Virtual reality in cognitive and affective neuroscience.

ABSTRACT Background This subgroup analysis investigates the clinical characteristics, treatment patterns, and outcomes of chronic lymphocytic leukemia (CLL) in the Middle East (ME). Research design and methods Retrospective study retrieved medical records of CLL patients treated for ≥12 months (CLL-treated) or treatment-naïve (TN). Results included data from 442 CLL-treated and 123 TN patients across five ME countries. Results CLL-treated group mean age: 61.1 ± 11.28 years; TN cohort: 63.5 ± 13.53 years. Most patients in both groups were male. CLL-IPI scores were mostly unavailable in both cohorts. Cytogenetic abnormalities were tested in 20.6% of CLL-treated and 18.7% of TN cohorts, with del(17p) being the most common abnormality (12.4% and 11.4%, respectively). TP53 aberrations were found in 9.0% of CLL-treated and 9.8% of TN cohorts. Regarding first-line treatment, 76.5% received CIT, while 17.9% received targeted therapies, with ibrutinib (75.9%) being the most common. CIT use led to significant resource utilization, including outpatient visits, length of stay, and blood transfusions. Conclusion CLL management in the ME is characterized by suboptimal utilization of risk-based treatment and genetic testing despite access to targeted therapy. Poor CLL outcomes stem from reliance on CIT, associated with an unfavorable safety profile and healthcare resource use compared to targeted therapies in the region. Clinical trial registration www.clinicaltrials.gov identifier is NCT04964908.

ABSTRACT Introduction Medicine-related harm associated with polypharmacy is a pertinent global health challenge. Deprescribing (reducing or stopping) medicines that cause more potential harm than benefit could mitigate the risk of medicine-related harm. However, the existing deprescribing research-to-practice gap threatens the long-term sustainability and scalability of deprescribing efforts. Research design and methods To address this, key stakeholders including healthcare practitioners, academics, policymakers and representatives of peak professional organizations, gathered at a World Café workshop to reflect on progress made in the deprescribing research and practice landscape while exploring the top future priorities for deprescribing. Results Thirty participants agreed on three top priorities: improving the clinical management of deprescribing; engaging consumers and gaining their perspectives; and raising awareness to enhance communication. Emerging themes and related barriers and catalysts were derived and mapped to a socio-ecological model offering a bird-eye’s view of these factors on an individual, interpersonal, organizational, and societal level. Conclusions Our World Cafe’ highlights opportunities for future deprescribing research and practice. To promote the uptake of deprescribing in practice, catalysts could include leveraging new technology, promoting deprescribing via social media and optimizing workforce staff and knowledge. Ultimately, this knowledge may motivate deprescribing efforts and bridge the research-to-practice gap.

The study examined assessing the awareness and use of ICT tools among university students in Nigeria: Implications for digital literacy. ICT tools in the case of higher education denote a vast array of technological tools utilized in communication, creation, dissemination, storage, and management of information. The research design used was a descriptive survey research. The study population consisted of undergraduate students in the Nigerian universities. Currently, according to the National Universities Commission (NUC) data, more than 2.3 million undergraduates study at 43 federal universities, 48 state universities, and 79 private universities of the country. A sample population of 585 respondents was picked out of an estimated population of 656 qualified students who had undergone or had access to institutional ICT facilities in the chosen universities, or participated in ICT-related courses. The findings indicate that ICT use in communication, internet use, and social networking were highly used by students and academic ICT use was less prevalent (data analysis, reference management, and e-library access). The results have wide implications on the development of digital-literacy in universities in Nigeria. The study concluded that ICT awareness among the Nigerian university students is high, but not evenly distributed among the institutions. Most students are conversant with the general ICT tools yet they have inadequate skills on academic-specific technologies that are vital in academics interaction. It was recommended that ICT cannot be isolated to be taught in computer science departments but it must be mainstreamed to all courses. The course evaluation may incorporate the Internet-based elements, online portfolios or e-projects submission to promote active ICT use.

Happy mothers, healthy minds: Maternal welfare and children's early development in the global south.

Mapping genetic and genomic competency in undergraduate nursing education: A scoping review.

The neutron total cross section is fundamental nuclear data crucial for the design of nuclear energy systems and research in nuclear physics. For graphite, an important reactor moderator, significant discrepancies exist among the major evaluated nuclear data libraries concerning its high-energy neutron total cross section, particularly in the resonance structures and the regions above 20 MeV. These uncertainties constrain the precise design of advanced nuclear systems. To resolve these controversies and provide benchmark experimental data, this study performed a high-accuracy measurement of the neutron total cross section of natural carbon from 0.3 eV to 50 MeV using the transmission method combined with the time-of-flight (TOF) technique. The experiment was conducted at the back-streaming white neutrons (Back-n) at the China Spallation Neutron Source (CSNS), utilizing the NTOX spectrometer equipped with a multi-cell fission chamber. The neutron emission time (t<sub>0</sub>) was precisely calibrated using the prompt γ-flash from the spallation reaction. The 77-meter flight path was accurately calibrated using the known standard fission resonance peaks of <sup>235</sup>U at 8.774 eV, 12.386 eV, and 19.288 eV. For the energy region above 100 keV, a Bayesian iterative algorithm was applied to unfold the double-bunch problem, effectively resolving the overlap of TOF spectra from neutrons produced in different beam bunches. The experimental results show excellent agreement with the ENDF/B-Ⅷ.1 evaluation and existing experimental data in the EXFOR database within the 0.3 eV-100 keV region. Owing to the high statistical accuracy, approximately 97.6% of the data points have statistical uncertainties of less than 1%, with the vast majority of total uncertainties better than 2%, significantly reducing the uncertainty level in this energy region. In the 100 keV– 50 MeV energy range, the data align with the overall trends observed in mainstream evaluation databases. No significant resonance effect was detected at 4.93 MeV, providing high-quality reference data for clarifying the resonance structure at this energy point. Systematically evaluated data above 20 MeV are currently only available in JENDL-5. The measurement results of this work provide indispensable high-quality experimental data to fill the high-energy data gap and to drive updates of the relevant evaluated libraries. This study not only provides critical benchmark data for the international nuclear data re-evaluation, especially for the CENDL-3.2 library which currently lacks complete data for natural carbon, but also systematically validates the methodological reliability of obtaining wide-energy-range, high-precision neutron total cross section data at the CSNS Back-n beamline.

Innovative nanosensing methods for lysozyme identification.

Sweet aftertaste (SA) is a unique taste phenomenon, specifically referring to a long-lasting, pleasant sweetness and comfort in the mouth after consuming certain foods. It has garnered increasing attention as a key driver of innovation in the food industry. However, the mechanisms underlying its formation and the specific material basis responsible for its perception remain inadequately elucidated, impeding the development of foods with an SA. This comprehensive review systematically examines the natural sources and material basis of SA, critically evaluates existing theoretical explanations, and proposes a novel dynamic synergy theory to describe the formation mechanism and enhancement principles of SA. Furthermore, the impact of food processing and manufacturing technologies on the perception and intensity of SA is thoroughly discussed. By integrating emerging technologies such as artificial intelligence and biological taste sensors, this article advances the concept of establishing objective-subjective integrated flavor evaluation approaches tailored to SA. The health implications and current challenges associated with this sensory phenomenon are also analyzed. Finally, grounded in the dynamic synergy theory, this article proposes innovative food design strategies aimed at achieving dual "SA-function" benefits, providing a forward-looking reference for future research and industrial applications in functional and sensory-oriented food design.

How microplastics influence the health and microbiota of aquatic invertebrates: A review.

Introduction: this study aims to design and develop digital job sheet-based learning media to improve the quality of learning in Software Engineering courses at universities.Methods: the study uses a Research and Development (R&D) design with the ADDIE model (Analysis, Design, Development, Implementation, Evaluation). The media developed integrates Flip PDF, YouTube, Google Sheets, and Quizizz. Validity testing was conducted by experts in subject matter, media, and language; practicality testing involved lecturers and students; while effectiveness testing used a single-group pretest-posttest design with paired t-test analysis and N-Gain.Results: the validation results showed an average of 85,68 % with a category of highly valid. Practicality obtained 86,33 % (lecturers) and 84,05 % (students), indicating that the media is very practical. Effectiveness analysis showed a significant increase in student learning outcomes (p < 0,05) with a moderate N-Gain score (0,46).Conclusions: this study provides empirical evidence that the integration of digital job sheets with multimedia elements supports the principles of constructivist learning and increases interactivity and learning outcomes. The developed media can be a reference for smart learning inovation in the future.

Structural design and performance research of high-temperature double-layer composite solid electrolyte

Self-building sodium modified g-C3N4/CN for fast kinetics in sodium‑sulfur batteries by first-principles calculations.

In recent years, digital technologies have been recognized as one of the key enablers for improving supply chains. By utilizing machine learning algorithms and data analytics, these technologies can forecast demand, optimize inventory management, and make supply chain processes more efficient. This study employed a descriptive–correlational research design. The statistical population consisted of employees from several companies across different industries, totaling approximately 270 individuals. Using multi-stage cluster random sampling and based on Morgan’s table, a sample size of 155 participants was determined. The study used the standardized questionnaire developed by Han et al. (2017), which consists of 22 closed-ended items measured on a five-point Likert scale. To assess the reliability of the questionnaire, Cronbach’s alpha coefficient was calculated using SPSS software, and the overall reliability was found to be 0.90, indicating satisfactory internal consistency. The results revealed that the first hypothesis — the existence of a significant relationship between digital technologies and supply chain optimization in various industries — was supported. The second hypothesis demonstrated a significant relationship between digital technologies and company performance across industries. Additionally, the third hypothesis indicated a significant relationship between the implementation of blockchain-based and cloud computing systems and the reduction of supply chain costs and cycle time.

This study investigates the efficacy of open peer assessment rubrics in an interconnected system of learning, specifically students' motivation, engagement, and self-awareness. The research was conducted in the period 2022–2023, and 98 university students participated in peer assessment. The study employs an action research design, with the view to resolve three central research concerns: (1) How confident do students feel and course content to be following peer assessment? (2) To what degree are students happy with their learning experience following peer assessment? (3) To what degree does the openness of the evaluation process affect students' sense of objectivity and fairness? These questions are examined using measures related to confidence in the quality of peer feedback (CO), value given to peer feedback as an instructional technique (VIM), and enjoyment of peer feedback as a worthwhile competence (VPS). The results indicate that peer assessment enhances students' performance significantly, enhances participation, and ignites motivation. Openness within the assessment process increases learners' confidence levels, encourages multiple viewpoints, and strengthens their understanding of course content. Yet, there were concerns regarding the influence of peer assessment on final course grades and hence the necessity of teacher intervention in maintaining fairness and accuracy of the assessment process. The findings highlight the necessity of weighing the benefits of peer assessment with requisite teacher intervention in maintaining openness of the assessment process that leads to fairer and more productive learning environments. The study concludes with implications for enhanced assessment practice and facilitating a transparent and inclusive learning culture.

This study aimed to design and evaluate an advanced Long Short-Term Memory (LSTM) deep learning model to accurately detect fraudulent claims in supplementary health insurance by leveraging sequential data patterns and domain-specific engineered features. An applied research design was used to build a robust fraud detection framework. A dataset of 20,000 health insurance claims was obtained from a supplementary insurance provider, containing both legitimate and fraudulent cases. Raw relational tables — including policy, insured individuals, claims, disease registry, and branch information — were merged into a single structured dataset using SQL. Rigorous data preprocessing was performed: irrelevant variables were removed, highly correlated features were eliminated through correlation analysis, dimensionality reduction was applied via Principal Component Analysis (PCA), and extreme outliers were excluded using the interquartile range (IQR) method. All numerical features were standardized, and class imbalance was addressed by weighting fraudulent cases during training. The processed data were reshaped into sequences suitable for LSTM input and divided into training and testing sets. An LSTM model with 32 hidden units and a Sigmoid output layer was trained using the Adam optimizer and binary cross-entropy loss, with performance validated through k-fold cross-validation. The LSTM model achieved outstanding predictive performance, with an overall accuracy of 100% on the test dataset. Both fraudulent and non-fraudulent claims reached perfect precision, recall, and F1-scores. Macro and weighted averages also recorded 1.00 across all metrics, indicating the model’s ability to detect rare fraudulent events without sacrificing specificity. By combining advanced deep learning with systematic data preparation and domain-informed feature engineering, the proposed LSTM framework effectively identified complex fraud patterns in supplementary health insurance. This approach offers a scalable and reliable solution to strengthen fraud risk management and reduce financial losses in the insurance sector.

This study investigates the interaction of a science teacher’s pedagogical content knowledge (PCK) components during the instruction of seventh-grade physics topics—work/energy and simple machines. It aims to explore how these components interconnect and influence instructional practices, particularly in the context of teaching gifted students. The study employed a qualitative research design, using classroom observations as the primary data source. It utilized the Alternative Mapping Approach (AMA), an adaptation of Park and Chen’s (2012) mapping-out method, to analyze PCK interactions. Content analysis and the constant comparative method were applied to interpret the data and construct detailed PCK interaction maps. The study yielded four key findings. First, PCK interaction maps are topic-specific and differ in complexity and structure. Second, student-related factors—such as learning difficulties, misconceptions, and characteristics of gifted learners—significantly influenced pedagogical decisions. Third, the teacher’s knowledge of instructional strategies played a central role in initiating interactions, while knowledge of learners was crucial in addressing instructional challenges. Lastly, contextual factors, particularly the needs of gifted students, required enrichment activities that demonstrated dynamic interactions between knowledge of curriculum and other PCK components. This study contributes to PCK research by introducing the Alternative Mapping Approach (AMA) as a novel and effective tool for capturing the complexity and sequence of PCK component interactions. It highlights the importance of considering contextual and qualitative dimensions in understanding how PCK is enacted in real classroom settings, particularly in differentiated instruction for gifted learners.

Introduction: This study examines the effectiveness and management of triage systems, particularly the Manchester Triage System (MTS), in Portuguese emergency services. It explores the extent to which current screening practices align with the goals of prioritising critical patients and managing limited healthcare resources efficiently.Methods: A qualitative research design was adopted, using semi-structured interviews with healthcare professionals working in emergency departments in Portugal. Thematic analysis was conducted to compare frontline experiences with theoretical insights from the existing literature, enabling a contextual assessment of the MTS’s operational effectiveness.Results: The Manchester Triage System was generally perceived as valid and functional. However, professionals reported challenges such as delayed diagnostic procedures, lack of material resources, and inconsistent adherence to triage priorities. Key suggestions included refining workflow structures, improving digital systems, and strengthening nurse training in Basic and Advanced Life Support.Conclusions: Reorganising emergency departments—by more clearly separating high- and low-priority cases, improving staff allocation, and enhancing IT infrastructure—can reduce waiting times, improve patient flow, and increase clinical safety. These changes may enhance patient satisfaction, reduce inequalities in emergency care delivery, and mitigate burnout among healthcare professionals.

This study aimed to design and empirically test a gamification model based on customer engagement indicators to enhance customer loyalty and improve business performance in small and medium-sized online businesses. A mixed-methods research design was employed, combining qualitative and quantitative approaches. In the qualitative phase, semi-structured interviews were conducted with 12 experts in digital marketing, user experience design, and online business management to identify key gamification components and mechanisms. Data were analyzed using thematic analysis with open, axial, and selective coding. In the quantitative phase, a researcher-developed questionnaire, informed by the qualitative findings and theoretical literature, was distributed to 384 active customers of Iranian online businesses who interacted with gamified elements through e-commerce platforms. Convenience sampling was used, and data were analyzed using SmartPLS 3.2 for confirmatory factor analysis, structural equation modeling (SEM), and model fit assessment. Inferential analysis showed that gamification had a significant positive effect on customer engagement (β = 0.72, t = 12.58, p < 0.001) and that customer engagement significantly influenced customer loyalty (β = 0.69, t = 11.76, p < 0.001). Gamification also had a direct positive effect on loyalty (β = 0.34, t = 6.32, p < 0.001), with a substantial total effect (β = 0.83, t = 15.89, p < 0.001). R² values indicated that gamification explained 52% of the variance in engagement and, with engagement, 61% of the variance in loyalty. The Q² value (0.440) demonstrated strong predictive relevance, and model fit indices (SRMR = 0.068; GoF = 0.470) confirmed good model adequacy. Gamification is an effective strategy for increasing customer engagement and loyalty in small and medium-sized online businesses, with engagement serving as a partial mediator in the gamification–loyalty relationship. These findings support the integration of gamification into SME digital marketing strategies to strengthen customer relationships and enhance performance outcomes.

The use of Aboriginal and Torres Strait Islander health programs is linked to improved health outcomes for Aboriginal and Torres Strait Islander communities. This study sought to understand the strengths within Aunty Roma's falls prevention program that have facilitated successful implementation, continued engagement and improved health outcomes. This study is the first to explore perspectives surrounding an Aboriginal and Torres Strait Islander falls prevention program that has demonstrated long-term success in a hospital environment. Indigenous methodologies were used to inform a qualitative descriptive research design and gather Aboriginal and Torres Strait Islander perspectives through a yarning circle and individual yarns. An Aboriginal Reference Group was established to provide community control over the research process and ensure prioritisation of local Aboriginal and Torres Strait Islander knowledge and cultural values. Thematic analysis was used to produce key themes. Yarning was conducted with two healthcare workers and nine group members involved in Aunty Roma's. Themes emerging from the data included community ownership, staff commitment, cultural safety, flexibility, social connection and relationships and improved health access and outcomes. This research demonstrates that through strong partnerships with staff and the community, and commitment to long-term implementation, hospital-based Aboriginal and Torres Strait Islander falls prevention programs can be culturally safe and successful in the long term. SO WHAT?: These findings will benefit the community involved through documentation of factors contributing to success, driving continual improvement of the program. The findings may also inform implementation of similar programs in the future.