2 research outputs found
The Effectiveness of Quality of Life Therapy on Depression and Anxiety among Patients with Multiple Sclerosis
Introduction: Many patients with incurable disease, like multiple sclerosis (MS), experience depression and anxiety, therefore, this study aimed at investigating the effectiveness of quality of life therapy (QOLT) among patients with multiple sclerosis (MS), depression and anxiety. Methods: The design of the study was semi-experimental with an experimental group and a control group, including three phases; a pre-test, a post-test and a follow-up test. Statistical population consisted of patients suffering from MS referring to health centers in Shiraz, Iran. 24 patients were selected using convenience-sampling method. They were randomly assigned to the experimental and control groups (each 12 members). The experimental group received QOLT during eight 50minute weekly sessions. The control group received no treatment. All participants completed the Beck Depression Inventory–II and Beck Anxiety Inventory. The data were analyzed using the analysis of covariance method. Results: Comparison of the means of depression and anxiety scores of two groups in post-test phase showed that quality of life therapies had significant effects on reduction of depression (p<0.001) and anxiety (p<0.001) rates in MS patients. Comparing two groups in a follow-up phase showed that the effect of intervention was stable. Conclusion: This research showed that quality of life therapy has decreased depression and anxiety rate significantly in MS patients. It seems that using psychological intervention can improve the well-being of MS patients and they can benefit from the positive effects of a quality time program. Therefore, it is recommended that, in addition to prescription of medications, psychological interventions be done for these patients. Declaration of Interest: Non
Unified Transcriptomic Signature of Arbuscular Mycorrhiza Colonization in Roots of Medicago truncatula by Integration of Machine Learning, Promoter Analysis, and Direct Merging Meta-Analysis
Plant root symbiosis with Arbuscular mycorrhizal (AM) fungi improves uptake of water and mineral nutrients, improving plant development under stressful conditions. Unraveling the unified transcriptomic signature of a successful colonization provides a better understanding of symbiosis. We developed a framework for finding the transcriptomic signature of Arbuscular mycorrhiza colonization and its regulating transcription factors in roots of Medicago truncatula. Expression profiles of roots in response to AM species were collected from four separate studies and were combined by direct merging meta-analysis. Batch effect, the major concern in expression meta-analysis, was reduced by three normalization steps: Robust Multi-array Average algorithm, Z-standardization, and quartiling normalization. Then, expression profile of 33685 genes in 18 root samples of Medicago as numerical features, as well as study ID and Arbuscular mycorrhiza type as categorical features, were mined by seven models: RELIEF, UNCERTAINTY, GINI INDEX, Chi Squared, RULE, INFO GAIN, and INFO GAIN RATIO. In total, 73 genes selected by machine learning models were up-regulated in response to AM (Z-value difference > 0.5). Feature weighting models also documented that this signature is independent from study (batch) effect. The AM inoculation signature obtained was able to differentiate efficiently between AM inoculated and non-inoculated samples. The AP2 domain class transcription factor, GRAS family transcription factors, and cyclin-dependent kinase were among the highly expressed meta-genes identified in the signature. We found high correspondence between the AM colonization signature obtained in this study and independent RNA-seq experiments on AM colonization, validating the repeatability of the colonization signature. Promoter analysis of upregulated genes in the transcriptomic signature led to the key regulators of AM colonization, including the essential transcription factors for endosymbiosis establishment and development such as NF-YA factors. The approach developed in this study offers three distinct novel features: (I) it improves direct merging meta-analysis by integrating supervised machine learning models and normalization steps to reduce study-specific batch effects; (II) seven attribute weighting models assessed the suitability of each gene for the transcriptomic signature which contributes to robustness of the signature (III) the approach is justifiable, easy to apply, and useful in practice. Our integrative framework of meta-analysis, promoter analysis, and machine learning provides a foundation to reveal the transcriptomic signature and regulatory circuits governing Arbuscular mycorrhizal symbiosis and is transferable to the other biological settings