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BACKGROUND: Diffusion tensor imaging (DTI) can characterize eloquent white matter tracts affected by brain arteriovenous malformations (AVMs). However, DTI interpretation can be difficult in ruptured cases due to the presence of blood products. The authors present the case of a ruptured pediatric AVM in the corticospinal tract (CST) and discuss how DTI at different time points informed the treatment. OBSERVATIONS: A 9-year-old female presented with a sudden headache and left hemiparesis. She was found to have a Spetzler-Martin grade III, Supplementary grade I AVM in the right caudate and centrum semiovale, with obliteration and corresponding reduced fractional anisotropy (FA), fiber density (FD), and tract count (TC) of the adjacent CST on DTI. The patient remained stable and was scheduled for elective resection following a 6-week period to facilitate hematoma resorption. After 6 weeks, repeat DTI showed part of the nidus within intact CST fibers with concordant improvement in FA, FD, and TC. Considering the nidus location, CST integrity, and motor function recovery, surgery was deferred in favor of stereotactic radiosurgery. LESSONS: In ruptured AVMs, DTI may initially create an incomplete picture and false assumptions about white matter tract integrity. DTI should be repeated if delayed treatment is appropriate to ensure informed decision-making and prevent avoidable permanent neurological deficits. https://thejns.org/doi/10.3171/CASE24225.

Background: Mobile phones have become a widely accepted learning mode due to the impact of COVID-19. This study explores the mobile technology acceptance, among nursing students at selected educational institutions in South India. Materials and Methods: Quantitative cross-sectional descriptive design. First-year 176 B.Sc. nursing students who underwent blended learning were selected by the purposive sampling method. The tool "Technology Acceptance Model" was used to collect responses. Bivariate analysis was used to determine the relationship between the demographic and study-related variables with the mobile technology acceptance using SPSS version 25.0. Results: The majority 73.9% of the students belonged to the age group of 18-19 years, females 76.7% and, 98.9% were unmarried. Among the constructs of TAM, a mean (SD) value of 22.08 (2.26) was found for material (mobile device audio/video) characteristics the mean (SD) value was 22.08 (2.26), attitude about use 17.58 (1.95), behavioral intention 17.46 (1.78) and system characteristics 17.21 (2.27). The mobile technology acceptance revealed that 126 (71.6%) strongly agreed, 49 (27.8%) agreed, and 1 (0.6%) was neutral with a mean (SD) of 105.19 (8.68), respectively. A positive correlation was found between the system characteristics, material characteristics, perceived ease to use, perceived usefulness, attitude about the use, behavioral intention with a P value <0.001. There was a statistically significant association between Mobile technology acceptance and time spent by the students for independent studies shown the Chi-square value of 12.7, with P value <0.05. Conclusion: Nursing students had a positive acceptance and behavior toward smartphone use.

Abstract Epigenetic modifications control gene expression and are essential for turning genes on and off to regulate and maintain differentiated cell types. Epigenetics are also modified by a multitude of environmental exposures, including diet and pollutants, allowing an individual’s environment to influence gene expression and resultant phenotypes and clinical outcomes. These epigenetic modifications due to gene–environment interactions can also be transmitted across generations, raising the possibility that environmental influences that occurred in one generation may be transmitted beyond the second generation, exerting a long-lasting effect. In this review, we cover the known mechanisms of epigenetic modification acquisition, reprogramming and persistence, animal models and human studies used to understand multigenerational epigenetic transmission, and examples of environmentally induced epigenetic change and its transmission across generations. We highlight the importance of environmental health not only on the current population but also on future generations that will experience health outcomes transmitted through epigenetic inheritance.

The purpose of this study is to investigate the factors that influence users’ behavioral intentions to adopt Artificial Intelligence for personal financial management using an Innovation Diffusion Theory (IDT) framework. An analysis of empirical data collected from 246 users is conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). Findings reveal that users’ intentions to embrace AI in financial management are influenced by a variety of factors, including relative advantage, compatibility, and observability. As a result of this study, we offer insights into the intricate interplay of factors affecting the adoption of artificial intelligence in personal finance.

Drawing mainly on Lewin's 3-Phase Process of Change framework, in the study reported on here we explored the role of principals in facilitating curriculum reforms. A case study design within an interpretivist paradigm was employed to determine the experiences and perceptions of principals, school management teams (SMTs) and teachers of how principals managed curriculum changes in their schools. Change is deemed a necessary and significant component of educational praxis. Principals, as instructional leaders, are compelled to be the driving force in managing curriculum reforms, ensuring that changes are conscientiously aligned to high learner achievement. Data were gathered through personal and focus-group interviews using a purposeful sample comprising of 4 principals, 13 SMT members and 9 teachers. Findings reveal that principals were not directly involved in the facilitation of curriculum changes but delegated this to deputy principals and heads of departments. For SMTs and principals to effectively manage curriculum changes, a collaborative culture must exist in schools. It is recommended that principals should make a paradigm shift by placing high priority to the procurement of adequate resources, providing support and development to teachers, and keeping abreast with latest trends in teaching. This approach will result in high learner achievement and educational standards.

Abstract Lung and colon cancers are leading contributors to cancer-related fatalities globally, distinguished by unique histopathological traits discernible through medical imaging. Effective classification of these cancers is critical for accurate diagnosis and treatment. This study addresses critical challenges in the diagnostic imaging of lung and colon cancers, which are among the leading causes of cancer-related deaths worldwide. Recognizing the limitations of existing diagnostic methods, which often suffer from overfitting and poor generalizability, our research introduces a novel deep learning framework that synergistically combines the Xception and MobileNet architectures. This innovative ensemble model aims to enhance feature extraction, improve model robustness, and reduce overfitting. Our methodology involves training the hybrid model on a comprehensive dataset of histopathological images, followed by validation against a balanced test set. The results demonstrate an impressive classification accuracy of 99.44%, with perfect precision and recall in identifying certain cancerous and non-cancerous tissues, marking a significant improvement over traditional approach. The practical implications of these findings are profound. By integrating Gradient-weighted Class Activation Mapping (Grad-CAM), the model offers enhanced interpretability, allowing clinicians to visualize the diagnostic reasoning process. This transparency is vital for clinical acceptance and enables more personalized, accurate treatment planning. Our study not only pushes the boundaries of medical imaging technology but also sets the stage for future research aimed at expanding these techniques to other types of cancer diagnostics.