Relative concentrations of haemostatic factors and cytokines in solvent/detergent-treated and fresh-frozen plasma

Background Indications, efficacy, and safety of plasma products are highly debated. We compared the concentrations of haemostatic proteins and cytokines in solvent/detergent-treated plasma (SDP) and fresh-frozen plasma (FFP). Methods Concentrations of the following parameters were measured in 25 SDP and FFP samples: fibrinogen (FBG), factor (F) II, F V, F VII, F VIII, F IX, F X, F XIII, von Willebrand factor (vWF), D-Dimers, ADAMTS-13 protease, tumour necrosis factor-α (TNF-α), interleukin (IL)-1β, IL-6, IL-8, and IL-10. Results Mean FBG concentrations in SDP and FFP were similar, but in FFP, the range was larger than in SDP (P<0.01). Mean F II, F VII, F VIII, F IX, and F XIII levels did not differ significantly. Higher concentrations of F V (P<0.01), F X (P<0.05), vWF (P<0.01), and ADAMTS-13 (P<0.01) were found in FFP. With the exception of F VIII and F IX, the range of concentrations for all of these factors was smaller (P<0.05) in SDP than in FFP. Concentrations of TNF-α, IL-8, and IL-10 (all P<0.01) were higher in FFP than in SDP, again with a higher variability and thus larger ranges (P<0.01). Conclusions Coagulation factor content is similar for SDP and FFP, with notable exceptions of less F V, vWF, and ADAMTS-13 in SDP. Cytokine concentrations (TNFα, IL-8, and IL-10) were significantly higher in FFP. The clinical relevance of these findings needs to be established in outcome studie

Ground beetle (Coleoptera: Carabidae) assemblages in the Conservation Reserve Program crop rotation systems in interior Alaska

To improve knowledge of ground beetle communities and the influence of habitat succession on these communities in Alaska, adult ground beetle (Coleoptera: Carabidae) activity and diversity was documented on Conservation Research Program (CRP) agricultural lands in Delta Junction, Alaska (64ºN, 145ºW). Twenty species, based on a total sample of 6,116 specimens, were collected during 2006 and 2007 from plots that were in the CRP for 9 years (young-field plots) and 19 years (old-field plots). Two species, Cymindis cribricollis Dejean and Amara obesa Say, are reported for the first time for Alaska. Species richness of carabids for our study plots was estimated using the Chao 1 and Chao 2 estimators to be 22 and 28 species, respectively.  Ninety-four percent of the specimens belonged to five species Pterostichus adstrictus Eschscholtz (42.9%), Agonum cupreum Dejean (17.9%), Calathus ingratus Dejean (15%), Amara obesa (11.1%), and Dicheirotrichus cognatus (Gyllenhal) (7.1%). Only Ag. cupreum showed significant effects based on plot age. The majority of carabid activity occurred late in the season, from mid September to early October. A comparison of our findings is made with historical data (1943-1956) from the collection of the Matanuska Experiment Station now incorporated into the University of Alaska Museum Insect Collection

Multiscale analysis of human tissue engineered matrices for heart valve tissue engineering applications

Human tissue-engineered matrices (hTEMs) have been proposed as a promising approach for in-situ tissue engineered heart valves (TEHVs). However, there is still a limited understanding on how ECM composition in hTEMs develops over tissue culture time. Therefore, we performed a longitudinal hTEM assessment by 1) multiscale evaluation of hTEM composition during culture time (2, 4, 6-weeks), using (immuno)histology, biochemical assays, and mass spectrometry (LC-MS/MS); 2) analysis of protein pathways involved in ECM development using gene set enrichment analysis (GSEA); and 3) assessment of hTEM mechanical characterization using uniaxial tensile testing. Finally, as proof-of-concept, TEHVs manufactured using 6-weeks hTEM samples were tested in a pulse duplicator. LC-MS/MS confirmed the tissue culture time-dependent increase in ECM proteins observed in histology and biochemical assays, revealing the most abundant collagens (COL6,COL12), proteoglycans (HSPG2,VCAN), and glycoproteins (FN,TNC). GSEA identified the most represented protein pathways in the hTEM at 2-weeks (mRNA metabolic processes), 4-weeks (ECM production), and 6-weeks (ECM organization and maturation). Uniaxial mechanical testing showed increased stiffness and stress at failure, and reduction in strain over tissue culture time. hTEM-based TEHVs demonstrated promising in vitro performance at both pulmonary and aortic pressure conditions, with symmetric leaflet coaptation and no stenosis. In conclusion, ECM protein abundance and maturation increased over tissue culture time, with consequent improvement of hTEM mechanical characterics. These findings suggest that longer tissue culture impacts tissue organization, leading to an hTEM that may be suitable for high-pressure applications. STATEMENT OF SIGNIFICANCE: : It is believed that the composition of the extracellular matrix (ECM) in the human tissue engineered matrices (hTEM) may favor tissue engineered heart valve (TEHV) remodeling upon implantation. However, the exact protein composition of the hTEM, and how this impacts tissue mechanical properties, remains unclear. Hence, we developed a reproducible rotation-based tissue culture method to produce hTEM samples. We performed a longitudinal assessment using different analytical techniques and mass spectrometry. Our data provided an in-depth characterization of the hTEM proteome with focus on ECM components, their development, and how they may impact the mechanical properties. Based on these results, we manufactured functional hTEM-based TEHVs at aortic-like condition in vitro. These outcomes pose an important step in translating hTEM-based TEHVs into clinics and in predicting their remodeling potential upon implantation

Endothelial Progenitor Cells as Biomarkers of Cardiovascular Pathologies: A Narrative Review

Endothelial progenitor cells (EPC) may influence the integrity and stability of the vascular endothelium. The association of an altered total EPC number and function with cardiovascular diseases (CVD) and risk factors (CVF) was discussed; however, their role and applicability as biomarkers for clinical purposes have not yet been defined. Endothelial dysfunction is one of the key mechanisms in CVD. The assessment of endothelial dysfunction in vivo remains a major challenge, especially for a clinical evaluation of the need for therapeutic interventions or for primary prevention of CVD. One of the main challenges is the heterogeneity of this particular cell population. Endothelial cells (EC) can become senescent, and the majority of circulating endothelial cells (CEC) show evidence of apoptosis or necrosis. There are a few viable CECs that have properties similar to those of an endothelial progenitor cell. To use EPC levels as a biomarker for vascular function and cumulative cardiovascular risk, a correct definition of their phenotype, as well as an update on the clinical application and practicability of current isolation methods, are an urgent priority. Keywords: biomarker; cardiovascular disease; endothelial cells; progenitor

Endothelial Progenitor Cells as Biomarkers of Cardiovascular Pathologies: A Narrative Review

Endothelial progenitor cells (EPC) may influence the integrity and stability of the vascular endothelium. The association of an altered total EPC number and function with cardiovascular diseases (CVD) and risk factors (CVF) was discussed; however, their role and applicability as biomarkers for clinical purposes have not yet been defined. Endothelial dysfunction is one of the key mechanisms in CVD. The assessment of endothelial dysfunction in vivo remains a major challenge, especially for a clinical evaluation of the need for therapeutic interventions or for primary prevention of CVD. One of the main challenges is the heterogeneity of this particular cell population. Endothelial cells (EC) can become senescent, and the majority of circulating endothelial cells (CEC) show evidence of apoptosis or necrosis. There are a few viable CECs that have properties similar to those of an endothelial progenitor cell. To use EPC levels as a biomarker for vascular function and cumulative cardiovascular risk, a correct definition of their phenotype, as well as an update on the clinical application and practicability of current isolation methods, are an urgent priority

Predicting Cell Cycle Regulated Genes by Causal Interactions

The fundamental difference between classic and modern biology is that technological innovations allow to generate high-throughput data to get insights into molecular interactions on a genomic scale. These high-throughput data can be used to infer gene networks, e.g., the transcriptional regulatory or signaling network, representing a blue print of the current dynamical state of the cellular system. However, gene networks do not provide direct answers to biological questions, instead, they need to be analyzed to reveal functional information of molecular working mechanisms. In this paper we propose a new approach to analyze the transcriptional regulatory network of yeast to predict cell cycle regulated genes. The novelty of our approach is that, in contrast to all other approaches aiming to predict cell cycle regulated genes, we do not use time series data but base our analysis on the prior information of causal interactions among genes. The major purpose of the present paper is to predict cell cycle regulated genes in S. cerevisiae. Our analysis is based on the transcriptional regulatory network, representing causal interactions between genes, and a list of known periodic genes. No further data are used. Our approach utilizes the causal membership of genes and the hierarchical organization of the transcriptional regulatory network leading to two groups of periodic genes with a well defined direction of information flow. We predict genes as periodic if they appear on unique shortest paths connecting two periodic genes from different hierarchy levels. Our results demonstrate that a classical problem as the prediction of cell cycle regulated genes can be seen in a new light if the concept of a causal membership of a gene is applied consequently. This also shows that there is a wealth of information buried in the transcriptional regulatory network whose unraveling may require more elaborate concepts than it might seem at first

Dynamics of quantum quenching for BCS-BEC systems in the shallow BEC regime

The problem of coupled Fermi-Bose mixtures of an ultracold gas near a narrow Feshbach resonance is approached through the time-dependent and complex Ginzburg-Landau (TDGL) theory. The dynamical system is constructed using Ginzburg-Landau-Abrikosov-Gor'kov (GLAG) path integral methods with the single mode approximation for the composite Bosons, and the equilibrium states are obtained in the BEC regime for adiabatic variations of the Feshbach detuning along the stationary solutions of the dynamical system. Investigations into the rich superfluid dynamics of this system in the shallow BEC regime yields the onset of multiple interference patterns in the dynamics as the system is quenched from the deep-BEC regime. This results in a partial collapse and revival of the coherent matter wave field of the BEC, whose temporal profile is reported.Comment: 24 pages, 7 figures. Submitted to European Journal of Physics Plu

Integrative Network Biology: Graph Prototyping for Co-Expression Cancer Networks

Network-based analysis has been proven useful in biologically-oriented areas, e.g., to explore the dynamics and complexity of biological networks. Investigating a set of networks allows deriving general knowledge about the underlying topological and functional properties. The integrative analysis of networks typically combines networks from different studies that investigate the same or similar research questions. In order to perform an integrative analysis it is often necessary to compare the properties of matching edges across the data set. This identification of common edges is often burdensome and computational intensive. Here, we present an approach that is different from inferring a new network based on common features. Instead, we select one network as a graph prototype, which then represents a set of comparable network objects, as it has the least average distance to all other networks in the same set. We demonstrate the usefulness of the graph prototyping approach on a set of prostate cancer networks and a set of corresponding benign networks. We further show that the distances within the cancer group and the benign group are statistically different depending on the utilized distance measure

Construction and refinement of preference ordered decision classes

Preference learning methods are commonly used in multicriteria analysis. The working principle of these methods is similar to classical machine learning techniques. A common issue to both machine learning and preference learning methods is the difficulty of the definition of decision classes and the assignment of objects to these classes, especially for large datasets. This paper proposes two procedures permitting to automatize the construction of decision classes. It also proposes two simple refinement procedures, that rely on the 80-20 principle, permitting to map the output of the construction procedures into a manageable set of decision classes. The proposed construction procedures rely on the most elementary preference relation, namely dominance relation, which avoids the need for additional information or distance/(di)similarity functions, as with most of existing clustering methods. Furthermore, the simplicity of the 80-20 principle on which the refinement procedures are based, make them very adequate to large datasets. Proposed procedures are illustrated and validated using real-world datasets