Bidirectional crosstalk between the epithelial-mesenchymal transition and immunotherapy: A bibliometric study.

Immunotherapy has recently attracted considerable attention. However, currently, a thorough analysis of the trends associated with the epithelial-mesenchymal transition (EMT) and immunotherapy is lacking. In this study, we used bibliometric tools to provide a comprehensive overview of the progress in EMT-immunotherapy research. A total of 1,302 articles related to EMT and immunotherapy were retrieved from the Web of Science Core Collection (WOSCC). The analysis indicated that in terms of the volume of research, China was the most productive country (49.07%, 639), followed by the United States (16.89%, 220) and Italy (3.6%, 47). The United States was the most influential country according to the frequency of citations and citation burstiness. The results also suggested that Frontiers in Immunotherapy can be considered as the most influential journal with respect to the number of articles and impact factors. "Immune infiltration," "bioinformatics analysis," "traditional Chinese medicine," "gene signature," and "ferroptosis" were found to be emerging keywords in EMT-immunotherapy research. These findings point to potential new directions that can deepen our understanding of the mechanisms underlying the combined effects of immunotherapy and EMT and help develop strategies for improving immunotherapy.

Promoting the adoption of local governmental policy on the reimbursement of chronic disease medicines (PAPMed): study protocol of a field-based cluster randomized trial in rural Nantong, China.

BACKGROUND: Among rural Chinese patients with non-communicable diseases (NCDs), low socioeconomic status increases the risk of developing NCDs and associated financial burdens in paying for medicines and treatments. Despite the chronic disease medicine reimbursement policy of the local government in Nantong City, China, various barriers prevent patients from registering for and benefitting from the policy. This study aims to develop a behavior science-based intervention program for promoting the adoption of the policy and to evaluate the effectiveness of the program compared with usual practices. METHODS: Barriers and opportunities affecting stakeholders in adopting the policy were identified through contextual research and summarized through behavior mapping. The intervention is designed to target these barriers and opportunities through behavior science theories and will be evaluated through a 6-month cluster randomized controlled trial in Tongzhou District, Nantong, China. A total of 30 villages from two townships are randomized in a 1:1 ratio to either the intervention or the control arm (usual practices). Village doctors in the intervention arm (1) receive systematic training on policy details, registration procedures, and intervention protocol, (2) promote the policy and encourage registration, (3) follow up with patients in the first, third, and sixth months after the intervention, and (4) receive financial incentives based on performance. The primary outcome is policy registration rate and the secondary outcomes include the number of patients registering for the policy, medical costs saved, frequency of village doctor visits, and health measures such as blood pressure and glucose levels. DISCUSSION: This study is one of very few that aims to promote adoption of NCDs outpatient medication reimbursement policies, and the first study to evaluate the impact of these policies on patients' financial and physical wellbeing in China. The simple, feasible, and scalable intervention is designed based on the theories of behavior science and is applicable to similar low-income regions nationwide where outpatient medical costs remain a financial burden for patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT04731194 , registered on 29 January 2021; Chinese Clinical Trial Registry ChiCTR2100042152 , registered on 14 January 14 2021.

Dense Residual Network: Enhancing global dense feature flow for character recognition.

Deep Convolutional Neural Networks (CNNs), such as Dense Convolutional Network (DenseNet), have achieved great success for image representation learning by capturing deep hierarchical features. However, most existing network architectures of simply stacking the convolutional layers fail to enable them to fully discover local and global feature information between layers. In this paper, we mainly investigate how to enhance the local and global feature learning abilities of DenseNet by fully exploiting the hierarchical features from all convolutional layers. Technically, we propose an effective convolutional deep model termed Dense Residual Network (DRN) for the task of optical character recognition. To define DRN, we propose a refined residual dense block (r-RDB) to retain the ability of local feature fusion and local residual learning of original RDB, which can reduce the computing efforts of inner layers at the same time. After fully capturing local residual dense features, we utilize the sum operation and several r-RDBs to construct a new block termed global dense block (GDB) by imitating the construction of dense blocks to adaptively learn global dense residual features in a holistic way. Finally, we use two convolutional layers to design a down-sampling block to reduce the global feature size and extract more informative deeper features. Extensive results show that our DRN can deliver enhanced results, compared with other related deep models.