Meng Huang

Despite the various mechanisms that involved in the pathogenesis of Alzheimer's disease (AD), neuronal damage and synaptic dysfunction are the key events leading to cognition impairment. Therefore, neuroprotection and neurogenesis would provide essential alternatives to the rescue of AD cognitive function. Here we demonstrated that extracellular vesicles secreted from adipose-derived mesenchymal stem cells (ADSCs-derived EVs, abbreviated as EVs) entered the brain quickly and efficiently following intranasal administration, and majorly accumulated in neurons within the central nervous system (CNS). Proteomics analysis showed that EVs contained multiple proteins possessing neuroprotective and neurogenesis activities, and neuronal RNA sequencing showed genes enrichment in neuroprotection and neurogenesis following the treatment with EVs. As a result, EVs exerted powerful neuroprotective effect on Aβ1–42 oligomer or glutamate-induced neuronal toxicity, effectively ameliorated neurologic damage in the whole brain areas, remarkably increased newborn neurons and powerfully rescued memory deficits in APP/PS1 transgenic mice. EVs also reduced Aβ deposition and decreased microglia activation although in a less extent. Collectively, here we provide direct evidence that ADSCs-derived EVs may potentially serve as an alternative for AD therapy through alleviating neuronal damage and promoting neurogenesis.

Brain drug delivery is one of the most important bottlenecks in the development of drugs for the central nervous system. Cumulative evidence has emerged that extracellular vesicles (EVs) play a key role in intercellular communication. Exosomes, a subgroup of EVs, have received the most attention due to their capability in mediating the horizontal transfer of their bioactive inclusions to neighboring and distant cells, and thus specifically regulating the physiological and pathological functions of the recipient cells. This native and unique signaling mechanism confers exosomes with great potential to be developed into an effective, precise, and safe drug delivery system. Here, we provide an overview into the challenges of brain drug delivery and the function of exosomes in the brain under physiological and pathological conditions, and discuss how these natural vesicles could be harnessed for brain drug delivery and for the therapy of brain diseases.

In response to the tenets of Industry 4.0, operation optimization in industrial processes has become a significant research topic. However, the uncertainties prevailing in the process pose challenges to production operations, especially the feedstock properties. In this work, the operation optimization study is performed on a distillation unit (DU), a typical plant in the industrial process. To enhance production performance, a modeling and operation optimization strategy based on feedstock property and production features is presented. One of the difficulties is how to uncover features from high-dimensional and imperfect data, where imperfect data refers to product quality data that is unavailable online. In the strategy, we inject the inherent characteristic of the process into the data-driven method to extract the feedstock property in a data-based and knowledge-oriented manner. Further, optimal feature representation and process modeling can be achieved by customizing the network structure. The operation optimization problem is formulated to adjust the top temperature of the distillation column (TTDC) to achieve satisfactory production under varying feedstock properties. Experimental results illustrate that the process model based on feedstock property and production features (PM-FP-PF) can better fit the physical process mechanism even based on incomplete information in industrial data. Industrial experiments have shown the proposed strategy has advanced generalization ability to the different feedstock properties. The proposed operation optimization strategy (OOS) improves the product qualification rate and has broad application prospects in industrial processes with similar features. Note to Practitioners—Industrial processes suffer from a variety of disturbances that interrupt the smooth operation of the system, such as varying feedstock properties. How to deal with them is the key to improve the product qualification rate. In this work, we propose a data-driven modeling and operation optimization framework to improve product quality under varying feedstock properties. The dynamic variation characteristics of the feedstock properties can be obtained by analyzing the physical properties of the production unit. This is used as a basis for representing and extracting feedstock properties in a data-driven way. Further, a process model based on feedstock property and production features (PM-FP-PF) is built to predict product quality. An operation optimization strategy with production capacity consideration is established. It can determine the optimal operation action required by the current system, mitigating the uncertainty of feedstock property. This operation optimization system has been applied to a distillation unit in the hydrofining process. The application results show that the process model achieves satisfactory estimation accuracy, and the operation optimization strategy has improved production performance.

The economic performance of a fluid catalytic cracking unit (FCCU) plays an essential role in the refinery’s benefit. A model-based real-time optimization (RTO) is widely applied to improve the unit’s economic performance. However, reactions in an FCCU are complex. Also, it is difficult to obtain a model that is the same as the plant’s, which leads to an economy loss. What is more, aperiodic perturbations in the feedstock quality make it more difficult to apply the RTO. To further improve the economic performance, an enhancing transient event trigger RTO for FCCU operation was proposed, which integrated the modifier adaptation method and trigger strategy. The modifier adaptation method was adopted to cope with model mismatch. In addition, because of the fast dynamics of a riser, the measures of a riser can partly reflect the steady feature of the whole plant. Hence, the measurements are utilized to calculate the modifiers of the objective function at a high frequency. To avoid useless calculations, a trigger event strategy is also proposed, judging whether the modified optimization problem should be resolved or not. In the simulation results, the proposed scheme was applied to an FCCU model, which was based on a refinery factory in Jiujiang, China. Different situations were demonstrated, and the results verified the efficiency of the proposed scheme.