LIPID-BASED PACLITAXEL AND DOXORUBICIN NANOPARTICLES TO OVERCOME P-GP-MEDIATED DRUG RESISTANCE IN SOLID TUMORS

Multidrug resistance (MDR) is a major obstacle limiting chemotherapeutic efficacy. The purpose of these studies was to investigate the potential application of injectable paclitaxel (PX) and doxorubicin (Dox)-loaded nanoparticles (NPs) engineered from oil-in-water microemulsion precursors for overcoming P-glycoprotein (P-gp)- mediated drug resistance in solid tumors. An in-vitro study was performed to test whether the oil (stearyl alcohol and cetyl alcohol) used to make lipid nanoparticles could be metabolized. The results showed that the concentrations of the fatty alcohols within nanoparticles, which were quantitatively determined over time by gas chromatography, decreased to only 10-20% of the initial concentration after 15-24 h of incubation with horse liver dehydrogenase (HLADH) and NAD+ at 37ºC. Moreover, the surfactant Brij 78 (polyoxyethylene 20-sterayl ether) in the nanoparticles influenced the activity of the enzyme. Novel Cremophor EL-free paclitaxel-loaded nanoparticles were developed using experimental design combining Taguchi array and sequential simplex optimization. The resulting PX G78 and PX BTM NPs were stable at 4ºC over five months and in PBS at 37ºC over 102 h. Release of PX from PX NPs was slow and sustained without initial burst release. Interestingly, PX BTM NPs could be lyophilized without cryoprotectants and without changing any physiochemical properties and bioactivities. Cytotoxicity studies in breast cancer MDA-MB-231 cells showed that PX NPs have similar anti-cancer activities compared to Taxol. Optimized Dox-loaded NPs were prepared using an ion-pair agent, sodium tetradecyl sulfate (STS), to mask Dox charge and to enhance its entrapment in NPs. In-vitro cytotoxicity studies were carried out in both sensitive and resistant human cancer cells treated with PX and Dox-loaded NPs. All of drug-loaded NPs decreased IC50 values by 6-13-fold in resistant cells compared to free drugs. A series of in-vitro assays were used to understand the underlying mechanisms. The results, in part, showed that the NPs inhibited P-gp and transiently depleted ATP, leading to enhanced uptake and prolonged retention of the drugs in P-gp-overexpressing cancer cells. Finally, in-vivo anti-cancer efficacy studies were performed using pegylated PX BTM NPs after intravenous (i.v.) injection and showed marked anti-cancer efficacy in nude mice bearing resistant NCI/ADR-RES tumors versus all control groups. These results suggest that NPs may be used to both target drug and biological mechanisms to overcome MDR

Reinforcement learning-based approximate optimal control for attitude reorientation under state constraints

This paper addresses the attitude reorientation problems of rigid bodies under multiple state constraints. A novel reinforcement learning (RL)-based approximate optimal control method is proposed to make the trade-off between control cost and performance. The novelty lies in that it guarantees constraint handling abilities on attitude forbidden zones and angular-velocity limits. To achieve this, barrier functions are employed to encode the constraint information into the cost function. Then an RL-based learning strategy is developed to approximate the optimal cost function and control policy. A simplified critic-only neural network (NN) is employed to replace the conventional actor-critic structure once adequate data is collected online. This design guarantees the uniform boundedness of reorientation errors and NN weight estimation errors subject to the satisfaction of a finite excitation condition, which is a relaxation compared with the persistent excitation condition that is typically required for this class of problems. More importantly, all underlying state constraints are strictly obeyed during the online learning process. The effectiveness and advantages of the proposed controller are verified by both numerical simulations and experimental tests based on a comprehensive hardware-in-loop testbed

Optimal tracking control for uncertain nonlinear systems with prescribed performance via critic-only ADP

This paper addresses the tracking control problem for a class of nonlinear systems described by Euler-Lagrange equations with uncertain system parameters. The proposed control scheme is capable of guaranteeing prescribed performance from two aspects: 1) A special parameter estimator with prescribed performance properties is embedded in the control scheme. The estimator not only ensures the exponential convergence of the estimation errors under relaxed excitation conditions but also can restrict all estimates to pre-determined bounds during the whole estimation process; 2) The proposed controller can strictly guarantee the user-defined performance specifications on tracking errors, including convergence rate, maximum overshoot, and residual set. More importantly, it has the optimizing ability for the trade-off between performance and control cost. A state transformation method is employed to transform the constrained optimal tracking control problem to an unconstrained stationary optimal problem. Then a critic-only adaptive dynamic programming algorithm is designed to approximate the solution of the Hamilton-Jacobi-Bellman equation and the corresponding optimal control policy. Uniformly ultimately bounded stability is guaranteed via Lyapunov-based stability analysis. Finally, numerical simulation results demonstrate the effectiveness of the proposed control scheme

BIOMECHANICAL ANALYSIS OF THE PADDLING TECHNIQUE AND THE VELOCITY OF 1000M FULL PADDLING EVENT: A CASE STUDY.

Biomechanical analysis from data obtained by video camera was used to investigate the paddling technique and the velocity of 1000m full paddling event. The results showed the characteristics and the advantages of Meng’s paddling technique. The data also revealed deficiencies and provided a set of kinematics parameters for evaluation, diagnosis and training of paddling techniques

Development of hierarchical porous NiO-YSZ with improved mechanical properties

 The thesis developed an hierarchical porous NiO/YSZ with high mechanical performance using a novel process. This process fabricates initial scaffolds with a controllable porosity by enhancing the surface energy of poly methyl methacrylate (PMMA) for the assembly of NiO-YSZ/PMMA. It maintains the hierarchical porous structure using two-step sintering (TSS) to restrict the growth of nanoparticles, and improves the mechanical properties in combination with a bimodal distribution of NiO/YSZ nano-particles