Comparison the effect of valerian and gabapentin on RLS and sleep quality in hemodialysis patients: A randomized clinical trial.

INTRODUCTION: This study aimed to compare the effect of valerian and gabapentin on restless legs syndrome (RLS) and sleep quality in HD patients. METHODS: In this cross over clinical trial study, 40 HD patients allocated into a valerian and gabapentin group. In the first phase of the study, Group A received valerian and Group B received gabapentin 1 h before bedtime for 1 month. In the second phase, the two groups' treatment regimen was swapped. After a 1-month washout period, the same process was repeated on the crossover groups. RESULTS: After the first phase, the mean score of RLS was lower in the gabapentin group. But there was no statistically significant difference between the two groups in terms of sleep quality score before and after the first and second interventions. CONCLUSION: Gabapentin is more effective than valerian in improving RLS, but both are equally effective in improving sleep quality.

GC-CNNnet: Diagnosis of Alzheimer's Disease with PET Images Using Genetic and Convolutional Neural Network.

There is a wide variety of effects of Alzheimer's disease (AD), a neurodegenerative disease that can lead to cognitive decline, deterioration of daily life, and behavioral and psychological changes. A polymorphism of the ApoE gene ε 4 is considered a genetic risk factor for Alzheimer's disease. The purpose of this paper is to demonstrate that single-nucleotide polymorphic markers (SNPs) have a causal relationship with quantitative PET imaging traits. Additionally, the classification of AD is based on the frequency of brain tissue variations in PET images using a combination of k-nearest-neighbor (KNN), support vector machine (SVM), linear discrimination analysis (LDA), and convolutional neural network (CNN) techniques. According to the results, the suggested SNPs appear to be associated with quantitative traits more strongly than the SNPs in the ApoE genes. Regarding the classification result, the highest accuracy is obtained by the CNN with 91.1%. These results indicate that the KNN and CNN methods are beneficial in diagnosing AD. Nevertheless, the LDA and SVM are demonstrated with a lower level of accuracy.

A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer's Disease Using EEG Signals.

This study will concentrate on recent research on EEG signals for Alzheimer's diagnosis, identifying and comparing key steps of EEG-based Alzheimer's disease (AD) detection, such as EEG signal acquisition, preprocessing function extraction, and classification methods. Furthermore, highlighting general approaches, variations, and agreement in the use of EEG identified shortcomings and guidelines for multiple experimental stages ranging from demographic characteristics to outcomes monitoring for future research. Two main targets have been defined based on the article's purpose: (1) discriminative (or detection), i.e., look for differences in EEG-based features across groups, such as MCI, moderate Alzheimer's disease, extreme Alzheimer's disease, other forms of dementia, and stable normal elderly controls; and (2) progression determination, i.e., look for correlations between EEG-based features and clinical markers linked to MCI-to-AD conversion and Alzheimer's disease intensity progression. Limitations mentioned in the reviewed papers were also gathered and explored in this study, with the goal of gaining a better understanding of the problems that need to be addressed in order to advance the use of EEG in Alzheimer's disease science.

Single and Combined Neuroimaging Techniques for Alzheimer's Disease Detection.

Alzheimer's disease (AD) consists of the gradual process of decreasing volume and quality of neuron connection in the brain, which consists of gradual synaptic integrity and loss of cognitive functions. In recent years, there has been significant attention in AD classification and early detection with machine learning algorithms. There are different neuroimaging techniques for capturing data and using it for the classification task. Input data as images will help machine learning models to detect different biomarkers for AD classification. This marker has a more critical role for AD detection than other diseases because beta-amyloid can extract complex structures with some metal ions. Most researchers have focused on using 3D and 4D convolutional neural networks for AD classification due to reasonable amounts of data. Also, combination neuroimaging techniques like functional magnetic resonance imaging and positron emission tomography for AD detection have recently gathered much attention. However, gathering a combination of data can be expensive, complex, and tedious. For time consumption reasons, most patients prefer to throw one of the neuroimaging techniques. So, in this review article, we have surveyed different research studies with various neuroimaging techniques and ML methods to see the effect of using combined data as input. The result has shown that the use of the combination method would increase the accuracy of AD detection. Also, according to the sensitivity metrics from different machine learning methods, MRI and fMRI showed promising results.

Improving the Practice of Nutrition Therapy in the NRITLD Critically Ill Patients: An International Quality Improvement Project.

BACKGROUND: In previous decades several studies have been performed demonstrating that providing appropriate nutritional support to intensive care unit patients affects complications, time of mechanical ventilation, length of ICU stay, and risk of death. In this study we provided a report of nutrition statuses in Masih Daneshvari's ICU as compared to 156 ICUs from 20 countries that participated in an international nutrition survey. MATERIALS AND METHODS: All patients admitted to an intensive care unit during a specified one-month period who required artificial nutrition were included in this study. Characteristics of patients, performance of nutrition practices, and ICU outcomes were registered for all patients and compared with data from 156 other intensive care units from various countries around the world. RESULTS: Twenty patients, of which 11(55%) were males and 9(45%) were females, were included in this study. The median age was 50.5 yrs (IQR: 40.5-56.0). Seventeen (85%) of them had EN nutrition only, 2(10%) had PN nutrition only, and 1(5%) had both EN and PN nutrition during their stay in the ICU. The adequacy of calorie intake was 67.6% (vs. 61.1% in all 157 ICUs) and the adequacy of protein intake was 84.9% (vs. 56.7% in 157 ICUs). CONCLUSION: In our ICU, enteral feeding was superior to parenteral feeding. Also we considered early initiation of enteral feeding within 48 hours following ICU admission. We just used polymeric formula during this study. As a result of formula variation limits, we overestimated calories and protein needs. Glutamine and Selenium supplementations have not been used yet for patient in our ICU, regardless of their proven benefits in oxidative stress conditions like pulmonary diseases. Therefore, limited use of supplementations like selenium is inevitably among the disadvantages of Masih Daneshvari Hospital's ICU, which is a tertiary-care center for chronic pulmonary diseases.