Introduction:Emerging from the epidemiological transition and accelerated aging process, China’s fragmentated healthcare systems struggle to meet the demands of the population. On Sept 1st 2017, China’s National Health and Family Planning Commission encouraged all cities to learn from the Luohu model of integration adopted in Luohu as an approach to meeting these challenges. In this paper, we study the integration process, analyze the core mechanisms, and conduct preliminary evaluations of integrated policy development in the Luohu model.Policy development:The Luohu hospital group was established in Aug 2015, consists of five district hospitals, 23 community health stations and an institute of precision medicine. The group adopted a series of professional, organizational, system, functional and normative strategies for integrated care, which was provided for the residents of Luohu, especially for the elderly population and patients with chronic conditions. According to a preliminary evaluation of the past two years, the Luohu model showed improvement in the structure and process towards integrated care. New preventive programs conducted in the hospital group resulted in changes of disease incidence. Residents were more satisfied with the Luohu model. However, spending exceeded the global budget for health insurance because of short-term increases in the demand for health care.Lessons learned:First, engagement of multiple stakeholders is essential for the design and implementation of reform. Second, organizational integration is a prerequisite for integrated care in China. Third, effective care integration requires alignment with payment reforms. Fourth, normative integration could promote collaboration in an integrated healthcare system.Conclusion:Core strategies and mechanisms of the Luohu model will promote integrated care in urban China and other countries facing the same challenges. However, it is necessary to study the effects of the Luohu model over the long term and continue to strive for integrated care.

RNA endowed with both protein-coding and noncoding functions is referred to as ‘dual-function RNA’, ‘binary functional RNA (bifunctional RNA)’ or ‘cncRNA (coding and noncoding RNA)’. Recently, an increasing number of cncRNAs have been identified, including both translated ncRNAs (ncRNAs with coding functions) and untranslated mRNAs (mRNAs with noncoding functions). However, an appropriate database for storing and organizing cncRNAs is still lacking. Here, we developed cncRNAdb, a manually curated database of experimentally supported cncRNAs, which aims to provide a resource for efficient manipulation, browsing and analysis of cncRNAs. The current version of cncRNAdb documents about 2600 manually curated entries of cncRNA functions with experimental evidence, involving more than 2,000 RNAs (including over 1300 translated ncRNAs and over 600 untranslated mRNAs) across over 20 species. In summary, we believe that cncRNAdb will help elucidate the functions and mechanisms of cncRNAs and develop new prediction methods. The database is available at http://www.rna-society.org/cncrnadb/.

Using qualitative and quantitative methodologies, delivery models and policies on mental health care in China during the period of 1949–2009 were reviewed and characteristics of different stages of the mental health-care development were also analysed in this period. Recent studies demonstrate that mental health-care services in China are being transformed from large mental hospital-based pattern to community-based pattern in the past six decades. Combining the international experiences with current strategies and situations of Chinese health care, we provided the outlook for mental health-care services in the next decade in China. In addition, we proposed relevant policy recommendations that mainly focus on the equity and availability of mental health-care services with the purpose of promoting community-based health services.

Determining the number of clusters in a data set is an essential yet difficult step in cluster analysis. Since this task involves more than one criterion, it can be modeled as a multiple criteria decision making (MCDM) problem. This paper proposes a multiple criteria decision making (MCDM)-based approach to estimate the number of clusters for a given data set. In this approach, MCDM methods consider different numbers of clusters as alternatives and the outputs of any clustering algorithm on validity measures as criteria. The proposed method is examined by an experimental study using three MCDM methods, the well-known clustering algorithm–k-means, ten relative measures, and fifteen public-domain UCI machine learning data sets. The results show that MCDM methods work fairly well in estimating the number of clusters in the data and outperform the ten relative measures considered in the study.