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 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.

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

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 F-fluorodeoxyglucose positron emission tomography (F-FDG PET) images and combine DLR features with clinical parameters (DLR+C) to improve diagnostic performance.

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.

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).