Corneal confocal microscopy : a novel means to detect nerve fibre damage in idiopathic small fibre neuropathy

Patients with idiopathic small fibre neuropathy (ISFN) have been shown to have significant intraepidermal nerve fibre loss and an increased prevalence of impaired glucose tolerance (IGT). It has been suggested that the dysglycemia of IGT and additional metabolic risk factors may contribute to small nerve fibre damage in these patients. Twenty-five patients with ISFN and 12 aged-matched control subjects underwent a detailed evaluation of neuropathic symptoms, neurological deficits (Neuropathy deficit score (NDS); Nerve Conduction Studies (NCS); Quantitative Sensory Testing (QST) and Corneal Confocal Microscopy (CCM)) to quantify small nerve fibre pathology. Eight (32%) patients had IGT. Whilst all patients with ISFN had significant neuropathic symptoms, NDS, NCS and QST except for warm thresholds were normal. Corneal sensitivity was reduced and CCM demonstrated a significant reduction in corneal nerve fibre density (NFD) (Pb0.0001), nerve branch density (NBD) (Pb0.0001), nerve fibre length (NFL) (Pb0.0001) and an increase in nerve fibre tortuosity (NFT) (Pb0.0001). However these parameters did not differ between ISFN patients with and without IGT, nor did they correlate with BMI, lipids and blood pressure. Corneal confocal microscopy provides a sensitive non-invasive means to detect small nerve fibre damage in patients with ISFN and metabolic abnormalities do not relate to nerve damage.

Effects of the architecture of tissue engineering scaffolds on cell seeding and culturing

The advance of rapid prototyping techniques has significantly improved control over the pore network architecture of tissue engineering scaffolds. In this work we assessed the influence of scaffold pore architecture on cell seeding and static culturing, by comparing a computer‐designed gyroid architecture fabricated by stereolithography to a random‐pore architecture resulting from salt‐leaching. The scaffold types showed comparable porosity and pore size values, but the gyroid type showed a more than tenfold higher permeability due to the absence of size‐limiting pore interconnections. The higher permeability significantly improved the wetting properties of the hydrophobic scaffolds, and increased the settling speed of cells upon static seeding of immortalised mesenchymal stem cells. After dynamic seeding followed by 5 days of static culture, gyroid scaffolds showed large cell populations in the centre of the scaffold, while salt‐leached scaffolds were covered with a cell‐sheet on the outside and no cells were found in the scaffold centre. It was shown that interconnectivity of the pores and permeability of the scaffold prolongs the time of static culture before overgrowth of cells at the scaffold periphery occurs. Furthermore, novel scaffold designs are proposed to further improve the transport of oxygen and nutrients throughout the scaffolds, and to create tissue engineering grafts with designed, pre‐fabricated vasculature.

Migration of breast cancer cells : understanding the roles of volume exclusion and cell-to-cell adhesion

We study MCF-7 breast cancer cell movement in a transwell apparatus. Various experimental conditions lead to a variety of monotone and nonmonotone responses which are difficult to interpret. We anticipate that the experimental results could be caused by cell-to-cell adhesion or volume exclusion. Without any modeling, it is impossible to understand the relative roles played by these two mechanisms. A lattice-based exclusion process random-walk model incorporating agent-to-agent adhesion is applied to the experimental system. Our combined experimental and modeling approach shows that a low value of cell-to-cell adhesion strength provides the best explanation of the experimental data suggesting that volume exclusion plays a more important role than cell-to-cell adhesion. This combined experimental and modeling study gives insight into the cell-level details and design of transwell assays.

Particulate texture image analysis with applications

Texture analysis and textural cues have been applied for image classification, segmentation and pattern recognition. Dominant texture descriptors include directionality, coarseness, line-likeness etc. In this dissertation a class of textures known as particulate textures are defined, which are predominantly coarse or blob-like. The set of features that characterise particulate textures are different from those that characterise classical textures. These features are micro-texture, macro-texture, size, shape and compaction. Classical texture analysis techniques do not adequately capture particulate texture features. This gap is identified and new methods for analysing particulate textures are proposed. The levels of complexity in particulate textures are also presented ranging from the simplest images where blob-like particles are easily isolated from their back- ground to the more complex images where the particles and the background are not easily separable or the particles are occluded. Simple particulate images can be analysed for particle shapes and sizes. Complex particulate texture images, on the other hand, often permit only the estimation of particle dimensions. Real life applications of particulate textures are reviewed, including applications to sedimentology, granulometry and road surface texture analysis. A new framework for computation of particulate shape is proposed. A granulometric approach for particle size estimation based on edge detection is developed which can be adapted to the gray level of the images by varying its parameters. This study binds visual texture analysis and road surface macrotexture in a theoretical framework, thus making it possible to apply monocular imaging techniques to road surface texture analysis. Results from the application of the developed algorithm to road surface macro-texture, are compared with results based on Fourier spectra, the auto- correlation function and wavelet decomposition, indicating the superior performance of the proposed technique. The influence of image acquisition conditions such as illumination and camera angle on the results was systematically analysed. Experimental data was collected from over 5km of road in Brisbane and the estimated coarseness along the road was compared with laser profilometer measurements. Coefficient of determination R2 exceeding 0.9 was obtained when correlating the proposed imaging technique with the state of the art Sensor Measured Texture Depth (SMTD) obtained using laser profilometers.

Aerial image analysis using spiking neural networks with application to power line corridor monitoring

Trees, shrubs and other vegetation are of continued importance to the environment and our daily life. They provide shade around our roads and houses, offer a habitat for birds and wildlife, and absorb air pollutants. However, vegetation touching power lines is a risk to public safety and the environment, and one of the main causes of power supply problems. Vegetation management, which includes tree trimming and vegetation control, is a significant cost component of the maintenance of electrical infrastructure. For example, Ergon Energy, the Australia’s largest geographic footprint energy distributor, currently spends over $80 million a year inspecting and managing vegetation that encroach on power line assets. Currently, most vegetation management programs for distribution systems are calendar-based ground patrol. However, calendar-based inspection by linesman is labour-intensive, time consuming and expensive. It also results in some zones being trimmed more frequently than needed and others not cut often enough. Moreover, it’s seldom practicable to measure all the plants around power line corridors by field methods. Remote sensing data captured from airborne sensors has great potential in assisting vegetation management in power line corridors. This thesis presented a comprehensive study on using spiking neural networks in a specific image analysis application: power line corridor monitoring. Theoretically, the thesis focuses on a biologically inspired spiking cortical model: pulse coupled neural network (PCNN). The original PCNN model was simplified in order to better analyze the pulse dynamics and control the performance. Some new and effective algorithms were developed based on the proposed spiking cortical model for object detection, image segmentation and invariant feature extraction. The developed algorithms were evaluated in a number of experiments using real image data collected from our flight trails. The experimental results demonstrated the effectiveness and advantages of spiking neural networks in image processing tasks. Operationally, the knowledge gained from this research project offers a good reference to our industry partner (i.e. Ergon Energy) and other energy utilities who wants to improve their vegetation management activities. The novel approaches described in this thesis showed the potential of using the cutting edge sensor technologies and intelligent computing techniques in improve power line corridor monitoring. The lessons learnt from this project are also expected to increase the confidence of energy companies to move from traditional vegetation management strategy to a more automated, accurate and cost-effective solution using aerial remote sensing techniques.

Towards an automatic system for road lane marking extraction in large-scale aerial images acquired over rural areas by hierarchical image analysis and Gabor filter

An automatic approach to road lane marking extraction from high-resolution aerial images is proposed, which can automatically detect the road surfaces in rural areas based on hierarchical image analysis. The procedure is facilitated by the road centrelines obtained from low-resolution images. The lane markings are further extracted on the generated road surfaces with 2D Gabor filters. The proposed method is applied on the aerial images of the Bruce Highway around Gympie, Queensland. Evaluation of the generated road surfaces and lane markings using four representative test fields has validated the proposed method.

Determination of stress-strain characteristics of railhead steel using image analysis

True stress-strain curve of railhead steel is required to investigate the behaviour of railhead under wheel loading through elasto-plastic Finite Element (FE) analysis. To reduce the rate of wear, the railhead material is hardened through annealing and quenching. The Australian standard rail sections are not fully hardened and hence suffer from non-uniform distribution of the material property; usage of average properties in the FE modelling can potentially induce error in the predicted plastic strains. Coupons obtained at varying depths of the railhead were, therefore, tested under axial tension and the strains were measured using strain gauges as well as an image analysis technique, known as the Particle Image Velocimetry (PIV). The head hardened steel exhibit existence of three distinct zones of yield strength; the yield strength as the ratio of the average yield strength provided in the standard (σyr=780MPa) and the corresponding depth as the ratio of the head hardened zone along the axis of symmetry are as follows: (1.17 σyr, 20%), (1.06 σyr, 20%- 80%) and (0.71 σyr, > 80%). The stress-strain curves exhibit limited plastic zone with fracture occurring at strain less than 0.1.

Molecular biomarkers in non-small-cell lung cancer : a retrospective analysis of data from the phase 3 FLEX study

Background: Findings from the phase 3 FLEX study showed that the addition of cetuximab to cisplatin and vinorelbine significantly improved overall survival, compared with cisplatin and vinorelbine alone, in the first-line treatment of EGFR-expressing, advanced non-small-cell lung cancer (NSCLC). We investigated whether candidate biomarkers were predictive for the efficacy of chemotherapy plus cetuximab in this setting. Methods: Genomic DNA extracted from formalin-fixed paraffin-embedded (FFPE) tumour tissue of patients enrolled in the FLEX study was screened for KRAS codon 12 and 13 and EGFR kinase domain mutations with PCR-based assays. In FFPE tissue sections, EGFR copy number was assessed by dual-colour fluorescence in-situ hybridisation and PTEN expression by immunohistochemistry. Treatment outcome was investigated according to biomarker status in all available samples from patients in the intention-to-treat population. The primary endpoint in the FLEX study was overall survival. The FLEX study, which is ongoing but not recruiting participants, is registered with ClinicalTrials.gov, number NCT00148798. Findings: KRAS mutations were detected in 75 of 395 (19%) tumours and activating EGFR mutations in 64 of 436 (15%). EGFR copy number was scored as increased in 102 of 279 (37%) tumours and PTEN expression as negative in 107 of 303 (35%). Comparisons of treatment outcome between the two groups (chemotherapy plus cetuximab vs chemotherapy alone) according to biomarker status provided no indication that these biomarkers were of predictive value. Activating EGFR mutations were identified as indicators of good prognosis, with patients in both treatment groups whose tumours carried such mutations having improved survival compared with those whose tumours did not (chemotherapy plus cetuximab: median 17·5 months [95% CI 11·7-23·4] vs 8·5 months [7·1-10·8], hazard ratio [HR] 0·52 [0·32-0·84], p=0·0063; chemotherapy alone: 23·8 months [15·2-not reached] vs 10·0 months [8·7-11·0], HR 0·35 [0·21-0·59], p<0·0001). Expression of PTEN seemed to be a potential indicator of good prognosis, with patients whose tumours expressed PTEN having improved survival compared with those whose tumours did not, although this finding was not significant (chemotherapy plus cetuximab: median 11·4 months [8·6-13·6] vs 6·8 months [5·9-12·7], HR 0·80 [0·55-1·16], p=0·24; chemotherapy alone: 11·0 months [9·2-12·6] vs 9·3 months [7·6-11·9], HR 0·77 [0·54-1·10], p=0·16). Interpretation: The efficacy of chemotherapy plus cetuximab in the first-line treatment of advanced NSCLC seems to be independent of each of the biomarkers assessed. Funding: Merck KGaA. © 2011 Elsevier Ltd.

Corneal confocal microscopy detects early nerve regeneration in diabetic neuropathy after simultaneous pancreas and kidney transplantation

Diabetic neuropathy is associated with increased morbidity and mortality. To date, limited data in subjects with impaired glucose tolerance and diabetes demonstrate nerve fiber repair after intervention. This may reflect a lack of efficacy of the interventions but may also reflect difficulty of the tests currently deployed to adequately assess nerve fiber repair, particularly in short-term studies. Corneal confocal microscopy (CCM) represents a novel noninvasive means to quantify nerve fiber damage and repair. Fifteen type 1 diabetic patients undergoing simultaneous pancreas-kidney transplantation (SPK) underwent detailed assessment of neurologic deficits, quantitative sensory testing (QST), electrophysiology, skin biopsy, corneal sensitivity, and CCM at baseline and at 6 and 12 months after successful SPK. At baseline, diabetic patients had a significant neuropathy compared with control subjects. After successful SPK there was no significant change in neurologic impairment, neurophysiology, QST, corneal sensitivity, and intraepidermal nerve fiber density (IENFD). However, CCM demonstrated significant improvements in corneal nerve fiber density, branch density, and length at 12 months. Normalization of glycemia after SPK shows no significant improvement in neuropathy assessed by the neurologic deficits, QST, electrophysiology, and IENFD. However, CCM shows a significant improvement in nerve morphology, providing a novel noninvasive means to establish early nerve repair that is missed by currently advocated assessment techniques.

Comparing skin biopsy with confocal microscopy : diagnostic yield of nerve fiber density

Background and aims: The assessment of intra-epidermal nerve fiber density (IENFD) in skin biopsies and corneal nerve fiber density (CNFD) using corneal confocal microscopy (CCM) provides promising techniques to detect small nerve fiber damage in patients with peripheral neuropathy. To help define the clinical utility of each of these techniques in patients with diabetic neuropathy we have assessed sensitivity and specificity of IENFD and CNFD in predicting the following: 1) diabetic polyneuropathy (DPN); 2) risk of foot ulceration (RFU); 3) initial small fiber neuropathy (iSFN); 4) severe small fiber neuropathy (sSFN)...