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Segmentation of dried amnion biomaterial tends to produce invalid (outlier) contour point detections due to texture and colour inhomogeneity of the biomaterial. In this paper, a novel implementation of a non-stationary Bayesian learning process for outlier contour point removal of amnion segmentations is presented. This outlier removal method is independent to algorithms used for the contour detection. The Bayesian process uses a non-stationary kernel to learn a function with complex shape that maps image features in a region-of-interest around each contour point to a discrete output. Based on this output, a contour point can be determined as valid or invalid (outlier). The hyper-parameters of the non-stationary kernel are learned by maximising the marginal likelihood of the combined likelihood of data and the prior of the kernel parameters. Moreover, a novel combination of gradient-ascend and harmonic heuristic search methods is presented to find the optimal hyper-parameters. To validate the method, experiments are conducted to detect and ignore invalid contour points on amnion biomaterial images. A comparison of the proposed method with a logistic regression classification as the baseline is performed. The results show that the proposed method can significantly improve the contour detection by removing outliers and, hence, can reduce waste of uncut biomaterials.

Amniotic membrane transplantation is an established therapeutic and biological adjunct for several clinical situations, including treatment of diabetic foot ulcers and ocular surface disease. However, poorly standardized and validated clinical preparation and storage procedures can render the final product highly variable and an unpredictable biomaterial. We have therefore developed a novel, standardized method for processing and dry-preserving amniotic membrane, minimizing biochemical, compositional, and structure damage to produce a potentially superior membrane suitable for clinical use. The intellectual property associated with this methodology was patented by the University of Nottingham and licensed to NuVision® Biotherapies which formed the basis of the Tereo® manufacturing process which is used to manufacture Omnigen®.

Amniotic membrane (AM) is used to treat a range of ophthalmic indications but must be presented in a non-contaminated state. AM from elective caesarean sections contains natural microbial contamination, requiring removal during processing protocols. The aim of this study was to assess the ability of antibiotic decontamination of AM, during processing by innovative low-temperature vacuum-drying. Bioburden of caesarean section AM was assessed, and found to be present in low levels. Subsequently, the process for producing vacuum-dried AM (VDAM) was assessed for decontamination ability, by artificially loading with Staphylococcus epidermidis at different stages of processing. The protocol was highly efficient at removing bioburden introduced at any stage of processing, with antibiotic treatment and drying the most efficacious steps. The antibacterial activity of non-antibiotic treated AM compared to VDAM was evaluated using minimum inhibitory/biocidal concentrations (MIC/MBC), and disc diffusion assays against Meticillin-resistant Staphylococcus aureus, Meticillin-resistant S. epidermidis, Escherichia coli, Pseudomonas aeruginosa and Enterococcus faecalis. Antibacterial activity without antibiotic was low, confirmed by high MIC/MBC, and a no inhibition on agar lawns. However, VDAM with antibiotic demonstrated effective antibacterial capacity against all bacteria. Therefore, antibiotic decontamination is a reliable method for sterilisation of AM and the resultant antibiotic reservoir is effective against gram-positive and –negative bacteria.

Laser cutting processes offer high-quality and fast cutting capability across a wide variety of materials, including metals, plastics and organic tissues. To enable 2D laser cutting process, a set of (x, y) Cartesian coordinates that form a cutting trajectory have to be given to a machine controller to perform the cutting process. Automatically determining the cutting trajectory from an image of materials with inhomogeneous, crease and transparency characteristics, for example biomaterials, is difficult.In this paper, an image processing algorithm for determining and optimising the trajectory of a 2D laser cutting process is presented. Using this optimised 2D trajectory, uncut material wastes from the laser cutting process can be substantially reduced. The waste reductions are mainly obtained from optimised cutting area allocation and defective cut avoidance by manual cutting. In addition, the presented algorithm accommodates different cutting shapes, determined by a user, to maximise material cut from the laser cutting process.Case studies of thin and transparent amnion biomaterials cutting are presented to demonstrate the proposed algorithm to optimise the 2D laser cutting trajectory of the biomaterials. The algorithm has been tested to determine the optimised 2D cutting trajectory for a rectangle, circle and random shape amnion biomaterials. Results show that uncut materials can be minimised up to 2%, 3% and 5% of the total material of rectangle, circle and random shapes, respectively, by using this algorithm.

Nonunion fractures and large bone defects are significant targets for osteochondral tissue engineering strategies. A major hurdle in the use of these therapies is the foreign body response of the host. Herein, we report the development of a bone tissue engineering scaffold with the ability to release anti-inflammatory drugs, in the hope of evading this response. Porous, sintered scaffolds composed of poly(D,L-lactic acid-co-glycolic acid) (PLGA) and poly(ethylene glycol) (PEG) were prepared with and without the anti-inflammatory drug diclofenac sodium. Analysis of drug release over time demonstrated a profile suitable for the treatment of acute inflammation with ∼80% of drug released over the first 4 days and a subsequent release of around 0.2% per day. Effect of drug release was monitored using an in vitro osteoblast inflammation model, comprised of mouse primary calvarial osteoblasts stimulated with proinflammatory cytokines interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and interferon-γ (IFN-γ). Levels of inflammation were monitored by cell viability and cellular production of nitric oxide (NO) and prostaglandin E2 (PGE2). The osteoblast inflammation model revealed that proinflammatory cytokine addition to the medium reduced cell viability to 33%, but the release of diclofenac sodium from scaffolds inhibited this effect with a final cell viability of ∼70%. However, releasing diclofenac sodium at high concentrations had a toxic effect on the cells. Proinflammatory cytokine addition led to increased NO and PGE2 production; diclofenac-sodium-releasing scaffolds inhibited NO release by ∼64% and PGE2 production by ∼52%, when the scaffold was loaded with the optimal concentration of drug. These observations demonstrate the potential use of PLGA/PEG scaffolds for localized delivery of anti-inflammatory drugs in bone tissue engineering applications.

The accurate study of cellular microenvironments is limited by the lack of technologies that can manipulate cells in 3D at a sufficiently small length scale. The ability to build and manipulate multicellular microscopic structures will facilitate a more detailed understanding of cellular function in fields such as developmental and stem cell biology. We present a holographic optical tweezers based technology to accurately generate bespoke cellular micro-architectures. Using embryonic stem cells, 3D structures of varying geometries were created and stabilized using hydrogels and cell-cell adhesion methods. Control of chemical microenvironments was achieved by the temporal release of specific factors from polymer microparticles positioned within these constructs. Complex co-culture micro-environmental analogues were also generated to reproduce structures found within adult stem cell niches. The application of holographic optical tweezers-based micromanipulation will enable novel insights into biological microenvironments by allowing researchers to form complex architectures with sub-micron precision of cells, matrices and molecules.