ObjectiveTo integrate person imagery from drawing tests in screening for mental disorders through meta-analysis to identify indicators that can effectively predict mental disorders. MethodsA computerized search of CNKI, WanFang Data, VIP, PubMed, Web of Science, and EBSCO databases was conducted to collect studies related to mental disorders and drawing tests, with a search timeframe of the period from the creation of the database to May 8, 2023. Meta-analysis was performed using CMA 3.0 after two researchers independently screened the literature, extracted information, and assessed the risk of bias. ResultsA total of 43 studies were included, with 791 independent effect sizes and 8 444 subjects. Meta-analysis revealed that a total of 29 person imagery traits significantly predicted mental disorders, which could be categorized into 7 types according to the features: absent, bizarre, blackened, simplified, static, detailed, and holistic. The subgroup analysis revealed that the specific indicators of affective disorders included "excessive separation among items", "oversimplified person", "rigid and static person" and "hands behind the back". The specific indicators of thought disorders were "absence of limbs", "absence of facial features" and "disproportionate body proportions". Moreover, there were seven common indicators of mental disorders, including "oversimplified drawing", "very small drawing", "very small person", "weak or intermittent lines", "single line limb", "absence of hands or feet" and "no expression or dullness''. ConclusionThe findings could provide a reference standard for selection and interpretation of drawing indicators, promote standardization of the drawing test, and enhance the accuracy of results in screening for mental disorders.
The interaction mechanism between mental disorders and diabetes is complex, involving genetics, endocrine metabolism, inflammation, oxidative stress and other aspects, which makes it difficult to treat patients with mental disorders complicated by diabetes. Such patients mostly use drugs and non-drug interventions to relieve symptoms of mental disorders and improve blood sugar levels, but the mechanism of mental disorders and diabetes needs to be systematically summarized and needs practical means to intervene. This article starts with the pathogenesis of diabetes and then describes the interaction mechanism of schizophrenia, bipolar disorder, depression and diabetes in detail. Finally, the intervention measures for patients with mental disorders complicated by diabetes are summarized, which aims to provide a reference for medical staff engaged in related work.
The causes of mental disorders are complex, and early recognition and early intervention are recognized as effective way to avoid irreversible brain damage over time. The existing computer-aided recognition methods mostly focus on multimodal data fusion, ignoring the asynchronous acquisition problem of multimodal data. For this reason, this paper proposes a framework of mental disorder recognition based on visibility graph (VG) to solve the problem of asynchronous data acquisition. First, time series electroencephalograms (EEG) data are mapped to spatial visibility graph. Then, an improved auto regressive model is used to accurately calculate the temporal EEG data features, and reasonably select the spatial metric features by analyzing the spatiotemporal mapping relationship. Finally, on the basis of spatiotemporal information complementarity, different contribution coefficients are assigned to each spatiotemporal feature and to explore the maximum potential of feature so as to make decisions. The results of controlled experiments show that the method in this paper can effectively improve the recognition accuracy of mental disorders. Taking Alzheimer's disease and depression as examples, the highest recognition rates are 93.73% and 90.35%, respectively. In summary, the results of this paper provide an effective computer-aided tool for rapid clinical diagnosis of mental disorders.
ObjectiveTo systematically review the efficacy of different drugs for patients with methamphetamine-induced psychotic disorders by network meta-analysis.MethodsAn electronical search was conducted in PubMed, The Cochrane Library, Web of Science, EMbase, CNKI, CBM, WanFang Data and VIP databases from inception to October 2016 to collect randomized controlled trials (RCTs) about different drugs for methamphetamine-induced psychotic disorders. Two reviewers independently screened literature, extracted data and assessed the risk bias of included studies, and then RevMan 5.3, R 3.3.2 and JAGS 4.2.0 softwares were used to perform network meta-analysis.ResultsA total of 16 RCTs involving 1 676 patients and 9 kinds of drugs were included. The results of network meta-analysis showed that: compared with the placebo group, olanzapine (OR=28.00, 95%CI 8.10 to 110.00), risperidone (OR=20.00, 95%CI 7.70 to 58.00), quetiapine (OR=30.00, 95%CI 6.60 to 160.00), ziprasidone (OR=28.00, 95%CI 3.70 to 230.00), chlorpromazine (OR=29.00, 95%CI 5.00 to 200.00), aripiprazole (OR=13.00, 95%CI 1.70 to 93.00), haloperidol (OR=19.00, 95%CI 2.10 to 190.00) could significantly improve the psychotic disorders of patients with methamphetamine, respectively, in which quetiapine was the best choice. There were no significant differences between any other pairwise comparisons of these different drugs.ConclusionFor the treatment of psychotic disorders caused by methamphetamine, quetiapine should be of a priority choice, follows by ziprasidone, chlorpromazine, olanzapine, risperidone, aripiprazole or haloperidol in a descending priority. Due to limited quality and quantity of the included studies, more high-quality studies are needed to verify above conclusion.
ObjectiveTo explore the related factors and nursing countermeasures for psychonosema in postoperative laryngeal cancer patients. MethodsWe retrospectively analyzed the clinical data of eight patients who accepted laryngectomy and developed psychonosema from January 2008 to April 2013. The related factors for psychonosema in these patients were analyzed and nursing countermeasures were summarized. ResultsEight patients had different degree of psychonosema, and it was correlated with psychological factors, various channels of undesirable stimulation, sleep disorders, drug and other factors. After treatment and careful nursing, within three to seven days, all patients' abnormal mental symptoms were alleviated, and all of them were discharged. ConclusionThere are many factors which can cause psychonosema after laryngectomy for laryngeal carcinoma. Medical staff should try to reduce or avoid inducing factors. Once it happens, medical staff should carry out psychiatric treatment in time to avoid accidents and promote the rehabilitation of patients.