Autologous fibroblasts induce fibrosis of the nucleus pulposus to maintain the stability of degenerative intervertebral discs

Lumbar degenerative disc diseases cause low back pain(LBP). The maintenance of the height and stability of the intervertebral disc(IVD) space is an effective treatment for LBP. The following study evaluated the effects of fibroblast injection on intervertebral disc degeneration(IDD) in a preclinical setting. Compared with the IDD group, the fibroblast treatment group demonstrated effective maintenance of IVD height, reduced endplate degeneration, and improved nuclear magnetic resonance signals and overall histological structure. In doing so, fibrotic IVDs maintained the stability and biomechanics of the vertebra. This finding is in agreement with clinical findings that human nucleus pulposus(NP) fibrosis is essential for the maintenance of IVD height and mechanical properties in patients following percutaneous endoscopic lumbar discectomy(PELD). Mechanistically, we demonstrated that injected fibroblasts not only proliferated but also induced NP cells to adopt a fibrotic phenotype via the secretion of TGF-β.Finally, to better mimic human conditions, the efficacy of autologous fibroblast injection in the treatment of IDD was further examined in a nonhuman primate cynomolgus monkey model due to their capacity for upright posture. We showed that the injection of fibroblasts could maintain the IVD height and rescue IVD signals in cynomolgus monkeys. Taken together, the results of our study reveal that autologous fibroblast injection can enhance the natural process of fibrosis during acute and subacute stages of stress-induced IDD. Fibrotic IVDs can maintain the stability, biological activity, and mechanical properties of the intervertebral space, thus providing a new direction for the treatment of intervertebral space-derived lumbar degenerative diseases.

The study on psychological resilience of tinnitus and associated influencing factors

The association between tinnitus and psychological resilience is an underdeveloped area of research.This cross-sectional study investigated such associations and factors potentially affecting resilience in 61 patients.Demographic and psychometric data were collected by questionnaires.The ConnoreDavidson Resilience Scale(CD-RISC),Medical Coping Modes Questionnaire(MCMQ),Satisfaction with Life Scale(SWLS),General Self-Efficacy Scale(GSES),Big Five Inventory(BFI)and Perceived Social Support Scale(PSSS)were completed by participants.Data were analyzed using independent t-test and Pearson's correlation analysis and multiple linear regression modeling.The CD-RISC score was relatively low(66.97±15.71),negatively correlated with tinnitus(r=0.276,p<0.001)and associated with age(r=0.270,P<0.001).As protective factors,SWLS(r=0.486,p<0.001),GSES(r=0.555,p<0.001),PSSS(r=0.538,p<0.001)and extraversion were positively correlated with CD-RISC and BFI scores(r=0.287,p<0.001).We also detected a negative correlation with neuroticism(r=0.395,p<0.001),which is a known risk factor for worse CD-RISC scores.Identifying protective and risk factors for psychological resilience can be used to predict treatment outcomes in tinnitus patients,which will help devise personalized solutions and improve patients'quality of life.

LogUAD: Log Unsupervised Anomaly Detection Based on Word2Vec

System logs record detailed information about system operation and areimportant for analyzing the system's operational status and performance. Rapidand accurate detection of system anomalies is of great significance to ensure system stability. However, large-scale distributed systems are becoming more andmore complex, and the number of system logs gradually increases, which bringschallenges to analyze system logs. Some recent studies show that logs can beunstable due to the evolution of log statements and noise introduced by log collection and parsing. Moreover, deep learning-based detection methods take a longtime to train models. Therefore, to reduce the computational cost and avoid loginstability we propose a new Word2Vec-based log unsupervised anomaly detection method (LogUAD). LogUAD does not require a log parsing step and takesoriginal log messages as input to avoid the noise. LogUAD uses Word2Vec togenerate word vectors and generates weighted log sequence feature vectors withTF-IDF to handle the evolution of log statements. At last, a computationally effi-cient unsupervised clustering is exploited to detect the anomaly. We conductedextensive experiments on the public dataset from Blue Gene/L (BGL). Experimental results show that the F1-score of LogUAD can be improved by 67.25%compared to LogCluster.

Research and Application of 3G Electrical Safety Job Site Intelligent Monitoring Device

This paper describes the shortcomings and difficulties of power company security construction, such as site management for construction site security monitoring personnel is limited, in recent years , rural power grids and Urban Network alteration Faced with new situation. The use of advanced science and technology and communication terminal in order to better strengthen the means of power construction site safety supervision, improve the level of safety production supervision, design and development of a new electrical safety job site intelligent monitoring devices. The device consists of three parts of the remote wide angle 360 degrees of portable video surveillance equipment and 3G smart terminal equipment and portable battery. Through the application of such a device, professionals can remotely monitor the construction job site safety, diagnose, and effectively improve the security of the electricity sector management and reduce security risks and personnel on-site monitoring costs for improving the security of the entire power industry field operations with significance.