The effects of PVP(FE(III)) catalyst and polymer molecular weight on gene delivery via biodegradable crosslinked polyethylenimine

Human gene therapy has faced many setbacks due to the immunogenicity and oncogenity of viruses. Safe and efficient alternative gene delivery vehicles are needed to implement gene therapy in clinical practice. Polymeric vectors are an attractive option due to their availability, simple chemistry, and low toxicity and immunogenicity. Our group has previously reported biodegradable polyethylenimines (PEI) that show high transfection efficiency and low toxicity by cross-linking 800 Da PEI with diacrylate cross-linkers using Michael addition. However, the synthesis was difficult to control, inconsistent, and resulted in polymers with a narrow range of molecular weights. In the present work, we utilized a heterogenous PVP(Fe(III)) catalyst to provide a more controllable PEI crosslinking reaction and wider range of biodegradable PEIs. The biodegradable PEIs reported here have molecular weights ranging from 1.2 kDa to 48 kDa, are nontoxic in MDA-MB-231 cells, and show low toxicity in HeLa cells. At their respective optimal polymer:DNA ratios, these biodegradable PEIs demonstrated about 2-5-fold higher transfection efficiency and 2-7-fold higher cellular uptake, compared unmodified 25 kDa PEI. The biodegradable PEIs show similar DNA condensation properties as unmodified PEI but more readily unpackage DNA, based on ethidium bromide exclusion and heparan sulfate competitive displacement assays, which could contribute to their improved transfection efficiency. Overall, the synthesis reported here provides a more robust, controlled reaction to produce cross-linked biodegradable PEIs that show enhanced gene delivery, low toxicity, and high cellular uptake and can potentially be used for future in vivo studies.

Soft computing based parameter identification in pavements and geomechanical systems

Accurate estimation of road pavement geometry and layer material properties through the use of proper nondestructive testing and sensor technologies is essential for evaluating pavement’s structural condition and determining options for maintenance and rehabilitation. For these purposes, pavement deflection basins produced by the nondestructive Falling Weight Deflectometer (FWD) test data are commonly used. The nondestructive FWD test drops weights on the pavement to simulate traffic loads and measures the created pavement deflection basins. Backcalculation of pavement geometry and layer properties using FWD deflections is a difficult inverse problem, and the solution with conventional mathematical methods is often challenging due to the ill-posed nature of the problem. In this dissertation, a hybrid algorithm was developed to seek robust and fast solutions to this inverse problem. The algorithm is based on soft computing techniques, mainly Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) as well as the use of numerical analysis techniques to properly simulate the geomechanical system. A widely used pavement layered analysis program ILLI-PAVE was employed in the analyses of flexible pavements of various pavement types; including full-depth asphalt and conventional flexible pavements, were built on either lime stabilized soils or untreated subgrade. Nonlinear properties of the subgrade soil and the base course aggregate as transportation geomaterials were also considered. A computer program, Soft Computing Based System Identifier or SOFTSYS, was developed. In SOFTSYS, ANNs were used as surrogate models to provide faster solutions of the nonlinear finite element program ILLI-PAVE. The deflections obtained from FWD tests in the field were matched with the predictions obtained from the numerical simulations to develop SOFTSYS models. The solution to the inverse problem for multi-layered pavements is computationally hard to achieve and is often not feasible due to field variability and quality of the collected data. The primary difficulty in the analysis arises from the substantial increase in the degree of non-uniqueness of the mapping from the pavement layer parameters to the FWD deflections. The insensitivity of some layer properties lowered SOFTSYS model performances. Still, SOFTSYS models were shown to work effectively with the synthetic data obtained from ILLI-PAVE finite element solutions. In general, SOFTSYS solutions very closely matched the ILLI-PAVE mechanistic pavement analysis results. For SOFTSYS validation, field collected FWD data were successfully used to predict pavement layer thicknesses and layer moduli of in-service flexible pavements. Some of the very promising SOFTSYS results indicated average absolute errors on the order of 2%, 7%, and 4% for the Hot Mix Asphalt (HMA) thickness estimation of full-depth asphalt pavements, full-depth pavements on lime stabilized soils and conventional flexible pavements, respectively. The field validations of SOFTSYS data also produced meaningful results. The thickness data obtained from Ground Penetrating Radar testing matched reasonably well with predictions from SOFTSYS models. The differences observed in the HMA and lime stabilized soil layer thicknesses observed were attributed to deflection data variability from FWD tests. The backcalculated asphalt concrete layer thickness results matched better in the case of full-depth asphalt flexible pavements built on lime stabilized soils compared to conventional flexible pavements. Overall, SOFTSYS was capable of producing reliable thickness estimates despite the variability of field constructed asphalt layer thicknesses.