Serum Sirtuin1 level decreases in Parkinson's disease and vascular parkinsonism: A prospective observational study.

OBJECTIVE: The present study aimed to investigate levels and clinical significance of serum SIRT1 in Parkinson's disease (PD) and Vascular parkinsonism (VP). METHODS: This prospective observational research enrolled a total of 165 VP and 159 PD patients who were admitted during March 2018 to December 2021. Blood samples and medical characteristics were also obtained from 160 healthy volunteers. The serum Sirtuin1 (SIRT1) and cytokines levels of all subjects were measured by enzyme-linked immunosorbent assay (ELISA) method. Demographic and clinical data were also collected. Statistical analysis was conducted using SPSS software with P < 0.05 as statistically different. RESULTS: The mean age, the UPDRSIII score of VP patients was significantly higher compared with the PD patients (p<0.05), while the MMSE score of VP patients was significantly lower than the PD patients (p<0.001). The serum SIRT1 levels of the VP patients were remarkably lower than the PD patients or the healthy persons (p<0.05). Pearson's analysis showed that SIRT1 levels were negatively correlated with levels of IL-6, TNF- α and hcy. The UPDRSIII of SIRT1 low levels group was remarkably higher than the SIRT1 high levels group (p=0.048), while the MMSE score was lower than the SIRT1 high levels group (p<0.001). In addition, ROC curves showed that SIRT1 could be a potential diagnostic biomarker of VP. SIRT1 was a risk factor for VP. CONCLUSION: Our present study indicated that SIRT1 associated with disease severity and could discriminate PD from VP.

The trajectory characteristics and clinical significance of the left-sided lumbar segmental artery: a prospective cross-sectional radio-anatomical study.

Background: Vascular injury to the lumbar segmental arteries is a devastating complication in minimally invasive lumbar interbody fusion. Previous studies on the anatomy of the lumbar segmental arteries are limited. This prospective cross-sectional study aims to quantitatively describe the brief trajectory of the lumbar segmental arteries on the left side (SegAL) and to discuss its clinical significance. Methods: One hundred and two asymptomatic volunteers were prospectively enrolled and underwent computed tomography angiography (CTA). Anatomical parameters including the existence rate, relative positions and directions of SegAL were measured. Mann-Whitney U tests were performed, and statistical significance was set at P<0.05. Results: A total of 404 lumbar SegAL were identified. The SegAL of L1, L2 and L3 were identified in all subjects while the L4 SegAL were absent in 9 of 102 (8.8%) and the L5 SegAL were absent in 97 of 102 (95.1%) volunteers. In 25 of 97 (25.8%) volunteers without the L5 SegAL, the branches of the L4 SegAL ran along the disks. Meanwhile, the branches of L3 intersecting over the intervertebral discs (IVD) were found in 8 of 9 (88.9%) subjects without the L4 SegAL and in 4 of 93 (4.3%) subjects with L4 SegAL. The branch angles between the L1, L2 SegAL and the aorta were significantly acute (P<0.05). The L3 SegAL ran approximately vertically with the aorta while the branch angles of the L4 SegAL were significantly blunt (P<0.05). according to the distances measured, on the anterior vertebral walls, the SegAL of L1 and L2 were significantly closer to the inferior vertebral walls than the SegAL of L3 and L4, while on the posterior vertebral walls, the L3 and L4 SegAL were significantly closer to the inferior walls. Conclusions: Arterial branches may course over the L3-4 and L4-5 IVD spaces and the branches over the L3-4 disks are more likely to be present when L4 segmental arteries are absent, thus posing potential risks of arterial complications. Because of the SegAL adjacent to the disks, the risk of arterial injury may be higher anteriorly at L1 and L2 and higher posteriorly at L3 and L4.

Deep-Learning Radiomics for Discrimination Conversion of Alzheimer's Disease in Patients With Mild Cognitive Impairment: A Study Based on 18F-FDG PET Imaging.

Objectives: Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the older people. Some types of mild cognitive impairment (MCI) are the clinical precursors of AD, while other MCI forms tend to remain stable over time and do not progress to AD. To discriminate MCI patients at risk of AD from stable MCI, we propose a novel deep-learning radiomics (DLR) model based on 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images and combine DLR features with clinical parameters (DLR+C) to improve diagnostic performance. Methods: 18F-fluorodeoxyglucose positron emission tomography (PET) data from the Alzheimer's disease Neuroimaging Initiative database (ADNI) were collected, including 168 patients with MCI who converted to AD within 3 years and 187 patients with MCI without conversion within 3 years. These subjects were randomly partitioned into 90 % for the training/validation group and 10 % for the independent test group. The proposed DLR approach consists of three steps: base DL model pre-training, network features extraction, and integration of DLR+C, where a convolution network serves as a feature encoder, and a support vector machine (SVM) operated as the classifier. In comparative experiments, we compared our DLR+C method with four other methods: the standard uptake value ratio (SUVR) method, Radiomics-ROI method, Clinical method, and SUVR + Clinical method. To guarantee the robustness, 10-fold cross-validation was processed 100 times. Results: Under the DLR model, our proposed DLR+C was advantageous and yielded the best classification performance in the diagnosis of conversion with the accuracy, sensitivity, and specificity of 90.62 ± 1.16, 87.50 ± 0.00, and 93.39 ± 2.19%, respectively. In contrast, the respective accuracy of the other four methods reached 68.38 ± 1.27, 73.31 ± 6.93, 81.09 ± 1.97, and 85.35 ± 0.72 %. These results suggested the DLR approach could be used successfully in the prediction of conversion to AD, and that our proposed DLR-combined clinical information was effective. Conclusions: This study showed DLR+C could provide a novel and valuable method for the computer-assisted diagnosis of conversion to AD from MCI. This DLR+C method provided a quantitative biomarker which could predict conversion to AD in MCI patients.

Analysis of the Curvature and Morphologic Features of the Lumbar Vertebral Endplates Through the Transverse Section: A Radioanatomical Study.

BACKGROUND: Knowledge concerning the curvature of the vertebrae through the transverse section is of clinical significance. However, relevant reports are scarce. This study investigated the features based on the cross-sections of lumbar vertebral endplates to provide information for clinical practice. METHODS: Computed tomography images of 78 subjects were retrospectively reviewed. The geometric morphometrics was performed, and the curvature of the vertebral endplates was calculated by the self-written MATLAB algorithm. The principal component analysis, the canonical variate analysis, the discriminant function analysis, and the Mann-Whitney U test were performed. Statistical significance was set at P < 0.05. RESULTS: No gender difference was found. In contrast, a morphologic difference was found between the superior and inferior lumbar vertebral endplates and between different segments. More specifically, the shape of the endplates gradually changes from the renal shape at superior L1 to the shell-like shape at inferior L5. The mean curvature values of the lateral anterior border were all around 0.60 cm-1, whereas the mean curvature values of the lateral posterior borders range from 0.66 to 1.09 cm-1 from L1 to L5. From L1 to L3, the mean and maximum curvature of the lateral posterior superior vertebral endplates decrease. The trend could also be found on the lateral posterior border of the inferior endplates from L1 to L3. CONCLUSIONS: The current study described morphologic variations and curvature of the lumbar vertebral endplates, which have not been reported previously. The different curvature distribution could provide important information for surgeons and manufacturers.

Use of a Sparse-Response Deep Belief Network and Extreme Learning Machine to Discriminate Alzheimer's Disease, Mild Cognitive Impairment, and Normal Controls Based on Amyloid PET/MRI Images.

In recent years, interest has grown in using computer-aided diagnosis (CAD) for Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI). However, existing CAD technologies often overfit data and have poor generalizability. In this study, we proposed a sparse-response deep belief network (SR-DBN) model based on rate distortion (RD) theory and an extreme learning machine (ELM) model to distinguish AD, MCI, and normal controls (NC). We used [18F]-AV45 positron emission computed tomography (PET) and magnetic resonance imaging (MRI) images from 340 subjects enrolled in the ADNI database, including 116 AD, 82 MCI, and 142 NC subjects. The model was evaluated using five-fold cross-validation. In the whole model, fast principal component analysis (PCA) served as a dimension reduction algorithm. An SR-DBN extracted features from the images, and an ELM obtained the classification. Furthermore, to evaluate the effectiveness of our method, we performed comparative trials. In contrast experiment 1, the ELM was replaced by a support vector machine (SVM). Contrast experiment 2 adopted DBN without sparsity. Contrast experiment 3 consisted of fast PCA and an ELM. Contrast experiment 4 used a classic convolutional neural network (CNN) to classify AD. Accuracy, sensitivity, specificity, and area under the curve (AUC) were examined to validate the results. Our model achieved 91.68% accuracy, 95.47% sensitivity, 86.68% specificity, and an AUC of 0.87 separating between AD and NC groups; 87.25% accuracy, 79.74% sensitivity, 91.58% specificity, and an AUC of 0.79 separating MCI and NC groups; and 80.35% accuracy, 85.65% sensitivity, 72.98% specificity, and an AUC of 0.71 separating AD and MCI groups, which gave better classification than other models assessed.