Enhancers are a class of cis-regulatory elements that can increase gene transcription by forming loops in intergenic regions, introns and exons. Enhancers, as well as their associated target genes, and transcription factors (TFs) that bind to them, are highly associated with human disease and biological processes. Although some enhancer databases have been published, most only focus on enhancers identified by high-throughput experimental techniques. Therefore, it is highly desirable to construct a comprehensive resource of manually curated enhancers and their related information based on low-throughput experimental evidences. Here, we established a comprehensive manually-curated enhancer database for human and mouse, which provides a resource for experimentally supported enhancers, and to annotate the detailed information of enhancers. The current release of ENdb documents 737 experimentally validated enhancers and their related information, including 384 target genes, 263 TFs, 110 diseases and 153 functions in human and mouse. Moreover, the enhancer-related information was supported by experimental evidences, such as RNAi, in vitro knockdown, western blotting, qRT-PCR, luciferase reporter assay, chromatin conformation capture (3C) and chromosome conformation capture-on-chip (4C) assays. ENdb provides a user-friendly interface to query, browse and visualize the detailed information of enhancers. The database is available at http://www.licpathway.net/ENdb.

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Abstract. In user-pay public private partnership (PPP) projects, private sectors collect user fees to cover cost and reap revenue. For projects that cannot be self-financed, public sectors usually invest public funds to make them financially feasible. The concession agreement allocates revenues and risks, and lies in the center of balancing public and private interests. However, stakeholders may have contrary opinions regarding the optimization of concession agreement. While private sectors are concerned about earning money, public sectors pay more attention to the efficient use of public funds. To address this challenge, this paper firstly identifies several key concessionary items, including concession period, concession price, capital structure and government subsidy. Then, a multi-objective optimization model is presented using discounted cash flow method, in which key concessionary items act as decision variables and public and private interests are represented by two sub-objectives. Subsequently, the model is solved using non-dominated sorting genetic algorithm-II (NSGA-II). Furthermore, a numerical case based on Beijing No. 4 Metro Line is provided to demonstrate the application of the model. Results show that the proposed model can produce a series of viable combinations of concessionary items that balance public and private interests, which provides practical references for relative decision making activities.

An updated LncTarD 2.0 database provides a comprehensive resource on key lncRNA–target regulations, their influenced functions and lncRNA-mediated regulatory mechanisms in human diseases. LncTarD 2.0 is freely available at (http://bio-bigdata.hrbmu.edu.cn/LncTarD or https://lnctard.bio-database.com/). LncTarD 2.0 was updated with several new features, including (i) an increased number of disease-associated lncRNA entries, where the current release provides 8360 key lncRNA–target regulations, with 419 disease subtypes and 1355 lncRNAs; (ii) predicted 3312 out of 8360 lncRNA–target regulations as potential diagnostic or therapeutic biomarkers in circulating tumor cells (CTCs); (iii) addition of 536 new, experimentally supported lncRNA–target regulations that modulate properties of cancer stem cells; (iv) addition of an experimentally supported clinical application section of 2894 lncRNA–target regulations for potential clinical application. Importantly, LncTarD 2.0 provides RNA-seq/microarray and single-cell web tools for customizable analysis and visualization of lncRNA–target regulations in diseases. RNA-seq/microarray web tool was used to mining lncRNA–target regulations in both disease tissue samples and CTCs blood samples. The single-cell web tools provide single-cell lncRNA–target annotation from the perspectives of pan-cancer analysis and cancer-specific analysis at the single-cell level. LncTarD 2.0 will be a useful resource and mining tool for the investigation of the functions and mechanisms of lncRNA deregulation in human disease.

<b><i>Purpose:</i></b> The aim of this study was to compare the prevalence of diabetic retinopathy (DR) and diabetic macular edema (DME), as well as their risk factors in patients with early-onset diabetes (EOD, ≤40 years) and late-onset diabetes (LOD, &#x3e;40 years). <b><i>Methods:</i></b> Patients were recruited from a community-based study, Fushun Diabetic Retinopathy Cohort Study (FS-DIRECT), conducted between July 2012 and May 2013 in China. The presence and severity of DR and DME were determined by a modified Early Treatment Diabetic Retinopathy Study (ETDRS) retinopathy scale of six-field fundus photographs. <b><i>Results:</i></b> A total of 1,932 patients (796 male, 41.2%) with gradable fundus photography were included. The prevalence of any DR and DME was 67.0% (95% confidence interval [CI]: 60.3–73.7%) and 39.3% (95% CI: 32.1–46.5%) in the EOD patients, respectively, which were both significantly higher than that in the LOD patients (DR: 41.9%, 39.6–44.2%, <i>p</i> &#x3c; 0.001; DME: 14.4%, 12.7–16.1%, <i>p</i> &#x3c; 0.001). Insulin use was associated with both the presence of DR and DME in both EOD and LOD patients. Besides insulin use, a high level of income (odds ratio [OR], 95% CI: 0.05, 0.01–0.51) was negatively associated with DR, and higher high-density lipoprotein (OR, 95% CI: 4.14, 1.44–11.91) was associated with DME among EOD patients. <b><i>Conclusion:</i></b> In this sample of patients with type 2 diabetes, both prevalence of DR and DME were apparently higher in patients who developed diabetes ≤40 years of age than those who developed it later.

Purpose. To evaluate the prevalence and causes of visual impairment in a group of community people with type 2 diabetes mellitus (T2DM) in Northeast China. Methods. Population-based cross-sectional survey. Patients diagnosed with T2DM residing in 15 communities in Fushun, Northeast China, were enrolled between July 2012 and May 2013. All participants underwent an extensive and standardized eye examination (visual acuity testing, slit-lamp, and fundus examination). Low vision was defined as presenting VA of better-seeing eye <20/60 and ≥20/400, and blindness was defined as VA <20/400, according to the World Health Organization (WHO) definitions. The primary causes of blindness and low vision were assessed by senior ophthalmologists. Results. Visual acuity measurements were available for 1998 (89.8%) of 2224 subjects in the study. The prevalence of bilateral blindness and low vision defined was 0.90% and 10.81%. Uncorrected refractive error was the first leading cause of low vision (75.0%) and blindness (38.9%). After correcting the refractive error, the first leading cause of low vision was cataract (44.4%), followed by diabetic retinopathy (29.6%) and myopic maculopathy (18.5%), while the first leading cause of blindness was proliferative DR (45.4%), followed by cataract (36.4%) and myopic maculopathy (18.2%). Conclusions. This study suggested a high prevalence of low vision and blindness in this study cohort. Uncorrected refractive error and cataract remain the leading cause of visual impairment, but the major challenge is the early diagnosis and intervention of diabetic retinopathy to reduce diabetes-related blindness.

ObjectiveThis study aimed to assess the knowledge, attitude, and practice (KAP) of diabetic subjects with diabetic retinopathy (DR) and those without DR (NDR) in an urban community in Northeast China, as well as their risk factors in subjects with DR and NDR.MethodsA community-based survey involving 1,662 subjects was conducted in Fushun, China, between July 2012 and May 2013. The subjects included diabetics with DR (n = 783) and those NDR (n = 879), and questionnaires were completed to collect information about their sociodemographic and healthcare characteristics. A Chi-square test and multiple logistic analyses were performed to analyze the data.ResultsAmong the DR group, 21.88% had a good knowledge of DR, 94.15% had a positive attitude, and 68.07% followed good practice, whereas 20.98% of the NDR group had a good knowledge of DR, 94.18% had a positive attitude, and 66.92% followed good practice. There was no significant difference in the KAP of the two groups of subjects. In the NDR group, a good level of knowledge was associated with a high-level of education (OR = 0.1, 0.2; p &lt; 0.05), a good attitude was associated with retirement (OR = 0.2; p &lt; 0.05), and good practice was associated with being female, having a high-level of education, and the type of treatment (OR = 0.5, 0.4, 2.3, 3.1; p &lt; 0.05). In the DR group, good practice was associated with older age and retirement (OR = 0.6, 0.4; p &lt; 0.05).ConclusionsThere was no significant difference between the DR and NDR subjects in the overall levels of KAP, but both groups showed a poor level of knowledge. Age, gender, education, occupation, and type of treatment were the main factors associated with the KAP scores, more risk factors in the NDR group than in the DR group. There is an urgent need for coordinated educational campaigns with a prioritized focus on the northeast region of China, especially NDR group.

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