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EFA with raters left in sample 1

Additional file 2: Fig. 2. Frequency of FP terms predicted by CLAMP, cTAKES, and MetaMap. The frequencies of FP terms predicted by the three tools were combined and are shown as a pie graph. Prioritized CUIs represent the set of CUIs from CUI predictions shared by the three tools in the same sentence and co-occurring with a general BM term; non-prioritized CUIs represent all other CUIs. The normalized entity name for the CUI is displayed in square brackets beside the CUI. The number in each section of the pie graph, which the size of the section is scaled to, represents the frequency as a percentage of all FP predictions from CLAMP, cTAKES, and MetaMap combined, and only percentages greater than 2 are labelled.

Two-factor solution Procrustes rotation with raters left in

Procrustes rotation testing 3-factor solution with raters in sample 1

Abstract Background Parkinson���s disease is a disabling neurodegenerative movement disorder characterized by dopaminergic neuron loss induced by ��-synuclein oligomers. There is an urgent need for disease-modifying therapies for Parkinson���s disease, but drug discovery is challenged by lack of in vivo models that recapitulate early stages of neurodegeneration. Invertebrate organisms, such as the nematode worm Caenorhabditis elegans, provide in vivo models of human disease processes that can be instrumental for initial pharmacological studies. Methods To identify early motor impairment of animals expressing ��-synuclein in dopaminergic neurons, we first used a custom-built tracking microscope that captures locomotion of single C. elegans with high spatial and temporal resolution. Next, we devised a method for semi-automated and blinded quantification of motor impairment for a population of simultaneously recorded animals with multi-worm tracking and custom image processing. We then used genetic and pharmacological methods to define the features of early motor dysfunction of ��-synuclein-expressing C. elegans. Finally, we applied the C. elegans model to a drug repurposing screen by combining it with an artificial intelligence platform and cell culture system to identify small molecules that inhibit ��-synuclein oligomers. Screen hits were validated using in vitro and in vivo mammalian models. Results We found a previously undescribed motor phenotype in transgenic ��-synuclein C. elegans that correlates with mutant or wild-type ��-synuclein protein levels and results from dopaminergic neuron dysfunction, but precedes neuronal loss. Together with artificial intelligence-driven in silico and in vitro screening, this C. elegans model identified five compounds that reduced motor dysfunction induced by ��-synuclein. Three of these compounds also decreased ��-synuclein oligomers in mammalian neurons, including rifabutin which has not been previously investigated for Parkinson���s disease. We found that treatment with rifabutin reduced nigrostriatal dopaminergic neurodegeneration due to ��-synuclein in a rat model. Conclusions We identified a C. elegans locomotor abnormality due to dopaminergic neuron dysfunction that models early ��-synuclein-mediated neurodegeneration. Our innovative approach applying this in vivo model to a multi-step drug repurposing screen, with artificial intelligence-driven in silico and in vitro methods, resulted in the discovery of at least one drug that may be repurposed as a disease-modifying therapy for Parkinson���s disease.

EFA with Raters removed sample 1

Abstract The chess game comprises different domains of cognitive function, demands great concentration and attention and is present in many cultures as an instrument of literacy, learning and entertainment. Over the years, many effects of the game on the brain have been studied. Seen that, we reviewed the current literature to analyze the influence of chess on cognitive performance, decision-making process, linking to historical neurological and psychiatric disorders as we describe different diseases related to renowned chess players throughout history, discussing the influences of chess on the brain and behavior.

Abstract Background Determining the mental capacity of psychiatric patients for making healthcare related decisions is crucial in clinical practice. This meta-review of review articles comprehensively examines the current evidence on the capacity of patients with a mental illness to make medical care decisions. Methods Systematic review of review articles following PRISMA recommendations. PubMed, Scopus, CINAHL and PsycInfo were electronically searched up to 31 January 2020. Free text searches and medical subject headings were combined to identify literature reviews and meta-analyses published in English, and summarising studies on the capacity of patients with serious mental illnesses to make healthcare and treatment related decisions, conducted in any clinical setting and with a quantitative synthesis of results. Publications were selected as per inclusion and exclusion criteria. The AMSTAR II tool was used to assess the quality of reviews. Results Eleven publications were reviewed. Variability on methods across studies makes it difficult to precisely estimate the prevalence of decision-making capacity in patients with mental disorders. Nonetheless, up to three-quarters of psychiatric patients, including individuals with serious illnesses such as schizophrenia or bipolar disorder may have capacity to make medical decisions in the context of their illness. Most evidence comes from studies conducted in the hospital setting; much less information exists on the healthcare decision making capacity of mental disorder patients while in the community. Stable psychiatric and non-psychiatric patients may have a similar capacity to make healthcare related decisions. Patients with a mental illness have capacity to judge risk-reward situations and to adequately decide about the important treatment outcomes. Different symptoms may impair different domains of the decisional capacity of psychotic patients. Decisional capacity impairments in psychotic patients are temporal, identifiable, and responsive to interventions directed towards simplifying information, encouraging training and shared decision making. The publications complied satisfactorily with the AMSTAR II critical domains. Conclusions Whilst impairments in decision-making capacity may exist, most patients with a severe mental disorder, such as schizophrenia or bipolar disorder are able to make rational decisions about their healthcare. Best practice strategies should incorporate interventions to help mentally ill patients grow into the voluntary and safe use of medications.

Abstract Background There are numerous barriers to identifying patients with silent brain infarcts (SBIs) and white matter disease (WMD) in routine clinical care. A natural language processing (NLP) algorithm may identify patients from neuroimaging reports, but it is unclear if these reports contain reliable information on these findings. Methods Four radiology residents reviewed 1000 neuroimaging reports (RI) of patients age > 50 years without clinical histories of stroke, TIA, or dementia for the presence, acuity, and location of SBIs, and the presence and severity of WMD. Four neuroradiologists directly reviewed a subsample of 182 images (DR). An NLP algorithm was developed to identify findings in reports. We assessed interrater reliability for DR and RI, and agreement between these two and with NLP. Results For DR, interrater reliability was moderate for the presence of SBIs (k = 0.58, 95 % CI 0.46–0.69) and WMD (k = 0.49, 95 % CI 0.35–0.63), and moderate to substantial for characteristics of SBI and WMD. Agreement between DR and RI was substantial for the presence of SBIs and WMD, and fair to substantial for characteristics of SBIs and WMD. Agreement between NLP and DR was substantial for the presence of SBIs (k = 0.64, 95 % CI 0.53–0.76) and moderate (k = 0.52, 95 % CI 0.39–0.65) for the presence of WMD. Conclusions Neuroimaging reports in routine care capture the presence of SBIs and WMD. An NLP can identify these findings (comparable to direct imaging review) and can likely be used for cohort identification.