Drug Repurposing: A Systematic Approach to Evaluate Candidate Oral Neuroprotective Interventions for Secondary Progressive Multiple Sclerosis

Objective: To develop and implement an evidence based framework to select, from drugs already licenced, candidate oral neuroprotective drugs to be tested in secondary progressive multiple sclerosis. Design: Systematic review of clinical studies of oral putative neuroprotective therapies in MS and four other neurodegenerative diseases with shared pathological features, followed by systematic review and meta-analyses of the in vivo experimental data for those interventions. We presented summary data to an international multi-disciplinary committee, which assessed each drug in turn using pre-specified criteria including consideration of mechanism of action. Results: We identified a short list of fifty-two candidate interventions. After review of all clinical and pre-clinical evidence we identified ibudilast, riluzole, amiloride, pirfenidone, fluoxetine, oxcarbazepine, and the polyunsaturated fatty-acid class (Linoleic Acid, Lipoic acid; Omega-3 fatty acid, Max EPA oil) as lead candidates for clinical evaluation. Conclusions: We demonstrate a standardised and systematic approach to candidate identification for drug rescue and repurposing trials that can be applied widely to neurodegenerative disorders

OAE: The Ontology of Adverse Events

A medical intervention is a medical procedure or application intended to relieve or prevent illness or injury. Examples of medical interventions include vaccination and drug administration. After a medical intervention, adverse events (AEs) may occur which lie outside the intended consequences of the intervention. The representation and analysis of AEs are critical to the improvement of public health. Description: The Ontology of Adverse Events (OAE), previously named Adverse Event Ontology (AEO), is a community-driven ontology developed to standardize and integrate data relating to AEs arising subsequent to medical interventions, as well as to support computer-assisted reasoning. OAE has over 3,000 terms with unique identifiers, including terms imported from existing ontologies and more than 1,800 OAE-specific terms. In OAE, the term ‘adverse event’ denotes a pathological bodily process in a patient that occurs after a medical intervention. Causal adverse events are defined by OAE as those events that are causal consequences of a medical intervention. OAE represents various adverse events based on patient anatomic regions and clinical outcomes, including symptoms, signs, and abnormal processes. OAE has been used in the analysis of several different sorts of vaccine and drug adverse event data

OAE: The Ontology of Adverse Events

A medical intervention is a medical procedure or application intended to relieve or prevent illness or injury. Examples of medical interventions include vaccination and drug administration. After a medical intervention, adverse events (AEs) may occur which lie outside the intended consequences of the intervention. The representation and analysis of AEs are critical to the improvement of public health. Description: The Ontology of Adverse Events (OAE), previously named Adverse Event Ontology (AEO), is a community-driven ontology developed to standardize and integrate data relating to AEs arising subsequent to medical interventions, as well as to support computer-assisted reasoning. OAE has over 3,000 terms with unique identifiers, including terms imported from existing ontologies and more than 1,800 OAE-specific terms. In OAE, the term ‘adverse event’ denotes a pathological bodily process in a patient that occurs after a medical intervention. Causal adverse events are defined by OAE as those events that are causal consequences of a medical intervention. OAE represents various adverse events based on patient anatomic regions and clinical outcomes, including symptoms, signs, and abnormal processes. OAE has been used in the analysis of several different sorts of vaccine and drug adverse event data

An ontology to standardize research output of nutritional epidemiology : from paper-based standards to linked content

Background: The use of linked data in the Semantic Web is a promising approach to add value to nutrition research. An ontology, which defines the logical relationships between well-defined taxonomic terms, enables linking and harmonizing research output. To enable the description of domain-specific output in nutritional epidemiology, we propose the Ontology for Nutritional Epidemiology (ONE) according to authoritative guidance for nutritional epidemiology. Methods: Firstly, a scoping review was conducted to identify existing ontology terms for reuse in ONE. Secondly, existing data standards and reporting guidelines for nutritional epidemiology were converted into an ontology. The terms used in the standards were summarized and listed separately in a taxonomic hierarchy. Thirdly, the ontologies of the nutritional epidemiologic standards, reporting guidelines, and the core concepts were gathered in ONE. Three case studies were included to illustrate potential applications: (i) annotation of existing manuscripts and data, (ii) ontology-based inference, and (iii) estimation of reporting completeness in a sample of nine manuscripts. Results: Ontologies for food and nutrition (n = 37), disease and specific population (n = 100), data description (n = 21), research description (n = 35), and supplementary (meta) data description (n = 44) were reviewed and listed. ONE consists of 339 classes: 79 new classes to describe data and 24 new classes to describe the content of manuscripts. Conclusion: ONE is a resource to automate data integration, searching, and browsing, and can be used to assess reporting completeness in nutritional epidemiology

The impact of impaired self-awareness on the assessment of fatigue and rehabilitation in brain injury

This portfolio thesis involves three parts. Part one includes a systematic literature review, part two includes an empirical paper and part three includes the appendices. Part one- Systematic Literature Review The Systematic Literature Review explored the impact of impaired self-awareness (ISA) on the process of rehabilitation in acquired brain injury populations. This review identified 16 studies which were analysed using Narrative Synthesis. Four key themes arose from the analysis, including goal setting, treatment adherence, engagement and willingness to change and time spent in hospital. The findings explored the impact that ISA can have on different areas of the rehabilitation process and how this can impact on recovery. The clinical implications and areas for further research are described. Part two- Empirical Paper The empirical paper is part of a larger project to validate and explore the Brain Injury Fatigue Scale (BIFS). The BIFS is an unpublished measure of fatigue that is widely used in clinical practice. This study investigated the degree of agreement between the self and proxy (i.e., carer/relative/friend) ratings of the BIFS and explored what variables best predict any differences in scores, including level of awareness and patients’ mood. Eleven individuals with acquired brain injuries (ABI) or neurological conditions and their proxies completed the BIFS and Patient Competency Rating Scale (PCRS). Patients also completed the Hospital Anxiety and Depression Scale (HADS) and their demographic data was collected. This study found that that 63.64% of patients rated their fatigue within the same clinical cut off category as their proxies’ ratings. It was also found that ISA and mood did not predict BIFS-Discrepancy scores. This study therefore found a moderate level of agreement between patient and proxy BIF ratings; however, it also emphasises the importance of using proxy ratings scales within this area, which has not previously been explored. Further self and proxy ratings of fatigue is required. Part three- Part three includes the appendices relating to the systematic literature review and the empirical paper, as well as the epistemological and reflective statements

Living with Multiple Sclerosis: A Phenomenological Study of Worries, Concern and Psychological Problems in Iranian Patients with MS

Multiple sclerosis (MS), as a progressive and degenerative illness, has an impact on different aspects of individual lives and may lead to difficulties, concerns, and worries in patients. The aim of the present study was to investigate concerns, worries and problems in patients with MS. We used a descriptive phenomenological qualitative approach. Participants were volunteers purposively selected based on their availability. We carried out deep interviews with 15 MS patients and analyzed the detailed information obtained from these interviews by using Colaizzi’s method. We extracted six essential themes and thirty-four sub-themes associated with MS from the content of the interviews. The main themes were labelled “Confronting existential concerns,” “Crisis of facing up with the illness,” “Suffering from the illness,” “Relationship,” “Confrontation with spirituality and religion,” “Searching for tranquility.” Results of the present study also reiterated the following: Patients with MS seem to lose meaning of life and this together with problems in dealing with existential concerns, may lead to the “disintegration of self,” hence resulting in considerable psychological disturbance and distress. It is concluded that the illness evokes psychological injury such as existential anxiety, relationship disturbance and hopelessness, and these psychological injuries can lead to relapsing of MS

Stroke outcome measurements from electronic medical records : cross-sectional study on the effectiveness of neural and nonneural classifiers

Background: With the rapid adoption of electronic medical records (EMRs), there is an ever-increasing opportunity to collect data and extract knowledge from EMRs to support patient-centered stroke management. Objective: This study aims to compare the effectiveness of state-of-the-art automatic text classification methods in classifying data to support the prediction of clinical patient outcomes and the extraction of patient characteristics from EMRs. Methods: Our study addressed the computational problems of information extraction and automatic text classification. We identified essential tasks to be considered in an ischemic stroke value-based program. The 30 selected tasks were classified (manually labeled by specialists) according to the following value agenda: tier 1 (achieved health care status), tier 2 (recovery process), care related (clinical management and risk scores), and baseline characteristics. The analyzed data set was retrospectively extracted from the EMRs of patients with stroke from a private Brazilian hospital between 2018 and 2019. A total of 44,206 sentences from free-text medical records in Portuguese were used to train and develop 10 supervised computational machine learning methods, including state-of-the-art neural and nonneural methods, along with ontological rules. As an experimental protocol, we used a 5-fold cross-validation procedure repeated 6 times, along with subject-wise sampling. A heatmap was used to display comparative result analyses according to the best algorithmic effectiveness (F1 score), supported by statistical significance tests. A feature importance analysis was conducted to provide insights into the results. Results: The top-performing models were support vector machines trained with lexical and semantic textual features, showing the importance of dealing with noise in EMR textual representations. The support vector machine models produced statistically superior results in 71% (17/24) of tasks, with an F1 score >80% regarding care-related tasks (patient treatment location, fall risk, thrombolytic therapy, and pressure ulcer risk), the process of recovery (ability to feed orally or ambulate and communicate), health care status achieved (mortality), and baseline characteristics (diabetes, obesity, dyslipidemia, and smoking status). Neural methods were largely outperformed by more traditional nonneural methods, given the characteristics of the data set. Ontological rules were also effective in tasks such as baseline characteristics (alcoholism, atrial fibrillation, and coronary artery disease) and the Rankin scale. The complementarity in effectiveness among models suggests that a combination of models could enhance the results and cover more tasks in the future. Conclusions: Advances in information technology capacity are essential for scalability and agility in measuring health status outcomes. This study allowed us to measure effectiveness and identify opportunities for automating the classification of outcomes of specific tasks related to clinical conditions of stroke victims, and thus ultimately assess the possibility of proactively using these machine learning techniques in real-world situations