Relationship between the use of electronic devices and susceptibility to multiple sclerosis

Management of Trauma-Induced Multiple Sclerosis with Paleo Diet and Integrative Therapies: A Case Report

Introduction

Trauma continues to be noted in studies as a risk factor for autoimmune diseases such as multiple sclerosis (MS). Successful therapeutic interventions that support the reduction of numbing episodes associated with MS may include diet, stress reduction techniques, and biofield therapy.

Case Description

This case report highlights the successful reduction of numbing episodes, stress, and fatigue in an individual 38-year-old Caucasian female patient using nutrition and body-mind-spirit practices. This individual continued conventional interventions while starting medical nutrition therapy. Over the course of 120 days, her treatment was augmented with a Paleolithic-style diet, which eliminated dairy and ultra-processed foods. Other dietary recommendations suggested were the increase of polyunsaturated fatty acids (PUFAs) and monounsaturated fatty acids (MUFAs), decrease of saturated fatty acids (SFAs), and increase in protein and CO2-promoting foods. Integrative recommendations were biofield therapy and for stress reduction were psychotherapy, reduced exercise intensity, lifestyle recovery from trauma, body-mind-spirit practices, and herbal supplementation.

Conclusion

Integrative therapies such as diet, stress reduction techniques, and biofield therapy may be used as therapeutic protocols for trauma-induced MS patients. Population-based clinical studies should be done to understand the role of these therapies in synchronization.

Detecting prostate cancer using deep learning convolution neural network with transfer learning approach

Prostate Cancer in men has become one of the most diagnosed cancer and also one of the leading causes of death in United States of America. Radiologists cannot detect prostate cancer properly because of complexity in masses. In recent past, many prostate cancer detection techniques were developed but these could not diagnose cancer efficiently. In this research work, robust deep learning convolutional neural network (CNN) is employed, using transfer learning approach. Results are compared with various machine learning strategies (Decision Tree, SVM different kernels, Bayes). Cancer MRI database are used to train GoogleNet model and to train Machine Learning classifiers, various features such as Morphological, Entropy based, Texture, SIFT (Scale Invariant Feature Transform), and Elliptic Fourier Descriptors are extracted. For the purpose of performance evaluation, various performance measures such as specificity, sensitivity, Positive predictive value, negative predictive value, false positive rate and receive operating curve are calculated. The maximum performance was found with CNN model (GoogleNet), using Transfer learning approach. We have obtained reasonably good results with various Machine Learning Classifiers such as Decision Tree, Support Vector Machine RBF kernel and Bayes, however outstanding results were obtained by using deep learning technique.