Effects of Various Flavonoids on the -Synuclein Fibrillation Process

α-Synuclein aggregation and fibrillation are closely associated with the formation of Lewy bodies in neurons and are implicated in the causative pathogenesis of Parkinson's disease and other synucleinopathies. Currently, there is no approved therapeutic agent directed toward preventing the protein aggregation, which has been recently shown to have a key role in the cytotoxic nature of amyloidogenic proteins. Flavonoids, known as plant pigments, belong to a broad family of polyphenolic compounds. Over 4,000 flavonoids have been identified from various plants and foodstuffs derived from plants and have been demonstrated as potential neuroprotective agents. In this study 48 flavonoids belonging to several classes with structures differing in the position of double bonds and ring substituents were tested for their ability to inhibit the fibrillation of α-synuclein in vitro. A variety of flavonoids inhibited α-synuclein fibrillation, and most of the strong inhibitory flavonoids were also found to disaggregate preformed fibrils

Evaluating the financial protection of patients with chronic disease by health insurance in rural China

Background A growing number of developing countries are developing health insurance schemes that aim to protect households, particularly the poor, from financial catastrophe and impoverishment caused by unaffordable medical care. This paper investigates the extent to which patients suffering from chronic disease in rural China face catastrophic expenditure on healthcare, and how far the New Co-operative Medical Insurance Scheme (NCMS) offers them financial protection against this. Methods A household survey was conducted in six counties in Ningxia Autonomous Region and Shandong Province, with a total of 6,147 rural households, including 3944 individual chronic disease patients. Structured questionnaires were used with chronic disease patients to investigate: their basic social and economic characteristics, including income and expenditure levels and NCMS membership; and their health care utilization, associated healthcare costs and levels of reimbursement by NCMS. 'Catastrophic' expenditure was defined as healthcare expenditure of more than 40% of household non-food expenditure. Results Expenditure for chronic diseases accounted for an average of 27% of annual non-food per capita expenditure amongst NCMS members in Shandong and 35% in Ningxia. 14-15% of families in both provinces spent more than 40% of their non-food expenditure on chronic healthcare costs. Between 8 and 11% of non NCMS members and 13% of NCMS members did not seek any medical care for chronic illness. A greater proportion of NCMS members in the poorest quintile faced catastrophic expenditure as compared to those in the richest quintile in both study sites. A slightly higher proportion of non-NCMS members than NCMS member households faced catastrophic expenditure, but the difference was not statistically significant. Conclusion A significant proportion of patients with chronic diseases face catastrophic healthcare costs and these are especially heavy for the poor. The NCMS offers only a limited degree of financial protection. The heavy financial burden of healthcare for chronic disease poses an urgent challenge to the NCMS. There is an urgent need for a clear policy on how to offer financial protection to those with chronic disease

Measuring and evaluating progress towards Universal Health Coverage in China.

BACKGROUND: This paper aims to develop a Chinese version of Universal Health Coverage (UHC) indices and to measure China's progress towards UHC. METHODS: Nineteen indicators were selected based on expert consultations to construct indices of accessibility and affordability to measure UHC. Data were drawn from health statistics yearbooks, nationally representative surveys, and health system reform surveillance. The index of accessibility includes absolute accessibility (to essential health services), relative accessibility (to hospital care) and people's subjective perceptions. The index of affordability includes absolute affordability (the incidence of catastrophic health expenditure, CHE), relative affordability (the composition of health expenditure), and people's subjective perceptions. RESULTS: The indices of accessibility and affordability both showed steady increases over the 17 years considered. Absolute accessibility had the most significant improvement (from 23.6 in 2002 to 73.8 in 2018), while the index of relative accessibility decreased from 81.4 in 2002 to 67.3 in 2018. The index of absolute affordability decreased significantly from 46.6 in 2002 to 30.5 in 2010 and then exhibited an increasing trend afterwards, reaching 52.1 in 2018. The index of relative affordability continuously increased during the observation period, from 35.3 to 75.4. CONCLUSIONS: China has made great progress in increasing the accessibility and affordability of health services since the health system reforms in 2009. However, integrating primary health care and hospital care and containing escalating medical expenditure to further reduce patients' financial burdens are key challenges for strengthening the Chinese health system

How does the New Cooperative Medical Scheme influence health service utilization? A study in two provinces in rural China

<p>Abstract</p> <p>Background</p> <p>Many countries are developing health financing mechanisms to pursue the goal of universal coverage. In China, a rural health insurance system entitled New Cooperative Medical Scheme (NCMS) is being developed since 2003. Although there is concern about whether the NCMS will influence the serious situation of inequity in health service utilization in rural China, there is only limited evidence available. This paper aims to assess the utilisation of outpatient and inpatient services among different income groups and provinces under NCMS in rural China.</p> <p>Methods</p> <p>Using multistage sampling processes, a cross-sectional household survey including 6,147 rural households and 22,636 individuals, was conducted in six counties in Shandong and Ningxia Provinces, China. Chi-square test, Poisson regression and log-linear regression were applied to analyze the association between NCMS and the utilization of outpatient and inpatient services and the length of stay for inpatients. Qualitative methods including individual interview and focus group discussion were applied to explain and complement the findings from the household survey.</p> <p>Results</p> <p>NCMS coverage was 95.9% in Shandong and 88.0% in Ningxia in 2006. NCMS membership had no significant association with outpatient service utilization regardless of income level and location.</p> <p>Inpatient service utilization has increased for the high income group under NCMS, but for the middle and low income, the change was not significant. Compared with non-members, NCMS members from Ningxia used inpatient services more frequently, while members from Shandong had a longer stay in hospital.</p> <p>High medical expenditure, low reimbursement rate and difference in NCMS policy design between regions were identified as the main reasons for the differences in health service utilization.</p> <p>Conclusions</p> <p>Outpatient service utilization has not significantly changed under NCMS. Although utilization of inpatient service in general has increased under NCMS, people with high income tend to benefit more than the low income group. While providing financial protection against catastrophic medical expenditure is the principal focus of NCMS, this study recommends that outpatient services should be incorporated in future NCMS policy development. NCMS policy should also be more equity oriented to achieve its policy goal.</p

Counting with Adaptive Auxiliary Learning

This paper proposes an adaptive auxiliary task learning based approach for object counting problems. Unlike existing auxiliary task learning based methods, we develop an attention-enhanced adaptively shared backbone network to enable both task-shared and task-tailored features learning in an end-to-end manner. The network seamlessly combines standard Convolution Neural Network (CNN) and Graph Convolution Network (GCN) for feature extraction and feature reasoning among different domains of tasks. Our approach gains enriched contextual information by iteratively and hierarchically fusing the features across different task branches of the adaptive CNN backbone. The whole framework pays special attention to the objects' spatial locations and varied density levels, informed by object (or crowd) segmentation and density level segmentation auxiliary tasks. In particular, thanks to the proposed dilated contrastive density loss function, our network benefits from individual and regional context supervision in terms of pixel-independent and pixel-dependent feature learning mechanisms, along with strengthened robustness. Experiments on seven challenging multi-domain datasets demonstrate that our method achieves superior performance to the state-of-the-art auxiliary task learning based counting methods. Our code is made publicly available at: https://github.com/smallmax00/Counting_With_Adaptive_Auxiliar

Dynamic Semantic-Based Spatial Graph Convolution Network for Skeleton-Based Human Action Recognition

Graph convolutional networks (GCNs) have attracted great attention and achieved remarkable performance in skeleton-based action recognition. However, most of the previous works are designed to refine skeleton topology without considering the types of different joints and edges, making them infeasible to represent the semantic information. In this paper, we proposed a dynamic semantic-based graph convolution network (DS-GCN) for skeleton-based human action recognition, where the joints and edge types were encoded in the skeleton topology in an implicit way. Specifically, two semantic modules, the joints type-aware adaptive topology and the edge type-aware adaptive topology, were proposed. Combining proposed semantics modules with temporal convolution, a powerful framework named DS-GCN was developed for skeleton-based action recognition. Extensive experiments in two datasets, NTU-RGB+D and Kinetics-400 show that the proposed semantic modules were generalized enough to be utilized in various backbones for boosting recognition accuracy. Meanwhile, the proposed DS-GCN notably outperformed state-of-the-art methods. The code is released here https://github.com/davelailai/DS-GCN</jats:p