Traditional dressings used for wound repair, such as gauze, have shortcomings; for example, they cannot provide a suitable microenvironment for wound recovery. Therefore, it is necessary to find a better dressing to overcome shortcomings. Hydrogel provides a suitable wet environment, has good biocompatibility, and has a strong swelling rate to absorb exudate. Nanomaterial in hydrogels has been used to improve their performance and overcome the shortcomings of current hydrogel dressings. Hydrogel dressing can also be loaded with nanodrug particles to exert a better therapeutic effect than conventional drugs and to make the dressing more practical. This article reviews the application of nanotechnology in hydrogels related to wound healing and discusses the application prospects of nanohydrogels. After searching for hydrogel articles related to wound healing, we found that nanomaterial can not only enhance the mechanical strength, antibacterial properties, and adhesion of hydrogels but also achieve sustained drug release. From the perspective of clinical application, these characteristics are significant for wound healing. The combination of nanomaterial and hydrogel is an ideal dressing with broad application prospects for wound healing in the future.

BackgroundLysosome are involved in nutrient sensing, cell signaling, cell death, immune responses and cell metabolism, which play an important role in the initiation and development of multiple tumors. However, the biological function of lysosome in gastric cancer (GC) has not been revealed. Here, we aim to screen lysosome-associated genes and established a corresponding prognostic risk signature for GC, then explore the role and underlying mechanisms.MethodsThe lysosome-associated genes (LYAGs) were obtained from MSigDB database. Differentially expressed lysosome-associated genes (DE-LYAGs) of GC were acquired based on the TCGA database and GEO database. According to expression profiles of DE-LYAGs, we divided the GC patients into different subgroups and then explored tumor microenvironment (TME) landscape and immunotherapy response in LYAG subtypes using GSVA, ESTIMATE and ssGSEA algorithms. Univariate Cox regression analysis, LASSO algorithm and multivariate Cox regression analysis were adopted to identify the prognostic LYAGs and then establish a risk model for patients with GC. The Kaplan-Meier analysis, Cox regression analysis and ROC analysis were utilized to evaluate the performance of the prognostic risk model. Clinical GC specimens were also used to verify the bioinformatics results by qRT-PCR assay.ResultsThirteen DE-LYAGs were obtained and utilized to distinguish three subtypes in GC samples. Expression profiles of the 13 DE-LYAGs predicted prognosis, tumor-related immunological abnormalities and pathway dysregulation in these three subtypes. Furthermore, we constructed a prognostic risk model for GC based on DEG in the three subtypes. The Kaplan-Meier analysis suggested that higher risk score related to short OS rate. The Cox regression analysis and ROC analysis indicated that risk model had independent and excellent ability in predicting prognosis of GC patients. Mechanistically, a remarkable difference was observed in immune cell infiltration, immunotherapy response, somatic mutation landscape and drug sensitivity. qRT-PCR results showed that compared with corresponding adjacent normal tissues, most screened genes showed significant abnormal expressions and the expression change trends were consistent with the bioinformatics results.ConclusionsWe established a novel signature based on LYAGs which could be served as a prognostic biomarker for GC. Our study might provide new insights into individualized prognostication and precision treatment for GC.

This paper presents the motion control and software design of an on-line automatic recognition system for automobile aluminum wheels. The system mainly consists of a recognition station, two laser displacement sensors, a ball-screw, an industrial computer with data processing software, and a programmable logic controller (PLC). The motion control, which incorporates technologies of microelectronic and computer combining the information and intelligence with mechanical devices and power equipments, ensures stability of the wheel recognition system. Each wheel can be identified correctly in less than 20 seconds due to the robust processing software, and the designed action has been achieved accurately. In addition, the man-machine interface is concisely clear and can be easily manipulated and upgraded. The recognition system could meet the requirement of modern wheel manufacturing with a great deal of flexibility.

Abstract. Identifying various types of aluminum wheels was traditionally performed manually, which could result in low efficiency, limited reliability, poor accuracy, and high labor cost. This paper presents the design and implementation of an on-line automatic recognition system for aluminum wheels based on laser trigonometry principle. The system mainly consists of a recognition station, two laser displacement sensors, a ball-screw, an industrial computer with data processing software, and a programmable logic controller (PLC). Robust algorithms, as well as an ingenious database for storing information of wheels in different styles and size are introduced for identifying the wheels. The feature data of a special wheel, such as the diameter, thickness and number of spokes, can be determining accurately by scanning the wheel using two laser displacement sensors and compare well with those in the database. Results show that the system identifies each wheel correctly in less than 20 seconds. The stability of this system is excellent. Significant cost saving, low error rate, and high efficiency can be achieved.