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Leukocytes play a vital role in immune responses, including defending against invasive pathogens, reconstructing impaired tissue, and maintaining immune homeostasis. When the immune system is activated in vivo, leukocytes accomplish a series of orderly and complex regulatory processes. While cancer and inflammation‐related diseases like sepsis are critical medical difficulties plaguing humankind around the world, leukocytes have been shown to largely gather at the focal site, and significantly contribute to inflammation and cancer progression. Therefore, the living leukocyte‐based drug delivery systems have attracted considerable attention in recent years due to the innate and specific targeting effect, low immunogenicity, improved therapeutic efficacy, and low reverse effect. In this review, the recent advances in the development of living leukocyte‐based drug delivery systems including macrophages, neutrophils, and lymphocytes as promising treatment strategies for cancer and inflammation‐related diseases are introduced. The advantages, current challenges, and limitations of these delivery systems are also discussed, as well as perspectives on the future development of precision and targeted therapy in the clinics are provided. Collectively, it is expected that such kind of living cell‐based drug delivery system is promising to improve or even revolutionize the treatments of cancers and inflammation‐related diseases in the clinics.

Rapid development of 3D printing techniques has led to the design of navigation templates to assist with accurate insertion of pedicle screws in last decades. However, there are still without the precise step-by-step methods to design 3D navigation templates from computed tomography (CT) images. Our present article provides a detailed protocol to allow the readers or researchers to obtain the 3D navigation template easily, and assist with pedicle screw insertion in their future research and surgery. Using 3D navigation template-assisted pedicle screw fixation in spine surgery is low cost and can decrease the radiation exposure to both patients and surgeons.

Fault prognostic is one of the most important problems in equipment health management system. This paper presents a hybrid method of mixture of Gaussian hidden Markov model (MG-HMM) and fixed size least squares support vector regression (FS-LSSVR) for fault prognostic. The system is established based on three parts. The first part trains the MG-HMM and FS-LSSVR model. According to the known samples, several MG-HMM models can be learned based on expectation maximization (EM) algorithm. Then, the forward variables can be calculated based on these MG-HMM models. Based on these forward variables, the corresponding FS-LSSVR models are built. All the MG-HMM models and corresponding FS-LSSVR models are combined into a model library. The second part recognizes the unknown sample based on the model library. This part obtains the MG-HMM model and FS-LSSVR model by maximization likelihood calculation between the unknown sample and MG-HMM models. The third part of the system calculates the forward variables based on the MG-HMM obtained from the second part. These forward variables are inputted into the corresponding FS-LSSVR model to compute the remaining useful life (RUL) of the unknown sample. Finally, we carry out experiments on benchmark data set to verify the proposed method. The results illustrate the effectiveness of the hybrid method.

This paper presents a generalized stochastic Petri-net model for performance analysis and allocation optimization of a repair system with interchangeable inventory. A repair system with interchangeable-inventory is an important type of repair-service, which features components that are assembled circularly with interchangeable-component inventory. Despite improved efficiency, major difficulties in model formulation and performance analysis arise due to a complex fork/join structure and the presence of interchangeable-component inventory. In this study, by applying a generalized stochastic Petri-net to model the fork/join structure and interchangeable inventory, a system with one overhaul center and one interchangeable-component repair shop was defined in the Petri-net by places, transitions and tokens. A performance analysis with single and multiple parameters and simulation experiments was performed according to the real field data of high-speed railway locomotives overhaul. With appropriate weights on each portion of the system resources, the optimal design scenario for the allocation of a specific repair system was achieved to control the overall expenditure.

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