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Comparison of recorded diagnoses of multiple sclerosis (MS) and related disorders and disease-modifying therapies (DMTs) in The Danish Multiple Sclerosis Registry (DMSR) with other nationwide health registries. The aim of the study is to describe and compare information on diagnoses of MS and related disorders and treatments with DMTs available in three national registries, highlighting the key differences relevant to MS research and providing insight for researchers for their choice of data source(s) suitable for their study. DMSR is a disease registry encompassing information on persons with MS and related disorders. The Danish National Patient Registry (DNPR) is a registry of activities at Danish hospitals. The Danish National Hospital Medication Registry (DNHMR) contains information on in-hospital prescription medications. The population comprised all persons in DMSR in 2023 who were alive or born after and residing in Denmark on January 1, 1995 (N = 26,474). For this population, we identified DNPR contacts with diagnoses of MS or related disorders and initiated DMTs in DNHMR. Diagnostic and demographic characteristics were reported as recorded in DMSR for the total population, and the subset included in DMSR but not in DNPR. Characteristics of the part of the population identified in DNPR were reported as recorded in DNPR. We calculated the proportions of DMT treatments in DMSR identified in DNHMR. Of the 26,474 persons, 23,857 (90.1%) were recorded with a diagnosis of MS or a related disorder in DNPR. Most (86.6%) of the 2617 persons not identified in DNPR were diagnosed before 1995. The proportion of persons with MS recorded without specification of the disease phenotype was 23.5% in the DMSR and 76.2% in the DNPR. After 2005, only 1.8% of persons with MS were recorded with an unspecified phenotype in DMSR. Of a total of 18,168 initiated DMT treatments in DMSR, 7230 (39.8%) were identified in DNHMR, with proportions ranging from 0.0% (mitoxantrone) to 88.5% (ocrelizumab). DMSR is suitable for disease-specific research addressing treatment efficacy, disease development, and long-term outcomes. In contrast, DNPR is well suited for broad epidemiological studies involving various health conditions and hospital utilization. The DNHMR will, when matured, be useful for studies involving medical treatment and comedication. However, as each registry has limitations, for most studies the best approach will be combining registries, depending on the research question.

Background: Environmental factors are known to influence the clinical presentation of patients with multiple sclerosis. This study aims to compare the demographic and clinical characteristics of multiple sclerosis patients treated at two diverse geographical settings. Methods: A cross-sectional, observational cohort study was conducted in two MS centers: the Danish Multiple Sclerosis Center (DMSC) in Copenhagen, Denmark and the Regional MS Center in Târgu Mureș, Romania. We compared patients’ demographic and clinical characteristics between MS centers, including sex distribution, current age, MS onset age, latest EDSS scores, symptomatology at disease onset, MS phenotype and type of ongoing DMT. Results: In both cohorts, sex distribution was similar, with females constituting 69.2% in DMSC, and 65.7% in Târgu Mureș. Pyramidal symptoms at MS onset were predominant among Targu Mures patients (32.7%), while sensory symptoms were more frequent among DMSC patients (33%). Progressive forms of MS were more prevalent in Târgu Mureș (22.6%) compared to DMSC (9.9%). High-efficacy DMTs were on use by 58.3% patients in DMSC and only by 29.4% patients in Târgu Mureș, who were mostly on low-efficacy DMTs (54.4% vs. 12.4% in DMSC). Conclusions: The study highlights both shared and distinct characteristics of MS patients treated in these two centers. These findings underscore the importance of regional considerations in the management and treatment of MS.

In this paper, a framework based on a sparse Mixture of Experts (MoE) architecture is proposed for the federated learning and application of a distributed classification model in domains (like cybersecurity and healthcare) where different parties of the federation store different subsets of features for a number of data instances. The framework is designed to limit the risk of information leakage and computation/communication costs in both model training (through data sampling) and application (leveraging the conditional-computation abilities of sparse MoEs). Experiments on real data have shown the proposed approach to ensure a better balance between efficiency and model accuracy, compared to other VFL-based solutions. Notably, in a real-life cybersecurity case study focused on malware classification (the KronoDroid dataset), the proposed method surpasses competitors even though it utilizes only 50% and 75% of the training set, which is fully utilized by the other approaches in the competition. This method achieves reductions in the rate of false positives by 16.9% and 18.2%, respectively, and also delivers satisfactory results on the other evaluation metrics. These results showcase our framework’s potential to significantly enhance cybersecurity threat detection and prevention in a collaborative yet secure manner.

Background In Denmark, specialized multiple sclerosis (MS) clinics offer free-of-charge treatment to people with MS. However, not all people with MS attend regular clinical follow-up. Objective To identify people with MS who do not attend Danish MS clinics and identify barriers to treatment. Methods The Danish Multiple Sclerosis Registry was linked to other national Danish registries with follow-up from 2000–2020. We used a time-dependent Cox regression to rank factors associated with low attendance to clinical follow-up visits based on the magnitude of hazard ratios (HRs). Results We included 10,175 adults with MS, of which 3862 (38%) had less than one visit annually. The five top-ranked factors that reduced the risk of visits occurring included never having received diseases modifying treatment (HR: 0.48; 95%CI: 0.46–0.49), been diagnosed with MS before 2009 (0.79; 0.78–0.81), association with MS center in an outer region of Denmark (0.82; 0.80–0.84), having progressive MS type (0.88; 0.86–0.91) and not having received symptomatic treatment at diagnosis (0.91; 0.89–0.93). Conclusion Our results highlight disease-specific and geographic inequalities in the management of people with MS in Denmark. Strategies to prevent this inequality, especially for people with progressive phenotypes and those who need supportive and non-medical treatment and care, should be implemented.

Introduction: Prescribing guidance for disease-modifying treatment (DMT) in multiple sclerosis (MS) is centred on a clinical diagnosis of relapsing–remitting MS (RRMS). DMT prescription guidelines and monitoring vary across countries. Standardising the approach to diagnosis of disease course, for example, assigning RRMS or secondary progressive MS (SPMS) diagnoses, allows examination of the impact of health system characteristics on the stated clinical diagnosis and treatment access. Methods: We analysed registry data from six cohorts in five countries (Czech Republic, Denmark, Germany, Sweden and United Kingdom) on patients with an initial diagnosis of RRMS. We standardised our approach utilising a pre-existing algorithm (DecisionTree, DT) to determine patient diagnoses of RRMS or secondary progressive MS (SPMS). We identified five global drivers of DMT prescribing: Provision, Availability, Funding, Monitoring and Audit, data were analysed against these concepts using meta-analysis and univariate meta-regression. Results: In 64,235 patients, we found variations in DMT use between countries, with higher usage in RRMS and lower usage in SPMS, with correspondingly lower usage in the UK compared to other registers. Factors such as female gender (p = 0.041), increasing disability via Expanded Disability Status Scale (EDSS) score (p = 0.004), and the presence of monitoring (p = 0.029) in SPMS influenced the likelihood of receiving DMTs. Standardising the diagnosis revealed differences in reclassification rates from clinical RRMS to DT-SPMS, with Sweden having the lowest rate Sweden (Sweden 0.009, range: Denmark 0.103 – UK portal 0.311). Those with higher EDSS at index (p < 0.03) and female gender (p < 0.049) were more likely to be reclassified from RRMS to DT-SPMS. The study also explored the impact of diagnosis on DMT usage in clinical SPMS, finding that the prescribing environment and auditing practices affected access to treatment. Discussion: This highlights the importance of a healthcare system’s approach to verifying the clinical label of MS course in facilitating appropriate prescribing, with some flexibility allowed in uncertain cases to ensure continued access to treatment.

During colony relocation, the selection of a new nest involves exploration and assessment of potential sites followed by colony movement on the basis of a collective decision making process. Hygiene and pathogen load of the potential nest sites are factors worker scouts might evaluate, given the high risk of epidemics in group-living animals. Choosing nest sites free of pathogens is hypothesized to be highly efficient in invasive ants as each of their introduced populations is often an open network of nests exchanging individuals (unicolonial) with frequent relocation into new nest sites and low genetic diversity, likely making these species particularly vulnerable to parasites and diseases. We investigated the nest site preference of the invasive pharaoh ant, Monomorium pharaonis, through binary choice tests between three nest types: nests containing dead nestmates overgrown with sporulating mycelium of the entomopathogenic fungus Metarhizium brunneum (infected nests), nests containing nestmates killed by freezing (uninfected nests), and empty nests. In contrast to the expectation pharaoh ant colonies preferentially (84%) moved into the infected nest when presented with the choice of an infected and an uninfected nest. The ants had an intermediate preference for empty nests. Pharaoh ants display an overall preference for infected nests during colony relocation. While we cannot rule out that the ants are actually manipulated by the pathogen, we propose that this preference might be an adaptive strategy by the host to \"immunize\" the colony against future exposure to the same pathogenic fungus.

Understanding the long-term safety of disease-modifying therapies for multiple sclerosis (MS) in routine clinical practice can be undertaken through registry-based studies. However, variability of data quality across such sources poses the challenge of data fit for regulatory decision-making. CLARION, a non-interventional cohort safety study of cladribine tablets, combines aggregated data from MS registries/data sources, except in Germany (which utilizes primary data collection). We describe the application of key data quality indicators (DQIs) within CLARION to evaluate data quality over time, as recommended by the European Medicines Agency (EMA) guideline on registry-based studies. DQIs were defined with participating registries/sources; they were used to assess data quality according to the EMA Data Quality Framework, addressing consistency, accuracy, completeness, and study representativeness. DQIs were associated with potential remedial measures if data quality was not met. DQIs were summarized overall and for individual MS registries/data sources to November 1, 2022. A total of 28 DQIs were analyzed using data from 5069 patients arising from eight MS registries/data sources and 14 countries. The Representativeness DQIs showed that 72.0% of patients were female, median age at MS diagnosis was 29.0 to 43.3 years, and 93.5% had relapsing-remitting MS. Consistency DQIs showed a total of 2899 patients had achieved at least two years of follow-up; 6.9% did not have any recorded visits during this timeframe. Discrepant values were assessed as part of Accuracy DQIs, and improvements over time were noted for recorded dates of MS onset and diagnosis. Regarding Completeness DQIs, 191/5069 (3.8%) patients were lost to follow-up. The application of 28 DQIs within the CLARION study has helped with understanding, not only intrinsic and question-specific determinants of data quality, but also tracking the quality of post-authorization safety data obtained from MS registries/data sources, thereby providing a foundation for the regulatory decision-making process.

Background To assign a course of secondary progressive multiple sclerosis (MS) (SPMS) may be difficult and the proportion of persons with SPMS varies between reports. An objective method for disease course classification may give a better estimation of the relative proportions of relapsing–remitting MS (RRMS) and SPMS and may identify situations where SPMS is under reported. Materials and methods Data were obtained for 61,900 MS patients from MS registries in the Czech Republic, Denmark, Germany, Sweden, and the United Kingdom (UK), including date of birth, sex, SP conversion year, visits with an Expanded Disability Status Scale (EDSS) score, MS onset and diagnosis date, relapses, and disease-modifying treatment (DMT) use. We included RRMS or SPMS patients with at least one visit between January 2017 and December 2019 if ≥ 18 years of age. We applied three objective methods: A set of SPMS clinical trial inclusion criteria (“EXPAND criteria”) modified for a real-world evidence setting, a modified version of the MSBase algorithm, and a decision tree-based algorithm recently published. Results The clinically assigned proportion of SPMS varied from 8.7% (Czechia) to 34.3% (UK). Objective classifiers estimated the proportion of SPMS from 15.1% (Germany by the EXPAND criteria) to 58.0% (UK by the decision tree method). Due to different requirements of number of EDSS scores, classifiers varied in the proportion they were able to classify; from 18% (UK by the MSBase algorithm) to 100% (the decision tree algorithm for all registries). Objectively classified SPMS patients were older, converted to SPMS later, had higher EDSS at index date and higher EDSS at conversion. More objectively classified SPMS were on DMTs compared to the clinically assigned. Conclusion SPMS appears to be systematically underdiagnosed in MS registries. Reclassified patients were more commonly on DMTs.

Intrusion detection tools have largely benefitted from the usage of supervised classification methods developed in the field of data mining. However, the data produced by modern system/network logs pose many problems, such as the streaming and non-stationary nature of such data, their volume and velocity, and the presence of imbalanced classes. Classifier ensembles look a valid solution for this scenario, owing to their flexibility and scalability. In particular, data-driven schemes for combining the predictions of multiple classifiers have been shown superior to traditional fixed aggregation criteria (e.g., predictions’ averaging and weighted voting). In intrusion detection settings, however, such schemes must be devised in an efficient way, since (part of) the ensemble may need to be re-trained frequently. A novel ensemble-based framework is proposed here for the online intrusion detection, where the ensemble is updated through an incremental stream-oriented learning scheme, correspondingly to the detection of concept drifts. Differently from mainstream ensemble-based approaches in the field, our proposal relies on deriving, though an efficient genetic programming (GP) method, an expressive kind of combiner function defined in terms of (non-trainable) aggregation functions. This approach is supported by a system architecture, which integrates different kinds of functionalities, ranging from the drift detection, to the induction and replacement of base classifiers, up to the distributed computation of GP-based combiners. Experiments on both artificial and real-life datasets confirmed the validity of the approach.

Detecting deviant traces in business process logs is crucial for modern organizations, given the harmful impact of deviant behaviours (e.g., attacks or faults). However, training a Deviance Prediction Model (DPM) by solely using supervised learning methods is impractical in scenarios where only few examples are labelled. To address this challenge, we propose an Active-Learning-based approach that leverages multiple DPMs and a temporal ensembling method that can train and merge them in a few training epochs. Our method needs expert supervision only for a few unlabelled traces exhibiting high prediction uncertainty. Tests on real data (of either complete or ongoing process instances) confirm the effectiveness of the proposed approach.