A Knowledge-based Integrative Modeling Approach for <em>In-Silico</em> Identification of Mechanistic Targets in Neurodegeneration with Focus on Alzheimer’s Disease

Dementia is the progressive decline in cognitive function due to damage or disease in the body beyond what might be expected from normal aging. Based on neuropathological and clinical criteria, dementia includes a spectrum of diseases, namely Alzheimer's dementia, Parkinson's dementia, Lewy Body disease, Alzheimer's dementia with Parkinson's, Pick's disease, Semantic dementia, and large and small vessel disease. It is thought that these disorders result from a combination of genetic and environmental risk factors. Despite accumulating knowledge that has been gained about pathophysiological and clinical characteristics of the disease, no coherent and integrative picture of molecular mechanisms underlying neurodegeneration in Alzheimer’s disease is available. Existing drugs only offer symptomatic relief to the patients and lack any efficient disease-modifying effects. The present research proposes a knowledge-based rationale towards integrative modeling of disease mechanism for identifying potential candidate targets and biomarkers in Alzheimer’s disease. Integrative disease modeling is an emerging knowledge-based paradigm in translational research that exploits the power of computational methods to collect, store, integrate, model and interpret accumulated disease information across different biological scales from molecules to phenotypes. It prepares the ground for transitioning from ‘descriptive’ to “mechanistic” representation of disease processes. The proposed approach was used to introduce an integrative framework, which integrates, on one hand, extracted knowledge from the literature using semantically supported text-mining technologies and, on the other hand, primary experimental data such as gene/protein expression or imaging readouts. The aim of such a hybrid integrative modeling approach was not only to provide a consolidated systems view on the disease mechanism as a whole but also to increase specificity and sensitivity of the mechanistic model by providing disease-specific context. This approach was successfully used for correlating clinical manifestations of the disease to their corresponding molecular events and led to the identification and modeling of three important mechanistic components underlying Alzheimer’s dementia, namely the CNS, the immune system and the endocrine components. These models were validated using a novel in-silico validation method, namely biomarker-guided pathway analysis and a pathway-based target identification approach was introduced, which resulted in the identification of the MAPK signaling pathway as a potential candidate target at the crossroad of the triad components underlying disease mechanism in Alzheimer’s dementia

Direct Use of Information Extraction from Scientific Text for Modeling and Simulation in the Life Sciences

Purpose: To demonstrate how the information extracted from scientific text can be directly used in support of life science research projects. In modern digital-based research and academic libraries, librarians should be able to support data discovery and organization of digital entities in order to foster research projects effectively; thus we speculate that text mining and knowledge discovery tools could be of great assistance to librarians. Such tools simply enable librarians to overcome increasing complexity in the number as well as contents of scientific literature, especially in the emerging interdisciplinary fields of science. In this paper we present an example of how evidences extracted from scientific literature can be directly integrated into in silico disease models in support of drug discovery projects. Design/methodology/approach: The application of text-mining as well as knowledge discovery tools are explained in the form of a knowledge-based workflow for drug target candidate identification. Moreover, we propose an in silico experimentation framework for the enhancement of efficiency and productivity in the early steps of the drug discovery workflow. Findings: Our in silico experimentation workflow has been successfully applied to searching for hit and lead compounds in the World-wide In Silico Docking On Malaria (WISDOM) project and to finding novel inhibitor candidates. Practical implications: Direct extraction of biological information from text will ease the task of librarians in managing digital objects and supporting research projects. We expect that textual data will play an increasingly important role in evidence-based approaches taken by biomedical and translational researchers. Originality / value: Our proposed approach provides a practical example for the direct integration of text- and knowledge-based data into life science research projects, with the emphasis on its application by academic and research libraries in support of scientific projects

Lipid Profile and the Risk of Stroke: A Study from North of Iran

Stroke is the second cause of mortality in the world and third in Iran and lipid abnormalities are the main cause of stroke. The relation of dyslipidemia and the risk of stroke is mater of controversy. The aim of this paper is to determine the relationship of dyslipidemia and the risk of stroke in Sayad Shirazi hospital, Gorgan, Northeastern Iran. Retrospectively, we investigated all medical records with a diagnosis of stroke based on International Classification of Diseases, Revision 10, from August 2015 to August 2016 in Sayyad Shirazi hospital. We include those records with laboratory reports on serum lipid profile. The National Cholesterol Education Program Adult Treatment Panel III guideline was used to classifying lipid profile. The Data management and analysis was performed using SPSS 20. Out of 415 identified records, 9.6% had an unspecified diagnosis of stroke subtype. Only, in 160 records the lipid parameters were measured. The majority of cases with dyslipidemia was men (56.6%) and age older than 60 years (71%). There was a significant difference between ethnic groups and dyslipidemia (p=0.04) and between discharge outcome and lipid profile in women (p=0.05). Furthermore, the relation between dyslipidemia and another comorbid risk factor for stroke including diabetes (p=0.004), ischemic heart disease (0.035), and prior stroke (0.002) was significant. This study has shown that dyslipidemia coexisting with diabetes, ischemic heart diseases, and prior stroke increases the risk of stroke especially in older age. In general, therefore, it seems that lipid-lowering therapy must be one of the priorities in this population

'HypothesisFinder:' a strategy for the detection of speculative statements in scientific text.

Speculative statements communicating experimental findings are frequently found in scientific articles, and their purpose is to provide an impetus for further investigations into the given topic. Automated recognition of speculative statements in scientific text has gained interest in recent years as systematic analysis of such statements could transform speculative thoughts into testable hypotheses. We describe here a pattern matching approach for the detection of speculative statements in scientific text that uses a dictionary of speculative patterns to classify sentences as hypothetical. To demonstrate the practical utility of our approach, we applied it to the domain of Alzheimer's disease and showed that our automated approach captures a wide spectrum of scientific speculations on Alzheimer's disease. Subsequent exploration of derived hypothetical knowledge leads to generation of a coherent overview on emerging knowledge niches, and can thus provide added value to ongoing research activities

Effectiveness of Cognitive Behavioral Therapy Training in Reducing Depression in Visually Impaired Male Students

Objectives: According to the prevalence of psychological problems, especially depression in people with visual impairment, this study aimed at investigating the effectiveness of group training of cognitive behavioral therapy in reducing depression in visually impaired male students.&nbsp; Methods: This study employed a quasi-experimental design, with pre-test and post-test and control group. The study population included 30 students with visual impairment from high school and pre-university levels. The subjects studied at the Martyr Mohebi School in Tehran which is for visually impaired students. The subjects were selected by convenience sampling method and were assigned randomly to experimental and control groups (15 subjects in each group). The second version of the Beck Depression Inventory (BDI)-II was used as pre-test and post-test for both groups. The experimental group received 10 sessions (twice a week) of cognitive behavioral therapy while the control group followed their daily routine. Results: Data analysis was done using statistical software SPSS (version 21). ANCOVA test was performed to examine differences between the two groups. The findings of this study indicate that cognitive-behavioral therapy training was significantly effective in reducing depressive symptoms of male students with visual impairment in the experimental group (P<0.01). Discussion: The findings demonstrated that cognitive behavioral therapy was significantly effective in improving depression of male students with visual impairment in experimental group. The group training needs to be adopted by medical practitioners on a cohort for validating its effectiveness on a larger scale

Linking hypothetical knowledge patterns to disease molecular signatures for biomarker discovery in Alzheimer’s disease

Background A number of compelling candidate Alzheimer’s biomarkers remain buried within the literature. Indeed, there should be a systematic effort towards gathering this information through approaches that mine publicly available data and substantiate supporting evidence through disease modeling methods. In the presented work, we demonstrate that an integrative gray zone mining approach can be used as a way to tackle this challenge successfully. Methods The methodology presented in this work combines semantic information retrieval and experimental data through context-specific modeling of molecular interactions underlying stages in Alzheimer’s disease (AD). Information about putative, highly speculative AD biomarkers was harvested from the literature using a semantic framework and was put into a functional context through disease- and stage-specific models. Staging models of AD were further validated for their functional relevance and novel biomarker candidates were predicted at the mechanistic level. Results Three interaction models were built representing three stages of AD, namely mild, moderate, and severe stages. Integrated analysis of these models using various arrays of evidence gathered from experimental data and published knowledge resources led to identification of four candidate biomarkers in the mild stage. Mode of action of these candidates was further reasoned in the mechanistic context of models by chains of arguments. Accordingly, we propose that some of these ‘emerging’ potential biomarker candidates have a reasonable mechanistic explanation and deserve to be investigated in more detail. Conclusions Systematic exploration of derived hypothetical knowledge leads to generation of a coherent overview on emerging knowledge niches. Integrative analysis of this knowledge in the context of disease mechanism is a promising approach towards identification of candidate biomarkers taking into consideration the complex etiology of disease. The added value of this strategy becomes apparent particularly in the area of biomarker discovery for neurodegenerative diseases where predictive biomarkers are desperately needed

Relationship of Deterministic Thinking With Loneliness and Depression in the Elderly

Objectives Deterministic thinking as a destructive factor in disrupting the balance of hope and fear plays an important role in mental health, especially depression and anxiety .This distortion is caused by cognitive inflexibility in the mind. This study was conducted to investigate the relationship between deterministic thinking and depression and sense of loneliness in older adults.&nbsp; Methods & Materials The type of study was descriptive-correlational. The population included all the older adults over 60 years who were living in a nursing home in 2014-15 in Karaj. Of them, 142 individuals were selected (male and female) by available sampling method. They were then asked to respond to deterministic thinking questionnaire, Geriatric Depression Scale, and UCLA loneliness scale. The data collected were imported to AMOS software and analyzed by path analysis model.&nbsp; Results The results of the path analysis model showed that deterministic thinking has a significant and positive relationship with depression (P=0.001) and sense of loneliness variable (P=0.001). It also has a significant effect on the prediction of sense of loneliness and depression with effect size of 0.26 and 0.28, respectively. The mean age was 67.2 years for women and 65.4 years for men. The mean score deterministic thinking, sense of loneliness and depression in women and men respectively were 118/50, 70/80, and 12/55. Conclusion According to the results, it can be said that deterministic thinking has a significant relationship with depression and sense of loneliness in older adults. So, deterministic thinking acts as a predictor of depression and sense of loneliness in older adults. Therefore, psychological interventions for challenging cognitive distortion of deterministic thinking and attention to mental health in older adult are very important.&nbsp