to employ the invaScope Raman system within the bladder, capturing in vivo spectroscopic Raman data followed by standard histo- and cytopathological examinations of urological tissue (considered the gold standard). The collected data were analyzed and correlated with histopathological findings post-procedure. Additionally, the study aimed to assess the feasibility of using diagnostic equipment, probes, and software for application in a clinical setting, evaluating usability aspects that are important during surgical procedures. This research represents a pivotal step toward advancing Raman spectroscopy for routine clinical use in characterizing bladder lesions. Full article

In the modern era, AI-driven algorithms have significantly influenced medical diagnosis and therapy. In this pilot study, we propose using Streamlit 1.38.0 to create an interactive dashboard, PoAna .v1—Pose Analysis, as a new approach to address these concerns. In real-time, our system accurately tracks and evaluates individualized rehabilitation exercises for patients suffering from low back pain using features such as exercise visualization and guidance, real-time feedback and monitoring, and personalized exercise plans. This dashboard was very effective for tracking rehabilitation progress. We recruited 32 individuals to participate in this pilot study. We monitored an individual’s overall performance for one week. Of the participants, 18.75% engaged in rehabilitative exercises less frequently than twice daily; 81.25% did so at least three times daily. The proposed Long Short-Term Memory (LSTM) architecture had a training accuracy score of 98.8% and a testing accuracy of 99.7%, with an average accuracy of 10-fold cross-validation of 98.54%. On the pre- and post-test assessments, there is a significant difference between pain levels, with a p < 0.05 and a t-stat value of 12.175. The proposed system’s usability score is 79.375, indicating that it provides a user-friendly environment for the user to use the PoAna .v1 web application. So far, our research suggests that the Streamlit 1.38.0-based dashboard improves patients’ engagement, adherence, and success with exercise. Future research aims to add more characteristics that can improve the complete care of low back pain (LBP) and validate the effectiveness of this intervention in larger patient cohorts. Full article

Brain–computer interfaces (BCIs) are promising tools for motor neurorehabilitation. Achieving a balance between classification accuracy and system responsiveness is crucial for real-time applications. This study aimed to assess how the duration of time windows affects performance, specifically classification accuracy and the false positive rate, to optimize the temporal parameters of MI-BCI systems. We investigated the impact of time window duration on classification accuracy and false positive rate, employing Linear Discriminant Analysis (LDA), Multilayer Perceptron (MLP), and Support Vector Machine (SVM) on data acquired from six post-stroke patients and on the external BCI IVa dataset. EEG signals were recorded and processed using the Common Spatial Patterns (CSP) algorithm for feature extraction. Our results indicate that longer time windows generally enhance classification accuracy and reduce false positives across all classifiers, with LDA performing the best. However, to maintain the real-time responsiveness, crucial for practical applications, a balance must be struck. The results suggest an optimal time window of 1–2 s, offering a trade-off between classification performance and excessive delay to guarantee the system responsiveness. These findings underscore the importance of temporal optimization in MI-BCI systems to improve usability in real rehabilitation scenarios. Full article

Traditional Chinese villages, recognized as national heritage sites, are invaluable cultural assets. Since 2012, efforts have focused on their preservation, though issues remain such as prioritizing authenticity over usability and material over cultural elements. This paper adopts the Historic Urban Landscape (HUL) approach, emphasizing the integration of physical heritage with daily life to shift value assessment. The fundamental argument of this paper is that the HUL method can serve as a new tool for preserving the historical heritage and landscape of rural areas, particularly in the context of assessing and formulating preservation frameworks for traditional Chinese villages. Through the case study of Tangfang village’s new conservation plan, the research demonstrates that the value of traditional villages lies in the continuous interaction between daily activities and physical space, including landscape, functional, and spiritual dimensions. The study concludes that a preservation framework based on local residents’ daily lives ensures more effective heritage conservation and landscape preservation by addressing both material and human values. Full article

Objectives: Our aim was to study how hemodialysis patients with varying levels of literacy would use a diet and fluid intake monitoring mobile application (DIMA-P) and what would be its impact on their dietary behaviors. Materials and Methods: We developed a mobile application using user-centered methods and informed by the Integrated Theory of Health Behavior Change (ITHBC). Eight hemodialysis patients were recruited to use the application to record and monitor their diet and fluid intakes for a 6-week study. Overall, the participants had low literacy, numeracy, and technical skills. We collected the data on application usage and administered usability and context-of-use questionnaires to gain insights into the participants’ interaction with the application. The participants’ portion estimation skills and dietary self-regulation self-efficacy were assessed using various tests. In addition, interdialytic weight gain data were collected to assess the impact of app usage on the participants’ health outcomes. Results: The application usage patterns varied, with a general trend towards frequent use (n = 5) correlating with engagement in self-monitoring. The participants gave high comprehensibility, user-friendliness, satisfaction, and usefulness ratings, suggesting that the app was well designed and the target users could easily navigate and interact with the features. While the participants improved in estimating portion sizes, the impact on measuring skills was variable. There was also an improvement in the participants’ dietary self-regulation self-efficacy post-study. The interdialytic weight gain trends indicated a slight improvement in fluid and diet management. Conclusion: People with different literacy skills can effectively use icon-based interfaces for portion size estimation and develop personalized usage patterns to self-regulate their fluid and dietary intakes. Moreover, they can experience an enhancement in their dietary self-efficacy skills by using a mobile application aimed at providing nutritional feedback. Furthermore, this research shows that the constructs of the ITHBC are effective in promoting dietary behavior change in a population with varying literacy skills. The target users can benefit from explicitly visualizing the relationship between their health outcomes and the factors influencing those outcomes. These user ambitions could be supported by developing machine learning models. Future research should also focus on enhancing the mechanisms by which technology can further enhance each component of the ITHBC framework. Full article