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Abstract Apolipoprotein A-IMilano (Apo A-IMilano) is a natural mutant of Apolipoprotein. It is currently the only protein that can clear arterial wall thrombus deposits and promptly alleviate acute myocardial ischemia. Apo A-IMilano is considered as the most promising therapeutic protein for treating atherosclerotic diseases without obvious toxic or side effects. However, the current biopharmaceutical platforms are not efficient for developing Apo A-IMilano. The objectives of this research were to express Apo A-IMilano using the genetic transformation ability of N. tabacum. The method is to clone the coding sequence of Apo A-IMilano into the plant binary expression vector pCHF3 with a Flag/His6/GFP tag. The constructed plasmid was transformed into N. tabacum by a modified agrobacterium-mediated method, and transformants were selected under antibiotic stress. PCR, RT-qPCR, western blot and co-localization analysis was used to further verify the resistant N. tabacum. The stable expression and transient expression of N. tabacum were established, and the pure product of Apo A-IMilano was obtained through protein A/G agarose. The results showed that Apo A-IMilano was expressed in N. tabacum with a yield of 0.05 mg/g leaf weight and the purity was 90.58% ± 1.65. The obtained Apo A-IMilano protein was subjected to amino acid sequencing. Compared with the theoretical sequence of Apo A-IMilano, the amino acid coverage was 86%, it is also found that Cysteine replaces Arginine at position 173, which indicates that Apo A-IMilano, a mutant of Apo A-I, is accurately expressed in N. tabacum. The purified Apo A-IMilano protein had a lipid binding activity. The established genetic modification N. tabacum will provide a cost-effective system for the production of Apo A-IMilano. Regarding the rapid propagation of N. tabacum, this system provides the possibility of large-scale production and accelerated clinical translation of Apo A-IMilano. Graphical Abstract

This paper presents a methodology for actively discovering knowledge in transport hotline databases by analyzing complaints reported by citizens, aiming to assist transportation management departments in planning actions to investigate and improve service quality. The proposed model uses text mining techniques and applies latent Dirichlet allocation (LDA) to identify topics that are related to transportation services. Consequently, we actively analyzed over 230,000 phone calls occurring in a certain province between 1st January and 31st December 2021. Specifically, we actively analyzed nearly 22,000 phone calls about the taxi industry within a selected city, and identified six topics, including lost and found (27.1%), car blocking (20.6%), attitude and behavior (17.1%), online car-hailing (12.8%), illegal operations (11.2%), and fare issues (11.2%). By actively referring to past and ongoing best practices, we actively recommend several policy implications. The proposed method thus actively transforms the service center record into a customer feedback-based assessment system to intently monitor drivers’ professionalism while efficiently addressing customers’ complaints and concerns.