A digital signal processing approach for affective sensing of a computer user through pupil diameter monitoring

Recent research has indicated that the pupil diameter (PD) in humans varies with their affective states. However, this signal has not been fully investigated for affective sensing purposes in human-computer interaction systems. This may be due to the dominant separate effect of the pupillary light reflex (PLR), which shrinks the pupil when light intensity increases. In this dissertation, an adaptive interference canceller (AIC) system using the H∞ time-varying (HITV) adaptive algorithm was developed to minimize the impact of the PLR on the measured pupil diameter signal. The modified pupil diameter (MPD) signal, obtained from the AIC was expected to reflect primarily the pupillary affective responses (PAR) of the subject. Additional manipulations of the AIC output resulted in a processed MPD (PMPD) signal, from which a classification feature, PMPDmean, was extracted. This feature was used to train and test a support vector machine (SVM), for the identification of stress states in the subject from whom the pupil diameter signal was recorded, achieving an accuracy rate of 77.78%. The advantages of affective recognition through the PD signal were verified by comparatively investigating the classification of stress and relaxation states through features derived from the simultaneously recorded galvanic skin response (GSR) and blood volume pulse (BVP) signals, with and without the PD feature. The discriminating potential of each individual feature extracted from GSR, BVP and PD was studied by analysis of its receiver operating characteristic (ROC) curve. The ROC curve found for the PMPDmean feature encompassed the largest area (0.8546) of all the single-feature ROCs investigated. The encouraging results seen in affective sensing based on pupil diameter monitoring were obtained in spite of intermittent illumination increases purposely introduced during the experiments. Therefore, these results confirmed the benefits of using the AIC implementation with the HITV adaptive algorithm to isolate the PAR and the potential of using PD monitoring to sense the evolving affective states of a computer user.

Hydroxyapatite-nanotube composites and coatings for orthopedic applications

Hydroxyapatite (HA) has received wide attention in orthopedics, due to its biocompatibility and osseointegration ability. Despite these advantages, the brittle nature and low fracture toughness of HA often results in rapid wear and premature fracture of implant. Hence, there is a need to improve the fracture toughness and wear resistance of HA without compromising its biocompatibility. ^ The aim of the current research is to explore the potential of nanotubes as reinforcement to HA for orthopedic implants. HA- 4 wt.% carbon nanotube (CNT) composites and coatings are synthesized by spark plasma sintering and plasma spraying respectively, and investigated for their mechanical, tribological and biological behavior. CNT reinforcement improves the fracture toughness (>90%) and wear resistance (>66%) of HA for coating and free standing composites. CNTs have demonstrated a positive influence on the proliferation, differentiation and matrix mineralization activities of osteoblasts, during in-vitro biocompatibility studies. In-vivo exposure of HA-CNT coated titanium implant in animal model (rat) shows excellent histocompatibility and neobone integration on the implant surface. The improved osseointegration due to presence of CNTs in HA is quantified by the adhesion strength measurement of single osteoblast using nano-scratch technique. ^ Considering the ongoing debate about cytotoxicity of CNTs in the literature, the present study also suggests boron nitride nanotube (BNNT) as an alternative reinforcement. BNNT with the similar elastic modulus and strength as CNT, were added to HA. The resulting composite having 4 wt.% BNNTs improved the fracture toughness (∼85%) and wear resistance (∼75%) of HA in the similar range as HA-CNT composites. BNNTs were found to be non-cytotoxic for osteoblasts and macrophages. In-vitro evaluation shows positive role of BNNT in osteoblast proliferation and viability. Apatite formability of BNNT surface in ∼4 days establishes its osseointegration ability.^

In vivo tissue diagnosis for myocardial infarction using optical spectroscopy with novel spectral interpretation algorithms

In recent decades, the rapid development of optical spectroscopy for tissue diagnosis has been indicative of its high clinical value. The goal of this research is to prove the feasibility of using diffuse reflectance spectroscopy and fluorescence spectroscopy to assess myocardial infarction (MI) in vivo. The proposed optical technique was designed to be an intra-operative guidance tool that can provide useful information about the condition of an infarct for surgeons and researchers. ^ In order to gain insight into the pathophysiological characteristics of an infarct, two novel spectral analysis algorithms were developed to interpret diffuse reflectance spectra. The algorithms were developed based on the unique absorption properties of hemoglobin for the purpose of retrieving regional hemoglobin oxygenation saturation and concentration data in tissue from diffuse reflectance spectra. The algorithms were evaluated and validated using simulated data and actual experimental data. ^ Finally, the hypothesis of the study was validated using a rabbit model of MI. The mechanism by which the MI was induced was the ligation of a major coronary artery of the left ventricle. Three to four weeks after the MI was induced, the extent of myocardial tissue injury and the evolution of the wound healing process were investigated using the proposed spectroscopic methodology as well as histology. The correlations between spectral alterations and histopathological features of the MI were analyzed statistically. ^ The results of this PhD study demonstrate the applicability of the proposed optical methodology for assessing myocardial tissue damage induced by MI in vivo. The results of the spectral analysis suggest that connective tissue proliferation induced by MI significantly alter the characteristics of diffuse reflectance and fluorescence spectra. The magnitudes of the alterations could be quantitatively related to the severity and extensiveness of connective tissue proliferation.^

Automated detection of hematological abnormalities through classification of flow cytometric data patterns

Flow Cytometry analyzers have become trusted companions due to their ability to perform fast and accurate analyses of human blood. The aim of these analyses is to determine the possible existence of abnormalities in the blood that have been correlated with serious disease states, such as infectious mononucleosis, leukemia, and various cancers. Though these analyzers provide important feedback, it is always desired to improve the accuracy of the results. This is evidenced by the occurrences of misclassifications reported by some users of these devices. It is advantageous to provide a pattern interpretation framework that is able to provide better classification ability than is currently available. Toward this end, the purpose of this dissertation was to establish a feature extraction and pattern classification framework capable of providing improved accuracy for detecting specific hematological abnormalities in flow cytometric blood data. ^ This involved extracting a unique and powerful set of shift-invariant statistical features from the multi-dimensional flow cytometry data and then using these features as inputs to a pattern classification engine composed of an artificial neural network (ANN). The contribution of this method consisted of developing a descriptor matrix that can be used to reliably assess if a donor’s blood pattern exhibits a clinically abnormal level of variant lymphocytes, which are blood cells that are potentially indicative of disorders such as leukemia and infectious mononucleosis. ^ This study showed that the set of shift-and-rotation-invariant statistical features extracted from the eigensystem of the flow cytometric data pattern performs better than other commonly-used features in this type of disease detection, exhibiting an accuracy of 80.7%, a sensitivity of 72.3%, and a specificity of 89.2%. This performance represents a major improvement for this type of hematological classifier, which has historically been plagued by poor performance, with accuracies as low as 60% in some cases. This research ultimately shows that an improved feature space was developed that can deliver improved performance for the detection of variant lymphocytes in human blood, thus providing significant utility in the realm of suspect flagging algorithms for the detection of blood-related diseases.^

Theoretical investigations of intercellular communication in the microcirculation: Conducted responses, myoendothelial projections and endothelium derived hyperpolarizing factor

The contractile state of microcirculatory vessels is a major determinant of the blood pressure of the whole systemic circulation. Continuous bi-directional communication exists between the endothelial cells (ECs) and smooth muscle cells (SMCs) that regulates calcium (Ca2+) dynamics in these cells. This study presents theoretical approaches to understand some of the important and currently unresolved microcirculatory phenomena. ^ Agonist induced events at local sites have been shown to spread long distances in the microcirculation. We have developed a multicellular computational model by integrating detailed single EC and SMC models with gap junction and nitric oxide (NO) coupling to understand the mechanisms behind this effect. Simulations suggest that spreading vasodilation mainly occurs through Ca 2+ independent passive conduction of hyperpolarization in RMAs. Model predicts a superior role for intercellular diffusion of inositol (1,4,5)-trisphosphate (IP3) than Ca2+ in modulating the spreading response. ^ Endothelial derived signals are initiated even during vasoconstriction of stimulated SMCs by the movement of Ca2+ and/or IP3 into the EC which provide hyperpolarizing feedback to SMCs to counter the ongoing constriction. Myoendothelial projections (MPs) present in the ECs have been recently proposed to play a role in myoendothelial feedback. We have developed two models using compartmental and 2D finite element methods to examine the role of these MPs by adding a sub compartment in the EC to simulate MP with localization of intermediate conductance calcium activated potassium channels (IKCa) and IP3 receptors (IP 3R). Both models predicted IP3 mediated high Ca2+ gradients in the MP after SMC stimulation with limited global spread. This Ca 2+ transient generated a hyperpolarizing feedback of ∼ 2–3mV. ^ Endothelium derived hyperpolarizing factor (EDHF) is the dominant form of endothelial control of SMC constriction in the microcirculation. A number of factors have been proposed for the role of EDHF but no single pathway is agreed upon. We have examined the potential of myoendothelial gap junctions (MEGJs) and potassium (K+) accumulation as EDHF using two models (compartmental and 2D finite element). An extra compartment is added in SMC to simulate micro domains (MD) which have NaKα2 isoform sodium potassium pumps. Simulations predict that MEGJ coupling is much stronger in producing EDHF than alone K+ accumulation. On the contrary, K+ accumulation can alter other important parameters (EC V m, IKCa current) and inhibit its own release as well as EDHF conduction via MEGJs. The models developed in this study are essential building blocks for future models and provide important insights to the current understanding of myoendothelial feedback and EDHF.^

A novel 3-D segmentation algorithm for anatomic liver and tumor volume calculations for liver cancer treatment planning

Three-Dimensional (3-D) imaging is vital in computer-assisted surgical planning including minimal invasive surgery, targeted drug delivery, and tumor resection. Selective Internal Radiation Therapy (SIRT) is a liver directed radiation therapy for the treatment of liver cancer. Accurate calculation of anatomical liver and tumor volumes are essential for the determination of the tumor to normal liver ratio and for the calculation of the dose of Y-90 microspheres that will result in high concentration of the radiation in the tumor region as compared to nearby healthy tissue. Present manual techniques for segmentation of the liver from Computed Tomography (CT) tend to be tedious and greatly dependent on the skill of the technician/doctor performing the task. ^ This dissertation presents the development and implementation of a fully integrated algorithm for 3-D liver and tumor segmentation from tri-phase CT that yield highly accurate estimations of the respective volumes of the liver and tumor(s). The algorithm as designed requires minimal human intervention without compromising the accuracy of the segmentation results. Embedded within this algorithm is an effective method for extracting blood vessels that feed the tumor(s) in order to plan effectively the appropriate treatment. ^ Segmentation of the liver led to an accuracy in excess of 95% in estimating liver volumes in 20 datasets in comparison to the manual gold standard volumes. In a similar comparison, tumor segmentation exhibited an accuracy of 86% in estimating tumor(s) volume(s). Qualitative results of the blood vessel segmentation algorithm demonstrated the effectiveness of the algorithm in extracting and rendering the vasculature structure of the liver. Results of the parallel computing process, using a single workstation, showed a 78% gain. Also, statistical analysis carried out to determine if the manual initialization has any impact on the accuracy showed user initialization independence in the results. ^ The dissertation thus provides a complete 3-D solution towards liver cancer treatment planning with the opportunity to extract, visualize and quantify the needed statistics for liver cancer treatment. Since SIRT requires highly accurate calculation of the liver and tumor volumes, this new method provides an effective and computationally efficient process required of such challenging clinical requirements.^

Positron emission tomography (PET) tumor segmentation and quantification: Development of new algorithms

Tumor functional volume (FV) and its mean activity concentration (mAC) are the quantities derived from positron emission tomography (PET). These quantities are used for estimating radiation dose for a therapy, evaluating the progression of a disease and also use it as a prognostic indicator for predicting outcome. PET images have low resolution, high noise and affected by partial volume effect (PVE). Manually segmenting each tumor is very cumbersome and very hard to reproduce. To solve the above problem I developed an algorithm, called iterative deconvolution thresholding segmentation (IDTS) algorithm; the algorithm segment the tumor, measures the FV, correct for the PVE and calculates mAC. The algorithm corrects for the PVE without the need to estimate camera's point spread function (PSF); also does not require optimizing for a specific camera. My algorithm was tested in physical phantom studies, where hollow spheres (0.5-16 ml) were used to represent tumors with a homogeneous activity distribution. It was also tested on irregular shaped tumors with a heterogeneous activity profile which were acquired using physical and simulated phantom. The physical phantom studies were performed with different signal to background ratios (SBR) and with different acquisition times (1-5 min). The algorithm was applied on ten clinical data where the results were compared with manual segmentation and fixed percentage thresholding method called T50 and T60 in which 50% and 60% of the maximum intensity respectively is used as threshold. The average error in FV and mAC calculation was 30% and -35% for 0.5 ml tumor. The average error FV and mAC calculation were ~5% for 16 ml tumor. The overall FV error was ∼10% for heterogeneous tumors in physical and simulated phantom data. The FV and mAC error for clinical image compared to manual segmentation was around -17% and 15% respectively. In summary my algorithm has potential to be applied on data acquired from different cameras as its not dependent on knowing the camera's PSF. The algorithm can also improve dose estimation and treatment planning.^

In vivo characterization of epileptic tissue with optical spectroscopy

For children with intractable seizures, surgical removal of epileptic foci, if identifiable and feasible, can be an effective way to reduce or eliminate seizures. The success of this type of surgery strongly hinges upon the ability to identify and demarcate those epileptic foci. The ultimate goal of this research project is to develop an effective technology for detection of unique in vivo pathophysiological characteristics of epileptic cortex and, subsequently, to use this technology to guide epilepsy surgery intraoperatively. In this PhD dissertation the feasibility of using optical spectroscopy to identify uniquein vivo pathophysiological characteristics of epileptic cortex was evaluated and proven using the data collected from children undergoing epilepsy surgery. ^ In this first in vivo human study, static diffuse reflectance and fluorescence spectra were measured from the epileptic cortex, defined by intraoperative ECoG, and its surrounding tissue from pediatric patients undergoing epilepsy surgery. When feasible, biopsy samples were taken from the investigated sites for the subsequent histological analysis. Using the histological data as the gold standard, spectral data was analyzed with statistical tools. The results of the analysis show that static diffuse reflectance spectroscopy and its combination with static fluorescence spectroscopy can be used to effectively differentiate between epileptic cortex with histopathological abnormalities and normal cortex in vivo with a high degree of accuracy. ^ To maximize the efficiency of optical spectroscopy in detecting and localizing epileptic cortex intraoperatively, the static system was upgraded to investigate histopathological abnormalities deep within the epileptic cortex, as well as to detect unique temporal pathophysiological characteristics of epileptic cortex. Detection of deep abnormalities within the epileptic cortex prompted a redesign of the fiberoptic probe. A mechanical probe holder was also designed and constructed to maintain the probe contact pressure and contact point during the time dependent measurements. The dynamic diffuse reflectance spectroscopy system was used to characterize in vivo pediatric epileptic cortex. The results of the study show that some unique wavelength dependent temporal characteristics (e.g., multiple horizontal bands in the correlation coefficient map γ(λref = 800 nm, λcomp ,t)) can be found in the time dependent recordings of diffuse reflectance spectra from epileptic cortex defined by ECoG.^

Multifunctional nanoparticles in cancer: In vitro characterization, in vivo distribution, and cellular response after laser-NIR dye-induced heating

A novel biocompatible and biodegradable polymer, termed poly(Glycerol malate co-dodecanedioate) (PGMD), was prepared by thermal condensation method and used for fabrication of nanoparticles (NPs). PGMD NPs were prepared using the single oil emulsion technique and loaded with an imaging/hyperthermia agent (IR820) and a chemotherapeutic agent (doxorubicin, DOX). The size of the void PGMD NPs, IR820-PGMD NPs and DOX-IR820-PGMD NPs were approximately 90 nm, 110 nm, and 125 nm respectively. An acidic environment (pH=5.0) induced higher DOX and IR820 release compared to pH=7.4. DOX release was also enhanced by exposure to laser, which increased the temperature to 42°C. Cytotoxicity of DOX-IR820-PGMD NPs was comparable in MES-SA but was higher in Dx5 cells compared to free DOX plus IR820 (p<0.05). The combination of hyperthermia (HT) and chemotherapy improved cytotoxicity in both cell lines. We also explored the cellular response after rapid, short-term and low thermal dose (laser/Dye/NP) induced-heating, and compared it to slow, long-term and high thermal dose cell incubator heating by investigating the reactive oxygen species (ROS) level, hypoxia-inducible factor-1&agr; (HIF-1&agr;) and vascular endothelial growth factor (VEGF) expression. The cytotoxicity of IR820-PGMD NPs after laser/Dye/NP HT resulted in higher cancer cell killing compared to incubator HT. ROS level, HIF-1&agr; and VEGF expression were elevated under incubator HT, while maintained at the baseline level under the laser/Dye/NP HT. In vivo mouse studies showed that NP formulation significantly improved the plasma half-life of IR820 after tail vein injection. Significant lower IR820 content was observed in kidney in DOX-IR820-PGMD NP treatment as compared to free IR820 treatment in our biodistribution studies (p<0.05). In conclusion, both IR820-PGMD NPs and DOX-IR820-PGMD NPs were successfully developed and used for both imaging and therapeutic purposes. Rapid and short-term laser/Dye/NP HT, with a low thermal dose, did not up-regulate HIF-1&agr; and VEGF expression, whereas slow and long-term incubator HT, with a high thermal dose, can enhance expression of both HIF-1&agr; and VEGF.^

Off-line and on-line affective recognition of a computer user through a biosignal processing approach

Physiological signals, which are controlled by the autonomic nervous system (ANS), could be used to detect the affective state of computer users and therefore find applications in medicine and engineering. The Pupil Diameter (PD) seems to provide a strong indication of the affective state, as found by previous research, but it has not been investigated fully yet. ^ In this study, new approaches based on monitoring and processing the PD signal for off-line and on-line affective assessment ("relaxation" vs. "stress") are proposed. Wavelet denoising and Kalman filtering methods are first used to remove abrupt changes in the raw Pupil Diameter (PD) signal. Then three features (PDmean, PDmax and PDWalsh) are extracted from the preprocessed PD signal for the affective state classification. In order to select more relevant and reliable physiological data for further analysis, two types of data selection methods are applied, which are based on the paired t-test and subject self-evaluation, respectively. In addition, five different kinds of the classifiers are implemented on the selected data, which achieve average accuracies up to 86.43% and 87.20%, respectively. Finally, the receiver operating characteristic (ROC) curve is utilized to investigate the discriminating potential of each individual feature by evaluation of the area under the ROC curve, which reaches values above 0.90. ^ For the on-line affective assessment, a hard threshold is implemented first in order to remove the eye blinks from the PD signal and then a moving average window is utilized to obtain the representative value PDr for every one-second time interval of PD. There are three main steps for the on-line affective assessment algorithm, which are preparation, feature-based decision voting and affective determination. The final results show that the accuracies are 72.30% and 73.55% for the data subsets, which were respectively chosen using two types of data selection methods (paired t-test and subject self-evaluation). ^ In order to further analyze the efficiency of affective recognition through the PD signal, the Galvanic Skin Response (GSR) was also monitored and processed. The highest affective assessment classification rate obtained from GSR processing is only 63.57% (based on the off-line processing algorithm). The overall results confirm that the PD signal should be considered as one of the most powerful physiological signals to involve in future automated real-time affective recognition systems, especially for detecting the "relaxation" vs. "stress" states.^

A study of branching fluid networks for enhancing the performance of thermal -fluid devices

Compact thermal-fluid systems are found in many industries from aerospace to microelectronics where a combination of small size, light weight, and high surface area to volume ratio fluid networks are necessary. These devices are typically designed with fluid networks consisting of many small parallel channels that effectively pack a large amount of heat transfer surface area in a very small volume but do so at the cost of increased pumping power requirements. ^ To offset this cost the use of a branching fluid network for the distribution of coolant within a heat sink is investigated. The goal of the branch design technique is to minimize the entropy generation associated with the combination of viscous dissipation and convection heat transfer experienced by the coolant in the heat sink while maintaining compact high heat transfer surface area to volume ratios. ^ The derivation of Murray's Law, originally developed to predict the geometry of physiological transport systems, is extended to heat sink designs which minimze entropy generation. Two heat sink designs at different scales are built, and tested experimentally and analytically. The first uses this new derivation of Murray's Law. The second uses a combination of Murray's Law and Constructal Theory. The results of the experiments were used to verify the analytical and numerical models. These models were then used to compare the performance of the heat sink with other compact high performance heat sink designs. The results showed that the techniques used to design branching fluid networks significantly improves the performance of active heat sinks. The design experience gained was then used to develop a set of geometric relations which optimize the heat transfer to pumping power ratio of a single cooling channel element. Each element can be connected together using a set of derived geometric guidelines which govern branch diameters and angles. The methodology can be used to design branching fluid networks which can fit any geometry. ^

An assessment of novel biodegradable magnesium alloys for endovascular biomaterial applications

Magnesium alloys have been widely explored as potential biomaterials, but several limitations to using these materials have prevented their widespread use, such as uncontrollable degradation kinetics which alter their mechanical properties. In an attempt to further the applicability of magnesium and its alloys for biomedical purposes, two novel magnesium alloys Mg-Zn-Cu and Mg-Zn-Se were developed with the expectation of improving upon the unfavorable qualities shown by similar magnesium based materials that have previously been explored. The overall performance of these novel magnesium alloys has been assessesed in three distinct phases of research: 1) analysing the mechanical properties of the as-cast magnesium alloys, 2) evaluating the biocompatibility of the as-cast magnesium alloys through the use of in-vitro cellular studies, and 3) profiling the degradation kinetics of the as-cast magnesium alloys through the use of electrochemical potentiodynamic polarization techqnique as well as gravimetric weight-loss methods. As compared to currently available shape memory alloys and degradable as-cast alloys, these experimental alloys possess superior as-cast mechanical properties with elongation at failure values of 12% and 13% for the Mg-Zn-Se and Mg-Zn-Se alloys, respectively. This is substantially higher than other as-cast magnesium alloys that have elongation at failure values that range from 7-10%. Biocompatibility tests revealed that both the Mg-Zn-Se and Mg-Zn-Cu alloys exhibit low cytotoxicity levels which are suitable for biomaterial applications. Gravimetric and electrochemical testing was indicative of the weight loss and initial corrosion behavior of the alloys once immersed within a simulated body fluid. The development of these novel as-cast magnesium alloys provide an advancement to the field of degradable metallic materials, while experimental results indicate their potential as cost-effective medical devices.^

Network construction and graph theoretical analysis of functional language networks in pediatric epilepsy

This dissertation introduces a new approach for assessing the effects of pediatric epilepsy on the language connectome. Two novel data-driven network construction approaches are presented. These methods rely on connecting different brain regions using either extent or intensity of language related activations as identified by independent component analysis of fMRI data. An auditory description decision task (ADDT) paradigm was used to activate the language network for 29 patients and 30 controls recruited from three major pediatric hospitals. Empirical evaluations illustrated that pediatric epilepsy can cause, or is associated with, a network efficiency reduction. Patients showed a propensity to inefficiently employ the whole brain network to perform the ADDT language task; on the contrary, controls seemed to efficiently use smaller segregated network components to achieve the same task. To explain the causes of the decreased efficiency, graph theoretical analysis was carried out. The analysis revealed no substantial global network feature differences between the patient and control groups. It also showed that for both subject groups the language network exhibited small-world characteristics; however, the patient's extent of activation network showed a tendency towards more random networks. It was also shown that the intensity of activation network displayed ipsilateral hub reorganization on the local level. The left hemispheric hubs displayed greater centrality values for patients, whereas the right hemispheric hubs displayed greater centrality values for controls. This hub hemispheric disparity was not correlated with a right atypical language laterality found in six patients. Finally it was shown that a multi-level unsupervised clustering scheme based on self-organizing maps, a type of artificial neural network, and k-means was able to fairly and blindly separate the subjects into their respective patient or control groups. The clustering was initiated using the local nodal centrality measurements only. Compared to the extent of activation network, the intensity of activation network clustering demonstrated better precision. This outcome supports the assertion that the local centrality differences presented by the intensity of activation network can be associated with focal epilepsy.^

Enhanced anchorage of tissue-engineered cartilage using an osteoinductive approach

Articular cartilage injuries occur frequently in the knee joint. Several methods have been implemented clinically, to treat osteochondral defects but none have been able to produce a long term, durable solution. Photopolymerizable cartilage tissue engineering approaches appear promising; however, fundamentally, forming a stable interface between the tissue engineered cartilage and native tissue, mainly subchondral bone and native cartilage, remains a major challenge. The overall objective of this research is to find a solution for the current problem of dislodgment of tissue engineered cartilage at the defect site for the treatment of degraded cartilage that has been caused due to knee injuries or because of mild to moderate level of osteoarthritis. For this, an in-vitro model was created to analyze the integration of tissue engineered cartilage with the bone, healthy and diseased cartilage over time. We investigated the utility of hydroxyapatite (HA) nanoparticles to promote controlled bone-growth across the bone-cartilage interface in an in vitro engineered tissue model system using bone marrow derived stem cells. We also investigated the application of HA nanoparticles to promote enhance integration between tissue engineered cartilage and native cartilage both in healthy and diseased states. Samples incorporated with HA demonstrated significantly higher interfacial shear strength (at the junction between engineered cartilage and engineered bone and also with diseased cartilage) compared to the constructs without HA (p < 0.05), after 28 days of culture. These findings indicate that the incorporation of HA nanoparticles permits more stable anchorage of the injectable hydrogel-based engineered cartilage construct via augmented integration between bone and cartilage.^

Sparse Sample Detection Using Magnetic Bead Manipulation on a Digital Microfluidic Device

This thesis demonstrates a new way to achieve sparse biological sample detection, which uses magnetic bead manipulation on a digital microfluidic device. Sparse sample detection was made possible through two steps: sparse sample capture and fluorescent signal detection. For the first step, the immunological reaction between antibody and antigen enables the binding between target cells and antibody-­‐‑ coated magnetic beads, hence achieving sample capture. For the second step, fluorescent detection is achieved via fluorescent signal measurement and magnetic bead manipulation. In those two steps, a total of three functions need to work together, namely magnetic beads manipulation, fluorescent signal measurement and immunological binding. The first function is magnetic bead manipulation, and it uses the structure of current-­‐‑carrying wires embedded in the actuation electrode of an electrowetting-­‐‑on-­‐‑dielectric (EWD) device. The current wire structure serves as a microelectromagnet, which is capable of segregating and separating magnetic beads. The device can achieve high segregation efficiency when the wire spacing is 50µμm, and it is also capable of separating two kinds of magnetic beads within a 65µμm distance. The device ensures that the magnetic bead manipulation and the EWD function can be operated simultaneously without introducing additional steps in the fabrication process. Half circle shaped current wires were designed in later devices to concentrate magnetic beads in order to increase the SNR of sample detection. The second function is immunological binding. Immunological reaction kits were selected in order to ensure the compatibility of target cells, magnetic bead function and EWD function. The magnetic bead choice ensures the binding efficiency and survivability of target cells. The magnetic bead selection and binding mechanism used in this work can be applied to a wide variety of samples with a simple switch of the type of antibody. The last function is fluorescent measurement. Fluorescent measurement of sparse samples is made possible of using fluorescent stains and a method to increase SNR. The improved SNR is achieved by target cell concentration and reduced sensing area. Theoretical limitations of the entire sparse sample detection system is as low as 1 Colony Forming Unit/mL (CFU/mL).