目的:探讨颅脑损伤合并视神经损伤的发病机制及治疗.方法:对23例颅脑损伤合并视神经损伤患者的临床资料做回顾性分析。结果:经过积极治疗部分患者视力有不同程度改善。结论:治疗颅脑损伤合并视神经损伤,强调神经外科和眼科协同处理,掌握治疗时机。
ObjectiveTo explore the morphological and functional features of tissue engineered composite constructed with bone mesenchymal stem cells (BMSCs) as seeding cells, thermosensitive collagen hydrogel (TCH) and poly-L-lactic acid (PLLA) as the extracellular matrix (ECM) scaffolds in the dynamic culture system. MethodsBMSCs were separated from long bones of Fischer344 rat, and cultured; and BMSCs at the 3rd generation were seeded on the ECM scaffold constructed with braided PLLA fiber and TCH. The BMSCs-ECM scaffold composite was cultured in the dynamic culture system which was designed by using an oscillating device at a frequency of 0.5 Hz and at swing angle of 70° (experimental group), and in the static culture system (control group) for 7 days. The general observation and scanning electron microscopy (SEM) observation were performed; total DNA content was measured at 0, 1, 3, and 7 days. ResultsPLLA was surrounded by collagen to form translucent gelatiniform in 2 groups; and compact membrane developed on the surface of PLLA. SEM observation showed that BMSCs had high viability and were fusiform in shape with microvilli on the surface of cells, and arranged in line; collagen and cells filled in the pores of PLLA fiber in the experimental group. The cells displayed a flat shape on the surface; there were less cells filling in the pores of PLLA fiber in the control group. At 1, 3, and 7 days, total DNA content in the experimental group was significantly higher than that in control group (P < 0.05). The total DNA content were increased gradually with time in 2 groups, showing significant difference between at 0 day and at 7 days (P < 0.05). ConclusionThe ECM constructed with TCH and PLLA has good biocompatibility. The dynamic cultivation system can promote the cell proliferation, distribution, and alignment on the surface of the composite, so it can be used for tissue engineered composite in vitro.
Objective To investigate the effect of glenohumeral bone structure on anterior shoulder instability by three-dimensional CT reconstruction. Methods The clinical data of 48 patients with unilateral anterior shoulder dislocation (instability group) and 46 patients without shoulder joint disease (control group) admitted between February 2012 and January 2024 were retrospectively analyzed. There was no significant difference in gender and side between the two groups (P>0.05). The patients were significantly younger in the instability group than in the control group (P<0.05). The glenoid joint morphological parameters such as glenoid height, glenoid width, ratio of glenoid height to width, glenoid inclination, the humeral containing angle, and glenoid version were measured on three-dimensional CT reconstruction of the glenoid. The differences of the above indexes between the two groups were compared, and the differences of the above indexes between the two groups were compared respectively in the male and the female. Random forest model was used to analyze the influencing factors of anterior shoulder instability. ResultsThe comparison between the two groups and the comparison between the two groups in the male and the female showed that the ratio of of the instability group glenoid height to width was larger than that of the control group, the glenoid width and humeral containing angle were smaller than those of the control group, and the differences were significant (P<0.05); there was no significant difference in glenoid height, glenoid inclination, and glenoid version between the two groups (P>0.05). The accuracy of the random forest model was 0.84. The results showed that the top four influencing factors of anterior shoulder instability were ratio of glenoid height to width, the humeral containing angle, age, and glenoid width. Conclusion Ratio of glenoid height to width and the humeral containing angle are important influencing factors of anterior shoulder instability.
Dementia is a neurodegenerative disease closely related to brain network dysfunction. In this study, we assessed the interdependence between brain regions in patients with early-stage dementia based on phase-lock values, and constructed a functional brain network, selecting network feature parameters for metrics based on complex network analysis methods. At the same time, the entropy information characterizing the EEG signals in time domain, frequency domain and time-frequency domain, as well as the nonlinear dynamics features such as Hjorth and Hurst indexes were extracted, respectively. Based on the statistical analysis, the feature parameters with significant differences between different conditions were screened to construct feature vectors, and finally multiple machine learning algorithms were used to realize the recognition of early categories of dementia patients. The results showed that the fusion of multiple features performed well in the categorization of Alzheimer’s disease, frontotemporal lobe dementia and healthy controls, especially in the identification of Alzheimer’s disease and healthy controls, the accuracy of β-band reached 98%, which showed its effectiveness. This study provides new ideas for the early diagnosis of dementia and computer-assisted diagnostic methods.