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In order to improve the quality of services at village clinics (VCs), which are important primary care service providers in rural China, the Chinese government has encouraged the township hospitals to own and manage VCs. There are currently three models of ownership and management of VCs: township hospital-owned and -managed (HVC), village committee-owned and -managed (VVC), and private-owned and -managed (PVC). This study aims to examine the association between these ownership models of VCs and patients' primary care experiences.Villagers were selected by multistage stratified sampling and their experiences with primary care were measured using the Primary Care Assessment Tool-Adult Edition (PCAT-AS). Data were collected through face-to-face interviews and the questionnaires administered by investigators in the cross-sectional study from February to April 2015. The PCAT scores were compared among the three models by covariance analysis, and multiple linear regression was used to analyze factors associated with the PCAT total scores.A total of 1491 questionnaires were collected. After controlling for covariates, HVCs reported the highest PCAT scores and satisfaction rate. In terms of the domains, HVC reported the highest scores in the coordination and comprehensiveness domains, while PVC had the highest scores in the first contact-accessibility domain. Multivariate linear regression showed that HVC, married participants, aged 60 and older, satisfied with the services, receiving six or more visits, and those with medical expenditures over 20% of their total family expenditures, were also positively associated with better primary care quality.This study demonstrates that villagers receiving medical care at HVCs perceived better primary care than those at PVCs and VVCs. In order to improve the quality of primary care at VCs, it is necessary to increase government subsidies for public service packages, tighten the township hospital's supervision of PVCs and VVCs, and develop performance-based incentive plans to motivate improvements in the accessibility of HVCs.

We report here the synthesis, characterization, and antibacterial evaluation of transition metal complexes of Ni, Cu, Co, Mn, Zn, and Cd (6a–f), using a Schiff base ligand (5) derived from naproxen (an anti-inflammatory drug) and 5-bromosalicylaldehyde by a series of reactions. The ligand and the synthesized complexes were characterized by elemental analysis, UV-Visible, FTIR, and XRD techniques. The ligand 5 behaves as a bidentate donor and coordinates with metals in square planar or tetrahedral fashion. In order to evaluate its bioactivity profile, we screened the Schiff base ligand and its metal complexes (6a–f) against different species of bacteria and the complexes were found to exhibit significant antibacterial activity. The complexes showed more potency against Bacillus subtilis as compared to the other species. Moreover, we modeled these complexes’ binding affinity against COX1 protein using computational docking.

Presently, many users are involved in multiple social networks. Identifying the same user in different networks, also known as anchor link prediction, becomes an important problem, which can serve numerous applications, e.g., cross-network recommendation, user profiling, etc. Previous studies mainly use hand-crafted structure features, which, if not carefully designed, may fail to reflect the intrinsic structure regularities. Moreover, most of the methods neglect the attribute information of social networks. In this paper, we propose a novel semi-supervised network-embedding model to address the problem. In the model, each node of the multiple networks is represented by a vector for anchor link prediction, which is learnt with awareness of observed anchor links as semi-supervised information, and topology structure and attributes as input. Experimental results on the real-world data sets demonstrate the superiority of the proposed model compared to state-of-the-art techniques.