Blockchain-Based Innovations for Population-Based Registries for Rare Neurodegenerative Diseases

Rare diseases are difficult if not impossible to study outside of population-based registries. Particularly in the context of rare neurodegenerative diseases characterized by case heterogeneity, difficult differential diagnosis by specialists, and small numbers of patients, registries make otherwise unfeasible incidence studies cost-effective and manageable. Building up and maintaining such registries is challenging and requires strong, active, and collaborative networks. Centralization around a leading institution provides structure and consistency, but this single-site storage leads to inefficiency and bottlenecks and is prone to failures, attacks, and manipulation. Furthermore, a substantial amount of trust is required between parties sharing data in a traditional registry. Patients are increasingly reluctant to share data in light of regular news reports about healthcare data breaches. Underfunded rare disease specialized centers are also hesitant to exchange with the leading institution out of fear that the low numbers of patients may seek treatment elsewhere. A lack of electronic health records and information system interoperability in certain settings leads to information silos and only further exacerbate the other issues. Blockchain technology may provide unique, innovative solutions to many of these challenges. Specifically, through digital trust and the use of an immutable distributed ledger, automated data transaction processing, guaranteed integrity, and enhanced security, blockchain technology seems to be perfectly suitable to optimize current population-based rare neurodegenerative disease registry construction and maintenance

Directed acyclic graphs and causal thinking in clinical risk prediction modeling

Background: In epidemiology, causal inference and prediction modeling methodologies have been historically distinct. Directed Acyclic Graphs (DAGs) are used to model a priori causal assumptions and inform variable selection strategies for causal questions. Although tools originally designed for prediction are finding applications in causal inference, the counterpart has remained largely unexplored. The aim of this theoretical and simulation-based study is to assess the potential benefit of using DAGs in clinical risk prediction modeling. Methods: We explore how incorporating knowledge about the underlying causal structure can provide insights about the transportability of diagnostic clinical risk prediction models to different settings. We further probe whether causal knowledge can be used to improve predictor selection in clinical risk prediction models. Results: A single-predictor model in the causal direction is likely to have better transportability than one in the anticausal direction in some scenarios. We empirically show that the Markov Blanket, the set of variables including the parents, children, and parents of the children of the outcome node in a DAG, is the optimal set of predictors for that outcome. Conclusions: Our findings provide a theoretical basis for the intuition that a diagnostic clinical risk prediction model including causes as predictors is likely to be more transportable. Furthermore, using DAGs to identify Markov Blanket variables may be a useful, efficient strategy to select predictors in clinical risk prediction models if strong knowledge of the underlying causal structure exists or can be learned

Blockchain-Based Innovations for Population-Based Registries for Rare Neurodegenerative Diseases

Rare diseases are difficult if not impossible to study outside of population-based registries. Particularly in the context of rare neurodegenerative diseases characterized by case heterogeneity, difficult differential diagnosis by specialists, and small numbers of patients, registries make otherwise unfeasible incidence studies cost-effective and manageable. Building up and maintaining such registries is challenging and requires strong, active, and collaborative networks. Centralization around a leading institution provides structure and consistency, but this single-site storage leads to inefficiency and bottlenecks and is prone to failures, attacks, and manipulation. Furthermore, a substantial amount of trust is required between parties sharing data in a traditional registry. Patients are increasingly reluctant to share data in light of regular news reports about healthcare data breaches. Underfunded rare disease specialized centers are also hesitant to exchange with the leading institution out of fear that the low numbers of patients may seek treatment elsewhere. A lack of electronic health records and information system interoperability in certain settings leads to information silos and only further exacerbate the other issues. Blockchain technology may provide unique, innovative solutions to many of these challenges. Specifically, through digital trust and the use of an immutable distributed ledger, automated data transaction processing, guaranteed integrity, and enhanced security, blockchain technology seems to be perfectly suitable to optimize current population-based rare neurodegenerative disease registry construction and maintenance

Study of Tailor Heat Treated Blanks Using the Fourier-series-based VFM

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Correction: Performance of risk prediction scores for cardiovascular mortality in older persons: External validation of the SCORE OP and appraisal

[This corrects the article DOI: 10.1371/journal.pone.0231097.

Public perspectives on protective measures during the COVID-19 pandemic in the Netherlands, Germany and Italy: A survey study

Background: The extent to which people implement government-issued protective measures is critical in preventing further spread of coronavirus disease 2019 (COVID-19) caused by coronavirus SARS-CoV-2. Our study aimed to describe the public belief in the effectiveness of protective measures, the reported implementation of these measures, and to identify communication channels used to acquire information on COVID-19 in European countries during the early stage of the pandemic. Methods and findings: An online survey available in multiple languages was disseminated starting on March 19th, 2020. After five days, we computed descriptive statistics for countries with more than 500 respondents. Each day, we assessed enacted community containment measures by stage of stringency (I-IV). In total, 9,796 adults responded, of whom 8,611 resided in the Netherlands (stage III), 604 in Germany (stage III), and 581 in Italy (stage IV). To explore possible dynamics as containment strategies intensified, we also included 1,365 responses submitted during the following week. Participants indicated support for governmental measures related to avoiding social gatherings, selective closure of public places, and hand hygiene and respiratory measures (range for all measures: 95.0%-99.7%). Respondents from the Netherlands less frequently considered a complete social lockdown effective (59.2%), compared to respondents in Germany (76.6%) or Italy (87.2%). Italian residents applied enforced social distancing measures more frequently (range: 90.2%-99.3%, German and Dutch residents: 67.5%-97.0%) and self-initiated hygienic and social distancing behaviors (range: 36.3%-96.6%, German and Dutch residents: 28.3%-95.7%). Respondents reported being sufficiently informed about the outbreak and behaviors to avoid infection (range: 90.2%-91.1%). Information channels most commonly reported included television newspapers, official health websites, and social media. One week later, we observed no major differences in submitted responses. Conclusions: During the early stage of the COVID-19 pandemic, belief in the effectiveness of protective measures among survey respondents from three European countries was high and participants reported feeling sufficiently informed. In March 2020, implementation of measures differed between countries and were highest among respondents from Italy, who were subjected to the most stringent lockdown measures and greatest COVID-19 burden in Europe during this period

Giving Researchers a Headache - Sex and Gender Differences in Migraine

Migraine is a common neurovascular disorder affecting ∼15% of the general population. Ranking second in the list of years lived with disability (YLD), people living with migraine are greatly impacted by this especially burdensome primary headache disorder. In ∼30% o

The Effect of Socioeconomic Factors and Indoor Residual Spraying on Malaria in Mangaluru, India: A Case-Control Study

India faces 0.5 million malaria cases annually, including half of all Plasmodium vivax malaria cases worldwide. This case–control study assessed socioeconomic determinants of urban malaria in coastal Mangaluru, Karnataka, southwestern India. Between June and December 2015, we recruited 859 malaria patients presenting at the governmental Wenlock Hospital and 2190 asymptomatic community controls. We assessed clinical, parasitological, and socioeconomic data. Among patients, p. vivax mono-infection (70.1%) predominated. Most patients were male (93%), adult (median, 27 years), had no or low-level education (70.3%), and 57.1% were daily labourers or construction workers. In controls (59.3% male; median age, 32 years; no/low-level education, 54.5%; daily labourers/construction workers, 41.3%), 4.1% showed asymptomatic Plasmodium infection. The odds of malaria was reduced among those who had completed 10th school grade (aOR, 0.3; 95% CI, 0.26–0.42), lived in a building with a tiled roof (aOR, 0.71; 95% CI, 0.53–0.95), and reported recent indoor residual spraying (aOR, 0.02; 95% CI, 0.01–0.04). In contrast, migrant status was a risk factor for malaria (aOR, 2.43; 95% CI, 1.60–3.67). Malaria in Mangaluru is influenced by education, housing condition, and migration. Indoor residual spraying greatly contributes to reducing malaria in this community and should be promoted, especially among its marginalised members.Peer Reviewe

Incidence of Syndromes Associated With Frontotemporal Lobar Degeneration in 9 European Countries

Importance Diagnostic incidence data for syndromes associated with frontotemporal lobar degeneration (FTLD) in multinational studies are urgent in light of upcoming therapeutic approaches.Objective To assess the incidence of FTLD across Europe.Design, Setting, and Participants The Frontotemporal Dementia Incidence European Research Study (FRONTIERS) was a retrospective cohort study conducted from June 1, 2018, to May 31, 2019, using a population-based registry from 13 tertiary FTLD research clinics from the UK, the Netherlands, Finland, Sweden, Spain, Bulgaria, Serbia, Germany, and Italy and including all new FTLD-associated cases during the study period, with a combined catchment population of 11 023 643 person-years. Included patients fulfilled criteria for the behavioral variant of frontotemporal dementia (BVFTD), the nonfluent variant or semantic variant of primary progressive aphasia (PPA), unspecified PPA, progressive supranuclear palsy, corticobasal syndrome, or frontotemporal dementia with amyotrophic lateral sclerosis (FTD-ALS). Data were analyzed from July 19 to December 7, 2021.Main Outcomes and Measures Random-intercept Poisson models were used to obtain estimates of the European FTLD incidence rate accounting for geographic heterogeneity.Results Based on 267 identified cases (mean [SD] patient age, 66.70 [9.02] years; 156 males [58.43%]), the estimated annual incidence rate for FTLD in Europe was 2.36 cases per 100 000 person-years (95% CI, 1.59-3.51 cases per 100 000 person-years). There was a progressive increase in FTLD incidence across age, reaching its peak at the age of 71 years, with 13.09 cases per 100 000 person-years (95% CI, 8.46-18.93 cases per 100 000 person-years) among men and 7.88 cases per 100 000 person-years (95% CI, 5.39-11.60 cases per 100 000 person-years) among women. Overall, the incidence was higher among men (2.84 cases per 100 000 person-years; 95% CI, 1.88-4.27 cases per 100 000 person-years) than among women (1.91 cases per 100 000 person-years; 95% CI, 1.26-2.91 cases per 100 000 person-years). BVFTD was the most common phenotype (107 cases [40.07%]), followed by PPA (76 [28.46%]) and extrapyramidal phenotypes (69 [25.84%]). FTD-ALS was the rarest phenotype (15 cases [5.62%]). A total of 95 patients with FTLD (35.58%) had a family history of dementia. The estimated number of new FTLD cases per year in Europe was 12 057.Conclusions and Relevance The findings suggest that FTLD-associated syndromes are more common than previously recognized, and diagnosis should be considered at any age. Improved knowledge of FTLD incidence may contribute to appropriate health and social care planning and in the design of future clinical trials.Peer reviewe

Behavioral psychological symptoms of dementia and functional connectivity changes: a network-based study

Behavioral and psychological symptoms of dementia (BPSD) are commonly observed since the early stage of Alzheimer's disease (AD) associated with structural brain changes. It is conceivable that they may also relate to functional brain changes. This resting-state functional MRI (RS-fMRI) study investigated the alterations within functional brain networks of a cohort of AD patients at different clinical stages who presented with BPSD. One hundred one AD patients and 56 patients with amnestic mild cognitive impairment underwent a neuropsychological evaluation including the Neuropsychiatry Inventory-12 (NPI-12). All patients and 35 healthy controls (HS) underwent 3T-MRI. Factor analysis was used to extract the principal factors from NPI-12, while RS-fMRI data were processed using graph theory to investigate functional connectivity. Five factors were extracted from NPI-12. Sixty-two percent of patients showed BPSD and functional brain connectivity changes in various networks compared to those without BPSD and HS. These changes contributed to account for patients' BPSD. This work opens new perspectives in terms of nonpharmacological interventions that might be designed to modulate brain connectivity and improve patients' BPSD