Quality Control in the Clinical Medical Laboratory Based on Mobile Medical Edge Computing.

Today, the IT departments of many healthcare organizations are suffering from data storage and network speed. Doctors diagnose patients to help them out, and this treatment process needs to be well managed. Clinical trial data are the main basis for judging the patient's condition in the medical process. With the rapid development of clinical laboratory technology in China, important achievements have been made in medicine. Clinical hospitals use a large number of different types of testing reagents and equipment, and the accuracy of testing data has become a key issue for testing results. The accuracy of testing data comes from the zero error of clinical testing, derived from the rigor and operational rigor of the testers in clinical testing. This article is dedicated to the quality control analysis and research based on mobile medical edge computing in clinical trials. This article introduces the relevant theories of mobile medical edge computing technology and quality control methods, with reference to the study on the general medical students' clinical competency in problem-solving, communication skills, procedure, history, and physical examination and critical and non-critical indicators in objective structured clinical examination (OSCE). In addition, a routine testing group and a quality control group for strengthening important aspects of medical testing were designed to conduct comparative experiments. Experiments have proved that the quality control group for strengthening the important links of medical testing has a higher index than the routine testing group in all aspects. Among them, the detection accuracy rate can reach up to 99.48%, which is of great significance for the improvement of the detection link of the patient's condition and the follow-up diagnosis and treatment link.

Determination of the optimal working performance matching through theoretical analysis and experimental study for a screw conveyor.

An experiment was conducted to investigate the effect of screw speed, feeding head speed, and the nature and type of granular material on the productivity and power consumption of a screw conveyor, and then determine the optimal speed. This experiment contains two parts. Part one was designed with 11 levels of screw speed (100-600r/min, interval 50r/min), three levels of feeding head speed (60,80 and 100r/min), and two kinds of granular materials (fine sand and soybeans). Part two was designed with 11 levels of screw speed (20-140r/min, interval 20r/min), three levels of feeding head speed (300,400 and 500r/min), and two kinds of granular materials (fine sand and soybeans). The results indicated that with the increase of the screw speed, the conveying performance will not always get better; with the rise of the feeding head speed, the conveying performance will get better. Due to the higher bulk density of sand, the productivity of conveying sand is higher for granular materials. Finally, for sand, the result indicates when the screw speed is 400r/min, and the feeding head speed is 120r/min, the conveying performance of sand determined by the productivity and power consumption is the best; for soybean, when the screw speed is 300r/min, and the feeding head speed is 140r/min, the conveying performance is the best.

Identification S100A9 as a potential biomarker in neuroblastoma.

BACKGROUND: More than half of Neuroblastoma (NB) patients presented with distant metastases and the relapse of metastatic patients was up to 90%. It is urgent to explore a biomarker that could facilitate the prediction of metastasis in NB patients. METHODS AND RESULTS: In the present study, we systematically analyzed Gene Expression Omnibus datasets and focused on identifying the critical molecular networks and novel key hub genes implicated in NB metastasis. In total, 176 up-regulated and 19 down-regulated differentially expressed genes (DEGs) were identified. Based on these DEGs, a PPI network composed of 150 nodes and 452 interactions was established. Through PPI network identification combined with qRT-PCR, ELISA and IHC, S100A9 was screened as an outstanding gene. Furthermore, in vitro tumorigenesis assays demonstrated that S100A9 overexpression enhanced the proliferation, migration and invasion of NB cells. CONCLUSIONS: Taken together, our findings suggested that S100A9 could participate in NB tumorigenesis and progression. In addition, S100A9 has the potential to be used as a promising clinical biomarker in the prediction of NB metastasis.