Search Results (2,089)

Against the dual backdrop of the rural revitalization strategy and the pursuit of high-quality, balanced urban–rural education, optimizing rural campus spaces has emerged as an important lever for addressing educational resource disparities and improving pedagogical quality. However, conventional evaluation of campus space optimization faces two systemic dilemmas. First, top-down decision-making often neglects the authentic needs of diverse stakeholders and place-based knowledge, resulting in spatial interventions that lose regional distinctiveness. Second, routine public participation is constrained by geographical barriers, time costs, and sample-size limitations, which can amplify professional cognitive bias and impede comprehensive feedback formation. The compounded effect of these challenges contributes to a disconnect between spatial optimization outcomes and perceived needs, thereby constraining the distinctive development of rural educational spaces. To address these constraints, this study proposes a novel method that integrates regional spatial feature recognition with digital media-based public perception assessment. At the data collection and ethical governance level, the study strictly adheres to platform compliance and academic ethics. A total of 12,800 preliminary comments were scraped from major social media platforms (e.g., Douyin, Dianping, and Xiaohongshu) and processed through a three-stage screening workflow—keyword screening–rule-based filtering–manual verification—to yield 8616 valid records covering diverse public groups across China. All user-identifying information was fully anonymized to ensure lawful use and privacy protection. At the analytical modeling level, we develop a Transformer-based deep learning system that leverages multi-head attention mechanisms to capture implicit spatial-sentiment features and metaphorical expressions embedded in review texts. Evaluation on an independent test set indicates a classification accuracy of 89.2%, aligning with balanced and stable scoring performance. Robustness is further strengthened by introducing an equal-weight alternative strategy and conducting stability checks to indicate the consistency of model outputs across weighting assumptions. At the scenario interpretation level, we combine grounded-theory coding with semantic network analysis to establish a three-tier spatial analysis framework—macro (landscape pattern/hydro-topological patterns), meso (architectural interface), and micro (teaching scenes/pedagogical scenarios)—and incorporate an interpretive stakeholder typology (tourists, residents, parents, and professional groups) to systematically identify and quantify key features shaping public spatial perception. Findings show that, at the macro level, naturally integrated scenarios—such as “campus–farmland integration” and “mountain–water embeddedness”—exhibit high affective association, aligning with the “mountain-water-field-village” spatial sequence logic and suggesting broad public endorsement of ecological campus concepts, whereas vernacular settlement-pattern scenarios receive relatively low attention due to cognitive discontinuities. At the meso level, innovative corridor strategies (e.g., framed vistas and expanded corridor spaces) strengthen the building–nature interaction and suggest latent value in stimulating exploratory spatial experience. At the micro level, place-based practice-oriented teaching scenes (e.g., intangible cultural heritage handcraft and creative workshops) achieve higher scores, aligning with the compatibility of vernacular education’s “differential esthetics,” while urban convergence-oriented interdisciplinary curriculum scenes suggest an interpretive gap relative to public expectations. These results indicate an embedded relationship between public perception and regional spatial features, which is further shaped by a multi-actor governance process—characterized by “Government + Influencers + Field Study”—that mediates how rural educational spaces are produced, communicated, and interpreted in digital environments. The study’s innovative value lies in integrating sociological theories (e.g., embeddedness) with deep learning techniques to fill the regional and multi-actor perspective gap in rural campus POE and to promote a methodological shift from “experience-based induction” toward a “data-theory” dual-drive model. The findings provide inferential evidence for rural campus renewal and optimization; the methodological pipeline is transferable to small-scale rural primary schools with media exposure and salient regional ecological characteristics, and it offers a new pathway for incorporating digital media-driven public perception feedback into planning and design practice. The research methodology of this study consists of four sequential stages, which are implemented in a systematic and progressive manner: First, data collection was conducted: Python and the Octopus Collector were used to crawl online comment data related to Fuwen Township Central Primary School, strictly complying with the user agreements of the Douyin, Dianping, and Xiaohongshu platforms. Second, semantic preprocessing was performed: The evaluation content was segmented to generate word frequency statistics and semantic networks; qualitative analysis was conducted using Origin software, and quantitative translation was realized via Sankey diagrams. Third, spatial scene coding was carried out: Combined with a spatial characteristic identification system, a macro–meso–micro three-tier classification system for spatial scene characteristics was constructed to encode and quantitatively express the textual content. Finally, sentiment quantification and correlation analysis was implemented: A deep learning model based on the Transformer framework was employed to perform sentiment quantification scoring for each comment; Sankey diagrams were used to quantitatively correlate spatial scenes with sentiment tendencies, thereby exploring the public’s perceptual associations with the architectural spatial environment of rural campuses. Full article

Background/Objectives: Synthetic artificial intelligence (AI) is increasingly used in cardiovascular medicine to generate realistic clinical data from limited samples while preserving patient privacy. Despite its promise, concerns remain regarding the clinical reliability of synthetic datasets, which hampers their integration into routine practice. This article introduces the TIMA method (Team-Implementation Multidisciplinary Approach), designed to involve clinicians directly in every phase of synthetic data development. The objective of this work is to describe the TIMA framework and to illustrate how structured clinician–data scientist collaboration can enhance the clinical robustness and plausibility of synthetic AI outputs. Methods: The TIMA approach structures the synthetic data generation workflow around continuous interaction between clinicians and data scientists. Cardiologists define clinical constraints, verify inter-variable relationships, and assess the coherence and plausibility of generated records. The framework is illustrated through multiple cardiology use cases, including atrial fibrillation risk prediction and surgical mortality estimation in infective endocarditis, to demonstrate its adaptability across different clinical contexts. Each phase includes iterative validation steps aimed at ensuring alignment with established clinical knowledge rather than reporting quantitative performance outcomes. Results: Application of the TIMA framework supported the development of synthetic datasets that adhered more closely to clinical logic and domain-specific constraints. Clinician–data scientist collaboration enabled early detection of implausible variable interactions, improved interpretability of synthetic data patterns, and enhanced internal consistency across different cardiology-oriented scenarios. Conclusions: TIMA represents a scalable and replicable methodological model for integrating synthetic AI into cardiology by embedding clinical expertise throughout the data generation process. Its structured, multidisciplinary workflow supports the production of synthetic data that is not only statistically coherent but also clinically meaningful, thereby strengthening trust and reliability in AI-assisted cardiovascular research. Full article

Background: Nutrition is a fundamental factor in the healthy growth and development of young athletes, as well as in supporting optimal sports performance. This study aimed to explore associations between Mediterranean diet adherence score, BMI and selected physical performance measures in youth handball players, by identifying distinct player profiles through a clustering approach. Methods: Thirty-five male youth handball players participated in the study. Mediterranean diet adherence was evaluated by means of a 16-item KIDMED questionnaire and total score, and physical performance was assessed using the countermovement jump (CMJ) test, the 505-change of direction test, linear straight sprints and isometric handgrip strength. Results: Cluster 1 goes more than one day a week to a fast-food restaurant, skips breakfast on more occasions and consumes sweets and candy more often. In addition, Cluster 2 showed better sprint (p = 0.019–0.053, ES = 0.39–0.47) and CMJ (p = 0.042; ES = 0.40) performance than Cluster 1. Conclusions: These findings present associations between dietary adherence, BMI, and selected physical performance measures in this specific cohort. Given the cross-sectional design and the small sample size, these findings should be interpreted with caution and do not allow causal inferences. Full article

Background. The Mediterranean dietary pattern (MDP) is recognised as one of the most evidence-based dietary models for the prevention of non-communicable diseases (NCDs). Its plant-rich composition suggests an inherently high potassium intake, yet epidemiological findings on the association between MDP adherence and potassium intake remain heterogeneous. The present review aims to summarise and critically evaluate the available evidence on the association between adherence to the MDP and dietary potassium intake in the adult population. Methods. We conducted a narrative review of observational, longitudinal and interventional studies evaluating the relationship between MDP adherence and dietary potassium intake (self-reported assessment and/or 24 h urinary potassium). MEDLINE/PubMed was searched from inception to 30 October 2025, additional studies were identified by reference screening. Results. From a total of 263 studies retrieved, 10 eligible studies (7 cross-sectional, 1 longitudinal, 2 randomised controlled trials) from Europe, Asia and North America were synthesised. Questionnaire-based studies consistently indicated higher potassium intake with greater MDP adherence, whereas biomarker-based findings were more variable and often attenuated, particularly in studies relying on single or unvalidated 24 h urine collections and selected samples. Overall risk of bias was high for most observational studies, while randomised trials were generally rated as having some concerns. Conclusions. Higher MDP adherence is generally associated with higher potassium intake, but estimates vary by how MDP adherence is defined and scored, the potassium assessment method, and population context. Current evidence remains insufficient to quantify potassium’s potential contribution as a candidate mediator without formal mediation analyses and robust exposure assessment, including repeated validated 24 h urine collections. Standardised scoring, routine reporting of potassium, sodium, and the Na/K ratio, and triangulation across dietary, biomarker and intervention evidence are key priorities to strengthen inference. Full article

Pancreatic cancer is a highly intractable malignancy that necessitates personalized treatment strategies. Conventional patient-derived models, such as three-dimensional organoids, are often limited by intellectual property constraints and high costs. In this study, we developed an affordable adherent culture system for patient-derived pancreatic cancer cells using a proprietary medium and laminin-coated dishes. Primary cultures were successfully established from 28 patients with pancreatic ductal adenocarcinoma, exceeding a 90% success rate. Validation of eight samples confirmed maintenance of epithelial cell adhesion molecule expression and preservation of oncogenic KRAS mutations. Transcriptomic profiling revealed consistent upregulation of a six-gene signature (FAP, IGFBP5, PRRX1, SPARC, WNT5A, and ADAMTS12), which is associated with malignancy. In vitro drug sensitivity assays revealed interpatient heterogeneity with preliminary clinical associations. In conclusion, this simplified platform provides high-purity cancer cells and serves as a functional precision medicine tool. Beyond conventional chemotherapy, this platform has the potential to support applications ranging from biomarker validation and exploratory preclinical testing of novel therapeutics, including immune checkpoint inhibitors and antibody–drug conjugates. This optimization can lead to personalized therapeutic strategies for pancreatic cancer. Full article

Background: Self-esteem plays a central role in adolescent psychological health and may be shaped by everyday health behaviors such as eating patterns and engagement in physical activity. However, evidence from Eastern European youth remains comparatively limited. Lower levels of self-worth during adolescence have been linked to increased vulnerability to maladaptive behaviors, including substance use. The present study aimed to explore preliminary associations between lifestyle behaviors, nutritional practices, and self-esteem in a sample of Romanian adolescents. Methods: A cross-sectional design was used, involving 113 participants aged 14–18 years. Self-esteem was assessed using the Rosenberg Self-Esteem Scale, while lifestyle behaviors were evaluated through a standardized questionnaire. Body mass index was calculated based on self-reported height and weight. Statistical analyses included Pearson correlation coefficients and multiple linear regression models. Results: Higher self-esteem scores were strongly associated with greater participation in physical activity and adherence to a balanced diet, while inverse relationships were observed with unhealthy dietary habits and higher BMI values. Physical activity emerged as the most influential predictor of self-esteem, accounting for over three-quarters of the variance in Rosenberg scale scores. Conclusions: In this preliminary analysis, physical activity and healthier dietary behaviors were associated with higher self-esteem scores among adolescents. Given the exploratory nature of the study, these findings should be interpreted with caution. They primarily serve to generate hypotheses and highlight the need for future studies with validated instruments, larger samples, and appropriate control for potential confounding factors to better elucidate the relationship between lifestyle behaviors and adolescent self-esteem. Full article

Background and Objectives: Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) remains a disease without a curative treatment. Hence, patient healthcare is mostly based on symptom management and the application of coping strategies, such as pacing. In this strategy, patients learn how to plan their daily physical and cognitive activities according to their perceived energy reservoir (or envelop). However, there is currently no evidence for the feasibility of pacing in Portugal, where ME/CFS is not well recognized. Materials and Methods: We implemented a 8-week pacing program in Portuguese patients with an official diagnosis of ME/CFS. We focused on recruitment feasibility, protocol adherence, and patient acceptability, with secondary exploratory analysis of pre- and post-intervention variations in the Chalder’s fatigue questionnaire and SF36 physical functioning scores. Results: We were able to recruit thirteen patients for the study. The patients attended, on average, seven out of the eight sessions expected per participant, with the majority adhering to the research protocol ($n=7;53.8\%$). In a post-intervention survey, the respondents ($n=10$) considered that the intervention addressed the specific needs of people living with ME/CFS. Concerning the outcome trends, the average fatigue score decreased from 27.5 at baseline to 17.7 after the intervention. The mean physical functioning score increased from 24.6 to 31.7. Conclusions: This exploratory study supported the feasibility of benchmark studies in Portugal with increased sample size, longer interventions, and including a control group (e.g., specialized medical care), with which eventual placebo effects can be better accounted for. Full article

Background/Objectives: The global prevalence of overweight and obesity among young adults has doubled since 1975, primarily due to unhealthy dietary habits and sedentary lifestyles. Understanding dietary patterns (DPs) in this population is essential for designing effective prevention strategies. This study aimed to characterize the dietary patterns and diet quality of university students and to examine their physical activity and associated health risks. Methods: A convenience sample of 136 participants (77.9% females, 22.1% males) was recruited. Data on clinical history, lifestyle behaviors, and physical activity were collected using a structured questionnaire. Dietary intake was assessed using a food frequency questionnaire and three 24-h dietary recalls. Intake was analyzed by food groups, total energy, and macronutrient and fiber composition. Principal component analysis was applied to identify DPs. Results: Three major DPs were identified: Ultra-Processed Foods, Variety Foods, and Traditional Mixed Mexican. Overall, participants showed low consumption of fiber, legumes, and nuts, coupled with high intake of animal-based foods. The mean daily energy intake was 2278 kcal for men and 2008 kcal for women. Although participants demonstrated higher adherence to the Traditional Mixed Mexican pattern, a strong tendency toward the Ultra-Processed Foods pattern was observed, which is linked to an increased risk of chronic diseases and poor nutritional outcomes. Conclusions: The findings highlight the urgent need for targeted dietary interventions among university students. Strategies should emphasize increased intake of fiber-rich plant foods, moderation of protein consumption, and reduction in refined carbohydrates and added sugars to promote healthier dietary habits and prevent chronic disease development. Full article

Background: Parkinson’s disease (PD) is characterized by motor disturbances that significantly impact balance, gait, and quality of life. Personalized Rhythmic Auditory Stimulation (pRAS) is an emerging rehabilitative approach that utilizes auditory entrainment to improve step and gait control. The aim of this systematic review is to critically summarize the data from the most recent evidence concerning the use of pRAS in gait rehabilitation for patients with Parkinson’s disease. Methods: A systematic review was conducted following PRISMA guidelines, including records that evaluated music-based or technological interventions based on personalized RAS. Primary outcomes included spatiotemporal gait parameters and distance covered. Results: Ten studies were included in the analysis. All the studies reported clinically relevant improvements: increases in gait speed, step length, and amplitude. Moreover, a reduction in freezing of gait episodes (up to 36%), greater walking distance, and good adherence were reported. Conclusions: Personalized, adaptive, or on-demand solutions proved more effective than traditional forms of cueing. Moreover, the available evidence suggests that pRAS constitutes an effective and safe rehabilitative option for gait disturbances in PD. However, further studies with larger sample sizes and prolonged follow-up periods are necessary to evaluate its long-term impact and transferability into clinical practice. Full article

The analysis of multivariate data is a central issue in biomedical research, where the accurate classification of patients and the extraction of reliable conclusions are of critical importance. Linear Discriminant Analysis (LDA) remains one of the most established methods for both dimensionality reduction and classification of data. In this paper, we examine in detail the theoretical foundations, assumptions, and statistical properties of LDA, and apply the method step by step to real data from the Breast Cancer Wisconsin (Diagnostic) database, which includes cellular features from breast biopsy samples with the aim of distinguishing benign from malignant tumors. Emphasis is placed on the importance of the method’s assumptions, such as multivariate normality, equality of covariance matrices, and absence of multicollinearity, demonstrating that their fulfillment leads to significant improvements in model performance. Specifically, careful preprocessing and strict adherence to these assumptions increase classification accuracy from $95.6\%$ ($94.7\%$ cross-validated) to $97.8\%$ ($97.4\%$ cross-validated). To our knowledge, this study is the first to demonstrate the dual use of LDA as both a dimensionality-reduction tool and a predictive classification model for this medical database within the same biomedical analysis framework. Moreover, we provide, for the first time, a systematic comparison between our assumption-aware LDA model and related studies employing the most accurate machine-learning classifiers reported in the literature for this dataset, showing that classical LDA achieves accuracy comparable to these more complex methods. The resulting discriminant model, which uses 13 variables out of the original 30, can be applied easily by clinical researchers to classify new cases as benign or malignant, while simultaneously providing interpretable coefficients that reveal the underlying relationships among variables. The implementation is carried out in the SPSS environment, following the theoretical steps described in the paper, thus offering a user-friendly and reproducible framework for reliable application. In addition, the study establishes a structured and transparent workflow for the proper application of LDA in biomedical research by explicitly linking assumption verification, preprocessing, dimensionality reduction, and classification. Full article

Background and Objectives: Medication adherence is a key determinant of treatment effectiveness in early-stage breast cancer, particularly during long-term systemic therapies. As breast cancer is increasingly diagnosed at younger ages, a growing number of women continue to carry active parenting responsibilities during treatment. However, the associations between parenting-related psychosocial factors and medication adherence remain insufficiently explored. This study aimed to examine the associations between parenting stress, spiritual well-being, and medication adherence in women with early-stage breast cancer who maintain active parenting roles. Materials and Methods: This multicenter, cross-sectional study included 432 women with early-stage (I–III) breast cancer receiving active systemic therapy across nine oncology centers. Parenting stress was assessed using the Parenting Stress Scale (PSS), spiritual well-being using the Functional Assessment of Chronic Illness Therapy–Spiritual Well-Being Scale (FACIT-Sp-12), and medication adherence using the 6-item Modified Morisky Adherence Scale (MMAS-6). Spearman correlation analyses and multivariable linear regression models were used to evaluate associations between variables. Mediation analysis was performed using Hayes’ PROCESS macro (Model 4) with 5000 bootstrap samples to assess statistical mediation. Results: Parenting stress was positively associated with poorer medication adherence (ρ = 0.248, p < 0.01), whereas spiritual well-being was negatively associated with non-adherence (ρ = −0.225, p < 0.01). Parenting stress showed a strong inverse association with spiritual well-being (ρ = −0.597, p < 0.01). In multivariable regression analyses, both parenting stress and spiritual well-being were independently associated with medication adherence (β = 0.180, p = 0.002 and β = −0.199, p = 0.001, respectively). Mediation analysis demonstrated a significant indirect statistical association between parenting stress and medication adherence through spiritual well-being (indirect effect = 0.0155), consistent with partial statistical mediation. Conclusions: Medication adherence among women with early-stage breast cancer and active parenting responsibilities is associated with psychosocial context in addition to clinical factors. Parenting stress is associated with poorer adherence, whereas greater spiritual well-being is associated with better adherence within a statistical mediation framework. These findings generate hypotheses for future longitudinal and interventional studies. Full article

Industrial Control Systems (ICSs), critical to infrastructure, face escalating cyber threats under Industry 4.0, yet existing intrusion detection methods are hindered by attack sample scarcity, spatiotemporal heterogeneity of industrial protocols, and resource constraints of embedded devices. This paper proposes a four-stage closed-loop intrusion detection framework for ICSs, with its core innovations integrating the following key components: First, a protocol-conditioned Conditional Generative Adversarial Network (CTGAN) is designed to synthesize realistic attack traffic by enforcing industrial protocol constraints and validating syntax through dual-path discriminators, ensuring generated traffic adheres to protocol specifications. Second, a three-tiered sliding window encoder transforms raw network flows into structured RGB images, capturing protocol syntax, device states, and temporal autocorrelation to enable multiresolution spatiotemporal analysis. Third, an Efficient Multiscale Attention Visual State Space Model (EMA-VSSM) is developed by integrating gate-enhanced state-space layers with multiscale attention mechanisms and contrastive learning, enhancing threat detection through improved long-range dependency modeling and spatial–temporal correlation capture. Finally, a lightweight EMA-VSSM student model, developed via hierarchical distillation, achieves a model compression rate of 64.8% and an inference efficiency enhancement of approximately 30% relative to the original model. Experimental results on a real-world ICS dataset demonstrate that this lightweight model attains an accuracy of 98.20% with a False Negative Rate (FNR) of 0.0316, outperforming state-of-the-art baseline methods such as XGBoost and Swin Transformer. By effectively balancing protocol compliance, multi-resolution feature extraction, and computational efficiency, this framework enables real-time deployment on resource-constrained ICS controllers. Full article

In digital-measurement-assisted assembly of large aircraft components, the spatial layout of Enhanced Reference System (ERS) points determines coordinate transformation accuracy and stability. To address manual layout limitations—specifically low efficiency, occlusion susceptibility, and physical deployment limitations—this paper proposes an adaptive planning method under engineering constraints. First, based on the Guide to the Expression of Uncertainty in Measurement (GUM) and weighted least squares, an analytical transformation sensitivity model is constructed. Subsequently, a multi-scale sample library generated via Monte Carlo sampling trains a high-precision BP neural network surrogate model, enabling millisecond-level sensitivity prediction. Combining this with ray-tracing occlusion detection, a weighted genetic algorithm optimizes transformation sensitivity, spatial uniformity, and station distance within feasible ground and tooling regions. Experimental results indicate that the method effectively avoids occlusion. Specifically, the Registration-Induced Error (RIE) is controlled at approximately 0.002 mm, and the Registration-Induced Loss Ratio (RILR) is maintained at about 10%. Crucially, comparative verification reveals an RIE reduction of approximately 40% compared to a feasible uniform baseline, proving that physics-based data-driven optimization yields superior accuracy over intuitive geometric distribution. By ensuring strict adherence to engineering constraints, this method offers a reliable solution that significantly enhances measurement reliability, providing solid theoretical support for automated digital twin construction. Full article

Background: Radiomics has emerged as a promising approach to non-invasively characterize the molecular landscape of gliomas, providing quantitative, high-dimensional data derived from routine MRI. Given the recent shift toward molecularly driven classification, radiomics may support precision oncology by predicting key genomic, epigenetic, and phenotypic alterations without the need for invasive tissue sampling. This systematic review aimed to synthesize current radiomics applications for the non-invasive prediction of molecular biomarkers in gliomas, evaluating methodological trends, performance metrics, and translational readiness. Methods: This review followed the PRISMA 2020 guidelines. A systematic search was conducted in PubMed, Ovid MEDLINE, and Scopus on 10 January 2025, and updated on 1 February 2025, using predefined MeSH terms and keywords related to glioma, radiomics, machine learning, deep learning, and molecular biomarkers. Eligible studies included original research using MRI-based radiomics to predict molecular alterations in human gliomas, with reported performance metrics. Data extraction covered study design, cohort size, MRI sequences, segmentation approaches, feature extraction software, computational methods, biomarkers assessed, and diagnostic performance. Methodological quality was evaluated using the Radiomics Quality Score (RQS), Image Biomarker Standardization Initiative (IBSI) criteria, and Newcastle–Ottawa Scale (NOS). Due to heterogeneity, no meta-analysis was performed. Results: Of 744 screened records, 70 studies met the inclusion criteria. A total of 10,324 patients were included across all studies (mean 140 patients/study, range 23–628). The most frequently employed MRI sequences were T2-weighted (59 studies, 84.3%), contrast-enhanced T1WI (53 studies, 75.7%), T1WI (50 studies, 71.4%), and FLAIR (48 studies, 68.6%); diffusion-weighted imaging was used in only 7 studies (12.8%). Manual segmentation predominated (52 studies, 74.3%), whereas automated approaches were used in 13 studies (18.6%). Common feature extraction platforms included 3D Slicer (20 studies, 28.6%) and MATLAB-based tools (17 studies, 24.3%). Machine learning methods were applied in 47 studies (67.1%), with support vector machines used in 29 studies (41.4%); deep learning models were implemented in 27 studies (38.6%), primarily convolutional neural networks (20 studies, 28.6%). IDH mutation was the most frequently predicted biomarker (49 studies, 70%), followed by ATRX (27 studies, 38.6%), MGMT methylation (8 studies, 11,4%), and 1p/19q codeletion (7 studies, 10%). Reported AUC values ranged from 0.80 to 0.99 for IDH, approximately 0.71–0.953 for 1p/19q, 0.72–0.93 for MGMT, and 0.76–0.97 for ATRX, with deep learning or hybrid pipelines generally achieving the highest performance. RQS values highlighted substantial methodological variability, and IBSI adherence was inconsistent. NOS scores indicated high-quality methodology in a limited subset of studies. Conclusions: Radiomics demonstrates strong potential for the non-invasive prediction of key glioma molecular biomarkers, achieving high diagnostic performance across diverse computational approaches. However, widespread clinical translation remains hindered by heterogeneous imaging protocols, limited standardization, insufficient external validation, and variable methodological rigor. Full article

Medication adherence following myocardial infarction (MI) is essential for effective secondary prevention, yet adherence rates remain suboptimal. Healthcare professionals (HCPs) are central to promoting adherence through clinical decision-making, patient education, and ongoing behavioural support. Understanding how HCPs perceive and experience the factors’ influencing adherence is key to developing effective, context-specific interventions. This study explored HCPs’ perspectives on medication adherence post-MI and identified behavioural determinants influencing medication management across the care pathway. A qualitative descriptive study was conducted using semi-structured interviews with HCPs in the southwest of Ireland. Participants included hospital pharmacists, community pharmacists, general practitioners (GPs), cardiologists, and nurses, recruited through purposive, convenience, and snowball sampling. Interviews were recorded, transcribed verbatim, and analysed using directed content analysis guided by the Theoretical Domains Framework (TDF). Twelve HCPs (eight female) were interviewed between December 2024 and May 2025, including four pharmacists, two GPs, three cardiologists and three nurses. Interviews lasted 30–50 min (mean 41 min). Analysis identified 15 facilitators, 13 barriers, and 7 dual-role determinants across 10 TDF domains. Novel contributions include demonstrating how HCPs’ real-world experiences contextualise adherence issues in the distinct post-MI setting characterised by abrupt care transitions, polypharmacy, and emotional vulnerability and identifying where HCPs feel most constrained and where their expertise could directly inform targeted intervention design. HCPs’ insights reveal complex, context-specific behavioural determinants influencing post-MI medication adherence and highlight the need for multidisciplinary, tailored, and system-level solutions. Enhancing collaboration, supporting patient-centred communication, and addressing resource barriers could empower HCPs to deliver more effective, personalised adherence support and inform the development of targeted intervention strategies. Full article