Glioma, a common and deadly brain tumor, requires early diagnosis for improved prognosis. However, low-quality Magnetic Resonance Imaging (MRI) technology in Sub-Saharan Africa (SSA) hinders accurate diagnosis. This paper presents our work in the BraTS Challenge on SSA Adult Glioma. We adopt the model from the BraTS-GLI 2021 winning solution and utilize it with three training strategies: (1) initially training on the BraTS-GLI 2021 dataset with fine-tuning on the BraTS-Africa dataset, (2) training solely on the BraTS-Africa dataset, and (3) training solely on the BraTS-Africa dataset with 2x super-resolution enhancement. Results show that initial training on the BraTS-GLI 2021 dataset followed by fine-tuning on the BraTS-Africa dataset has yielded the best results. This suggests the importance of high-quality datasets in providing prior knowledge during training. Our top-performing model achieves Dice scores of 0.882, 0.840, and 0.926, and Hausdorff Distance (95
Medical misinformation propagates rapidly across digital platforms, posing substantial risks to public health decision-making. Traditional content-based detection approaches exhibit limited effectiveness when confronting the nuanced language and domain-specific terminology inherent in health-related claims. While foundation language models demonstrate remarkable semantic understanding capabilities, their direct application to medical misinformation detection encounters two fundamental obstacles: hallucination-induced evidence unreliability and insufficient explainability in high-stakes health contexts. Rather than treating foundation models as monolithic end-to-end predictors, we reconceptualize them as modular evidence generators and verifiers within a collaborative reasoning architecture. We propose Medical misinformation GUard via multi-model Adversarial Reasoning and Dimensional evidence (MedGUARD), a two-stage framework that leverages foundation model collaboration for explainable detection. The evidence generation stage employs adaptive in-context learning and multi-model adversarial negotiation to synthesize reliable multidimensional evidence, effectively mitigating hallucination artifacts. The semantic fusion stage integrates claim representations with evidence-augmented features through multimodal factorized bilinear pooling. Experiments across four medical misinformation benchmarks demonstrate that MedGUARD achieves improvements of 3.2 to 4.5 F1 points over 25 baseline methods, with all advantages confirmed by McNemar’s significance test. Adversarial negotiation reduces hallucination rates by 66% to 70% while generating transparent reasoning trails across seven evidential dimensions. Computational efficiency analysis quantifies MedGUARD’s cost-performance trade-off against all baselines, and cross-domain generalization experiments confirm that its accuracy advantage holds when training and test distributions differ, enabling medical professionals to verify automated assessments before clinical deployment.
Background Children are at increased risk of medication-related harm because of immature physiological development and inappropriate self-medication. Medication literacy crucially predicts rational medication use, yet relevant research remains limited. Objective This study aims to investigate medication literacy and its associated factors among children from the perspective of Andersen's behavioral model and to identify central indicators through network analysis. Methods This nationwide cross-sectional survey was conducted in China from October 2023 to May 2024, involving 4110 children. Children's medication literacy was evaluated using the Medication Literacy Scale (MLS). Guided by Andersen's behavioral model, this study employed random forest regression, univariate and multivariate generalized linear model analyses, and network analysis to examine children's medication literacy and its associated factors. Results The median medication literacy score was 47 points. The item “Needing a lot of time to understand” in the Medication Literacy Scale exhibited the highest node strength within the network. Multivariate generalized linear model analysis indicated that several factors were positively associated with medication literacy, including health literacy, family health, self-efficacy, education level, and age. Additionally, health literacy showed the strongest positive association with medication literacy, with age and education level serving as central indicators. Conclusions The findings indicate substantial potential for improving children's medication literacy. Interventions should be designed and implemented based on central indicators, with targeted strategies aimed at enhancing children's medication recognition and promoting rational medication practices, thereby improving medication literacy levels.
This study presents a tannic acid (TA)-mediated strategy to engineer the structure and functionality of chitosan/ polyvinyl alcohol (CS/PVA) packaging films by simultaneously crosslinking polymers and integrating nanoparticles. TA served as a multifunctional structuring agent, forming extensive hydrogen-bonding networks within the CS/PVA matrix and coordinating with MgO-TiO2 nanoparticles to generate stable metal-phenolic interfaces. This dual interaction produced a more compact, integrated film network with enhanced interfacial compatibility and reduced polymer chain mobility. Consequently, the composite film showed a 79% increase in tensile strength (29.3 MPa), along with significant reductions in water vapor permeability (21%) and oxygen permeability (36.4%), indicating improved barrier performance through increased network density and tortuous diffusion pathways. TA-mediated coordination further modified the optical properties of MgO-TiO2, enabling visible-light responsiveness and promoting reactive oxygen species generation, which contributed to strong antibacterial activity, with bacterial reduction exceeding 5 log CFU mL- 1 against Escherichia coli and Staphylococcus aureus within 24 h. In addition, the films exhibited enhanced antioxidant activity and complete UV shielding. When applied to banana preservation, the optimized film effectively delayed ripening, extending shelf life by approximately four days while maintaining acceptable quality. Overall, this work highlights a synergistic approach to tailoring hybrid film networks via polyphenol-mediated crosslinking and metal-phenolic coordination, providing insights into structure-property relationships for the design of advanced active packaging materials.
