Detecting Bots Using a Hybrid Approach

Artificial intelligence (AI) remains a crucial aspect for improving our modern lives but it also casts several social and ethical issues. One issue is of major concern, investigated in this research, is the amount of content users consume that is being generated by a form of AI known as bots (automated software programs). With the rise of social bots and the spread of fake news more research is required to understand how much content generated by bots is being consumed. This research investigates the amount of bot generated content relating to COVID-19. While research continues to uncover the extent to which our social media platforms are being used as a terrain to spread information and misinformation, there still remain issues when it comes to distinguishing between social bots and humans that spread misinformation. Since online platforms have become a center for spreading fake information that is often accelerated using bots this research examines the amount of bot generated COVID-19 content on Twitter. A hybrid approach is presented to detect bots using a Covid-19 dataset of 71,908 tweets collected between January 22nd, 2020 and April 2020, when the total reported cases of Covid-19 were below 600 globally. Three experiments were conducted using user account features, topic analysis, and sentiment features to detect bots and misinformation relating to the Covid-19 pandemic. Using Weka Machine Learning Tool, Experiment I investigates the optimal algorithms that can be used to detect bots on Twitter. We used 10-fold cross validation to test for prediction accuracy on two labelled datasets. Each dataset contains a different set (category 1 and category 2) of four features. Results from Experiment I show that category 1 features (favorite count, listed count, name length, and number of tweets) combined with random forest algorithm produced the best prediction accuracy and performed better than features found in category 2 (follower count, following count, length of screen name and description length). The best feature was listed count followed by favorite count. It was also observed that using category 2 features for the two labelled datasets produced the same prediction accuracy (100%) when Tree based classifiers are used. To further investigate the validity of the features used in the two labelled datasets, in Experiment II, each labelled dataset from Experiment I was used as a training sample to classify two different labelled datasets. Results show that Category 1 features generated a 94% prediction accuracy as compared to 60% accuracy generated by category 2 features using the Random Forest algorithm. Experiment III applies the results from Experiment I and II to classify 39,091 account that posted Coronavirus related content. Using the random forest algorithm and features identified Experiment I and II, our classification framework detected 5867 out of 39,091 (15%) account as bots and 33,224 (85%) accounts as humans. Further analysis revealed that bot accounts generated 30% (1949/6446) of Coronavirus misinformation compared to 70% of misinformation created by human accounts. Closer examination showed that about 30% of misinformation created by humans were retweets of bot content. In addition, results suggest that bot accounts were involved in posting content on fewer topics compared to humans. Our results also show that bots generated more negative sentiments as compared to humans on Covid-19 related issues. Consequently, topic distribution and sentiment may further improve the ability to distinguish between bot and human accounts

Natural Products in Renal-Associated Drug Discovery

The global increase in the incidence of kidney failure constitutes a major public health problem. Kidney disease is classified into acute and chronic: acute kidney injury (AKI) is associated with an abrupt decline in kidney function and chronic kidney disease (CKD) with chronic renal failure for more than three months. Although both kidney syndromes are multifactorial, inflammation and oxidative stress play major roles in the diversity of processes leading to these kidney malfunctions. Here, we reviewed various publications on medicinal plants with antioxidant and anti-inflammatory properties with the potential to treat and manage kidney-associated diseases in rodent models. Additionally, we conducted a meta-analysis to identify gene signatures and associated biological processes perturbed in human and mouse cells treated with antioxidants such as epigallocatechin gallate (EGCG), the active ingredient in green tea, and the mushroom Ganoderma lucidum (GL) and in kidney disease rodent models. We identified EGCG- and GL-regulated gene signatures linked to metabolism; inflammation (NRG1, E2F1, NFKB1 and JUN); ion signalling; transport; renal processes (SLC12A1 and LOX) and VEGF, ERBB and BDNF signalling. Medicinal plant extracts are proving to be effective for the prevention, management and treatment of kidney-associated diseases; however, more detailed characterisations of their targets are needed to enable more trust in their application in the management of kidney-associated diseases

Functional network resilience to pathology in presymptomatic genetic frontotemporal dementia.

The presymptomatic phase of neurodegenerative diseases are characterized by structural brain changes without significant clinical features. We set out to investigate the contribution of functional network resilience to preserved cognition in presymptomatic genetic frontotemporal dementia. We studied 172 people from families carrying genetic abnormalities in C9orf72, MAPT, or PGRN. Networks were extracted from functional MRI data and assessed using graph theoretical analysis. We found that despite loss of both brain volume and functional connections, there is maintenance of an efficient topological organization of the brain's functional network in the years leading up to the estimated age of frontotemporal dementia symptom onset. After this point, functional network efficiency declines markedly. Reduction in connectedness was most marked in highly connected hub regions. Measures of topological efficiency of the brain's functional network and organization predicted cognitive dysfunction in domains related to symptomatic frontotemporal dementia and connectivity correlated with brain volume loss in frontotemporal dementia. We propose that maintaining the efficient organization of the brain's functional network supports cognitive health even as atrophy and connectivity decline presymptomatically.The Italian Ministry of Health The Canadian Institutes of Health Research as part of a Centres of Excellence in Neurodegeneration grant [grant number CoEN01

Downregulation of exosomal miR-204-5p and miR-632 as a biomarker for FTD: a GENFI study

OBJECTIVE: To determine whether exosomal microRNAs (miRNAs) in cerebrospinal fluid (CSF) of patients with frontotemporal dementia (FTD) can serve as diagnostic biomarkers, we assessed miRNA expression in the Genetic Frontotemporal Dementia Initiative (GENFI) cohort and in sporadic FTD. METHODS: GENFI participants were either carriers of a pathogenic mutation in progranulin, chromosome 9 open reading frame 72 or microtubule-associated protein tau or were at risk of carrying a mutation because a first-degree relative was a known symptomatic mutation carrier. Exosomes were isolated from CSF of 23 presymptomatic and 15 symptomatic mutation carriers and 11 healthy non-mutation carriers. Expression of 752 miRNAs was measured using quantitative PCR (qPCR) arrays and validated by qPCR using individual primers. MiRNAs found differentially expressed in symptomatic compared with presymptomatic mutation carriers were further evaluated in a cohort of 17 patients with sporadic FTD, 13 patients with sporadic Alzheimer's disease (AD) and 10 healthy controls (HCs) of similar age. RESULTS: In the GENFI cohort, miR-204-5p and miR-632 were significantly decreased in symptomatic compared with presymptomatic mutation carriers. Decrease of miR-204-5p and miR-632 revealed receiver operator characteristics with an area of 0.89 (90% CI 0.79 to 0.98) and 0.81 (90% CI 0.68 to 0.93), respectively, and when combined an area of 0.93 (90% CI 0.87 to 0.99). In sporadic FTD, only miR-632 was significantly decreased compared with AD and HCs. Decrease of miR-632 revealed an area of 0.90 (90% CI 0.81 to 0.98). CONCLUSIONS: Exosomal miR-204-5p and miR-632 have potential as diagnostic biomarkers for genetic FTD and miR-632 also for sporadic FTD

Conceptual framework for the definition of preclinical and prodromal frontotemporal dementia

The presymptomatic stages of frontotemporal dementia (FTD) are still poorly defined and encompass a long accrual of progressive biological (preclinical) and then clinical (prodromal) changes, antedating the onset of dementia. The heterogeneity of clinical presentations and the different neuropathological phenotypes have prevented a prior clear description of either preclinical or prodromal FTD. Recent advances in therapeutic approaches, at least in monogenic disease, demand a proper definition of these predementia stages. It has become clear that a consensus lexicon is needed to comprehensively describe the stages that anticipate dementia. The goal of the present work is to review existing literature on the preclinical and prodromal phases of FTD, providing recommendations to address the unmet questions, therefore laying out a strategy for operationalizing and better characterizing these presymptomatic disease stages

Downregulation of exosomal miR-204-5p and miR-632 as a biomarker for FTD: a GENFI study.

OBJECTIVE: To determine whether exosomal microRNAs (miRNAs) in cerebrospinal fluid (CSF) of patients with frontotemporal dementia (FTD) can serve as diagnostic biomarkers, we assessed miRNA expression in the Genetic Frontotemporal Dementia Initiative (GENFI) cohort and in sporadic FTD. METHODS: GENFI participants were either carriers of a pathogenic mutation in progranulin, chromosome 9 open reading frame 72 or microtubule-associated protein tau or were at risk of carrying a mutation because a first-degree relative was a known symptomatic mutation carrier. Exosomes were isolated from CSF of 23 presymptomatic and 15 symptomatic mutation carriers and 11 healthy non-mutation carriers. Expression of 752 miRNAs was measured using quantitative PCR (qPCR) arrays and validated by qPCR using individual primers. MiRNAs found differentially expressed in symptomatic compared with presymptomatic mutation carriers were further evaluated in a cohort of 17 patients with sporadic FTD, 13 patients with sporadic Alzheimer's disease (AD) and 10 healthy controls (HCs) of similar age. RESULTS: In the GENFI cohort, miR-204-5p and miR-632 were significantly decreased in symptomatic compared with presymptomatic mutation carriers. Decrease of miR-204-5p and miR-632 revealed receiver operator characteristics with an area of 0.89 (90% CI 0.79 to 0.98) and 0.81 (90% CI 0.68 to 0.93), respectively, and when combined an area of 0.93 (90% CI 0.87 to 0.99). In sporadic FTD, only miR-632 was significantly decreased compared with AD and HCs. Decrease of miR-632 revealed an area of 0.90 (90% CI 0.81 to 0.98). CONCLUSIONS: Exosomal miR-204-5p and miR-632 have potential as diagnostic biomarkers for genetic FTD and miR-632 also for sporadic FTD

Cognitive composites for genetic frontotemporal dementia: GENFI-Cog.

BACKGROUND: Clinical endpoints for upcoming therapeutic trials in frontotemporal dementia (FTD) are increasingly urgent. Cognitive composite scores are often used as endpoints but are lacking in genetic FTD. We aimed to create cognitive composite scores for genetic frontotemporal dementia (FTD) as well as recommendations for recruitment and duration in clinical trial design. METHODS: A standardized neuropsychological test battery covering six cognitive domains was completed by 69 C9orf72, 41 GRN, and 28 MAPT mutation carriers with CDR® plus NACC-FTLD ≥ 0.5 and 275 controls. Logistic regression was used to identify the combination of tests that distinguished best between each mutation carrier group and controls. The composite scores were calculated from the weighted averages of test scores in the models based on the regression coefficients. Sample size estimates were calculated for individual cognitive tests and composites in a theoretical trial aimed at preventing progression from a prodromal stage (CDR® plus NACC-FTLD 0.5) to a fully symptomatic stage (CDR® plus NACC-FTLD ≥ 1). Time-to-event analysis was performed to determine how quickly mutation carriers progressed from CDR® plus NACC-FTLD = 0.5 to ≥ 1 (and therefore how long a trial would need to be). RESULTS: The results from the logistic regression analyses resulted in different composite scores for each mutation carrier group (i.e. C9orf72, GRN, and MAPT). The estimated sample size to detect a treatment effect was lower for composite scores than for most individual tests. A Kaplan-Meier curve showed that after 3 years, ~ 50% of individuals had converted from CDR® plus NACC-FTLD 0.5 to ≥ 1, which means that the estimated effect size needs to be halved in sample size calculations as only half of the mutation carriers would be expected to progress from CDR® plus NACC FTLD 0.5 to ≥ 1 without treatment over that time period. DISCUSSION: We created gene-specific cognitive composite scores for C9orf72, GRN, and MAPT mutation carriers, which resulted in substantially lower estimated sample sizes to detect a treatment effect than the individual cognitive tests. The GENFI-Cog composites have potential as cognitive endpoints for upcoming clinical trials. The results from this study provide recommendations for estimating sample size and trial duration

Progranulin plasma levels predict the presence of GRN mutations in asymptomatic subjects and do not correlate with brain atrophy: results from the GENFI study.

We investigated whether progranulin plasma levels are predictors of the presence of progranulin gene (GRN) null mutations or of the development of symptoms in asymptomatic at risk members participating in the Genetic Frontotemporal Dementia Initiative, including 19 patients, 64 asymptomatic carriers, and 77 noncarriers. In addition, we evaluated a possible role of TMEM106B rs1990622 as a genetic modifier and correlated progranulin plasma levels and gray-matter atrophy. Plasma progranulin mean ± SD plasma levels in patients and asymptomatic carriers were significantly decreased compared with noncarriers (30.5 ± 13.0 and 27.7 ± 7.5 versus 99.6 ± 24.8 ng/mL, p 61.55 ng/mL, the test had a sensitivity of 98.8% and a specificity of 97.5% in predicting the presence of a mutation, independent of symptoms. No correlations were found between progranulin plasma levels and age, years from average age at onset in each family, or TMEM106B rs1990622 genotype (p > 0.05). Plasma progranulin levels did not correlate with brain atrophy. Plasma progranulin levels predict the presence of GRN null mutations independent of proximity to symptoms and brain atrophy

Patterns of gray matter atrophy in genetic frontotemporal dementia: results from the GENFI study.

Frontotemporal dementia (FTD) is a highly heritable condition with multiple genetic causes. In this study, similarities and differences of gray matter (GM) atrophy patterns were assessed among 3 common forms of genetic FTD (mutations in C9orf72, GRN, and MAPT). Participants from the Genetic FTD Initiative (GENFI) cohort with a suitable volumetric T1 magnetic resonance imaging scan were included (319): 144 nonmutation carriers, 128 presymptomatic mutation carriers, and 47 clinically affected mutation carriers. Cross-sectional differences in GM volume between noncarriers and carriers were analyzed using voxel-based morphometry. In the affected carriers, each genetic mutation group exhibited unique areas of atrophy but also a shared network involving the insula, orbitofrontal lobe, and anterior cingulate. Presymptomatic GM atrophy was observed particularly in the thalamus and cerebellum in the C9orf72 group, the anterior and medial temporal lobes in MAPT, and the posterior frontal and parietal lobes as well as striatum in GRN. Across all presymptomatic carriers, there were significant decreases in the anterior insula. These results suggest that although there are important differences in atrophy patterns for each group (which can be seen presymptomatically), there are also similarities (a fronto-insula-anterior cingulate network) that help explain the clinical commonalities of the disease

Ventricular volume expansion in presymptomatic genetic frontotemporal dementia

OBJECTIVE: To characterize the time course of ventricular volume expansion in genetic frontotemporal dementia (FTD) and identify the onset time and rates of ventricular expansion in presymptomatic FTD mutation carriers. METHODS: Participants included patients with a mutation in MAPT, PGRN, or C9orf72, or first-degree relatives of mutation carriers from the GENFI study with MRI scans at study baseline and at 1 year follow-up. Ventricular volumes were obtained from MRI scans using FreeSurfer, with manual editing of segmentation and comparison to fully automated segmentation to establish reliability. Linear mixed models were used to identify differences in ventricular volume and in expansion rates as a function of time to expected disease onset between presymptomatic carriers and noncarriers. RESULTS: A total of 123 participants met the inclusion criteria and were included in the analysis (18 symptomatic carriers, 46 presymptomatic mutation carriers, and 56 noncarriers). Ventricular volume differences were observed 4 years prior to symptom disease onset for presymptomatic carriers compared to noncarriers. Annualized rates of ventricular volume expansion were greater in presymptomatic carriers relative to noncarriers. Importantly, time-intensive manually edited and fully automated ventricular volume resulted in similar findings. CONCLUSIONS: Ventricular volume differences are detectable in presymptomatic genetic FTD. Concordance of results from time-intensive manual editing and fully automatic segmentation approaches support its value as a measure of disease onset and progression in future studies in both presymptomatic and symptomatic genetic FTD