Design and performance assessment of aortic heart valve for tissue engineering

Current artificial heart valves are classified as mechanical and bioprosthetic. An appealing pathway that promises to overcome the shortcomings of commercially available heart valves is offered by the interdisciplinary approach of cardiovascular tissue engineering. However, the mechanical properties of the Tissue Engineering Heart Valves (TEHV) are limited and generally fail in the long-term use. To meet this performance challenge novel biodegradable triblock copolymer poly(ethylene oxide)-polypropylene oxide)-poly(ethylene oxide) (PEO-PPO-PEO or F108) crosslinked to Silk Fibroin (F108-SilkC) to be used as tri-leaflet heart valve material was investigated. ^ Synthesis of ten polymers with varying concentration and thickness (55 µm, 75 µm and 100 µm) was achieved via a covalent crosslinking scheme using bifunctional polyethylene glycol diglycidyl ether (PEGDE). Static and fatigue testing were used to assess mechanical properties of films, and hydrodynamic testing was performed to determine performance under a simulated left ventricular flow regime. The crosslinked copolymer (F108-Silk C) showed greater flexibility and resilience, but inferior ultimate tensile strength, by increasing concentration of PEGDE. Concentration molar ratio of 80:1 (F108: Silk) and thickness of 75 µm showed longer fatigue life for both tension-tension and bending fatigue tests. Four valves out of twelve designed satisfactorily complied with minimum performance requirement ISO 5840, 2005. ^ In conclusion, it was demonstrated that the applicability of a degradable polymer in conjugation with silk fibroin for tissue engineering cardiovascular use, specifically for aortic valve leaflet design, met the performance demands. Thinner thicknesses (t<75 µm) in conjunction with stiffness lower than 320 MPa (80:1, F108: Silk) are essential for the correct functionality of proposed heart valve biomaterial F108-SilkC. Fatigue tests were demonstrated to be a useful tool to characterize biomaterials that undergo cyclic loading. ^

Signal processing of physiological signals for automated assessment of stress in computer users

The need to provide computers with the ability to distinguish the affective state of their users is a major requirement for the practical implementation of affective computing concepts. This dissertation proposes the application of signal processing methods on physiological signals to extract from them features that can be processed by learning pattern recognition systems to provide cues about a person's affective state. In particular, combining physiological information sensed from a user's left hand in a non-invasive way with the pupil diameter information from an eye-tracking system may provide a computer with an awareness of its user's affective responses in the course of human-computer interactions. In this study an integrated hardware-software setup was developed to achieve automatic assessment of the affective status of a computer user. A computer-based "Paced Stroop Test" was designed as a stimulus to elicit emotional stress in the subject during the experiment. Four signals: the Galvanic Skin Response (GSR), the Blood Volume Pulse (BVP), the Skin Temperature (ST) and the Pupil Diameter (PD), were monitored and analyzed to differentiate affective states in the user. Several signal processing techniques were applied on the collected signals to extract their most relevant features. These features were analyzed with learning classification systems, to accomplish the affective state identification. Three learning algorithms: Naïve Bayes, Decision Tree and Support Vector Machine were applied to this identification process and their levels of classification accuracy were compared. The results achieved indicate that the physiological signals monitored do, in fact, have a strong correlation with the changes in the emotional states of the experimental subjects. These results also revealed that the inclusion of pupil diameter information significantly improved the performance of the emotion recognition system. ^

Durability assessment of polymer trileaflet heart valves

The durability of a polymer trileaflet valve is dependent on leaflet stress concentrations, so valve designs that reduce stress can, hypothetically, increase durability. Design aspects that are believed to contribute to reduced leaflet stress include stent flexibility, parabolic coaptation curvature, and leaflet anisotropy. With this in mind, the purpose of this investigation was to elucidate what specific combinations of these parameters promote optimal acute and long-term valve function. A combination of four stent designs, seven leaflet reinforcement materials, and three coaptation geometries were evaluated through a combination of experimentation and modeling. Static tensile and Poisson’s ratio tests and dynamic tensile fatigue testing were used to evaluate the individual leaflet components; and hydrodynamic testing and accelerated valve fatigue was used to assess complete valve prototypes. The two most successful designs included a 0.40 mm thick knit-reinforced valve with a fatigue life of 10.35 years, and a 0.20 mm thick knit-reinforced valve with a 28.9 mmHg decrease in pressure drop over the former. A finite element model was incorporated to verify the impact of the above-mentioned parameters on leaflet stress concentrations. Leaflet anisotropy had a large impact on stress concentrations, and matching the circumferential modulus to that of the natural valve showed the greatest benefit. Varying the radial modulus had minimal impact. Varying coaptation geometry had no impact, but stent flexibility did have a marked effect on the stress at the top of the commissure, where a completely rigid stent resulted in a higher peak stress than a flexible stent (E = 385 MPa). In conclusion, stent flexibility and leaflet anisotropy do effect stress concentrations in the SIBS trileaflet valve, but coaptation geometry does not. Regions of high stress concentrations were linked to failure locations in vitro, so a fatigue prediction model was developed from the S/N curves generated during dynamic tensile testing of the 0.20 mm knit-reinforced leaflets. Failure was predicted at approximately 400 million cycles (10 years) at the top of the commissure. In vitro fatigue of this valve showed failure initiation after approximately 167 million cycles (4.18 years), but it was related to a design defect that is subsequently being changed.

Principal component analysis and assessment of language network activation patterns in pediatric epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: (1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; (2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and (3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.

Assessment of Higher-Education Hospitality Programs

The function of assessment in higher-education hospitality programs is to improve student learning. Although the assessment process is common in higher-education institutions, examples of assessment practices in hospitality programs have not been made available to academic practitioners. This paper describes a method successful at formulating assessment in a hospitality college professional program.

Multiplex PCR for Assessment of Tetracycline Resistance (tet) Genes in Antibiotic-Resistant Soil Bacteria and the Ecological Implications

Antibiotics are becoming increasingly prevalent in bacterial communities due to clinical and agricultural misuse and overuse in their environment. As exposure increases, so does the incidence of microbial resistance. Such is the case with bacterial resistance to tetracyclines, a phenotype often acquired through the horizontal gene transfer of tet genes between bacteria. The objective of this project was to analyze the bacterial diversity of tet resistance genes in soil from Miami-Dade County. Bacterial isolates were Gram-stained and the Kirby-Bauer antibiotic disk diffusion test was performed to determine each bacterium’s degree of resistance. The 16S rRNA gene from antibiotic-resistant isolates was amplified by PCR and sequenced to identify the isolates. All isolates’ tet genes were amplified by multiplex PCR, sequenced, and compared. Among eight isolates, three distinct species were positively identified based on their 16S rRNA sequences and four distinct tet genes were identified, though all tested susceptible to tetracycline via the Kirby-Bauer test. This project clarifies some aspects of the ecology of antibiotic resistance genes, their natural ecological function and the potential for the expansion of intrinsic multi-antibiotic resistance into new ecosystems and/or hosts.

Real-time biosensor for the assessment of nanotoxicity and cancer electrotherapy

Knowledge of cell electronics has led to their integration to medicine either by physically interfacing electronic devices with biological systems or by using electronics for both detection and characterization of biological materials. In this dissertation, an electrical impedance sensor (EIS) was used to measure the electrode surface impedance changes from cell samples of human and environmental toxicity of nanoscale materials in 2D and 3D cell culture models. The impedimetric response of human lung fibroblasts and rainbow trout gill epithelial cells when exposed to various nanomaterials was tested to determine their kinetic effects towards the cells and to demonstrate the biosensor's ability to monitor nanotoxicity in real-time. Further, the EIS allowed rapid, real-time and multi-sample analysis creating a versatile, noninvasive tool that is able to provide quantitative information with respect to alteration in cellular function. We then extended the application of the unique capabilities of the EIS to do real-time analysis of cancer cell response to externally applied alternating electric fields at different intermediate frequencies and low-intensity. Decreases in the growth profiles of the ovarian and breast cancer cells were observed with the application of 200 and 100 kHz, respectively, indicating specific inhibitory effects on dividing cells in culture in contrast to the non-cancerous HUVECs and mammary epithelial cells. We then sought to enhance the effects of the electric field by altering the cancer cell's electronegative membrane properties with HER2 antibody functionalized nanoparticles. An Annexin V/EthD-III assay and zeta potential were performed to determine the cell death mechanism indicating apoptosis and a decrease in zeta potential with the incorporation of the nanoparticles. With more negatively charged HER2-AuNPs attached to the cancer cell membrane, the decrease in membrane potential would thus leave the cells more vulnerable to the detrimental effects of the applied electric field due to the decrease in surface charge. Therefore, by altering the cell membrane potential, one could possibly control the fate of the cell. This whole cell-based biosensor will enhance our understanding of the responsiveness of cancer cells to electric field therapy and demonstrate potential therapeutic opportunities for electric field therapy in the treatment of cancer.

Durability Assessment of Polymer Trileaflet Heart Valves

The durability of a polymer trileaflet valve is dependent on leaflet stress concentrations, so valve designs that reduce stress can, hypothetically, increase durability. Design aspects that are believed to contribute to reduced leaflet stress include stent flexibility, parabolic coaptation curvature, and leaflet anisotropy. With this in mind, the purpose of this investigation was to elucidate what specific combinations of these parameters promote optimal acute and long-term valve function. A combination of four stent designs, seven leaflet reinforcement materials, and three coaptation geometries were evaluated through a combination of experimentation and modeling. Static tensile and Poisson’s ratio tests and dynamic tensile fatigue testing were used to evaluate the individual leaflet components; and hydrodynamic testing and accelerated valve fatigue was used to assess complete valve prototypes. The two most successful designs included a 0.40 mm thick knit-reinforced valve with a fatigue life of 10.35 years, and a 0.20 mm thick knit-reinforced valve with a 28.9 mmHg decrease in pressure drop over the former. A finite element model was incorporated to verify the impact of the above-mentioned parameters on leaflet stress concentrations. Leaflet anisotropy had a large impact on stress concentrations, and matching the circumferential modulus to that of the natural valve showed the greatest benefit. Varying the radial modulus had minimal impact. Varying coaptation geometry had no impact, but stent flexibility did have a marked effect on the stress at the top of the commissure, where a completely rigid stent resulted in a higher peak stress than a flexible stent (E = 385 MPa). In conclusion, stent flexibility and leaflet anisotropy do effect stress concentrations in the SIBS trileaflet valve, but coaptation geometry does not. Regions of high stress concentrations were linked to failure locations in vitro, so a fatigue prediction model was developed from the S/N curves generated during dynamic tensile testing of the 0.20 mm knit-reinforced leaflets. Failure was predicted at approximately 400 million cycles (10 years) at the top of the commissure. In vitro fatigue of this valve showed failure initiation after approximately 167 million cycles (4.18 years), but it was related to a design defect that is subsequently being changed.

Principal Component Analysis and Assessment of Language Network Activation Patterns in Pediatric Epilepsy

This dissertation establishes a novel data-driven method to identify language network activation patterns in pediatric epilepsy through the use of the Principal Component Analysis (PCA) on functional magnetic resonance imaging (fMRI). A total of 122 subjects’ data sets from five different hospitals were included in the study through a web-based repository site designed here at FIU. Research was conducted to evaluate different classification and clustering techniques in identifying hidden activation patterns and their associations with meaningful clinical variables. The results were assessed through agreement analysis with the conventional methods of lateralization index (LI) and visual rating. What is unique in this approach is the new mechanism designed for projecting language network patterns in the PCA-based decisional space. Synthetic activation maps were randomly generated from real data sets to uniquely establish nonlinear decision functions (NDF) which are then used to classify any new fMRI activation map into typical or atypical. The best nonlinear classifier was obtained on a 4D space with a complexity (nonlinearity) degree of 7. Based on the significant association of language dominance and intensities with the top eigenvectors of the PCA decisional space, a new algorithm was deployed to delineate primary cluster members without intensity normalization. In this case, three distinct activations patterns (groups) were identified (averaged kappa with rating 0.65, with LI 0.76) and were characterized by the regions of: 1) the left inferior frontal Gyrus (IFG) and left superior temporal gyrus (STG), considered typical for the language task; 2) the IFG, left mesial frontal lobe, right cerebellum regions, representing a variant left dominant pattern by higher activation; and 3) the right homologues of the first pattern in Broca's and Wernicke's language areas. Interestingly, group 2 was found to reflect a different language compensation mechanism than reorganization. Its high intensity activation suggests a possible remote effect on the right hemisphere focus on traditionally left-lateralized functions. In retrospect, this data-driven method provides new insights into mechanisms for brain compensation/reorganization and neural plasticity in pediatric epilepsy.

Real-time Biosensor for the Assessment of Nanotoxicity and Cancer Electrotherapy

Knowledge of cell electronics has led to their integration to medicine either by physically interfacing electronic devices with biological systems or by using electronics for both detection and characterization of biological materials. In this dissertation, an electrical impedance sensor (EIS) was used to measure the electrode surface impedance changes from cell samples of human and environmental toxicity of nanoscale materials in 2D and 3D cell culture models. The impedimetric response of human lung fibroblasts and rainbow trout gill epithelial cells when exposed to various nanomaterials was tested to determine their kinetic effects towards the cells and to demonstrate the biosensor’s ability to monitor nanotoxicity in real-time. Further, the EIS allowed rapid, real-time and multi-sample analysis creating a versatile, noninvasive tool that is able to provide quantitative information with respect to alteration in cellular function. We then extended the application of the unique capabilities of the EIS to do real-time analysis of cancer cell response to externally applied alternating electric fields at different intermediate frequencies and low-intensity. Decreases in the growth profiles of the ovarian and breast cancer cells were observed with the application of 200 and 100 kHz, respectively, indicating specific inhibitory effects on dividing cells in culture in contrast to the non-cancerous HUVECs and mammary epithelial cells. We then sought to enhance the effects of the electric field by altering the cancer cell’s electronegative membrane properties with HER2 antibody functionalized nanoparticles. An Annexin V/EthD-III assay and zeta potential were performed to determine the cell death mechanism indicating apoptosis and a decrease in zeta potential with the incorporation of the nanoparticles. With more negatively charged HER2-AuNPs attached to the cancer cell membrane, the decrease in membrane potential would thus leave the cells more vulnerable to the detrimental effects of the applied electric field due to the decrease in surface charge. Therefore, by altering the cell membrane potential, one could possibly control the fate of the cell. This whole cell-based biosensor will enhance our understanding of the responsiveness of cancer cells to electric field therapy and demonstrate potential therapeutic opportunities for electric field therapy in the treatment of cancer.