A complex case with generalized epilepsy, probable focal seizures, and functional seizures.

In this patient, now 42 years old, genetic generalized epilepsy (juvenile myoclonic epilepsy) manifested itself at the age of 13. At the age of 39, she experienced a status episode with prolonged ICU treatment. She was left with a left-sided hippocampal sclerosis and probably focal seizures. In addition, since the age of 24, the patient also experiences functional seizures on the background of a borderline personality disorder. While generalized epileptic seizures could be controlled with antiseizure medication (ASM), the patient was multiple times admitted to Emergency Departments for her functional seizures with subsequent intensive care treatments, including intubation. As a complication, the patient developed critical illness polyneuropathy and myopathy, resulting in wheelchair dependence. Additionally, she acquired a complex regional pain syndrome after extravasation of ASM. The report demonstrates the uncommon development of hippocampal sclerosis after a generalized tonic-clonic status epilepticus and the poor treatability of functional seizures as compared to generalized and focal seizures.

Factors associated with financial risk due to health spending in Argentina.

This article aims to assess the association between household demographic and socioeconomic characteristics and catastrophic health expenditure (CHE) in Argentina during 2017-2018. CHE was estimated as the proportion of household consumption capacity (using both income and total consumption in separate estimations) allocated for Out-of-Pocket Health Expenditure (OOP). For assessing the determinants, we estimated a generalized ordered logit model using different intensities of CHE (10%, 15%, 20% and 25%) as the ordinal dependent variable, and socioeconomic, demographic, and geographical variables as explanatory factors. We found that having members older than 65 years and with long-term difficulties increased the likelihood of incurring CHE. Additionally, having an economically inactive household head was identified as a factor that increases this probability. However, the research did not yield consistent results regarding the relationship between public and private health insurance and consumption capacity. Our results, along with the robustness checks, suggest that the magnitude of the coefficients for the household head characteristics could be exaggerated in studies that overlook the attributes of other household members. In addition, these results emphasize the significance of accounting for long-term difficulties and indicate that omitting this factor could overestimate the impact of members aged over 65.

Simultaneous Inference of Multiple Binary Endpoints in Biomedical Research: Small Sample Properties of Multiple Marginal Models and a Resampling Approach.

In biomedical research, the simultaneous inference of multiple binary endpoints may be of interest. In such cases, an appropriate multiplicity adjustment is required that controls the family-wise error rate, which represents the probability of making incorrect test decisions. In this paper, we investigate two approaches that perform single-step p $p$ -value adjustments that also take into account the possible correlation between endpoints. A rather novel and flexible approach known as multiple marginal models is considered, which is based on stacking of the parameter estimates of the marginal models and deriving their joint asymptotic distribution. We also investigate a nonparametric vector-based resampling approach, and we compare both approaches with the Bonferroni method by examining the family-wise error rate and power for different parameter settings, including low proportions and small sample sizes. The results show that the resampling-based approach consistently outperforms the other methods in terms of power, while still controlling the family-wise error rate. The multiple marginal models approach, on the other hand, shows a more conservative behavior. However, it offers more versatility in application, allowing for more complex models or straightforward computation of simultaneous confidence intervals. The practical application of the methods is demonstrated using a toxicological dataset from the National Toxicology Program.

Systematic solution for multi-model control approaches in nonlinear dynamic systems based on the s-gap metric.

This paper proposes a systematic approach for optimizing the distribution of local models in multi-model control systems (MMCS) to enhance overall robustness. While existing literature discusses this method for linear parameter varying (LPV) and uncertain linear time-invariant (LTI) systems, significant limitations persist in addressing nonlinear dynamic systems. Robust control tools like the gap metric and generalized stability margin (GSM) have limited effectiveness in analyzing the robustness of nonlinear feedback systems. To address these challenges, novel concepts of the gap metric and GSM are introduced to determine central operating points (COPs) within local operating areas (LOAs) across the total operating area (TOA). These COPs guide the extraction of affine disturbance local models (ADLMs). Additionally, an optimization problem based on the s-gap metric and GSM is presented to optimize COPs placement and LOAs boundaries. Challenges such as non-monotonic behavior of the cost function and complexity arising from the s-gap metric formulation necessitate novel solution methods. To address these, constraints are applied to the cost function, and a novel discrete optimization approach is introduced. Finally, theoretical findings are applied to the Duffing system, pH neutralization process, and continuous stirred tank reactor (CSTR) plant to evaluate the proposed method's effectiveness. This comprehensive validation across different systems underscores the versatility and practical utility of the proposed approach.

Robot-assisted deep brain stimulation of the centromedian nucleus of the thalamus for generalized epilepsy: targeting and operative video.

The centromedian (CM) nucleus of the thalamus is a promising target for a range of brain diseases including drug-resistant generalized and multifocal epilepsy. CM is highly connected to cortical and subcortical regions including frontoparietal/sensorimotor cortex, striatum, brainstem, and cerebellum, which are involved in some generalized epilepsy syndromes like Lennox-Gastaut syndrome (LGS). In this video, the authors describe their methodology for targeting CM for deep brain stimulation (DBS). Delineation of an optimal and consistent target will expand the efficacy of neuromodulation of CM in intractable epilepsy. The video can be found here: https://stream.cadmore.media/r10.3171/2024.4.FOCVID245.

Theory of mind in epilepsy.

Epilepsy is characterized by recurrent, chronic, and unprovoked seizures. Epilepsy has a significant negative impact on a patient's quality of life even if seizures are well controlled. In addition to the distress caused by seizures, patients with epilepsy (PwE) may suffer from cognitive impairment with serious social consequences such as poor interpersonal relationships, loss of employment, and reduced social networks. Pathological changes and functional connectivity abnormalities observed in PwE can disrupt the neural network responsible for the theory of mind. Theory of mind is the ability to attribute mental states to other people (intentions, beliefs, and emotions). It is a complex aspect of social cognition and includes cognitive and affective constructs. In recent years, numerous studies have assessed the relationship between social cognition, including the theory of mind, in PwE, and suggested impairment in this domain. Interventions targeting the theory of mind can be potentially helpful in improving the quality of life of PwE.

Covariate-adjusted generalized pairwise comparisons in small samples.

Semiparametric probabilistic index models allow for the comparison of two groups of observations, whilst adjusting for covariates, thereby fitting nicely within the framework of generalized pairwise comparisons (GPC). As with most regression approaches in this setting, the limited amount of data results in invalid inference as the asymptotic normality assumption is not met. In addition, separation issues might arise when considering small samples. In this article, we show that the parameters of the probabilistic index model can be estimated using generalized estimating equations, for which adjustments exist that lead to estimators of the sandwich variance-covariance matrix with improved finite sample properties and that can deal with bias due to separation. In this way, appropriate inference can be performed as is shown through extensive simulation studies. The known relationships between the probabilistic index and other GPC statistics allow to also provide valid inference for example, the net treatment benefit or the success odds.

Temperature modification of air pollutants and their synergistic effects on respiratory diseases in a semi-arid city in Northwest China.

Air pollutants and temperature are significant threats to public health, and the complex linkages between the environmental factors and their interactions harm respiratory diseases. This study is aimed to analyze the impact of air pollutants and meteorological factors on respiratory diseases and their synergistic effects in Dingxi, a city in northwestern China, from 2018 to 2020 using a generalized additive model (GAM). Relative risk (RR) was employed to quantitatively evaluate the temperature modification on the short-term effects of PM2.5 and O3 and the synergistic effects of air pollutants (PM2.5 and O3) and meteorological elements (temperature and relative humidity) on respiratory diseases. The results indicated that the RRs per inter-quatile range (IQR) rise in PM2.5 and O3 concentrations were (1.066, 95% CI: 1.009-1.127, lag2) and (1.037, 95% CI: 0.975-1.102, lag4) for respiratory diseases, respectively. Temperature stratification suggests that the influence of PM2.5 on respiratory diseases was significantly enhanced at low and moderate temperatures, and the risk of respiratory diseases caused by O3 was significantly increased at high temperatures. The synergy analysis demonstrated significant a synergistic effect of PM2.5 with low temperature and high relative humidity and an antagonistic effect of high relative humidity and O3 on respiratory diseases. The findings would provide a scientific basis for the impact of pollutants on respiratory diseases in Northwest China.

The global burden of breast cancer in women from 1990 to 2030: assessment and projection based on the global burden of disease study 2019.

Background and aim: This study aims to analyze the worldwide prevalence, mortality rates, and disability-adjusted life years (DALYs) attributed to breast cancer in women between 1990 and 2019. Additionally, it seeks to forecast the future trends of these indicators related to the burden of breast cancer in women from 2020 to 2030. Methods: Data from the Global Burden of Disease Study (GBD) 2019 was analyzed to determine the age-standardized incidence rate (ASIR) and age-standardized death rate (ASDR) of DALYs due to breast cancer in women across 204 countries and territories from 1990 to 2019. Socio-economic development levels of countries and regions were assessed using Socio-demographic Indexes, and trends in the burden of breast cancer in women worldwide from 2020 to 2030 were projected using generalized additive models (GAMs). Results: The estimated annual percentage change (EAPC) in the ASIR breast cancer in women globally was 0.36 from 1990 to 2019 and is expected to increase to 0.44 from 2020 to 2030. In 2019, the ASIR of breast cancer in women worldwide was 45.86 and is projected to reach 48.09 by 2030. The burden of breast cancer in women generally rises with age, with the highest burden expected in the 45-49 age group from 2020 to 2030. The fastest increase in burden is anticipated in Central sub-Saharan Africa (EAPC in the age-standardized death rate: 1.62, EAPC in the age-standardized DALY rate: 1.52), with the Solomon Islands (EAPC in the ASIR: 7.25) and China (EAPC in the ASIR: 2.83) projected to experience significant increases. Furthermore, a strong positive correlation was found between the ASIR breast cancer in women globally in 1990 and the projected rates for 2030 (r = 0.62). Conclusion: The anticipated increase in the ASIR of breast cancer in women globally by 2030 highlights the importance of focusing on women aged 45-49 in Central sub-Saharan Africa, Oceania, the Solomon Islands, and China. Initiatives such as breast cancer information registries, raising awareness of risk factors and incidence, and implementing universal screening programs and diagnostic tests are essential in reducing the burden of breast cancer and its associated morbidity and mortality.

Prospective cohort study evaluating efficacy and safety of efgartigimod in Chinese generalized myasthenia gravis patients.

Background: Despite the efficacy of efgartigimod demonstrated in ADAPT phase 3 trial, data specifically derived from Chinese participants are not available. Therefore, we aimed to evaluate the efficacy and safety of efgartigimod in Chinese patients with generalized myasthenia gravis (gMG). Methods: This is a prospective cohort study conducted in 8 hospitals across China. gMG patients received weekly intravenous infusions of efgartigimod (10 mg/kg) under a named patient program (NPP). The present study is an 8-week study, consisting of 4 consecutive doses of efgartigimod administered over 3 weeks (one cycle), followed by a 5-week follow-up period to assess the tolerability of efgartigimod's therapeutic effects. The primary outcome was the mean change in MG activities of daily living (MG-ADL) total score from baseline to 4 weeks. MG-ADL responder was defined as a ≥ 2-point improvement that persisted for 4 weeks, starting by week 4. Safety evaluations encompassed the monitoring of adverse events (AE) and serious AE (SAE) throughout the study. Results: Between 5 July 2022 and 25 August 2023, a total of 14 gMG patients were included. The mean age was 57.7 years, with a mean MG-ADL score of 10.86 ± 3.32. At week 4, MG-ADL scores showed a mean reduction of 6 points, reaching a maximum decline of 13 points. Among the patients, 85.7% (12/14) achieved MG-ADL responder status after one cycle of treatment. The most significant reduction in quantitative MG (QMG) scores also occurred at week 4, with a mean decrease of 7 points. Notably, the improvements in MG-ADL and QMG scores persisted until week 8. During treatment and follow-up period, only two mild neck rashes occurred and resolved promptly. No infections or SAE were reported. Discussion: A single cycle of efgartigimod treatment demonstrates effectiveness and the tolerability through week 8, with no new safety signals observed in Chinese gMG patients.

Uncertainty quantification for the random HIV dynamical model driven by drug adherence.

In HIV drug therapy, the high variability of CD4+ T cells and viral loads brings uncertainty to the determination of treatment options and the ultimate treatment efficacy, which may be the result of poor drug adherence. We develop a dynamical HIV model coupled with pharmacokinetics, driven by drug adherence as a random variable, and systematically study the uncertainty quantification, aiming to construct the relationship between drug adherence and therapeutic effect. Using adaptive generalized polynomial chaos, stochastic solutions are approximated as polynomials of input random parameters. Numerical simulations show that results obtained by this method are in good agreement, compared with results obtained through Monte Carlo sampling, which helps to verify the accuracy of approximation. Based on these expansions, we calculate the time-dependent probability density functions of this system theoretically and numerically. To verify the applicability of this model, we fit clinical data of four HIV patients, and the goodness of fit results demonstrate that the proposed random model depicts the dynamics of HIV well. Sensitivity analyses based on the Sobol index indicate that the randomness of drug effect has the greatest impact on both CD4+ T cells and viral loads, compared to random initial values, which further highlights the significance of drug adherence. The proposed models and qualitative analysis results, along with monitoring CD4+ T cells counts and viral loads, evaluate the influence of drug adherence on HIV treatment, which helps to better interpret clinical data with fluctuations and makes several contributions to the design of individual-based optimal antiretroviral strategies.

Assessing the spatiotemporal evolution and socioeconomic determinants of PM2.5-related premature deaths in China from 2000 to 2021.

China's swift socioeconomic development has led to extremely severe ambient PM2.5 levels, the associated negative health outcomes of which include premature death. However, a comprehensive explanation of the socioeconomic mechanism contributing to PM2.5-related premature deaths has not yet to be fully elucidated through long-term spatial panel data. Here, we employed a global exposure mortality model (GEMM) and the system generalized method of moments (Sys-GMM) to examine the primary determinants contributing to premature deaths in Chinese provinces from 2000 to 2021. We found that in the research period, premature deaths in China increased by 46 %, reaching 1.87 million, a figure that decreased somewhat after the COVID-19 outbreak. 62 thousand premature deaths were avoided in 2020 and 2021 compared to 2019, primarily due to the decline in PM2.5 concentrations. Premature deaths have increased across all provinces, particularly in North China, and a discernible spatial agglomeration effect was observed, highlighting effects on nearby provinces. The findings also underscored the significance of determinants such as urbanization, import and export trade, and energy consumption in exacerbating premature deaths, while energy intensity exerted a mitigating influence. Importantly, a U-shaped relationship between premature deaths and economic development was unveiled for the first time, implying the need for vigilance regarding potential health impact deterioration and the implementation of countermeasures as the per capita GDP increases in China. Our findings deserve attention from policymakers as they shed fresh insights into atmospheric control and Health China action.

Causal relations between ischemic stroke and epilepsy: A bidirectional Mendelian randomization study.

Background: Although previous studies have reported a bidirectional relationship between ischemic stroke (IS) and epilepsy, the existence of a causal nexus and its directionality remains a topic of controversy. Methods: The single nucleotide polymorphisms (SNPs) associated with IS were extracted from the Genome-Wide Association Study (GWAS) database. Pooled genetic data encompassing all epilepsy cases, as well as generalized and focal epilepsy subtypes, were acquired from the International League Against Epilepsy's GWAS study. In this study, the primary analysis approach utilized the inverse variance weighting (IVW) method as the main analytical technique. To enhance the robustness of the findings against potential pleiotropy, additional sensitivity analyses were conducted. Results: In the forward analysis, the IVW method demonstrated that IS was associated with an increased risk of all epilepsy (odds ratio (OR) = 1.127, 95 % confidence interval (CI) = 1.038-1.224, P = 0.004) and generalized epilepsy (IVW: OR = 1.340, 95 % CI = 1.162-1.546, P = 5.70 × 10-5). There was no substantial causal relationship observed between IS and focal epilepsy (P > 0.05). Furthermore, generalized epilepsy, focal epilepsy, and all epilepsy did not show a causal relationship with IS. Conclusion: This Mendelian randomization (MR) analysis demonstrates that IS increases the risk of developing epilepsy, especially generalized epilepsy. Conversely, no clear causal association was found between epilepsy and the onset of stroke. Therefore, the possible mechanisms of the effect of epilepsy on the pathogenesis of IS still need to be further investigated.

Altered serum interleukin-17A and interleukin-23A levels may be associated with the pathophysiology and development of generalized anxiety disorder.

In recent times, the pathogenesis of generalized anxiety disorder (GAD) and the influence of pro- and anti-inflammatory cytokines on it have garnered considerable interest. Cytokine research, especially Th-17 cytokine research on GAD patients, is limited. Here, we aim to assess the role of interleukin-17A (IL-17A) and interleukin-23A (IL-23A) in the pathophysiology and development of GAD. This investigation included 50 GAD patients and 38 age-sex-matched healthy controls (HCs). A psychiatrist diagnosed patients with GAD and assessed symptom severity using the DSM-5 and the GAD-7 scales. The serum concentrations of IL-17A and IL-23A were determined using commercially available ELISA kits. GAD patients exhibited elevated levels of IL-17A (77.14 ± 58.30 pg/ml) and IL-23A (644.90 ± 296.70 pg/ml) compared to HCs (43.50 ± 25.54 pg/ml and 334.40 ± 176.0 pg/ml). We observed a positive correlation between disease severity and cytokine changes (IL-23A: r = 0.359, p = 0.039; IL-17A: r = 0.397, p = 0.032). These findings indicate that IL-17A and IL-23A may be associated with the pathophysiology of GAD. ROC analysis revealed moderately higher AUC values (IL-23A: 0.824 and IL-17A: 0.710), demonstrating their potential to discriminate between patients and HCs. Also, the sensitivity values of both cytokines were relatively higher (IL-23A: 80.49% and IL-17A: 77.27%). According to the present findings, there may be an association between peripheral serum levels of IL-17A and IL-23A and the pathophysiology and development of GAD. These altered serum IL-17A and IL-23A levels may play a role in directing the early risk of developing GAD. We recommend further research to ascertain their exact role in the pathophysiology and their performance as risk assessment markers of GAD.

Genetic causality and metabolite pathway identifying the relationship of blood metabolites and psoriasis.

BACKGROUND: Psoriasis is a chronic inflammatory disease that causes significant disability. However, little is known about the underlying metabolic mechanisms of psoriasis. Our study aims to investigate the causality of 975 blood metabolites with the risk of psoriasis. MATERIALS AND METHODS: We mainly applied genetic analysis to explore the possible associations between 975 blood metabolites and psoriasis. The inverse variance weighted (IVW) method was used as the primary analysis to assess the possible association of blood metabolites with psoriasis. Moreover, generalized summary-data-based Mendelian randomization (GSMR) was used as a supplementary analysis. In addition, linkage disequilibrium score regression (LDSC) was used to investigate their genetic correction further. Metabolic pathway analysis of the most suggested metabolites was also performed using MetaboAnalyst 5.0. RESULTS: In our primary analysis, 17 metabolites, including unsaturated fatty acids, phospholipids, and triglycerides traits, were selected as potential factors in psoriasis, with odd ratios (OR) ranging from 0.986 to 1.01. The GSMR method confirmed the above results (ß = 0.001, p < 0.05). LDSC analysis mainly suggested the genetic correlation of psoriasis with genetic correlations (rg) from 0.088 to 0.155. Based on the selected metabolites, metabolic pathway analysis suggested seven metabolic pathways including ketone body that may be prominent pathways for metabolites in psoriasis. CONCLUSION: Our study supports the causal role of unsaturated fatty acid properties and lipid traits with psoriasis. These properties may be regulated by the ketone body metabolic pathway.

Exploring Phylogenetic Signal in Multivariate Phenotypes by Maximizing Blomberg's K.

Due to the hierarchical structure of the tree of life, closely related species often resemble each other more than distantly related species; a pattern termed phylogenetic signal. Numerous univariate statistics have been proposed as measures of phylogenetic signal for single phenotypic traits, but the study of phylogenetic signal for multivariate data, as is common in modern biology, remains challenging. Here we introduce a new method to explore phylogenetic signal in multivariate phenotypes. Our approach decomposes the data into linear combinations with maximal (or minimal) phylogenetic signal, as measured by Blomberg's K. The loading vectors of these phylogenetic components or K-components can be biologically interpreted, and scatterplots of the scores can be used as a low-dimensional ordination of the data that maximally (or minimally) preserves phylogenetic signal. We present algebraic and statistical properties, along with two new summary statistics, KA and KG, of phylogenetic signal in multivariate data. Simulation studies showed that KA and KG have higher statistical power than the previously suggested statistic Kmult, especially if phylogenetic signal is low or concentrated in a few trait dimensions. In two empirical applications to vertebrate cranial shape (crocodyliforms and papionins), we found statistically significant phylogenetic signal concentrated in a few trait dimensions. The finding that phylogenetic signal can be highly variable across the dimensions of multivariate phenotypes has important implications for current maximum likelihood approaches to phylogenetic signal in multivariate data.

A Note on Ising Network Analysis with Missing Data.

The Ising model has become a popular psychometric model for analyzing item response data. The statistical inference of the Ising model is typically carried out via a pseudo-likelihood, as the standard likelihood approach suffers from a high computational cost when there are many variables (i.e., items). Unfortunately, the presence of missing values can hinder the use of pseudo-likelihood, and a listwise deletion approach for missing data treatment may introduce a substantial bias into the estimation and sometimes yield misleading interpretations. This paper proposes a conditional Bayesian framework for Ising network analysis with missing data, which integrates a pseudo-likelihood approach with iterative data imputation. An asymptotic theory is established for the method. Furthermore, a computationally efficient Pólya-Gamma data augmentation procedure is proposed to streamline the sampling of model parameters. The method's performance is shown through simulations and a real-world application to data on major depressive and generalized anxiety disorders from the National Epidemiological Survey on Alcohol and Related Conditions (NESARC).

Spatial distribution and risk assessment of microplastics in surface waters of the St. Lawrence Estuary.

Development of effective prevention and mitigation strategies for marine plastic pollution requires a better understanding of the pathways and transport mechanisms of plastic waste. Yet the role of estuaries as a key interface between riverine inputs of plastic pollution and delivery to receiving marine environments remains poorly understood. This study quantified the concentration and distribution of microplastics (MPs) (50-3200 µm) in surface waters of the St. Lawrence Estuary (SLE) in eastern Canada. Microplastics were identified and enumerated based on particle morphology, colour, and size class. Fourier Transform Infrared (FTIR) spectroscopy was used on a subset of particles to identify polymers. Generalized linear models (Gamma distribution with log-link) examined the relationship between MP concentrations and oceanographic variables and anthropogenic sources. Finally, a risk assessment model, using MP concentrations and chemical hazards based on polymer types, estimated the MP pollution risk to ecosystem health. Mean surface MP concentration in the SLE was 120 ± 42 SD particles m-3; MP concentrations were highest in the fluvial section and lowest in the Northwest Gulf of St. Lawrence. However, MP concentrations exhibited high heterogeneity along the length and width of the SLE. Microplastics were elevated at stations located closer to wastewater treatment plant outflows and downstream sites with more agricultural land. Black, blue, and transparent fibers and fragments ≤250 µm were most commonly encountered. Predominant polymer types included polyethylene terephthalate, regenerated cellulose, polyethylene, and alkyds. While the overall risk to ecosystem health in the entire estuary was considered low, several stations, particularly near urban centres were at high or very high risk. This study provides new insights into the quantification and distribution of MPs and first estimates of the risk of MP pollution to ecosystem health in one of the world's largest estuaries.

New Paradigm of Identifiable General-response Cognitive Diagnostic Models: Beyond Categorical Data.

Cognitive diagnostic models (CDMs) are a popular family of discrete latent variable models that model students' mastery or deficiency of multiple fine-grained skills. CDMs have been most widely used to model categorical item response data such as binary or polytomous responses. With advances in technology and the emergence of varying test formats in modern educational assessments, new response types, including continuous responses such as response times, and count-valued responses from tests with repetitive tasks or eye-tracking sensors, have also become available. Variants of CDMs have been proposed recently for modeling such responses. However, whether these extended CDMs are identifiable and estimable is entirely unknown. We propose a very general cognitive diagnostic modeling framework for arbitrary types of multivariate responses with minimal assumptions, and establish identifiability in this general setting. Surprisingly, we prove that our general-response CDMs are identifiable under Q -matrix-based conditions similar to those for traditional categorical-response CDMs. Our conclusions set up a new paradigm of identifiable general-response CDMs. We propose an EM algorithm to efficiently estimate a broad class of exponential family-based general-response CDMs. We conduct simulation studies under various response types. The simulation results not only corroborate our identifiability theory, but also demonstrate the superior empirical performance of our estimation algorithms. We illustrate our methodology by applying it to a TIMSS 2019 response time dataset.

Therapeutic challenges and unmet needs in the management of myasthenia gravis: an Italian expert opinion.

Myasthenia gravis (MG) is a rare, autoimmune, neurological disorder. Most MG patients have autoantibodies against acetylcholine receptors (AChRs). Some have autoantibodies against muscle-specific tyrosine kinase (MuSK) or lipoprotein-receptor-related protein 4 (LRP4), and some are seronegative. Standard of care, which includes anti-cholinesterase drugs, thymectomy, corticosteroids (CS), and off-label use of non-steroidal immunosuppressive drugs (NSISTs), is bounded by potential side effects and limited efficacy in refractory generalized MG (gMG) patients. This highlights the need for new therapeutic approaches for MG. Eculizumab, a monoclonal antibody that inhibits the complement system, has been recently approved in Italy for refractory gMG. A panel of 11 experts met to discuss unmet therapeutic needs in the acute and chronic phases of the disease, as well as the standard of care for refractory patients. Survival was emphasized as an acute phase outcome. In the chronic phase, persistent remission and early recognition of exacerbations to prevent myasthenic crisis and respiratory failure were considered crucial. Refractory patients require treatments with fast onset of action, improved tolerability, and the ability to slow disease progression and increase life expectancy. The Panel agreed that eculizumab would presumably meet the therapeutic needs of many refractory gMG patients. The panel concluded that the unmet needs of current standard of care treatments for gMG are significant. Evaluating new therapeutic options accurately is essential to find the best balance between efficacy and tolerability for each patient. Collecting real-world data on novel molecules in routine clinical practice is necessary to address unmet needs.