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Exploring How Soccer Players With Perfectionism Navigate Challenges in Talent Pathways
Get PDFThe study provides a qualitative exploration of how soccer players reporting perfectionism navigate challenges in talent pathways. Eighteen players (10 females, eight males, M age = 16.17 years, SD = 3.47) from talent pathways with higher levels of perfectionism and perfectionistic cognitions (1 SD above the mean of samples from previous studies) participated in semistructured one-to-one interviews. Using semantic thematic analysis, seven themes were identified: cycles of anxiety, sadness at being a substitute, self-criticism and hopelessness during slumps, ruminating on mistakes, worthless when injured, shame in success and intolerance of defeat, and psychological distress. Participants experienced heightened anxiety, especially when substituted, and responded to poor performance, mistakes, and injuries with self-criticism and unhelpful emotions. Postmatch, they ruminated over both success and defeat, with some reporting extreme psychological difficulties. The findings highlight how aspiring soccer players perceived perfectionism as a barrier to overcoming challenges, hindering both their performance and well-being
Agricultural Application of Convolutional Neural Networks: A Case Study on Potato Plant Disease Detection Using Keras Image Generator and Data Augmentation Techniques
No full textCrop yields are severely impacted by plant diseases, leading to significant economic consequences. This study presents a plant disease prediction model that utilizes Convolutional Neural Networks (CNNs) and the Keras image augmentation technique. The CNN architecture includes multiple convolutional and pooling layers, as well as fully connected layers. Model training employs the Adam optimiser and categorical cross-entropy loss function, using a dataset of plant leaf images labelled with corresponding diseases for validation. After training the model with 10 epochs and a batch size of 32, an accuracy of 97% was achieved with a loss of 0.11. Validation accuracy and loss were 91% and 0.20, respectively. The Keras image augmentation technique was also evaluated for its effectiveness in generating new images from existing ones, which were used to test the model's ability to generalise when exposed to unseen data. The accuracy and loss on the test images were 95% and 0.25 and for augmented images were 94% and 0.22, respectively, demonstrating the model's potential for use in plant disease management as a diagnostic tool for farmers. This study is unique in combining CNN and Keras Image Generator for the detection of leaf diseases, and the results suggest that the proposed model could be useful for improving crop yields and farmers' income
Enhancing Endoscopic Precision: The Role of Artificial Intelligence in Modern Gastroenterology
Get PDFBackground
Endoscopy remains the gold standard for gastrointestinal diagnostics, enabling direct visualisation and intervention within the gastrointestinal (GI) tract. However, diagnostic accuracy and procedural outcomes vary significantly depending on endoscopist skill and experience, leading to potential missed lesions and inconsistent patient care. The integration of artificial intelligence (AI) into endoscopic practice offers a promising solution to address these limitations and enhance diagnostic precision.
Aim
This review explores the current applications of AI in endoscopy, focusing on image analysis, lesion detection, classification, and workflow optimisation, whilst evaluating the impact on clinical practice and identifying implementation challenges.
Method
A literature search was conducted using PubMed, Google Scholar, and IEEE Xplore databases for studies published between January 2010 and December 2024. Keywords included "Artificial Intelligence," "Endoscopy," "Gastrointestinal Diseases," "Image Analysis," and "Lesion Detection." Studies were selected based on their focus on AI applications in endoscopy with quantitative or qualitative data on performance and clinical impact.
Results
AI demonstrates exceptional capabilities in polyp detection, achieving detection rates that often surpass human practitioners, with systems like GI Genius showing high sensitivity and specificity. Convolutional Neural Networks excel in real-time lesion identification and classification, differentiating between benign and malignant growths with remarkable precision. AI also optimises endoscopic workflows through automated reporting and advanced training tools.
Conclusion
While AI integration shows promise for enhancing endoscopic diagnostic accuracy and procedural efficiency, successful implementation requires careful consideration of current limitations, including reliance on industry-sponsored studies, and addressing challenges in data quality, clinical workflow integration, and regulatory considerations. Future developments in advanced algorithms, personalised medicine, and telemedicine may further advance endoscopic practice and improve patient outcomes
Immune Organoids: A Review of Their Applications in Cancer and Autoimmune Disease Immunotherapy
Get PDFImmune organoids have emerged as a ground-breaking platform in immunology, offering a physiologically relevant and controllable environment to model human immune responses and evaluate immunotherapeutic strategies. Derived from stem cells or primary tissues, these three-dimensional constructs recapitulate key aspects of lymphoid tissue architecture, cellular diversity, and functional dynamics, providing a more accurate alternative to traditional two-dimensional cultures and animal models. Their ability to mimic complex immune microenvironments has positioned immune organoids at the forefront of cancer immunotherapy development, autoimmune disease modeling, and personalized medicine. This narrative review highlights the advances in immune organoid technology, with a focus on their applications in testing immunotherapies, such as checkpoint inhibitors, CAR-T cells, and cancer vaccines. It also explores how immune organoids facilitate the study of autoimmune disease pathogenesis with insights into their molecular basis and support in high-throughput drug screening. Despite their transformative potential, immune organoids face significant challenges, including the replication of systemic immune interactions, standardization of fabrication protocols, scalability limitations, biological heterogeneity, and the absence of vascularization, which restricts organoid size and maturation. Future directions emphasize the integration of immune organoids with multi-organ systems to better replicate systemic physiology, the development of advanced biomaterials that closely mimic lymphoid extracellular matrices, the incorporation of artificial intelligence (AI) to optimize organoid production and data analysis, and the rigorous clinical validation of organoid-derived findings. Continued innovation and interdisciplinary collaboration will be essential to overcome existing barriers, enabling the widespread adoption of immune organoids as indispensable tools for advancing immunotherapy, vaccine development, and precision medicine
A meta-analysis of multidimensional perfectionism and imposter phenomenon
No full textA meta-analysis is provided to disentangle the relationship between perfectionism and impostor phenomenon. Following a preregistered protocol, a systematic search provided 25 studies (N = 12,141) and 42 effect sizes. Perfectionistic strivings had a small positive relationship with impostor phenomenon (r+=.15[.07, 0.23]) and perfectionistic concerns had a large positive relationship with impostor phenomenon (r+=.61[.55, 0.65]). In turn, perfectionistic concerns made a substantially larger contribution to the overall effect of perfectionism (βPS + βPC = 0.57[.54, 0.60]). There was also evidence that the relationship with perfectionistic concerns was larger in studies with more females. The overlap between perfectionism and impostor phenomenon appears to relate mainly to a need to appear perfect to others. Future research should examine their development and mediating and moderating factors
Transforming healthcare delivery: A comprehensive review of digital integration, challenges, and best practices in integrated care systems
Get PDFDigital transformation in healthcare, particularly within Integrated Care Systems (ICS), offers significant potential to improve the quality, accessibility, and efficiency of care. This narrative review examines the best practices, challenges, and outcomes of digital healthcare transformation within ICS, with a focus on the integration of key technologies such as electronic health records (EHRs), telemedicine, and artificial intelligence (AI). The review highlights the critical role of leadership, stakeholder engagement, and staff training in overcoming barriers to successful digital adoption, including resistance to change, interoperability issues, and financial constraints. It further explores the impact of digital tools on patient outcomes, operational efficiency, and patient engagement. Despite the promise of digital transformation, several challenges persist, including technological barriers, regulatory complexities, and the need for significant investment in infrastructure. To maximize the benefits of digital healthcare tools, the review recommends fostering collaboration among healthcare providers, prioritizing staff involvement and leadership support, implementing digital solutions in phases, and addressing financial and regulatory challenges early in the planning process. By addressing these challenges and implementing recommended strategies, ICS can enhance care coordination, optimize resource utilization, and ultimately improve patient outcomes, paving the way for a more sustainable healthcare system
Examining the Relationship Between Physical Function and Anxiety/Depression in Parkinson's
Get PDFBackground: Parkinson's disease (PD) is a complex neurological disorder characterized by both motor and nonmotor symptoms, including tremor, muscle stiffness, anxiety, and depression. Objectives: The primary aim of this study was to examine the relationship between physical function and psychological symptoms, specifically anxiety and depression, in people with Parkinson's (PwP). The secondary aim was to explore whether any discrepancies between participant‐reported and clinician‐rated measures of physical function exist. Methods: This study utilized the Parkinson's Progression Markers Initiative (PPMI) dataset, analyzing data from 1065 individuals with PD. Correlational analyses assessed relationships between clinician‐rated and participant‐reported motor outcomes alongside psychological symptoms. Multiple linear regression (MLR) was employed to identify predictors of anxiety and depression. Results: In PwP, significant correlations were found between depression/anxiety and participant‐reported motor function (via MDS‐UPDRS Part II: r = 0.313 for depression, r = 0.284 for anxiety, p < 0.05). In contrast, correlations with clinician‐rated motor function (via MDS‐UPDRS Part III) were weaker (r = 0.079 for depression, p < 0.05; r = 0.054 for anxiety, p = 0.08). MLR analysis indicated that in PwP, age, cognition, and participant‐reported motor function explained 11.2% of the variance in depression and 10.5% in anxiety. Conclusions: This study highlights a discrepancy between psychological symptoms and their relationship with clinician‐rated versus participant‐reported motor function in PwP. Our findings suggest that factors such as age, cognitive level, and perceived physical function significantly influence this relationship. Consequently, it is crucial to consider psychological factors and participant‐reported motor function when conducting clinical assessments and treatment planning for individuals with PD