The interplay between stigma and sleep quality in breast cancer: A cross-sectional network analysis.

PURPOSE: Stigma, a subjective internal shame, arises from the association of cancer with death. Sleep quality can be considered a product of stigma. However, the extent of overlap or difference between the two remains unclear. METHODS: In total, 512 survivors with breast cancer were recruited from the "Be Resilient to Breast Cancer" project between May and August 2023. This study estimated the stigma, sleep quality, and their relationship by conducting a cross-sectional network analysis. The social impact scale and Pittsburgh Sleep Quality Index scale were employed in this study. RESULTS: The core symptom for stigma from the network analysis was alienation by people (Strength = 1.213, Betweenness = 13, Closeness = 0.00211). The core symptom for sleep quality were the sleep quality (Str = 1.114, Bet = 17, Clo = 0.01586). Regarding the combination network, results showed that self-isolation and daytime dysfunction were the bridge nodes and that daytime dysfunction was positively associated with feeling less capable than before (according to self) (r = 0.15). CONCLUSION: Our study demonstrates the core symptoms in different symptomatic networks, which can be targeted for treatment personalization and aid in the improvement of sleep quality and stigma in breast cancer patients.

Machine learning and statistic analysis to predict drug treatment outcome in pediatric epilepsy patients with tuberous sclerosis complex.

OBJECTIVES: We aimed to investigate the association between multi-modality features and epilepsy drug treatment outcomes and propose a machine learning model to predict epilepsy drug treatment outcomes with multi-modality features. METHODS: This retrospective study consecutively enrolled 103 epilepsy children with rare TSC. Multi-modality data were used to characterize risk factors for epilepsy drug treatment outcome of TSC, including clinical data, TSC1, and TSC2 genes test results, magnetic resonance imaging (MRI), computerized tomography (CT), and electroencephalogram (EEG). Three common feature selection methods and six common machine learning models were used to find the best combination of feature selection and machine learning model for epilepsy drug treatment outcomes prediction with multi-modality features for TSC clinical application. RESULTS: The analysis of variance based on selected 35 features combined with multilayer perceptron (MLP) model achieved the best area-under-curve score (AUC) of 0.812 (±0.005). Infantile spasms, EEG discharge type, epileptiform discharge in the right frontal area of EEG, drug-resistant epilepsy, gene mutation type, and type II lesions were positively correlated with drug treatment outcome. Age of onset and age of visiting doctors were negatively correlated with drug treatment outcome (p < 0.05). Our machine learning results found that among MRI features, lesion type is the most important in the outcome prediction, followed by location and quantity. CONCLUSION: We developed and validated an effective prediction model for epilepsy drug treatment outcomes of TSC. Our results suggested that multi-modality features analysis and MLP-based machine learning can predict epilepsy drug treatment outcomes of TSC.