Pro-angiogenic and anti-inflammatory regulation by functional peptides loaded in polymeric implants for soft tissue regeneration.

Inflammation and angiogenesis are inevitable in vivo responses to biomaterial implants. Continuous progress has been made in biomaterial design to improve tissue interactions with an implant by either reducing inflammation or promoting angiogenesis. However, it has become increasingly clear that the physiological processes of inflammation and angiogenesis are interconnected through various molecular mechanisms. Hence, there is an unmet need for engineering functional tissues by simultaneous activation of pro-angiogenic and anti-inflammatory responses to biomaterial implants. In this work, the modulus and fibrinogen adsorption of porous scaffolds were tuned to meet the requirements (i.e., ~100 kPa and ~10 nm, respectively), for soft tissue regeneration by employing tyrosine-derived combinatorial polymers with polyethylene glycol crosslinkers. Two types of functional peptides (i.e., pro-angiogenic laminin-derived C16 and anti-inflammatory thymosin ß4-derived Ac-SDKP) were loaded in porous scaffolds through collagen gel embedding so that peptides were released in a controlled fashion, mimicking degradation of the extracellular matrix. The results from (1) in vitro coculture of human umbilical vein endothelial cells and human blood-derived macrophages and (2) in vivo subcutaneous implantation revealed the directly proportional relationship between angiogenic activities (i.e., tubulogenesis and perfusion capacity) and inflammatory activities (i.e., phagocytosis and F4/80 expression) upon treatment with either type of peptide. Interestingly, cotreatment with both types of peptides upregulated the angiogenic responses, while downregulating the inflammatory responses. Also, anti-inflammatory Ac-SDKP peptides reduced production of pro-inflammatory cytokines (i.e., interleukin [IL]-1ß, IL-6, IL-8, and tumor necrosis factor alpha) even when treated in combination with pro-angiogenic C16 peptides. In addition to independent regulation of angiogenesis and inflammation, this study suggests a promising approach to improve soft tissue regeneration (e.g., blood vessel and heart muscle) when inflammatory diseases (e.g., ischemic tissue fibrosis and atherosclerosis) limit the regeneration process.

Decoupling polymer properties to elucidate mechanisms governing cell behavior.

Determining how a biomaterial interacts with cells ("structure-function relationship") reflects its eventual clinical applicability. Therefore, a fundamental understanding of how individual material properties modulate cell-biomaterial interactions is pivotal to improving the efficacy and safety of clinically translatable biomaterial systems. However, due to the coupled nature of material properties, their individual effects on cellular responses are difficult to understand. Structure-function relationships can be more clearly understood by the effective decoupling of each individual parameter. In this article, we discuss three basic decoupling strategies: (1) surface modification, (2) cross-linking, and (3) combinatorial approaches (i.e., copolymerization and polymer blending). Relevant examples of coupled material properties are briefly reviewed in each section to highlight the need for improved decoupling methods. This follows with examples of more effective decoupling techniques, mainly from the perspective of three primary classes of synthetic materials: polyesters, polyethylene glycol, and polyacrylamide. Recent strides in decoupling methodologies, especially surface-patterning and combinatorial techniques, offer much promise in further understanding the structure-function relationships that largely govern the success of future advancements in biomaterials, tissue engineering, and drug delivery.

Data mining: comparing the empiric CFS to the Canadian ME/CFS case definition.

This article contrasts two case definitions for myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS). We compared the empiric CFS case definition (Reeves et al., 2005) and the Canadian ME/CFS clinical case definition (Carruthers et al., 2003) with a sample of individuals with CFS versus those without. Data mining with decision trees was used to identify the best items to identify patients with CFS. Data mining is a statistical technique that was used to help determine which of the survey questions were most effective for accurately classifying cases. The empiric criteria identified about 79% of patients with CFS and the Canadian criteria identified 87% of patients. Items identified by the Canadian criteria had more construct validity. The implications of these findings are discussed.