Collaborative deep learning improves disease-related circRNA prediction based on multi-source functional information.

Emerging studies have shown that circular RNAs (circRNAs) are involved in a variety of biological processes and play a key role in disease diagnosing, treating and inferring. Although many methods, including traditional machine learning and deep learning, have been developed to predict associations between circRNAs and diseases, the biological function of circRNAs has not been fully exploited. Some methods have explored disease-related circRNAs based on different views, but how to efficiently use the multi-view data about circRNA is still not well studied. Therefore, we propose a computational model to predict potential circRNA-disease associations based on collaborative learning with circRNA multi-view functional annotations. First, we extract circRNA multi-view functional annotations and build circRNA association networks, respectively, to enable effective network fusion. Then, a collaborative deep learning framework for multi-view information is designed to get circRNA multi-source information features, which can make full use of the internal relationship among circRNA multi-view information. We build a network consisting of circRNAs and diseases by their functional similarity and extract the consistency description information of circRNAs and diseases. Last, we predict potential associations between circRNAs and diseases based on graph auto encoder. Our computational model has better performance in predicting candidate disease-related circRNAs than the existing ones. Furthermore, it shows the high practicability of the method that we use several common diseases as case studies to find some unknown circRNAs related to them. The experiments show that CLCDA can efficiently predict disease-related circRNAs and are helpful for the diagnosis and treatment of human disease.

Integrative Graph-Based Framework for Predicting circRNA Drug Resistance Using Disease Contextualization and Deep Learning.

Circular RNAs (circRNAs) play a crucial role in gene regulation and have been implicated in the development of drug resistance in cancer, representing a significant challenge in oncological therapeutics. Despite advancements in computational models predicting RNA-drug interactions, existing frameworks often overlook the complex interplay between circRNAs, drug mechanisms, and disease contexts. This study aims to bridge this gap by introducing a novel computational model, circRDRP, that enhances prediction accuracy by integrating disease-specific contexts into the analysis of circRNA-drug interactions. It employs a hybrid graph neural network that combines features from Graph Attention Networks (GAT) and Graph Convolutional Networks (GCN) in a two-layer structure, with further enhancement through convolutional neural networks. This approach allows for sophisticated feature extraction from integrated networks of circRNAs, drugs, and diseases. Our results demonstrate that the circRDRP model outperforms existing models in predicting drug resistance, showing significant improvements in accuracy, precision, and recall. Specifically, the model shows robust predictive capability in case studies involving major anticancer drugs such as Cisplatin and Methotrexate, indicating its potential utility in precision medicine. In conclusion, circRDRP offers a powerful tool for understanding and predicting drug resistance mediated by circRNAs, with implications for designing more effective cancer therapies.

Effects of Pharmacist Intervention on Community Control of Hypertension: A Randomized Controlled Trial in Zunyi, China.

OBJECTIVE: We aimed to test the effects of pharmacist intervention on the community control of hypertension through a comparative randomized controlled trial. METHODS: We recruited adult hypertensive patients with comorbidity or confusion with medication (n=636) from 2 community health centers in Zunyi, China. They were randomly and equally divided into 2 groups. Both groups received the usual care and participated in the community systematic management program of hypertension. Participants in the intervention group were given interventions from pharmacists, including a monthly review of medications, patient education, and medication adjustment advice to medical doctors over 6 months. Participants' blood pressure was assessed at baseline, 3 months, and 6 months. Participants' knowledge and medication adherence were measured using a questionnaire before and after the trial. RESULTS: Compared to the control group (n=298), a significantly higher percentage of participants in the intervention group (n=290) had their blood pressure under control 3 months (46.9% vs. 38.3%, P=.034) and 6 months (60.7% vs. 40.9%, P<.001) after the interventions. Difference-in-differences analyses showed that the pharmacist intervention resulted in an increase in knowledge scores by 12.55 points (P<.001), a decrease in systolic blood pressure by 6.65 mmHg (P=.001), and a decrease in diastolic blood pressure by 7.26 mmHg (P<.001) compared to the controls after adjustment for variations in potential confounding factors. The odds of participants passing the hypertension knowledge tests in the intervention group was 4.45 times those in the control group (P<.001). Similarly, it was found that the intervention group had higher odds of not needing any medication adjustments (adjusted odds ratio [AOR]=2.75, P<.001) and having their blood pressure under control (AOR=2.18, P=.002) compared to the control group. CONCLUSION: It is evident that pharmacist intervention has significant short-term effects on improving the knowledge and medication adherence of hypertensive patients, as well as timely medication adjustments from medical doctors, resulting in lowered blood pressure and an increased control rate. Further studies should explore the long-term sustainability of the effects of community pharmacist intervention.