Objective:To study the status quo of directive and nondirective support from family or friends among patients with diabetes in community and to explore the association between directive, nondirective support from family or friends and self-management behaviors.Methods: The data of the subjects were from the Community Diagnosis in Tongzhou District, Beijing survey.Questionnaire-based investigation, physical examination and glycated hemoglobin test were conducted on 474 eligible patients in community.The summary of diabetes self-care activities measure (SDSCA) was adopted to investigate the self-management behaviors, and directive and nondirective support scale among patients with diabetes (DNSS-PD) was adopted to measure the directive and nondirective support among patients with diabetes.Results: A total of 452 patients with diabetes were included in the analysis.The average score of directive support was 2.53±1.07, and only 27.6% patients had high level directive support.The average score of nondirective support was 3.13±1.12 and 50.8% patients had high level nondirective support.The average SDSCA total score was 35.38±14.21 and the corresponding scoring percentage was 45.95%.Only 20.6% patients had optimal self-management.The total score, score of diet and physical activity, general self-management situation, diet self-management situation and physical activity self-management situation of the patients in high level directive support group were significantly better than in low level directive support group.The total score, score of self-management behaviors in five aspects (diet, physical activity, blood glucose self-monitoring, foot care and smoking), general self-management situation, self-management situation of five aspects of patients in high level nondirective support group were significantly better than in low level nondirective support group.Gender (OR=2.729), nondirective support level (OR=4.890), age (OR=0.969) and body mass index (BMI) (OR=0.819) were important influencing factors of self-management behavior.Conclusion: The status quo of self-management among patients with diabetes is not optimistic.Patients obtain more nondirective support than directive support from family and friends.However, the support level of both nondirective and directive support should be improved in the future.Both high level directive support and high level nondirective support had positive correlation with better self-management behaviors.Nondirective support had positive correlation with five aspects of self-management behavior, while directive support had positive relation with self-management behavior in aspects of diet and physical activity.In addition, more consideration and support of self-management should be provided for older obese male patients with diabetes in community, especially nondirective support.

Continual reduction in sequencing cost is expanding the accessibility of genome sequencing data for routine clinical applications. However, the lack of methods to construct machine learning-based predictive models using these datasets has become a crucial bottleneck for the application of sequencing technology in clinics. Here, we develop a new algorithm, eTumorMetastasis, which transforms tumor functional mutations into network-based profiles and identifies network operational gene (NOG) signatures. NOG signatures model the tipping point at which a tumor cell shifts from a state that doesn't favor recurrence to one that does. We show that NOG signatures derived from genomic mutations of tumor founding clones (i.e., the'most recent common ancestor' of the cells within a tumor) significantly distinguish the recurred and non-recurred breast tumors as well as outperform the most popular genomic test (i.e., Oncotype DX). These results imply that mutations of the tumor founding clones are associated with tumor recurrence and can be used to predict clinical outcomes. As such, predictive tools could be used in clinics to guide treatment routes. Finally, the concepts underlying the eTumorMetastasis pave the way for the application of genome sequencing in predictions for other complex genetic diseases. eTumorMetastasis pseudocode and related data used in this study are available at https://github.com/WangEdwinLab/eTumorMetastasis.