Primary familial brain calcification (PFBC) is a rare neurodegenerative disorder with four causative genes (SLC20A2, PDGFRB, PDGFB, and XPR1) that have been identified. Here, we aim to describe the mutational spectrum of four causative genes in a series of 226 unrelated Chinese PFBC patients. Mutations in four causative genes were detected in 16.8% (38/226) of PFBC patients. SLC20A2 mutations accounted for 14.2% (32/226) of all patients. Mutations in the other three genes were relatively rare, accounting for 0.9% (2/226) of all patients, respectively. Clinically, 44.8% of genetically confirmed patients (probands and relatives) were considered symptomatic. The most frequent symptoms were chronic headache, followed by movement disorders and vertigo. Moreover, the total calcification score was significantly higher in the symptomatic group compared to the asymptomatic group. Functionally, we observed impaired phosphate transport induced by seven novel missense mutations in SLC20A2 and two novel mutations in XPR1. The mutation p.D164Y in XPR1 might result in low protein expression through an enhanced proteasome pathway. In conclusion, our study further confirms that mutations in SLC20A2 are the major cause of PFBC and provides additional evidence for the crucial roles of phosphate transport impairment in the pathogenies of PFBC.
Background: Previous work has described acute liver injury (ALI) in coronavirus disease 2019 (COVID-19) pneumonia patients, However, there is limited analyses available investigating chronic liver disease (CLD) in COVID-19 patients. This study aimed to investigate clinical characteristics and outcomes of CLD confirmed in COVID-19 patients.
Results: A total of 104 cases (each group containing 52 patients) were analyzed in this study. The CLD group showed an average of 14 (10.0~21.2) length of stay (LOS) days, compared to the group without CLD that only showed an average of 12.5 (10~16) LOS days (Relative Risk [RR] = 1.34, 95% CI (1.22~1.48), P<0.001; Adjusted Relative Risk was 1.24 (95% CI: 1.12~1.39)). The CLD group contained a higher mortality rate and slight liver injury. Furthermore, COX regression model analyses suggested that the neutrophil-to-lymphocyte ratio (NLR) was an independent predictor of mortality risk (P < 0.001) in the CLD group. Additionally, a high NLR significantly correlated with a shorter overall survival (P <0.001).
Conclusions: COVID-19 patients also diagnosed with CLD suffered longer LOS, slight liver injuries and a higher mortality when compared to COVID-19 patients without CLD. The NLR was an independent risk factor for in-hospital deaths. Increased expression of NLR was an indicator of poor prognosis in COVID-19 patients with CLD. Thus, COVID-19 patients diagnosed with CLD and who show a higher NLR need additional care.
Methods: A retrospective cohort study was performed at the Wuhan Jin Yin-tan Hospital from February 2, 2020 to April 2, 2020. COVID-19 patients diagnosed with CLD or not diagnosed with CLD were enrolled in this study. The clinical characteristics and outcomes of these patients were compared.
Schizophrenia is a brain disease that frequently occurs in young people. Early diagnosis and treatment can reduce family burdens and reduce social costs. There is no objective evaluation index for schizophrenia. In order to improve the classification effect of traditional classification methods on magnetic resonance data, a method of classification of functional magnetic resonance imaging data is proposed in conjunction with the convolutional neural network algorithm. We take functional magnetic resonance imaging (fMRI) data for schizophrenia as an example, to extract effective time series from preprocessed fMRI data, and perform correlation analysis on regions of interest, using transfer learning and VGG16 net, and the functional connection between schizophrenia and healthy controls is classified. Experimental results show that the classification accuracy of fMRI based on VGG16 is up to 84.3%. On the one hand, it can improve the early diagnosis of schizophrenia, and on the other hand, it can solve the classification problem of small samples and high-dimensional data and effectively improve the generalization ability of deep learning models.
Background: The cause of atherosclerosis is not known, and therefore the current treatment options are limited. In the present study, we aimed to investigate the effects of Phoenixin 20 and its receptor G protein-coupled receptor 173 (GPR173) against ox-LDL-induced endothelial dysfunction.Materials and Methods: Human aortic endothelial cells (HAECs) were treated with 10 μg/ml ox-LDL in the presence or absence of phoenixin 20. Gene expression of GPR173, ICAM-1, VCAM-1, IL-1β, IL-8, MCP-1, and NOX-4 were measured by real time PCR. Protein expression was assayed by western blot analysis. Secretions of pro-in ammatory cytokines were measured by ELISA. The attachment of THP-1 monocytes to HAECs was detected using calcein-AM staining. Transcriptional activity of NF-κB was measured using dual-luciferase reporter assay.Results: Our ndings indicate that ox-LDL signi cantly lowered the expression of GPR173 in HAECs and triggered an increase in ROS, NOX-4, and proin ammatory cytokine expression. Importantly, we demonstrate that agonism of GPR173 using phoenixin 20 signi cantly ameliorated these harmful effects of ox-LDL. We also show that agonism of GPR173 can prevent the attachment of monocytes to endothelial cells, which is an important therapeutic approach to prevent atherogenesis.
Conclusion:Here, for the rst time to our knowledge, we provide a basis for future research on the role of GPR173 as a new potential treatment against atherosclerosis.
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