Photochemical cross-linking of plastically compressed collagen gel produces an optimal scaffold for corneal tissue engineering

The experiments were designed to use photochemically cross-linked plastically compressed collagen (PCPCC) gel to support corneal epithelial cells. A plastically compressed collagen (PCC) scaffold was photo cross-linked by UVA in the presence of riboflavin to form a biomaterial with optimal mechanical properties. The breaking force, rheology, surgical suture strength, transparency, ultrastructure, and cell-based biocompatibility were compared between PCPCC and PCC gels. The breaking force increased proportionally with an increased concentration of riboflavin. The stress required to reach breaking point of the PCPCC scaffolds was over two times higher compared to the stress necessary to break PCC scaffolds in the presence of 0.1% riboflavin. Rheology results indicated that the structural properties of PCC remain unaltered after UVA cross-linking. The PCC gels were more easily broken than PCPCC gels when sutured on to bovine corneas. The optical density values of PCPCC and PCC showed no significant differences (p > 0.05). SEM analyses showed that the collagen fibres within the PCPCC gels were similar in morphology to PCC gels. No difference in cell-based biocompatibility was seen between the PCPCC and PCC scaffolds in terms of their ability to support the ex vivo expansion of corneal epithelial cells or their subsequent differentiation evidenced by similar levels of cytokeratin 14. In conclusion, PCPCC scaffold is an optimal biomaterial for use in therapeutic tissue engineering of the cornea.

Tissue engineering a fetal membrane

The aim of this study was to construct an artificial fetal membrane (FM) by combination of human amniotic epithelial stem cells (hAESCs) and a mechanically enhanced collagen scaffold containing encapsulated human amniotic stromal fibroblasts (hASFs). Such a tissue-engineered FM may have the potential to plug structural defects in the amniotic sac after antenatal interventions, or to prevent preterm premature rupture of the FM. The hAESCs and hASFs were isolated from human fetal amniotic membrane (AM). Magnetic cell sorting was used to enrich the hAESCs by positive ATP-binding cassette G2 selection. We investigated the use of a laminin/fibronectin (1:1)-coated compressed collagen gel as a novel scaffold to support the growth of hAESCs. A type I collagen gel was dehydrated to form a material mimicking the mechanical properties and ultra-structure of human AM. hAESCs successfully adhered to and formed a monolayer upon the biomimetic collagen scaffold. The resulting artificial membrane shared a high degree of similarity in cell morphology, protein expression profiles, and structure to normal fetal AM. This study provides the first line of evidence that a compacted collagen gel containing hASFs could adequately support hAESCs adhesion and differentiation to a degree that is comparable to the normal human fetal AM in terms of structure and maintenance of cell phenotype.

Achieving carbon reduction from listed buildings in the UK: A case study on The Royal Academy of Engineering, London

A carbon reduction strategy for a historic Grade 1 listed office building in London is presented. The study evaluates the impact of49 different carbon abatement options, quantified using building simulation software, auditing procedures and qualitative methods. The impact of each option is assessed against three criteria: carbon abatement potential, practicality and cost. The strategy comprises of18interventions,integrated within 12 key recommendations. Accumulative reduction of 37% (below a 2009 carbon emissions baseline)appears achievable and only feasible with heavy reliance on changes in occupant behaviour. This theme appears central in achieving realistic and significant carbon savings from listed buildings, where planning constraints relinquish potential for major building fabric alteration and renewable energy installations.

A combined engineering and statistical model of UK domestic appliance electrical load profiles

The development of a combined engineering and statistical Artificial Neural Network model of UK domestic appliance load profiles is presented. The model uses diary-style appliance use data and a survey questionnaire collected from 51 suburban households and 46 rural households during the summer of 2010 and2011 respectively. It also incorporates measured energy data and is sensitive to socioeconomic, physical dwelling and temperature variables. A prototype model is constructed in MATLAB using a two layer feed forward network with back propagation training which has a 12:10:24 architecture. Model outputs include appliance load profiles which can be applied to the fields of energy planning (microrenewables and smart grids), building simulation tools and energy policy.