Aging Male最新文献

Most current research on Alzheimer's disease (AD) is based on transverse measurements. Given the nature of neurodegeneration in AD progression, observing longitudinal changes in the structural features of brain networks over time may improve the accuracy of the predicted transformation and provide a good measure of the progression of AD. Currently, there is no cure for patients with existing AD dementia, but patients with mild cognitive impairment (MCI) in the prodromal stage of AD dementia may be diagnosed. The study of the early diagnosis of MCI and the prediction of MCI to AD transformation is of great significance for the monitoring of the MCI to AD transformation process. Despite the high rate of MCI conversion to AD, the neuropathological cause of MCI is heterogeneous. However, many people with MCI remain stable. Treatment options are different for patients with stable MCI and those with underlying dementia. Therefore, it is of great significance for clinical practice to predict whether patients with MCI will develop AD dementia. This paper proposes an improved algorithm that is based on a convolution neural network (CNN) with residuals combined with multi-layer long short-term memory (LSTM) to diagnose AD and predict MCI. Firstly, multi-time resting-state fMRI images were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database for preprocessing, and then an AAL brain partition template was used to construct a 90 × 90 functional connectivity (FC) network matrix of a whole-brain region of interest (ROI). Secondly, the diversity of training samples was increased by generating an adversarial network (GAN). Finally, a CNN with residuals and a multi-layer LSTM model were constructed to automatically classify and predict the functional adjacency matrix. This method can not only distinguish Alzheimer's disease from normal health conditions at multiple time points, but can also predict progressive MCI (pMCI) and stable MCI (sMCI) at multiple time points. The classification accuracies in AD vs. NC and sMCI vs.pMCI reached 93.5% and 75.5%, respectively.

Objectives: Erectile dysfunction (ED) is the persistent inability to attain and/or maintain erection sufficient for satisfactory sexual performance. Chronic kidney disease (CKD) is a problem with increasing incidence every day which disrupts quality of life significantly. We aimed to research whether ED is a warning symptom for the early stages of CKD or not.

Materials and methods: The records of 639 patients attending Ordu University due to ED were retrospectively investigated. According to International Index of Erectile Function (IIEF) scores and degree of ED, patients were compared in terms of GFR values.

Results: In 92.8% of patients, serum creatinine values were within normal limits (<1 mg/dL), while 30.5% of patients were observed to have GFR below 80. While stage 2 CKD was identified in 1% of the control group, this rate was calculated as 8% in the group with severe ED. In stage 1 and stage 2 CKD, IIEF scores were identified to be low by clear degree.

Conclusions: Results confirm that it was identified that the incidence of stage 1 and stage 2 CKD was higher among patients attending with ED compared to the control group. Just as ED may be an early clinical marker of coronary artery disease, it may be early warning symptom for CKD.