Rett syndrome (RTT), caused by loss-of-function mutations in the MECP2 gene, is a neurological disorder characterized by severe impairment of motor and cognitive functions. The aim of this study was to investigate the impact of vector design, dosage, and delivery route on the efficacy and safety of gene augmentation therapy in mouse models of RTT. Our results show that AAV-mediated delivery of MECP2 to Mecp2 null mice by systemic administration, and utilizing a minimal endogenous promoter, was associated with a narrow therapeutic window and resulted in liver toxicity at higher doses. Lower doses of this vector significantly extended the survival of mice lacking MeCP2 or expressing a mutant T158M allele but had no impact on RTT-like neurological phenotypes. Modifying vector design by incorporating an extended Mecp2 promoter and additional regulatory 3′ UTR elements significantly reduced hepatic toxicity after systemic administration. Moreover, direct cerebroventricular injection of this vector into neonatal Mecp2-null mice resulted in high brain transduction efficiency, increased survival and body weight, and an amelioration of RTT-like phenotypes. Our results show that controlling levels of MeCP2 expression in the liver is achievable through modification of the expression cassette. However, it also highlights the importance of achieving high brain transduction to impact the RTT-like phenotypes.

Healthcare staff routing to provide healthcare service to the patients is one of the real-world scheduling problems similar to multiple travelling salesman problems (MTSP). Healthcare staff members provide daily medical services at patients' homes. The service provider authority has to schedule these staff in an effective and efficient way so that it achieves the minimum total cost. The aim of this study is to propose an Adaptive Local Search based on Genetic Algorithm (GA) to solve Healthcare Staff Routing Problem. Two new types of Adaptive Local Searches have been proposed to explore the optimal solutions. Also, Immigrant Scheme has been applied to improve the performance of the proposed GA. With this feature, we make an effort to motivate the GA to replace population occasionally by calling the best GA chromosome when the GA struggles at the local optimal solution. By the proposed algorithm, an effective routing schedule for staff members is generated. Our empirical study demonstrates that the proposed GA with Adaptive Local Search and Immigrant Scheme outperforms its rival methods in terms of the sum of distances.

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