品管圈对缩短精神科急诊患者就诊等待时间的效果探讨

目的探讨品管圈对缩短精神科急诊患者就诊等待时间的效果。方法选取2019年7月至2020年6月于我院精神科急诊就诊的300例患者作为研究对象,150例予以常规就诊指导的患者纳入对照组,150例予以品管圈就诊指导的患者纳入观察组。比较两组的就诊等待时间及护理满意度。结果观察组的就诊等待时间显著短于对照组,护理满意度评分显著高于对照组(P<0.05)。结论品管圈可缩短精神科急诊患者的就诊等待时间,提高患者的护理满意度。     

Clinical observation on Xiaoyu decoction () plus psychotherapy in treating functional dyspepsia) plus psychotherapy in treating functional dyspepsia

Objective: To explore the relationship between mental symptoms and functional dyspepsia (FD), and the effect of Xiaoyu decoction (XYD) plus psychotherapy on FD.Methods: SCL-90 scale and FD symptom scale were used to estimate the condition of 56 healthy subjects and 56 patients of FD before and after 4 weeks treatment with XYD plus psychotherapy.Results: There was significant difference in SCL-90 scales between the healthy subjects and the FD patients before treatment (P < 0. 01). After treatment, the mental symptoms and the symptom of FD in the patients were markedly improved, as compared with those before treatment, the difference was significant (P < 0.01).Conclusion: Mental symptoms, such as depression and anxiety existed commonly in FD patients, were closely related to FD. XYD plus psychotherapy could cure it effectively.     

老年认知功能缺损者二项必选数字记忆测验结果分析

目的：探讨认知功能缺损程度对二项必选数字记忆测验(简称二项测验)成绩的影响。方法：用简易智力状态量表和韦氏成人智力量表简式调查出38例认知功能缺损者(研究组)和19例无认知功能缺损者(对照组)，同时实施二项数字测验。结果：(1)研究组与对照组二项测验容易条目、困难条目、总分的得分差异均无显著性。(2)智商70-89者困难条目分及总分显著高于智商50-69及34-49者；各组间容易条目分的差异均无显著性。(3)二项测验的假阳性率随着认知功能缺损的加重而有增加趋势。结论：二项测验成绩在一定程度上受认知功能缺损的影响，尤其是严重认知功能减退者，应注意假阳性率的问题。     

强迫症患者认知功能与病期的关系

目的：探讨强迫症的认知功能障碍与病期的关系。方法：分别用韦氏记忆测验，数字划销测验和威斯康星卡片分类测验评估25例急性强迫症和36例慢性强迫症患者的记忆、注意和执行功能。结果：急性强迫症患者的记忆测验中记图和数字划销测验中第二阶段失误率显著性较慢性强迫症差，其余两组间记忆测验、划销测验和威斯康星卡片分类测验各量表分无显著性差异。结论：强迫症的认知功能与病期无明显相关。     

住院精神分裂症患者疗效的影响因素对照研究

目的探讨住院精神分裂症患者疗效的影响因素。方法将203例精神分裂症患者按临床疗效分组,临床痊愈和显著好转者132例为显效组,临床好转和无效者71例为对照组,采用自编《住院精神分裂症患者一般情况调查表》进行回顾性统计、分析。结果住院患者中女性、低技术工人、阴性症状突出者、病前无诱因的疗效较差(P<0.05)。其疗效与住院天数、总住院费用成正相关,与日均费用成负相关(P<0.01)。结论精神分裂症药物治疗应充分、足程,临床应重视对女性、低技术工人的治疗观察,并加强精神疾病知识健康教育,同时健全社会保障体系以提高住院精神分裂症患者的疗效。     

Deep cross-view co-regularized representation learning for glioma subtype identification

The new subtypes of diffuse gliomas are recognized by the World Health Organization (WHO) on the basis of genotypes, e.g., isocitrate dehydrogenase and chromosome arms 1p/19q, in addition to the histologic phenotype. Glioma subtype identification can provide valid guidances for both risk-benefit assessment and clinical decision. The feature representations of gliomas in magnetic resonance imaging (MRI) have been prevalent for revealing underlying subtype status. However, since gliomas are highly heterogeneous tumors with quite variable imaging phenotypes, learning discriminative feature representations in MRI for gliomas remains challenging. In this paper, we propose a deep cross-view co-regularized representation learning framework for glioma subtype identification, in which view representation learning and multiple constraints are integrated into a unified paradigm. Specifically, we first learn latent view-specific representations based on cross-view images generated from MRI via a bi-directional mapping connecting original imaging space and latent space, and view-correlated regularizer and output-consistent regularizer in the latent space are employed to explore view correlation and derive view consistency, respectively. We further learn view-sharable representations which can explore complementary information of multiple views by projecting the view-specific representations into a holistically shared space and enhancing via adversary learning strategy. Finally, the view-specific and view-sharable representations are incorporated for identifying glioma subtype. Experimental results on multi-site datasets demonstrate the proposed method outperforms several state-of-the-art methods in detection of glioma subtype status.     

Joint hub identification for brain networks by multivariate graph inference

Recent developments in neuroimaging allow us to investigate the structural and functional connectivity between brain regions in vivo. Mounting evidence suggests that hub nodes play a central role in brain communication and neural integration. Such high centrality, however, makes hub nodes particularly susceptible to pathological network alterations and the identification of hub nodes from brain networks has attracted much attention in neuroimaging. Current popular hub identification methods often work in a univariate manner, i.e., selecting the hub nodes one after another based on either heuristic of the connectivity profile at each node or predefined settings of network modules. Since the topological information of the entire network (such as network modules) is not fully utilized, current methods have limited power to identify hubs that link multiple modules (connector hubs) and are biased toward identifying hubs having many connections within the same module (provincial hubs). To address this challenge, we propose a novel multivariate hub identification method. Our method identifies connector hubs as those that partition the network into disconnected components when they are removed from the network. Furthermore, we extend our hub identification method to find the population-based hub nodes from a group of network data. We have compared our hub identification method with existing methods on both simulated and human brain network data. Our proposed method achieves more accurate and replicable discovery of hub nodes and exhibits enhanced statistical power in identifying network alterations related to neurological disorders such as Alzheimer's disease and obsessive-compulsive disorder.     

Maternal insomnia during the COVID-19 pandemic: associations with depression and anxiety

Background

The COVID-19 outbreak has made people more prone to depression, anxiety and insomnia, and females are at a high risk of developing these conditions. As a special group, pregnant and lying-in women must pay close attention to their physical and mental health, as both have consequences for the mother and the fetus. However, knowledge regarding the status of depression, anxiety and insomnia among these women is limited.

Aim

This study aimed to examine insomnia and psychological factors among pregnant and lying-in women during the COVID-19 pandemic and provide theoretical support for intervention research.

Methods

In total, 2235 pregnant and lying-in women from 12 provinces in China were surveyed; their average age was 30.25 years (SD = 3.99, range = 19–47 years).

Participants and setting

The participants completed electronic questionnaires designed to collect demographic information and assess levels of depression, anxiety and insomnia.

Results

The prevalence of insomnia in the sample was 18.9%. Depression and anxiety were significant predictors of insomnia. Participants in high-risk areas, those with a disease history, those with economic losses due to the outbreak, and those in the postpartum period had significantly higher insomnia scores.

Discussion

The incidence of insomnia among pregnant and lying-in women is not serious in the context of the epidemic, which may be related to the sociocultural background and current epidemic situation in China.

Conclusion

Depression and anxiety are more indicative of insomnia than demographic variables.

     

Grey matter volume abnormalities in patients with bipolar I depressive disorder and unipolar depressive disorder:a voxelbased morphometry study

Bipolar disorder and unipolar depressive disorder(UD) may be different in brain structure. In the present study,we performed voxel-based morphometry(VBM) to quantify the grey matter volumes in 23 patients with bipolar I depressive disorder(BP1) and 23 patients with UD,and 23 age-,gender-,and educationmatched healthy controls(HCs) using magnetic resonance imaging. We found that compared with the HC and UD groups,the BP1 group showed reduced grey matter volumes in the right inferior frontal gyrus and middle cingulate gyrus,while the UD group showed reduced volume in the right inferior frontal gyrus compared to HCs. In addition,correlation analyses revealed that the grey matter volumes of these regions were negatively correlated with the Hamilton depression rating scores. Taken together,the results of our study suggest that decreased grey matter volume of the right inferior frontal gyrus is a common abnormality in BP1 and UD,and decreasedgrey matter volume in the right middle cingulate gyrus may be specifi c to BP1.