Regorafenib Treatment for Recurrent Glioblastoma Beyond Bevacizumab-Based Therapy: A Large, Multicenter, Real-Life Study

Background/Objectives: In the REGOMA trial, regorafenib demonstrated an overall survival advantage over lomustine, and it has become a recommended treatment for recurrent glioblastoma in guidelines. This study aimed to evaluate the effectiveness and safety of regorafenib as a third-line treatment for patients with recurrent glioblastoma who progressed while taking bevacizumab-based therapy. Methods: This retrospective, multicenter study in Turkey included 65 patients treated between 2021 and 2023 across 19 oncology centers. The main inclusion criteria were histologically confirmed isocitrate dehydrogenase (IDH)-wildtype glioblastoma, progression after second-line bevacizumab-based treatment, and an Eastern Cooperative Oncology Group (ECOG) performance status score of <= 2. Patients received regorafenib 160 mg once daily for the first 3 weeks of each 4-week cycle. Results: The median age of the patients was 53 years (18-67 years), with a median progression-free survival of 2.5 months (95% Confidence Interval: 2.23-2.75) and a median overall survival of 4.1 months (95% CI: 3.52-4.68). The median overall survival was improved in patients who received subsequent therapy after regorafenib treatment compared with those who did not (p = 0.022). Progression-free survival was longer in patients with ECOG 0-1 than in those with ECOG 2 (p = 0.042). The safety profile was consistent with that of the REGOMA trial, with no drug-related deaths observed. Conclusions: Regorafenib shows good efficacy and safety as a third-line treatment for recurrent glioblastoma after bevacizumab-based therapy. This study supports the use of regorafenib and emphasizes the need for further randomized studies to validate its role and optimize treatment strategies

Institutional Structure and Environmental Pollution: An Application within the Framework of the Environmental Kuznets Curve Hypothesis

The EKC hypothesis explains the relationship between economic growth and environmental degradation. However, criticisms of its fundamental assumptions suggest that this relationship should be examined more comprehensively. While the EKC hypothesis addresses the link between income levels and environmental quality, it may overlook the impact of institutional structures and policy factors. In this context, recent studies increasingly highlight the role of institutional structures in environmental degradation. Accordingly, this study aims to provide a comprehensive analysis of environmental sustainability by approaching the EKC hypothesis from the perspective of institutional quality. To determine the effects of institutional quality on environmental degradation, the study employs the Driscoll-Kraay estimation method. The analysis is conducted on a sample of SADC countries for the period 1990-2021. The findings indicate that institutional quality has a statistically significant and positive effect on environmental degradation; however, beyond a certain threshold, this effect reverses. Additionally, the impact of economic growth on environmental degradation is examined within the EKC framework, revealing that while per capita income initially increases environmental degradation, exceeding a certain income level leads to improvements in environmental quality. The findings confirm the validity of the EKC hypothesis in SADC countries and suggest that strong institutional structures can play a supportive role in promoting environmental sustainability. © 2025, Econjournals. All rights reserved

Navigating stress with creativity: The moderating role of conflict-induced creativity on job satisfaction among entrepreneurs

BackgroundEntrepreneurs frequently encounter high levels of job stress, which can undermine their job satisfaction. Although the negative consequences of stress are well-known, less attention has been paid to the role of Conflict-Induced Creativity in buffering these effects.ObjectiveThis study investigates how Conflict-Induced Creativity moderates the relationship between job stress and job satisfaction among entrepreneurs, addressing a gap in understanding adaptive mechanisms in high-pressure work environments.MethodsUsing a quantitative design, data were collected from 453 entrepreneurs through convenience sampling. Statistical analyses included factor analyses, correlations, regressions, and moderation testing via PROCESS MACRO in SPSS v.22.ResultsValidated scales assessed job satisfaction (α = 0.934), job stress (α = 0.919), and Conflict-Induced Creativity (α = 0.832). Job stress negatively predicted job satisfaction (β = -0.126, p < 0.001), while Conflict-Induced Creativity emerged as a strong positive predictor (β = 0.688, p < 0.001). Moderation analysis confirmed that Conflict-Induced Creativity significantly buffered the adverse effects of stress on satisfaction (b = 0.147, 95% CI [0.048, 0.245], p < 0.05).ConclusionConflict-Induced Creativity plays a protective role in stressful entrepreneurial contexts. Practically, fostering a work culture that supports Conflict-Induced Creativity can enhance resilience and job satisfaction among entrepreneurs, offering actionable insights for leadership development, training programs, and organizational policy.4059893

Parents' experiences on the management of the process after sexual abuse of their children in northern region of Turkey: Qualitative study

Background: Child abuse is a universal problem with medical, legal, and psychosocial dimensions that every child is at risk of encountering all over the world. Aim: The aim of this study was to evaluate the experiences of parents regarding the management of the process after sexual abuse of their children using a qualitative approach. Methods: In this qualitative study, semi-structured in-depth interviews were conducted with the parents of 15 children who were exposed to qualified sexual abuse living in a city in Northern Turkey. Criterion sampling method, one of the purposive sampling methods, was used to reach the sample group. Interviews continued until data saturation was achieved. All interviews were audio recorded and then transcribed. The data of the study were evaluated using thematic analysis. The study was conducted and reported according to the consolidated criteria for reporting qualitative research (COREQ) checklist. Results: In the analysis of the data, three themes (effects of child sexual abuse on the family, sexual abuse and process management, and attitudes and approaches to the child after sexual abuse), and nine sub-themes (mental, physical, social, reactions, difficulties experienced, coping, cognitive dimension, emotional dimension, behavioral dimension) were identified. Conclusion: As a result of the study, it was determined that parents were negatively affected psychologically by the sexual abuse of their children. This study reveals that the concept of family is very important in all aspects of the sexual abuse of children. As a result of the study, it was determined that families were not very effective in process management and some parents blamed themselves for the incident they experienced.4044382

Post-seismic structural assessment: advanced crack detection through complex feature extraction using pre-trained deep learning and machine learning integration

Earthquakes can often cause significant damage to buildings. After an earthquake, experts/managers need to make quick and accurate damage assessments. Traditionally, manual analysis processes have been widely used in damage assessment studies. The fact that these methods are time-consuming and based on human observation leads to certain limitations in damage assessment studies. In recent years, artificial intelligence techniques such as deep learning and machine learning have frequently been preferred in damage detection studies, and significant success has been achieved. This study aimed to automatically detect cracks/damages in the buildings in Diyarbakir city after the February 6, 2023 Kahramanmaras, Turkey earthquake. Our experimental dataset was collected by the researchers and named Kahramanmaras-Diyarbakir Earthquake Building Crack Dataset (KDBECD-2023). The data set consists of four categories in terms of damage level: undamaged, slightly damaged, moderately damaged, and heavily damaged buildings. DenseNet201 deep learning architecture and popular machine learning algorithms, Support Vector Machine, Random Forest, Decision Tree, and K-Nearest Neighbors (KNN) were used to classify cracks at different damage levels. In the experimental phase, feature extraction was performed with the DenseNet201 architecture. In addition, dimensional reduction was applied with the Principal Component Analysis method to reduce the computational complexity of the proposed hybrid study. According to the experimental results, the DenseNet201-KNN hybrid model gave the most successful result with an accuracy value of 94.62%. The results of this study can make important contributions to decision makers and experts in detecting cracks and damages in buildings after an earthquake. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025

Nursing students' cybersecurity practices and perceptions and cybersecurity crime awareness: A cross-sectional study

Background: Cybersecurity has become a critical issue with the increasing use of digital platforms in healthcare. Understanding nursing students' cybersecurity practices, perceptions, and cybercrime awareness is essential for improving healthcare security and developing strategies to mitigate cyber threats. Aim: This study aimed to determine nursing students' cybersecurity practices, perceptions, and cybercrime awareness. Design: A descriptive cross-sectional design was used. Setting: The study was conducted between April and June 2024 at a School of Nursing within a public university in Türkiye. Participants: A total of 434 undergraduate nursing students participated in the study. Methods: Data were collected face-to-face using a paper-and-pencil technique. The data collection tools used included the Personal Information Form, Cyber Security Scale (CSS), and Cyber Crime Awareness Scale (CAS). Data analysis utilised descriptive statistical methods, Pearson correlation analysis, independent samples t-test, ANOVA, and linear regression analysis. Results: The study revealed that 92.9 % of the students had not received any prior cybersecurity education. The mean CSS score was 87.50 ± 11.40, and the mean CAS score was 174.75 ± 36.75. A moderate positive correlation was found between the CSS and CAS scores (r = 0.576, p < 0.01). A positive relationship was found between computer usage skills and CSS scores (r = 0.190, p < 0.01), while a weak negative correlation was observed between internet usage duration and CSS scores (r = -0.095, p < 0.05). No relationships were identified between the CSS score and age, gender, or cybersecurity education. Linear regression analysis showed that higher computer usage skill levels were significantly associated with increased CSS scores (B = 1.129, p < 0.001). Conclusions: The findings highlight the importance of integrating cybersecurity education into the nursing curriculum. Enhancing cybersecurity awareness and practices may help protect patient data and support safer healthcare by better preparing nursing students for cyber threats.4055507

Optimizing Visibility of Historical Structures Using Modified Weighted Differential Evolution: Insights from the Kromni Valley, Gümüşhane, Türkiye

It is very important for historical structures to see each other in order to reveal the historical and cultural identity of a region. Historical structures in the Kromni Valley of Gümüşhane, located near the Sümela Monastery, served as places of worship, communication, trade, and social activity centers during their period of active use. This study analyses the spatial relationships of 38 historic buildings, including churches, chapels and castles, whose 3D models are created by in-situ measurements and point clouds obtained by unmanned aerial vehicles, using a 3D viewshed analysis using geographic information systems and remote sensing data. The research introduces a modified weighted differential evolution-based viewshed analysis (mWDE-WS) to enhance the visibility of these structures. In order to assess the applicability of the proposed method, a statistical comparison was conducted between four different Differential Evolution (DE) algorithms (standard DE, LSHADE, CobiDE, JADE and WDE) and the mWDE. The Wilcoxon signed-rank test indicates that mWDE is a more effective solution than alternative methods for addressing the relevant real-world issues. The study also integrates drainage network analysis to assess flood risks and the relationship between cultural structures and water flow. Findings show that historical structures in the region were built not randomly but within a rational approach and 64% of the study area is visible from structures and 2% of the area is visible from ten or more structures. mWDE-WS analysis revealed that the visible area could increase by 20% to 84.37% if the historic structures were placed in optimal locations. In addition, the historical structures were built away from 3rd order streams to minimize flood risk and humidity, demonstrating the community's awareness of the local topography and hydrology. © Author(s) 2025

Supervised classification-based framework for rock mass discontinuity identification using point cloud data

Mapping and evaluating rock mass discontinuities using point clouds is a critical task in mining, civil, and geological engineering. Rock discontinuities can significantly impact the integrity, strength, and stability of rock masses. The orientation of these discontinuities is also a key characteristic of the rock mass. Accurate orientation estimation from point clouds enables more precise predictions of rock mass behavior, leading to improved safety, more efficient excavation processes, reduced operational costs, and significant time savings. In this context, a supervised classification-based framework is proposed for calculating orientation parameters from point cloud. Supervised classification plays a crucial role in tasks where a model learns complex patterns from labeled data to accurately predict previously unseen instances. The proposed method consists of eight-steps, including: data collection, pre-processing (data filtering), adaptive neighborhood size selection (omnivariance-based), feature extraction (geometric features), feature selection (Minimum Redundancy Maximum Relevance method), classification (Support Vector Machine), clustering (connected component labeling), and plane fitting to calculate orientation parameters (dip angle and dip direction). The framework was applied to two real-world datasets and one synthetic dataset, which was tested in two different subsampled forms (random and uniform subsampling). The results statistically demonstrated that the technique was effective in detecting and characterizing rock mass discontinuities with high Accuracy, Recall, Precision, and F-Score values ranging from 94.64% to 99.57%. The deviations of the method in the measurements of the dip angle and the dip direction, compared to the manual measurements, range from 1% to 4%, indicating strong agreement with the manual measurements and the existing studies. © 202

Effects of Web-Based Psychotherapeutic Interventions on Depression in Mood Disorders: A Meta-Analysis Study

PURPOSE: To investigate the effects of web-based psychotherapeutic interventions on depression among individuals with mood disorders. METHOD: For this meta-analysis study, data were obtained from October to December 2023 by searching PubMed, Web of Science, EBSCOhost, Google Scholar, and YÖK Thesis Center for articles published in the past 5 years. In the first stage of the search, 12,056 records were obtained. After removing duplicate studies, 4,910 records were considered for title and abstract review. After this evaluation, 139 studies were identified for full-text review. After the review, six studies reporting results on the effectiveness of web-based psychotherapeutic interventions on depression among individuals with mood disorders were ultimately included. RESULTS: Web-based interventions had significant positive effects and provided decreases in depression levels (standardized mean difference = –0.168, 95% confidence interval [–0.315, –0.021]; Z = –2.243; p < 0.05). CONCLUSION: Web-based interventions for mood disorders may play an effective role in reducing the burden of chronic mental illness and improving patient outcomes. © SLACK INCORPORATED.3950866

How does loneliness affect satisfaction with life? What is the role of the perception of God in this interaction?

Introduction: This study examined the role of loneliness and the perception of God in affecting the satisfaction with life of Muslim individuals living alone in Turkey during the COVID-19 pandemic. Additionally, the study explored the regulatory role of the perception of God in the relationship between individuals' loneliness and satisfaction with life. Methods: The research is a cross-sectional study that evaluates individuals' loneliness, satisfaction with life, and perception of God. The study group consists of 378 individuals living alone in Turkey. Among the participants, 196 are women (51.9%) and 182 are men (48.1%). The UCLA loneliness scale, the satisfaction with life scale, the perception of God scale, and a personal information form were used as data collection tools in the study. Results: The examination of research findings indicated that the variables of loneliness, perception of God, and the interaction between loneliness and the perception of God explained 28% of the variance in individuals' satisfaction with life. We determined that satisfaction with life was affected significantly and positively by the perception of God (β = 0.28, p < 0.001) and significantly and negatively by loneliness (β = -0.38, p < 0.001). The interactional effect of the variables of loneliness and perception of God on satisfaction with life was also found to be significant (β = -0.10, p = 0.023). When we examined the details of the regulatory effect, we found that the effect of loneliness on satisfaction with life decreased even more in cases where the perception of God was high. Discussion: The research findings suggest that loneliness decreases life satisfaction, while positive self-image mitigates this effect. It can be stated that using belief-sensitive therapeutic approaches in the therapeutic process could contribute to alleviating the negative effects of loneliness.4003494