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Factors Affecting Customer Loyalty and Retention in UK Based One Stop Convenience Stores
No full textIn the cutthroat UK retail market convenience stores like One Stop especially need strong customer loyalty and retention to be successful. The research examines primary factors driving customer loyalty and retention at One Stop convenience stores located in both urban and rural areas. The research team utilized quantitative methods to gather data from 35 participants using structured questionnaires. Research indicates strong customer loyalty to One Stop convenience stores depends heavily on the quality of customer service together with diverse product offerings and competitive pricing and accessible store locations. Customer service stood out as the most powerful driver of loyalty which validated the importance of positive consumer experiences in developing customer allegiance. The findings show younger customers under 35 years old exhibit higher loyalty levels than older customers while women visit One Stop stores more frequently than men. Higher-income consumers demonstrate stronger brand allegiance as income levels influence customer loyalty. The research identifies contrasting loyalty drivers for rural versus urban markets because urban consumers value brand prestige and service reliability while rural consumers emphasize cost-effectiveness and product reach. One Stop evaluated its customer retention strategies finding that urban customers preferred loyalty programmes while rural customers responded better to promotional discounts. Research reveals an emotional disconnect in customer engagement which requires personalized approaches to build durable customer relationships. Retailers can utilize these insights to boost customer retention rates while refining service delivery and marketing approaches for different consumer groups to gain a competitive edge in the UK convenience store market
The Use and Perceptions of AI Chatbots in Medical Research: An International Cross-Sectional Survey
No full textBackground
Artificial Intelligence (AI) has undergone remarkable progress, leading to the development of advanced large language models (LLMs). Despite these LLMs' growing adoption, concerns persist regarding the scientific accuracy of AI-generated content, and their acceptance within academic publishing remains contentious. This study aimed to describe AI-chatbot use patterns and to assess medical researchers’ perceptions of impact on research credibility, ethical concerns, guideline awareness, and disclosure of future intentions.
Methodology
A cross-sectional survey-based study spread into Saudi Arabia, Nigeria, Tunis, and England from 2023 to 2024 surveyed researchers, excluding non-medical and non-publishing researchers.
Results
We analyzed 434 respondents; 175 (40.3%) reported AI-chatbot use. Use varied by country (32.8%-45.9%), but neither gender nor country was significantly associated with use. Older age and more senior roles were associated with lower odds of use (odds ratio (OR): ages 41-50 years, 0.32; residents, 0.31; consultants, 0.17; P ≤ 0.009). Awareness strongly predicted use (OR 15.53), as did guideline awareness (OR 2.47), trust (P = 0.005), hypothesis formation (P = 0.001), willingness to cite (P = 0.003), and future use (P < 0.001); intention to declare use during submission did not differ (P = 0.468).
Conclusions
Our study shows that medical researchers have a positive attitude toward using AI chatbots, but with ethical and accuracy concerns requiring further interventions to create systematic unified rules
Exploring the Role of Gut Microbiota in Atherosclerosis and Novel Therapeutics to Mitigate the Disease Process? A Comprehensive Review of Current Literature
No full textAtherosclerosis is an inflammatory process affecting the blood vessels, mainly the arteries. It is an established cause of a range of vascular disorders. Under homeostatic conditions, gut microbiota interacts with the host to affect its physiology and perform functions such as maintaining the integrity of the gut–epithelial barrier and immune regulation required for host survival. The gut microbial signature of an individual is a significant factor serving as a determinant of developing atherosclerotic diseases. Several factors, including the administration of antibiotics and consumption of a diet rich in processed saturated fats and carbohydrates, are known to eliminate the commensal microbes colonizing the gut, resulting in disrupted microbial ecology or dysbiosis. The gut microbiome synthesizes a range of metabolites implicated in atherosclerotic plaque formation. Dysbiosis impedes the metabolic processes and disrupts the gut–epithelial barrier that permeates the translocation of such gut metabolites and microbial endotoxins into the circulation where it interacts with the host immune cells and triggers inflammation. The current understanding regarding the integral role of the gut microbiome and its metabolites in atherosclerosis has paved the way toward discovering and applying novel nonpharmacological therapeutical interventions targeting to improve gut microbial diversity and neutralizing the effect of proinflammatory metabolites released by it. This review aims to provide a comprehensive account of the intricacies of the link existing between the human gut microbiome and atherosclerosis. Additionally, it explores novel therapeutics that carry the potential to mitigate the risk of developing atherosclerotic disorders via diverse mechanisms
Artificial Intelligence in Healthcare and Biomedical Visualization
No full textThis book opens with a broad survey of how AI and biomedical visualization are reshaping medicine today. It defines the global challenges—workforce shortages, exploding data volumes, and rising costs—that make AI’s intervention critical, and introduces the core technologies (machine learning, virtual reality, telemedicine) driving innovation across diagnostic, educational, and delivery systems. By framing these advances within real world pressures, the introduction establishes why a holistic, interdisciplinary treatment of AI’s role in healthcare is both timely and essential.
Delving deeper, the text is organized into ten thematically linked chapters. It begins by charting the anatomy of medical data and the visualization tools that make complex information interpretable, then moves into machine learning approaches for imaging and AI driven diagnostic platforms. Subsequent chapters explore immersive virtual reality simulations for clinician training, the ethical and regulatory imperatives of AI adoption, and the evolution of AI in medical education and surgical simulation. The book also examines frontier applications—autonomous health systems in spaceflight, AI supported care in low resource settings, and strategies for workforce adaptation—before concluding with a forward looking synthesis of best practices and future directions.
This volume is crafted for a diverse readership—clinicians seeking to integrate AI tools, researchers probing new algorithms, healthcare administrators designing policy, and students building interdisciplinary expertise. By combining case studies, evidence based analyses, and practical guidance, it equips readers to navigate AI’s complexities responsibly, optimize clinical workflows, and ultimately improve patient outcomes. Whether you’re on the front lines of care or shaping the next generation of medical innovation, this book delivers the insights and frameworks needed to harness AI’s transformative potential
Addition of Venetoclax to Azacitidine Did Not Improve Survival in Acute Myeloid Leukemia and Was Not Well Tolerated: Real World Experience
No full textIntroduction: Front-line therapy with Azacitidine (AZA) + Venetoclax (Ven) improved overall survival (OS) and remissions in acute myeloid leukemia (AML) patients ineligible for standard induction. Less is known about the outcome of AML treated with AZA + Ven in the “real world”. Methods: We assessed the comparative pattern of administration, tolerability, efficacy and safety of AZA vs. AZA + Ven administered at our cancer centre. We retrospectively reviewed all patients treated with AZA alone or AZA + Ven. Patients who received less than one cycle or proceeded with consolidative stem cell transplant were excluded. Results: A total of 53 patients, median age 77 years, received AZA, and 23 patients, median age 73 years, received AZA + Ven. Among those, 69% and 47.8% were ≥75 years old, respectively. Only 52% received Ven doses above 200 mg. Mean time on therapy was 13.1 months in AZA vs. 5.9 months in AZA + Ven. Treatment delays occurred in 22.6% of AZA and 34.8% of AZA + Ven patients, primarily due to infections and cytopenias. Neutropenia grade 3/4 occurred in 28.3% of AZA vs. 56.5% of AZA + Ven patients. Thrombocytopenia grade 3/4 occurred in 15.1% of AZA and 51.2% of AZA + Ven patients. Anemia grade 3/4 occurred in 5.7% of AZA vs. 30.4% of AZA + Ven patients. Moreover, 69.8% of AZA and 69.5% AZA + Ven patients reached stable disease/partial and complete remission. Median overall survival (OS) was similar: 18 months in AZA vs. 14 months in the AZA + Ven group, p = 0.905. Conclusions: In a community setting, the addition of Venetoclax to AZA did not improve overall survival or disease control, mainly due to low tolerability and higher toxicity. However, these results should be interpreted cautiously due to a significant imbalance in the cytogenetic risk profiles and lower tolerability in the combined group. This suggests the need for a larger study with adjusted analyses
Corrigendum to "Universal maternal testing for group B streptococcus in late pregnancy: process outcomes and alongside qualitative study for the GBS3 trial" [Early Hum. Dev. 213 (2026) Page 1-5/P106442].
No full text1-Year Outcomes of Novel Balloon-Expandable vs Contemporary Transcatheter Heart Valves in Severe Aortic Stenosis The LANDMARK Trial
No full textBackground In the LANDMARK trial, the Myval balloon-expandable transcatheter heart valve (THV) series was non-inferior to the most commonly used contemporary Sapien and Evolut Series THVs for the 30-day early safety endpoint in participants with symptomatic severe native aortic stenosis (AS). Objectives The current study reports clinical outcomes, hemodynamic performances and quality of life (QOL) at one year. Methods This open-label, non-inferiority trial enrolled 768 participants across 31 hospitals in Europe, New Zealand and Brazil. Participants were randomly assigned (1:1) to receive either a Myval THV series or a contemporary THV (Sapien or Evolut series). The composite endpoint at one year included all-cause mortality, all strokes, and procedure- or valve-related hospitalizations. Clinical efficacy was defined as freedom from the composite endpoint. As recommended in Valve Academic Research Consortium (VARC)-3, the above composite endpoint, combined with the assessment of QOL at baseline and 1 year with the Short Form-12, was reported as an extended composite endpoint. The non-inferiority hypothesis was prespecified for the assessment of the primary endpoint at 30 days. Considering the specific 1-year composite endpoints of VARC-3 and the event rate of 27.23% derived from recent studies, an a posteriori descriptive and exploratory non-inferiority hypothesis was introduced with a non-inferiority margin of 10.89%. The analysis was performed in the intention-to-treat population. Results The mean age was 80 years, 48% were women, and the median Society of Thoracic Surgeons Predicted Risk of Mortality score was 2.6%. There was no significant difference in the Kaplan-Meier estimates of freedom from the composite endpoint at 365 days (Myval THV 87.0% vs. Contemporary THVs 86.9%). The Myval THV series was non-inferior to the contemporary THVs for the composite endpoint (difference: -0.1%, one-sided 95% confidence interval: 3.9%, Pnon-inferiority < 0.0001). Similarly, there were no significant differences in freedom from the extended composite endpoint (80.5% vs 77.3%, difference: 3.2%, 95% CI: -2.9 to 9.2%, p=0.33). Conclusions In the treatment of symptomatic severe native AS, the clinical and hemodynamic outcomes of the Myval THV series were comparable to those of contemporary THVs for the 1-year composite of all-cause mortality, all strokes, or procedure- or valve-related hospitalizations
S07-1: Promoting Physical Literacy in Higher Education Physical Education: ePhyLi
No full textPurpose: The ePhyLi project, funded by the Erasmus+ Sport Programme (No. 101089928), aims to promote awareness and activity in health-enhancing physical activity (HEPA) environments and the adoption of a healthy lifestyle, addressing priority areas of the EU work plan for sport. This will be achieved by: (1) increasing the knowledge and understanding about the notion of physical literacy (PL) of university students who study physical education (PE), sport and exercise science, (2) properly preparing them for further promoting PL to their students, as part of their future teaching career, becoming PL advocators. Project Description: The project is implemented collaboratively by five partners across four European countries: Cyprus, Italy, France, and EU-wide with the support of EUPEA. The main target group consists of university students studying to become PE and school teachers, as well as pre-service and in-service PE teachers. Stakeholders involved included university educators, digital learning experts, and sport education professionals. Implementation involved the creation of three main outputs: (1) an e-book, featuring eight structured modules and available in four languages (English, Greek, Italian, and French); (2) the ePhyLi serious game, a mobile application reinforcing PL concepts through interactive gameplay; (3) an e-platform offering gamified learning units, educational comic books, and interactive activities. The project is currently undergoing its second pilot phase, implemented by all partners. This phase includes structured testing of both the e-learning platform and the app with university students and relevant stakeholders. Evaluation activities focus on assessing the tools’ effectiveness in enhancing participants’ understanding of PL, as well as their usability, user experience, and perceived usefulness of the two digital outputs. Conclusions: ePhyLi aspires to ultimately have a long-term impact on future (PE) teachers’ teaching and pedagogical practices and therefore the experience of children being taught in the future, with a subsequent impact on the currently sedentary population, as children will start to value and engage in physical activity more regularly. Also, the project will strengthen the competency profiles of HEIs staff when it comes to educating their university students in the field of PE, as well as when it comes to the use of digital tools in PE related courses. Keywords: Physical literacy, higher education, digital tools, physical educatio
ConCF: A Hybrid Deep Learning Model for Semantic-Aware Collaborative Filtering in Recommender Systems
No full textRecommender systems play a crucial role in modern digital platforms by enhancing user engagement across ecommerce, media streaming, and other online services. One of the most commonly used approaches to recommendation is Collaborative Filtering (CF), which analyzes past interactions between users and items to understand preferences and provide relevant recommendations. However, issues, such as data sparsity, the cold start problem, and item synonymy, reduce its effectiveness in practice. Current deep learning techniques address these problems only partially and typically treat them individually. This paper proposes a hybrid deep learning model called Collaborative Neural Content Fusion (ConCF). The model combines Neural Collaborative Filtering (NCF) with Convolutional Neural Networks (CNNs) on item metadata, including titles and genres. It simultaneously learns the latent interaction patterns and semantic content characteristics in an unsupervised system. To provide a fair comparison, four baseline models, namely Autoencoder, NCF, Convolutional Matrix Factorization (ConMF), and supervised ConMF, were re-implemented under a standardized preprocessing pipeline and evaluation protocol. The experiments were conducted using the MovieLens 1M dataset. The results show that ConCF performs better than the baselines with the smallest Root Mean Square Error (RMSE =0.877) and the greatest Recall@5 (53.70%) while the sparsity is 20%. These findings demonstrate that by leveraging both collaborative and semantic cues, ConCF achieves significant advantages, thereby establishing it as a scalable and generalizable framework for next-generation recommender systems