Polydopamine and collagen coated micro-grated polydimethylsiloxane for human mesenchymal stem cell culture

Natural tissues contain highly organized cellular architecture. One of the major challenges in tissue engineering is to develop engineered tissue constructs that promote cellular growth in physiological directionality. To address this issue, micro-patterned polydimethylsiloxane (PDMS) substrates have been widely used in cell sheet engineering due to their low microfabrication cost, higher stability, excellent biocompatibility, and most importantly, ability to guide cellular growth and patterning. However, the current methods for PDMS surface modification either require a complicated procedure or generate a non-uniform surface coating, leading to the production of poor-quality cell layers. A simple and efficient surface coating method is critically needed to improve the uniformity and quality of the generated cell layers. Herein, a fast, simple and inexpensive surface coating method was analyzed for its ability to uniformly coat polydopamine (PD) with or without collagen on micro-grated PDMS substrates without altering essential surface topographical features. Topographical feature, stiffness and cytotoxicity of these PD and/or collagen based surface coatings were further analyzed. Results showed that the PD-based coating method facilitated aligned and uniform cell growth, therefore holds great promise for cell sheet engineering as well as completely biological tissue biomanufacturing

Genome-wide characterization of RR gene family members in Zanthoxylum armatum and the subsequent functional characterization of the C-type RR

Response Regulators (RRs) are crucial regulators in plant development and stress responses, comprising A-type, B-type, C-type, and pseudo-RR subfamilies. However, previous studies have often focused on specific subfamilies, which restricts our understanding of the complete RR gene family. In this study, we conducted a comprehensive analysis of 63 RR members from Zanthoxylum armatum, using phylogenetic relationships, motif composition, cis-acting elements, gene duplication and collinearity analyses. Segmental repeats among ZaRR genes enhanced the various environmental adaptabilities of Z. armatum, and the B-type ZaRR exhibited significant collinearity with the RRs in P. trichocarpa and C. sinensis. Cis-element analysis indicated ZaRRs play a significant role in abiotic stress and phytohormone pathways, particularly in light, drought, cold, abscisic acid (ABA) and salicylic acid (SA) responses. Abundant Ethylene Response Factor (ERF) and reproduction-associated binding sites in ZaRR promoters suggested their roles in stress and reproductive processes. A-type ZaRRs were implicated in plant vegetative and reproductive growth, whereas B-type ZaRRs contributed to both growth and stress responses. C-type ZaRRs were associated with plant reproductive growth, whereas pseudo-RRs may function in plant stress responses, such as water logging, cold, and response to ethylene (ETH), SA, and jasmonic acid (JA). Ectopic expression of ZaRR24, a C-type RR, inhibits growth, induces early flowering, and shortens fruit length in Arabidopsis. ZaRR24 overexpression also affected the expression of A- and B-type RRs, as well as floral meristem and organ identity genes. These findings establish a solid and comprehensive foundation for RR gene research in Z. armatum, and provide a platform for investigating signal transduction in other woody plants

Integrated transcriptomics, proteomics and metabolomics-based analysis uncover TAM2-associated glycolysis and pyruvate metabolic remodeling in pancreatic cancer

IntroductionTumor-associated macrophage 2 (TAM2) abundantly infiltrates pancreatic ductal adenocarcinoma (PAAD), and its interaction with malignant cells is involved in the regulation of tumor metabolism. In this study, we explored the metabolic heterogeneity involved in TAM2 by constructing TAM2-associated metabolic subtypes in PAAD.Materials and methodsPAAD samples were classified into molecular subtypes with different metabolic characteristics based on a multi-omics analysis strategy. 20 PAAD tissues and 10 normal pancreatic tissues were collected for proteomic and metabolomic analyses. RNA sequencing data from the TCGA-PAAD cohort were used for transcriptomic analyses. Immunohistochemistry was used to assess TAM2 infiltration in PAAD tissues.ResultsThe results of transcriptomics and immunohistochemistry showed that TAM2 infiltration levels were upregulated in PAAD and were associated with poor patient prognosis. The results of proteomics and metabolomics indicated that multiple metabolic processes were aberrantly regulated in PAAD and that this dysregulation was linked to the level of TAM2 infiltration. WGCNA confirmed pyruvate and glycolysis/gluconeogenesis as co-expressed metabolic pathways of TAM2 in PAAD. Based on transcriptomic data, we classified the PAAD samples into four TAM2-associated metabolic subtypes (quiescent, pyruvate, glycolysis/gluconeogenesis and mixed). Metabolic subtypes were each characterized in terms of clinical prognosis, tumor microenvironment, immune cell infiltration, chemotherapeutic drug sensitivity, and functional mechanisms.ConclusionOur study confirmed that the metabolic remodeling of pyruvate and glycolysis/gluconeogenesis in PAAD was closely related to TAM2. Molecular subtypes based on TAM2-associated metabolic pathways provided new insights into prognosis prediction and therapy for PAAD patients

An analysis of farmers' perception of the new cooperative medical system in Liaoning Province, China

<p>Abstract</p> <p>Background</p> <p>Since 2003, the number of pilot areas of the New Rural Cooperative Medical System (NRCMS) has increased in rural China. And the major efforts have been concentrated on the enrollment of prospective members. In this study, we examined the satisfaction of the rural residents with the NRCMS as well as factors affecting their attitudes towards the NRCMS.</p> <p>Methods</p> <p>The data for this study were collected from a survey involving twenty counties in Liaoning Province. Interviews and focus groups were conducted between 10^thJanuary and 20^thAugust 2008. A total of 2,780 people aged 18-72 were randomly selected and interviewed. Data were evaluated by nonparametric tests and ordinal regression models.</p> <p>Results</p> <p>71.6% of the study subjects were satisfied with the NRCMS. Single factor analysis showed that attitudes towards the NRCMS were influenced by gender, age, marital status, and self-rated health status. In the ordinal regression analysis, gender, age, and self-rated health status affect satisfaction (P < 0.05).</p> <p>Conclusions</p> <p>We found that a considerable proportion of farmers were satisfied with the NRCMS. Gender, age, and self-rated health status had significant effects on farmers' attitudes towards the NRCMS. The Chinese Central Government attempted to adopt active measures in the future to continuously improve the NRCMS, including initiating educational programs, building new medical facilities and increasing financial investment.</p

Whole-genome sequencing of the snub-nosed monkey provides insights into folivory and evolutionary history

Colobines are a unique group of Old World monkeys that principally eat leaves and seeds rather than fruits and insects. We report the sequencing at 146× coverage, de novo assembly and analyses of the genome of a male golden snub-nosed monkey (Rhinopithecus roxellana) and resequencing at 30× coverage of three related species (Rhinopithecus bieti, Rhinopithecus brelichi and Rhinopithecus strykeri). Comparative analyses showed that Asian colobines have an enhanced ability to derive energy from fatty acids and to degrade xenobiotics. We found evidence for functional evolution in the colobine RNASE1 gene, encoding a key secretory RNase that digests the high concentrations of bacterial RNA derived from symbiotic microflora. Demographic reconstructions indicated that the profile of ancient effective population sizes for R. roxellana more closely resembles that of giant panda rather than its congeners. These findings offer new insights into the dietary adaptations and evolutionary history of colobine primates

Applying marginal value analysis in refinery planning

Most research work and refinery decision makers mainly focus on direct planning results such as the optimal combination of raw materials, unit loads, and production rates. There exists much more useful hidden information from an LP model such as marginal values of the feed stocks, the intermediate products, and the final products than direct planning results. One of the limitations of using marginal values is that they are only applied for stream flows at the solution point. We have no idea how these marginal values are changed beyond the solution. To tackle this problem, two analytical methods, namely sensitivity analysis ( SA) and parametric programming ( PP), are proposed and applied in MVA ( marginal value analysis). With these two analysis methods, MVA can be extended beyond the solution point and applied to process constraints. This article first uses a simple gasoline blending example to illustrate the required modeling techniques and procedures for performing these analyses. Then a multi-period refinery case study is presented to show how to interpret the results and apply the analyses to a real world refinery. The approach proposed here can be of great help for debottlenecking, retrofitting, pricing, and investment evaluation. The analytical methods proposed can also be generally applied to other chemical plants

Debottlenecking and retrofitting for a refinery using marginal value analysis, sensitivity analysis and parametric programming

A new analytical method called "marginal value analysis" is used in this paper to provide economic information for all stream flows inside a refinery. Three types of marginal values are defined that represent respectively the de-bottlenecking effect, production cost and product value of an intermediate material flow. Important insights are generated using the analysis to find production bottlenecks, assist in decision making and price intermediate materials, etc. Sensitivity analysis and parametric programming are studied in this paper to provide more comprehensive information for pricing, retrofitting and investment evaluation. Several case studies are used to illustrate the research on marginal value analysis, sensitivity analysis and parametric programming

Integrating Neural Network Models for Refinery Planning

This paper presents two artificial neural networks (ANN) to predict pour point (PP) and cold filter plugging point (CFPP) of diesel oil and octane number of gasoline respectively, using back-propagation (BP) algorithm with actual plant data. Appropriate topologies of ANNs were obtained and the training rates and momentum in ANNs were discussed. Detailed models of the most important process units such as Crude Distillation Unit (CDU) and Fluidized-bed Catalytic Cracking (FCC) in a refinery were developed. The developed ANN models and CDU, FCC models were integrated into the refinery planning model to replace the traditional linear processing unit models and the linear blending rule used in gasoline blending and diesel oil blending. With these accurate unit models and blending models, the planning model gives us much more reliable results which can be used for refinery business decision and configuration retrofitting, etc. Several case studies were used to illustrate the capabilities and effectiveness of the integrated model proposed. © 2003 Elsevier B.V. All rights reserved

Integrating CDU, FCC and product blending models into refinery planning

The accuracy of using linear models for crude distillation unit (CDU), fluidize-bed catalytic cracker (FCC) and product blending in refinery planning has been debated for decades. Inaccuracy caused by nonrigorous linear models may reduce the overall profitability or sacrifice product quality. On the other hand, using rigorous process models for refinery planning imposes unnecessary complications on the problem because these models lengthen the solution time and often hide critical issues and parameters for profit improvements. To overcome these problems, this paper presents a refinery planning model that utilizes simplified empirical nonlinear process models with considerations for crude characteristics, products' yields and qualities, etc. The proposed model can be easily solved with much higher accuracy than a traditional linear model. This paper will present how the CDU, FCC and product blending models are formulated and applied to refinery planning. Several case studies are used to illustrate the features of the refinery-planning model proposed. (c) 2005 Elsevier Ltd. All rights reserved