Parkinson’s disease is a common chronic progressive neurodegenerative disease, and its main pathological change is the degeneration and loss of dopaminergic neurons in substantia nigra striatum. Vitamin D receptors are widely distributed in neurons and glial cells, and the normal function of substantia nigra striatum system depends on the level of vitamin D and the normal expression of vitamin D receptors. In recent years, from basic to clinical research, there are some differences in the conclusion of the correlation of vitamin D and its receptor gene polymorphism with Parkinson’s disease. This paper aims to review the research on the correlation of vitamin D and vitamin D receptor gene polymorphism with Parkinson’s disease, and discuss the future research direction in this field.
Objective To evaluate the diagnostic accuracy of Wilson score for predicating difficult intubation. Methods Such databases as PubMed, EMbase, CNKI, WanFang Data and VIP were searched to collect the studies about Wilson score for predicating difficult intubation published from inception to January 2013. Two reviewers independently screened the studies, extracted the data, and assessed the methodological quality by QUADAS. The analysis was conducted by using Meta-Disc 1.4 software, and the random effect model was chosen to calculate the pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and the 95%CI. The summary receiver operating characteristic (SROC) curve was drawn and the area under the curve (AUC) was calculated in order to comprehensively assess the total diagnostic accuracy of Wilson score for predicating difficult intubation. Results A total of 9 studies involving 6 506 subjects were included. The results of meta-analysis showed that: the pooled sensitivity was 0.57 (95%CI 0.53 to 0.62), specificity was 0.89 (95%CI 0.88 to 0.90), positive likelihood ratio was 6.11 (95%CI 4.63 to 8.07), negative likelihood ratio was 0.52 (95%CI 0.41 to 0.66), diagnostic odds ratio was 12.76 (95%CI 8.60 to 18.93), and the AUC of SROC was 0.84. Conclusion Wilson score plays a role in predicating difficult intubation, while some other clinical indicators also need to be taken into consideration in its application.
The apical displacement of tricuspid valve leaflets complicated with significantly enlarged, thin and fibrotic wall of the right ventricle is prone to dysfunction of right heart. Therefore, the myocardial protection for the right ventricle is important. Based on the pathological changes, an algorithm of perioperative myocardial protection strategy is summarized. Firstly, we should clearly know that the right ventricular myocardium with severe lesions is much different from the unimpaired myocardium, because it is now on the margin of failure; secondly, right heart protection should be regarded as a systematic project, which runs through preoperative, intraoperative and postoperative periods, and requires close collaboration among surgeons, perfusionists, anesthesiologists and ICU physicians. In this article, we try to introduce the systematic project of the right heart protection, in order to improve the outcome of this population.
ObjectiveTo explore the clinical application value of antithrombin Ⅲ (ATⅢ) in pulmonary thromboembolism (PTE).MethodsA retrospective study included 204 patients with confirmed PTE who were admitted to Fujian Provincial Hospital from May 2012 to June 2019. The clinical data of the study included basic conditions, morbilities, laboratory examinations and scoring system within 24 hours after admission. The relationship between ATⅢ and PTE in-hospital death was analyzed, and the value of ATⅢ to optimize risk stratification was explored.ResultsFor ATⅢ, the area under receiver operating characteristic curve (AUC) of predicting in-hospital mortality was 0.719, with a cut-off value of 77.7% (sensitivity 64.71%, specificity 80.21%). The patients were divided into ATⅢ≤77.7% group (n=48) and ATⅢ>77.7% group (n=156) according to the cut-off value, and significant statistically differences were found in chronic heart failure, white blood cells count, platelets count, alanine aminotransferase (ALT), albumin and troponin I (P<0.05). According to the in-hospital mortality, patients were divided into a death group (n=17) and a survival group (n=187), and the differences in count of white blood cells, ATⅢ, D-dimer, ALT, albumin, estimated glomerular filtration rate and APACHEⅡ were statistically significant. Logistic regression analysis revealed that ATⅢ≤77.7% and white blood cells count were independent risk factors for in-hospital death. The risk stratification and the risk stratification combined ATⅢ to predict in-hospital death were evaluated by receiver operating characteristic curve, and the AUC was 0.705 and 0.813, respectively (P<0.05). A new scoring model of risk stratification combined with ATⅢ was showed by nomogram.ConclusionsATⅢ≤77.7% is an independent risk factor for in-hospital death, and is beneficial to optimize risk stratification. The mechanism may be related to thrombosis, right ventricular dysfunction and inflammatory response.
Objective To evaluate the associations of 16 variants in clopidogrel-relevant genes with early neurological deterioration (END) in acute ischemic stroke (AIS) patients receiving clopidogrel treatment. Methods AIS patients admitted to the Department of Neurology of three hospitals between June 2014 and January 2015 were included. The 16 variants in clopidogrel-relevant genes were examined using mass spectrometry. Gene-gene interactions were analyzed by generalized multifactor dimensionality reduction (GMDR) methods. The primary outcome was END within the 10 days of admission. Results A total of 375 patients with AIS were included. Among the 375 patients, 95 (25.33%) patients developed END within the first 10 days of admission. Among the 16 variants, only CYP2C19*2 rs4244285 AG+AA was associated with END using single-locus analytical approach (P<0.001). GMDR analysis revealed that there was a synergistic effect of gene-gene interactions among CYP2C19*2 rs4244285, P2Y12 rs16863323, and GPⅢa rs2317676 on risk for END (P=0.019). Cox regression analysis showed that the high-risk interactive genotype was independent predictor for END [hazard ratio=2.184, 95% confidence interval (1.472, 3.238), P=0.004]. Conclusions END is very common in patients with AIS. Interactions among CYP2C19*2 rs4244285, P2Y12 rs16863323, and GPⅢa rs2317676 may confer a higher risk for END. It may be very important to modify clopidogrel therapy for the patients carrying the high-risk interactive genotype.
Alzheimer’s disease (AD) is an irreversible neurodegenerative disorder that damages patients’ memory and cognitive abilities. Therefore, the diagnosis of AD holds significant importance. The interactions between regions of interest (ROIs) in the brain often involve multiple areas collaborating in a nonlinear manner. Leveraging these nonlinear higher-order interaction features to their fullest potential contributes to enhancing the accuracy of AD diagnosis. To address this, a framework combining nonlinear higher-order feature extraction and three-dimensional (3D) hypergraph neural networks is proposed for computer-assisted diagnosis of AD. First, a support vector machine regression model based on the radial basis function kernel was trained on ROI data to obtain a base estimator. Then, a recursive feature elimination algorithm based on the base estimator was applied to extract nonlinear higher-order features from functional magnetic resonance imaging (fMRI) data. These features were subsequently constructed into a hypergraph, leveraging the complex interactions captured in the data. Finally, a four-dimensional (4D) spatiotemporal hypergraph convolutional neural network model was constructed based on the fMRI data for classification. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database demonstrated that the proposed framework outperformed the Hyper Graph Convolutional Network (HyperGCN) framework by 8% and traditional two-dimensional (2D) linear feature extraction methods by 12% in the AD/normal control (NC) classification task. In conclusion, this framework demonstrates an improvement in AD classification compared to mainstream deep learning methods, providing valuable evidence for computer-assisted diagnosis of AD.