Fabrication of novel metal alloy foams for biomedical applications

Degeneration of the weight bearing bones of the ageing population often requires the inception of metallic biomaterials. Research in this area is receiving increased attention globally. However, most of today's artificial bone materials are dense and suffer from problems of adverse reaction, biomechanical mismatch and lack of appropriate space for the regeneration of new bone tissues. In the present study, novel ZrTi alloy foams with a porous structure and mechanical properties that are very close to those of bone were fabricated. These ZrTi alloy foams are biocompatible, and display a porous structure permitting the ingrowth of new bone tissues.

Three-dimensional porous polymer scaffolds for tissue engineering application

This thesis investigates three-dimensional porous polymer blend scaffolds fabricated using supercritical carbon dioxide combined with solvent etching. These scaffolds with improved pore structures and interconnectivity can be used in regeneration medicine and tissue engineering application.

Mechanical properties, in vitro corrosion and biocompatibility of newly developed biodegradable Mg-Zr-Sr-Ho alloys for biomedical applications

Our previous studies have demonstrated that Mg-Zr-Sr alloys can be anticipated as excellent biodegradable implant materials for load-bearing applications. In general, rare earth elements (REEs) are widely used in magnesium (Mg) alloys with the aim of enhancing the mechanical properties of Mg-based alloys. In this study, the REE holmium (Ho) was added to an Mg-1Zr-2Sr alloy at different concentrations of Mg1Zr2SrxHo alloys (x = 0, 1, 3, 5 wt. %) and the microstructure, mechanical properties, degradation behaviour and biocompatibility of the alloys were systematically investigated. The results indicate that the addition of Ho to Mg1Zr2Sr led to the formation of the intermetallic phases MgHo3, Mg2Ho and Mg17Sr2 which resulted in enhanced mechanical strength and decreased degradation rates of the Mg-Zr-Sr-Ho alloys. Furthermore, Ho addition (≤5 wt. %) to Mg-Zr-Sr alloys led to enhancement of cell adhesion and proliferation of osteoblast cells on the Mg-Zr-Sr-Ho alloys. The in vitro biodegradation and the biocompatibility of the Mg-Zr-Sr-Ho alloys were both influenced by the Ho concentration in the Mg alloys; Mg1Zr2Sr3Ho exhibited lower degradation rates than Mg1Zr2Sr and displayed the best biocompatibility compared with the other alloys.

Mobile health (mHealth) biomedical imaging paradigm

Technology assisted methods for medical diagnosis and biomedical health monitoring are rapidly shifting from classical invasive methods to handheld-based non-invasive approaches. Biomedical imagining is one of the most prominent practices of non-invasive mechanisms in medical applications. This paper considers the medical imaging schemes for Mobile Health (mHealth) applications and studies the feasibility of future mobile systems for accommodating image informatics capabilities.

pH-sensitive and targeted PLGA-based drug delivery to colorectal cancer

Investigation on targeted PLGA based drug delivery system for the therapy of colorectal cancer. The results from in-vitro cell experiments indicated that prepared systems have potent cytotoxicity and high affinity to HT-29 cancer cells. Results were published on Biomedical Engineering and Informatics and ICONN conference proceeding.

Nanofibers - production, properties and functional applications

With the rapid development of nanoscience and nanotechnology over the last decades, great progress has been made not only in the preparation and characterization of nanomaterials, but also in their functional applications. As an important one-dimensional nanomaterial, nanofibers have extremely high specific surface area because of their small diameters, and nanofiber membranes are highly porous with excellent pore interconnectivity. These unique characteristics plus the functionalities from the materials themselves impart nanofibers with a number of novel properties for applications in areas as various as biomedical engineering, wound healing, drug delivery and release control, catalyst and enzyme carriers, filtration, environment protection, composite reinforcement, sensors, optics, energy harvest and storage , and many others. More and more emphasis has recently been placed on large-scale nanofiber production, the key technology to the wide usages of nanofibers in practice. Tremendous efforts have been made on producing nanofibers from special materials. Concerns have been raised to the safety issue of nanofibrous materials. This book is a compilation of contributions made by experts who specialize in their chosen field. It is grouped into three sections composed of twenty-one chapters, providing an up-to-date coverage of nanofiber preparation, properties and functional applications. I am deeply appreciative of all the authors and have no doubt that their contribution will be a useful resource of anyone associated with the discipline of nanofibers.

Spike sorting using locality preserving projection with gap statistics and landmark-based spectral clustering

Understanding neural functions requires knowledge from analysing electrophysiological data. The process of assigning spikes of a multichannel signal into clusters, called spike sorting, is one of the important problems in such analysis. There have been various automated spike sorting techniques with both advantages and disadvantages regarding accuracy and computational costs. Therefore, developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice.

Ultrafine PDMS fibers: Preparation from in situ curing-electrospinning and mechanical characterization

Polydimethylsiloxane (PDMS) fibers with unexpected elasticity were prepared by a modified core-shell electrospinning method using a commercially-available liquid PDMS precursor (Sylgard 184) and polyvinylpyrrolidone (PVP) as core and sheath materials, respectively. The liquid PDMS precursor was crosslinked in situ to form a solid core when the newly-electrospun core-sheath nanofibers were deposited onto a hot-plate electrode collector. After dissolving the PVP sheath layer off the fibers, net PDMS fibers showed larger average diameter than core-sheath fibers, with an average diameter around 1.35 μm. The tensile properties of both single fibers and fibrous mats were measured. Single PDMS fibers had a tensile strength and elongation at break of 6.0 MPa and 212%, respectively, which were higher than those of PDMS cast film (4.9 MPa, 93%). The PDMS fiber mat had larger elongation at break than the single PDMS fibers, which can be drawn up to 403% their original length. Cyclic loading tests indicated a Mullin effect on the PDMS fiber mats. Such a superior elastic feature was attributed to the PDMS molecular orientation within fibers and the randomly-orientated fibrous structure. Highly-elastic, ultrafine PDMS fibers may find applications in strain sensors, biomedical engineering, wound healing, filtration, catalysis, and functional textiles. © The Royal Society of Chemistry 2014.

Closed loop deep brain stimulation: an evolving technology

Deep brain stimulation is an effective and safe medical treatment for a variety of neurological and psychiatric disorders including Parkinson's disease, essential tremor, dystonia, and treatment resistant obsessive compulsive disorder. A closed loop deep brain stimulation (CLDBS) system automatically adjusts stimulation parameters by the brain response in real time. The CLDBS continues to evolve due to the advancement in the brain stimulation technologies. This paper provides a study on the existing systems developed for CLDBS. It highlights the issues associated with CLDBS systems including feedback signal recording and processing, stimulation parameters setting, control algorithm, wireless telemetry, size, and power consumption. The benefits and limitations of the existing CLDBS systems are also presented. Whilst robust clinical proof of the benefits of the technology remains to be achieved, it has the potential to offer several advantages over open loop DBS. The CLDBS can improve efficiency and efficacy of therapy, eliminate lengthy start-up period for programming and adjustment, provide a personalized treatment, and make parameters setting automatic and adaptive.

Classification of neural action potentials using mean shift clustering

 Understanding neural functions requires the observation of the activities of single neurons that are represented via electrophysiological data. Processing and understanding these data are challenging problems in biomedical engineering. A microelectrode commonly records the activity of multiple neurons. Spike sorting is a process of classifying every single action potential (spike) to a particular neuron. This paper proposes a combination between diffusion maps (DM) and mean shift clustering method for spike sorting. DM is utilized to extract spike features, which are highly capable of discriminating different spike shapes. Mean shift clustering provides an automatic unsupervised clustering, which takes extracted features from DM as inputs. Experimental results show a noticeable dominance of the features extracted by DM compared to those selected by wavelet transformation (WT). Accordingly, the proposed integrated method is significantly superior to the popular existing combination of WT and superparamagnetic clustering regarding spike sorting accuracy.

Neural signal analysis by landmark-based spectral clustering with estimated number of clusters

Spike sorting plays an important role in analysing electrophysiological data and understanding neural functions. Developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice. This paper proposes an automatic unsupervised spike sorting method using the landmark-based spectral clustering (LSC) method in connection with features extracted by the locality preserving projection (LPP) technique. Gap statistics is employed to evaluate the number of clusters before the LSC can be performed. Experimental results show that LPP spike features are more discriminative than those of the popular wavelet transformation (WT). Accordingly, the proposed method LPP-LSC demonstrates a significant dominance compared to the existing method that is the combination between WT feature extraction and the superparamagnetic clustering. LPP and LSC are both linear algorithms that help reduce computational burden and thus their combination can be applied into realtime spike analysis.

Localization and degmentation of 3D intervertebral discs in MR Images by data driven estimation

This paper addresses the problem of fully-automatic localization and segmentation of 3D intervertebral discs (IVDs) from MR images. Our method contains two steps, where we first localize the center of each IVD, and then segment IVDs by classifying image pixels around each disc center as foreground (disc) or background. The disc localization is done by estimating the image displacements from a set of randomly sampled 3D image patches to the disc center. The image displacements are estimated by jointly optimizing the training and test displacement values in a data-driven way, where we take into consideration both the training data and the geometric constraint on the test image. After the disc centers are localized, we segment the discs by classifying image pixels around disc centers as background or foreground. The classification is done in a similar data-driven approach as we used for localization, but in this segmentation case we are aiming to estimate the foreground/background probability of each pixel instead of the image displacements. In addition, an extra neighborhood smooth constraint is introduced to enforce the local smoothness of the label field. Our method is validated on 3D T2-weighted turbo spin echo MR images of 35 patients from two different studies. Experiments show that compared to state of the art, our method achieves better or comparable results. Specifically, we achieve for localization a mean error of 1.6-2.0 mm, and for segmentation a mean Dice metric of 85%-88% and a mean surface distance of 1.3-1.4 mm.