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Abstract Aims/Introduction The association between serum angiopoietin‐like 4 (ANGPTL4) levels and the severity of diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus remains unclear. Methods A total of 1,115 type 2 diabetes mellitus patients were analyzed in this cross‐sectional study. DKD index included DKD stages defined by estimated glomerular filtration rate, the albuminuria grades and DKD risk management grades. Serum levels of ANGPTL4 and other biomarkers were detected. Multivariable‐adjusted linear and logistic analyses were used to study the association between ANGPTL4 and DKD. The protein levels of ANGPTL4 were assessed in the kidney. Renal tubular cells were stimulated with glucose to study ANGPTL4 expression. Results Compared with the participants in the third or fourth quantile of ANGPTL4, those in the first or second quantile of ANGPTL4 were younger, with lower glycated hemoglobin, triglycerides and urinary albumin creatinine ratio (all P

Epilepsy seizure prediction is vital for enhancing the quality of life for individuals with epilepsy. In this study, we introduce a novel hybrid deep learning architecture, merging DenseNet and Vision Transformer (ViT) with an attention fusion layer for seizure prediction. DenseNet captures hierarchical features and ensures efficient parameter usage, while ViT offers self-attention mechanisms and global feature representation. The attention fusion layer effectively amalgamates features from both networks, guaranteeing the most relevant information is harnessed for seizure prediction. The raw EEG signals were preprocessed using the short-time Fourier transform (STFT) to implement time–frequency analysis and convert EEG signals into time–frequency matrices. Then, they were fed into the proposed hybrid DenseNet–ViT network model to achieve end-to-end seizure prediction. The CHB-MIT dataset, including data from 24 patients, was used for evaluation and the leave-one-out cross-validation method was utilized to evaluate the performance of the proposed model. Our results demonstrate superior performance in seizure prediction, exhibiting high accuracy and low redundancy, which suggests that combining DenseNet, ViT, and the attention mechanism can significantly enhance prediction capabilities and facilitate more precise therapeutic interventions.

Abstract Esophageal cancer (EC) is a malignant tumor with high mortality. We aimed to find the optimal examined lymph node (ELN) count threshold and develop a model to predict survival of patients after radical esophagectomy. Two cohorts were analyzed: the training cohort which included 734 EC patients from the Chinese registry and the external testing cohort which included 3208 EC patients from the Surveillance, Epidemiology, and End Results (SEER) registry. Cox proportional hazards regression analysis was used to determine the prognostic value of ELNs. The cut-off point of the ELNs count was determined using R-statistical software. The prediction model was developed using random survival forest (RSF) algorithm. Higher ELNs count was significantly associated with better survival in both cohorts (training cohort: HR = 0.98, CI = 0.97–0.99, P

Abstract In the analysis of single-cell RNA-sequencing (scRNA-seq) data, how to effectively and accurately identify cell clusters from a large number of cell mixtures is still a challenge. Low-rank representation (LRR) method has achieved excellent results in subspace clustering. But in previous studies, most LRR-based methods usually choose the original data matrix as the dictionary. In addition, the methods based on LRR usually use spectral clustering algorithm to complete cell clustering. Therefore, there is a matching problem between the spectral clustering method and the affinity matrix, which is difficult to ensure the optimal effect of clustering. Considering the above two points, we propose the DLNLRR method to better identify the cell type. First, DLNLRR can update the dictionary during the optimization process instead of using the predefined fixed dictionary, so it can realize dictionary learning and LRR learning at the same time. Second, DLNLRR can realize subspace clustering without relying on spectral clustering algorithm, that is, we can perform clustering directly based on the low-rank matrix. Finally, we carry out a large number of experiments on real single-cell datasets and experimental results show that DLNLRR is superior to other scRNA-seq data analysis algorithms in cell type identification.

Background Integrated care is a global trend in international healthcare reform, particularly for piloting vertical integration involving hospitals and primary healthcare institutions (PHIs). However, evidence regarding the impact of vertical integration on primary healthcare has been mixed and limited. Our study aims to evaluate the empirical effects of vertical integration reform on PHIs in China, and examines variations across integration intensity (tight integration vs. loose collaboration). Methods This study used a longitudinal design. The time-varying difference-in-difference (DID) method with a fixedeffect model for panel data was adopted. A total of 370 PHIs in the eastern, central, and western areas of China from 2009 to 2018 were covered. Outcome measures included the indicators at three dimensions regarding inpatient and outpatient service volume, patient flow between PHIs and hospitals and quality of chronic disease care (hypertension and diabetes). Results Significant increases in absolute (the number) and relative (the ratio between PHIs and hospitals) volume of inpatient admissions have been found after reform under tight integration, peaking at 183% and 15.0% respectively, in the third reform year. The quality of hypertension and diabetes care (by indicators of control rate of blood pressure and blood glucose) showed significant improvements under both types of vertical integration after reform. It was much more distinct for the PHIs under tight integration, which had the most significant increase of 34.0% and 22.8% under tight integration for the control rate of hypertension and diabetes compared to the peak of 21.2% and 22.1% respectively under loose collaboration. Conclusion Our findings suggest that vertical integration (especially tight integration) in China significantly contributed to strengthening primary healthcare in terms of inpatient services and quality of hypertension and diabetes care, providing empirical evidence to other countries on integrating primary healthcare-based health systems.

BackgroundKnowledge regarding the treatment cost of coronavirus disease 2019 (COVID-19) in the real world is vital for disease burden forecasts and health resources planning. However, it is greatly hindered by obtaining reliable cost data from actual patients. To address this knowledge gap, this study aims to estimate the treatment cost and specific cost components for COVID-19 inpatients in Shenzhen city, China in 2020–2021.MethodsIt is a 2 years' cross-sectional study. The de-identified discharge claims were collected from the hospital information system (HIS) of COVID-19 designated hospital in Shenzhen, China. One thousand three hundred ninety-eight inpatients with a discharge diagnosis for COVID-19 from January 10, 2020 (the first COVID-19 case admitted in the hospital in Shenzhen) to December 31, 2021. A comparison was made of treatment cost and cost components of COVID-19 inpatients among seven COVID-19 clinical classifications (asymptomatic, mild, moderate, severe, critical, convalescent and re-positive cases) and three admission stages (divided by the implementation of different treatment guidelines). The multi-variable linear regression models were used to conduct the analysis.ResultsThe treatment cost for included COVID-19 inpatients was USD 3,328.8. The number of convalescent cases accounted for the largest proportion of all COVID-19 inpatients (42.7%). The severe and critical cases incurred more than 40% of treatment cost on western medicine, while the other five COVID-19 clinical classifications spent the largest proportion (32%−51%) on lab testing. Compared with asymptomatic cases, significant increases of treatment cost were observed in mild cases (by 30.0%), moderate cases (by 49.2%), severe cases (by 228.7%) and critical cases (by 680.7%), while reductions were shown in re-positive cases (by 43.1%) and convalescent cases (by 38.6%). The decreasing trend of treatment cost was observed during the latter two stages by 7.6 and 17.9%, respectively.ConclusionsOur findings identified the difference of inpatient treatment cost across seven COVID-19 clinical classifications and the changes at three admission stages. It is highly suggestive to inform the financial burden experienced by the health insurance fund and the Government, to emphasize the rational use of lab tests and western medicine in the COVID-19 treatment guideline, and to design suitable treatment and control policy for convalescent cases.

Automatic seizure detection system can serve as a meaningful clinical tool for the treatment and analysis of epilepsy using electroencephalogram (EEG) and has obtained rapid development. An automatic detection of epileptic seizure method based on kernel-based robust probabilistic collaborative representation (ProCRC) combined with graph-regularized non-negative matrix factorization (GNMF) is proposed in this work. The raw EEG signals are pre-processed through the wavelet transform to obtain time-frequency distribution of EEG signals as preliminary feature information and GNMF is further employed for dimension reduction, retaining and enhancing the productive feature information of EEG signals. Then, the test sample is represented using robust ProCRC that can decide whether the testing sample belongs to each class (seizure or non-seizure) by jointly maximizing the likelihood. In addition, the kernel trick is applied to improve the separability of non-linear high dimensional EEG signals in robust ProCRC. Finally, post-processing techniques are introduced to generate more accurate and reliable results. The average epoch-based sensitivity of 96.48%, event-based sensitivity of 93.65% and specificity of 98.55% are acquired in this method, which is evaluated on the public Freiburg EEG database.

Abstract Background The identification of cancer types is of great significance for early diagnosis and clinical treatment of cancer. Clustering cancer samples is an important means to identify cancer types, which has been paid much attention in the field of bioinformatics. The purpose of cancer clustering is to find expression patterns of different cancer types, so that the samples with similar expression patterns can be gathered into the same type. In order to improve the accuracy and reliability of cancer clustering, many clustering methods begin to focus on the integration analysis of cancer multi-omics data. Obviously, the methods based on multi-omics data have more advantages than those using single omics data. However, the high heterogeneity and noise of cancer multi-omics data pose a great challenge to the multi-omics analysis method. Results In this study, in order to extract more complementary information from cancer multi-omics data for cancer clustering, we propose a low-rank subspace clustering method called multi-view manifold regularized compact low-rank representation (MmCLRR). In MmCLRR, each omics data are regarded as a view, and it learns a consistent subspace representation by imposing a consistence constraint on the low-rank affinity matrix of each view to balance the agreement between different views. Moreover, the manifold regularization and concept factorization are introduced into our method. Relying on the concept factorization, the dictionary can be updated in the learning, which greatly improves the subspace learning ability of low-rank representation. We adopt linearized alternating direction method with adaptive penalty to solve the optimization problem of MmCLRR method. Conclusions Finally, we apply MmCLRR into the clustering of cancer samples based on multi-omics data, and the clustering results show that our method outperforms the existing multi-view methods.

Introduction: Implementing integrated care for the aged population has been regarded as a mechanism to achieve healthy ageing. However, evidence from undeveloped nations has been scant. This study aims to explore the integrated care experience in Anhui and Fujian Province of China based on the Rainbow Model of Integrated Care (RMIC). Methods: The qualitative study was conducted in Anhui (in the middle area) and Fujian Province (in the eastern area) between May and September in 2018. The interviewees included twenty-eight policy makers working at departments of health and civil affairs at different levels and seventeen heads of medical and elderly care institutions. Results: The preliminary progress of integrated care in the sample cities of two provinces are mainly shown at solid policy basis by multiple key government agencies and political commitment achieved (system integration); preliminary coordination mechanism established between medical and elderly care institutions (organizational integration); consolidation of multi-disciplinary collaboration (professional integration); and reinforced role of family doctor teams for community-home dwelling elderly (service integration). Main challenges are also identified at insufficient inter-agency coordination, weak service capacity, lack of sustainable funding schemes, low level of information integration, and shortage of professional supply. Conclusion: Our findings provide a feasible path for other countries to strengthen integrated care for the aged population, particularly for those confronting rapid population ageing but with fragmented health care and elderly care systems.

A calculation method considering ventilation resistance was proposed for designing a cluster-based inductive ventilation system for a real passenger-dedicated railway tunnel. In the system, jet fans were installed only at the two portals, instead of being arranged along the tunnel. A 20 MW train fire was simulated in Fire Dynamics Simulator (FDS) 6.6 to reproduce the designed fire, and the tunnel ventilation was validated using full-scale ventilation experiments. The simulation results showed that the growth of the ventilation velocities was periodically fluctuating. At a height of 2 m above the rail surface, the temperatures always remained below 60 °C, and the visibilities upstream of the fire were not affected by the smoke. It can be concluded that the designed ventilation scheme can effectively inhibit smoke backflow, and both the cluster-based inductive ventilation system and calculation method are feasible.

Abstract Background It has been widely accepted that long non-coding RNAs (lncRNAs) play important roles in the development and progression of human diseases. Many association prediction models have been proposed for predicting lncRNA functions and identifying potential lncRNA-disease associations. Nevertheless, among them, little effort has been attempted to measure lncRNA functional similarity, which is an essential part of association prediction models. Results In this study, we presented an lncRNA functional similarity calculation model, IDSSIM for short, based on an improved disease semantic similarity method, highlight of which is the introduction of information content contribution factor into the semantic value calculation to take into account both the hierarchical structures of disease directed acyclic graphs and the disease specificities. IDSSIM and three state-of-the-art models, i.e., LNCSIM1, LNCSIM2, and ILNCSIM, were evaluated by applying their disease semantic similarity matrices and the lncRNA functional similarity matrices, as well as corresponding matrices of human lncRNA-disease associations coming from either lncRNADisease database or MNDR database, into an association prediction method WKNKN for lncRNA-disease association prediction. In addition, case studies of breast cancer and adenocarcinoma were also performed to validate the effectiveness of IDSSIM. Conclusions Results demonstrated that in terms of ROC curves and AUC values, IDSSIM is superior to compared models, and can improve accuracy of disease semantic similarity effectively, leading to increase the association prediction ability of the IDSSIM-WKNKN model; in terms of case studies, most of potential disease-associated lncRNAs predicted by IDSSIM can be confirmed by databases and literatures, implying that IDSSIM can serve as a promising tool for predicting lncRNA functions, identifying potential lncRNA-disease associations, and pre-screening candidate lncRNAs to perform biological experiments. The IDSSIM code, all experimental data and prediction results are available online at https://github.com/CDMB-lab/IDSSIM .

Background and Objective: Improving quality of care is one of the primary goals in current Chinese hospital reforms. Teamwork can play an essential role. Characteristics of teamwork and interventions for improving teamwork in hospitals have been widely studied. However, most of these studies are from a Western context; evidence from China is scarce. Because of the contextual differences between China and Western countries, empirical evidence on teamwork from Western hospitals may have limited validity in China. This systematic review aims to advance the evidence base and understanding of teamwork in Chinese hospitals.Methods: Both English (i.e., Embase, Medline, and Web of Science) and Chinese databases (i.e., CNKI, CQVIP, and Wanfang) were searched for relevant articles until February 6, 2020. We included the studies that empirically researched teamwork in Chinese hospitals. Studies were excluded if they (1) were not conducted in hospitals in Mainland China, (2) did not research teamwork on team interventions, (3) were not empirical, (4) were not written in English or Chinese, (5) were not published in peer-reviewed journals, and (6) were not conducted in teams that provide direct patient care. Both deductive and inductive approaches were used to analyze data. The Mixed Methods Appraisal Tool (MMAT) was used to assess their methodological quality.Results: A total of 70 articles (i.e., 39 English articles and 31 Chinese articles) were included. The results are presented in two main categories: Teamwork components and Team interventions. The evidence regarding the relationships among inputs, processes, and outcomes is scarce and mostly inconclusive. The only conclusive evidence shows that females perceive better team processes than males. Similar types of training and tools were introduced as can be found in Western literature, all showing positive effects. In line with the Chinese health reforms, many of the intervention studies regard the introduction of multidisciplinary teams (MDTs). The evidence on the implementation of MDTs reveals that they have led to lower complication rates, shorter hospital stays, higher diagnosis accuracy, efficiency improvement, and a variety of better disease-specific clinical outcomes. Evidence on the effect on patient survival is inconclusive.Conclusion: The Chinese studies on teamwork components mainly focus on the input-process relationship. The evidence provided on this relationship is, however, mostly inconclusive. The intervention studies in Chinese hospitals predominantly focus on patient outcomes rather than organizational and employee outcomes. The introduction of training, tools, and MDTs generally shows promising results. The evidence from primary hospitals and rural areas, which are prioritized in the health reforms, is especially scarce. Advancing the evidence base on teamwork, especially in primary hospitals and rural areas, is needed and can inform policy and management to promote the health reform implementation.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020175069, identifier CRD42020175069.

Abstract Background ADP-glucose pyrophosphorylase (AGPase), the key enzyme in plant starch biosynthesis, is a heterotetramer composed of two identical large subunits and two identical small subunits. AGPase has plastidial and cytosolic isoforms in higher plants, whereas it is mainly detected in the cytosol of grain endosperms in cereal crops. Our previous results have shown that the expression of the TaAGPL1 gene, encoding the cytosolic large subunit of wheat AGPase, temporally coincides with the rate of starch accumulation and that its overexpression dramatically increases wheat AGPase activity and the rate of starch accumulation, suggesting an important role. Methods In this study, we performed yeast one-hybrid screening using the promoter of the TaAGPL1 gene as bait and a wheat grain cDNA library as prey to screen out the upstream regulators of TaAGPL1 gene. And the barley stripe mosaic virus-induced gene-silencing (BSMV-VIGS) method was used to verify the functional characterization of the identified regulators in starch biosynthesis. Results Disulfide isomerase 1-2 protein (TaPDIL1-2) was screened out, and its binding to the TaAGPL1-1D promoter was further verified using another yeast one-hybrid screen. Transiently silenced wheat plants of the TaPDIL1-2 gene were obtained by using BSMV-VIGS method under field conditions. In grains of BSMV-VIGS-TaPDIL1-2-silenced wheat plants, the TaAGPL1 gene transcription levels, grain starch contents, and 1000-kernel weight also significantly increased. Conclusions As important chaperones involved in oxidative protein folding, PDIL proteins have been reported to form hetero-dimers with some transcription factors, and thus, our results suggested that TaPDIL1-2 protein could indirectly and negatively regulate the expression of the TaAGPL1 gene and function in starch biosynthesis.

Electroencephalography (EEG) records the electrical activity of the brain, which is an important tool for the automatic detection of epileptic seizures. It is certainly a very heavy burden to only recognize EEG epilepsy manually, so the method of computer-assisted treatment is of great importance. This paper presents a seizure detection algorithm based on variational modal decomposition (VMD) and a deep forest (DF) model. Variational modal decomposition is performed on EEG recordings, and the first three variational modal functions (VMFs) are selected to construct the time–frequency distribution of the EEG signals. Then, the log−Euclidean covariance matrix (LECM) is computed to represent the EEG properties and form EEG features. The deep forest model is applied to complete the EEG signal classification, which is a non-neural network deep model with a cascade structure that performs feature learning through the forest. In addition, to improve the classification accuracy, postprocessing techniques are performed to generate the discriminant results by moving average filtering and adaptive collar expansion. The algorithm was evaluated on the Bonn EEG dataset and the Freiburg long−term EEG dataset, and the former achieved a sensitivity and specificity of 99.32% and 99.31%, respectively. The mean sensitivity and specificity of this method for the 21 patients in the Freiburg dataset were 95.2% and 98.56%, respectively, with a false detection rate of 0.36/h. These results demonstrate the superior performance advantage of our algorithm and indicate its great research potential in epilepsy detection.

In ancient villages, the spread of uninterrupted fires caused great damage to clustered wooden houses. Thus, the spread of fire among wooden houses should be systematically studied to explore its characteristics. Statistical analysis is a feasible way to study the characteristics and underlying mechanisms of fire in full-scale wooden houses. In this study, 4 full-scale wooden buildings were built in an ethnic village in Guizhou Province, and the fire spread test was conducted by igniting a 0.63-MW power wood crib. To investigate the fire spread, the visual characteristics were observed, and the temperatures and heat radiation at special locations were monitored with thermocouples and radiation flowmeters, respectively. The effect of relative slope, heat radiation, and wind direction on fire spread characteristics was established by mathematical statistics, and the measured temperatures were used to verify the statistics’ regularity. The results showed that in wooden houses, fire spread was mainly influenced by the slope, the distance between houses, and wind direction. When the inner wall of a wooden house is protected by a fireproof coating, the thermal radiation spread and fire spread are both slower. The slope and distance had the same influence weight (0.41) on fire spread; however, since they affect the process in different ways, they should be analyzed separately for fire risk evaluation. The findings of this study provide a theoretical foundation for understanding the fire spread process in wooden buildings.

IntroductionChina has made considerable progress with health system reforms in recent years. Rural China, however, has lagged behind as the diversity of needs of China's 3,000 rural counties were not always well addressed by national top-down reforms. China's Rural Health Reform Project Health XI (HXI) piloted a hybrid process of top down and bottom up implementation of health system reforms which were tailored to rural county level needs and covered a population of more than 21 million. Different studies provide evidence that HXI counties have achieved substantial benefits given the relatively limited investment. The Effectiveness of HXI subsequently raises the question how the hybrid approach may have resulted in effective implementation of interventions. We answer this question to advance understanding of hybrid approaches in general and in the rural Chinese context in particular, where the bottom-up elements might match poorly with the traditional organisational culture and learning style.Materials & methodsWe conducted an in-depth qualitative analysis in three 'best practice' counties, performing document-analyses, observations, semi-structured individual and group interviews. In alignment with the research question, this study is of an explorative nature and follows a sequence of deductive and inductive steps.ResultsHXI struggled initially as counties had difficulties to take initiative and autonomously select and adapt their own reforms. The initial reforms required multiple improvement iterations before achieving the planned results. The effectiveness of these bottom up reform processes has been aided by tight top down supervision and extensive domestic expert involvement. County level leadership is seen as essential to align the top down and bottom up structures and processes. Where successful, HXI has changed mind-sets and counties developed generic health improvement capabilities.ConclusionTailoring innovations to fit local needs formed a severe challenge for the three 'best practice' counties studied. A 'change of mindset' to actively take initiative and assume autonomy was needed to advance. Top down supervision and extensive support of experts was required to overcome the barriers. The studied counties finally achieved sustainable improvements and developed double loop learning capabilities beyond HXI objectives. Taken together, the above findings suggest that the continuum of healthcare reform implementation approaches in which hybrid approaches reside-from bottom up to top down-has two dimensions: a content dimension and a procedural dimension. Enabled by top down procedures, counties were able to bottom up tailor the content of best practice innovations to fit local needs.

Abstract Background China is in the process of integrating the new cooperative medical scheme (NCMS) and the urban residents’ basic medical insurance system (URBMI) into the urban and rural residents’ basic medical insurance system (URRBMI). However, how to integrate the financing policies of NCMS and URBMI has not been described in detail. This paper attempts to illustrate the differences between the financing mechanisms of NCMS and URBMI, to analyze financing inequity between urban and rural residents and to identify financing mechanisms for integrating urban and rural residents’ medical insurance systems. Methods Financing data for NCMS and URBMI (from 2008 to 2015) was collected from the China health statistics yearbook, the China health and family planning statistics yearbook, the National Handbook of NCMS Information, the China human resources and social security statistics yearbook, and the China social security yearbook. “Ability to pay” was introduced to measure inequity in health financing. Individual contributions to NCMS and URBMI as a function of per capita disposable income was used to analyze equity in health financing between rural and urban residents. Results URBMI had a financing mechanism that was similar to that used by NCMS in that public finance accounted for more than three quarters of the pooling funds. The scale of financing for NCMS was less than 5% of the per capita net income of rural residents and less than 2% of the per capita disposable income of urban residents for URBMI. Individual contributions to the NCMS and URBMI funds were less than 1% of their disposable and net incomes. Inequity in health financing between urban and rural residents in China was not improved as expected with the introduction of NCMS and URBMI. The role of the central government and local governments in financing NCMS and URBMI was oscillating in the past decade. Conclusions The scale of financing for URRBMI is insufficient for the increasing demands for medical services from the insured. The pooling fund should be increased so that it can better adjust to China’s rapidly aging population and epidemiological transitions as well as protect the insured from poverty due to illness. Individual contributions to the URBMI and NCMS funds were small in terms of contributors’ incomes. The role of the central government and local governments in financing URRBMI was not clearly identified. Individual contributions to the URRBMI fund should be increased to ensure the sustainable development of URRBMI. Compulsory enrollment should be required so that URRBMI improves the social medical insurance system in China.

This study attempts to investigate the effect of phenol on physicochemical properties and treatment performances of partial nitrifying granules (PNGs). Two sequencing batch reactors (SBRs) fed with synthetic ammonium wastewaters were operated in absence (R1) or presence (R2) of phenol. The PNGs in R1 maintained excellent partial nitrification performance and relatively stable physicochemical properties, and exhibited compact and regular shaped structure with a cocci-dominant surface. However, as phenol concentration was stepwise increased from 0 to 300 mg/L in R2, filamentous bacteria appeared and gradually dominated within granules, which in turn resulted in settleability deterioration. Most notably, granules in R2 got easier to agglomerate in the reactor walls and then been washed out with effluent, leading to significant biomass loss, frequent outflow pipe blockage, and eventual system failure. The extracellular polymeric substances (EPS) contents including proteins and polysaccharides in R2 reached 1.8 and 1.7 times of that in R1, respectively, indicating that the presence of phenol played an important role on EPS production. Removal efficiency of ammonium and phenol remained high, but dropped sharply when phenol concentration reached 300 mg/L. Moreover, the failed maintenance of partial nitrification was observed due to the revival of nitrite oxidizing bacteria (NOB) within granules after phenol exposure, which was confirmed by quantitative fluorescence in situ hybridization (FISH) analysis. Overall this study demonstrates that phenol had negative effects on PNGs, and pretreatment to eliminate phenolic substances is recommended when using PNGs for wastewater treatment.

OBJECTIVE:In China, patients increasingly choose to access already severely overcrowded higher level hospitals, leaving lower level facilities with low utilization rates. This situation undermines the effectiveness and efficiency of the health system. The situation tends to worsen despite policy measures aimed at improvement. We systematically review the factors affecting patient choice to synthesize scientific understanding of health system access in China. The review provides an evidence base for measures to direct patient flow towards lower level facilities. METHODS:We screened the peer-reviewed literature published from April 2009 to January 2016 that investigates Chinese patients' choice of health care facilities at different levels and assessed 45 studies in total. We applied two structured forms to extract data on each study's characteristics, methodology, and factors. RESULTS OF DATA SYNTHESIS:The results identified four factor types: 1) patient, 2) provider, 3) context and 4) composite: combined patient, provider, and/or context attributes. Patient factors are mentioned the most, but the evidence on patient factors is often inconclusive. Evidence suggests that the provider factors 'drug variety' and 'equipment', and composite factor 'perceived quality', push patients from lower levels towards higher levels. CONCLUSION:Underuse of primary care facilities and overcrowding of higher level facilities will likely be amplified by current demographic trends. Evidence suggests that improving drug availability, equipment and perceived quality of primary care services can improve the situation. Well-designed research that considers the interactions between factors is called for to better inform future interventions.