Sample Mean Research Articles

Fusarium Head Blight in Argentina, a Profile of Produced Mycotoxins and a Biocontrol Strategy in Barley During Micro-Malting Process

Barley (Hordeum vulgare L.) is the second winter crop in Argentina. In the national market, grains are mainly destined to produce malt for beer manufacture. Fusarium species are common, causing Fusarium Head Blight (FHB) in barley, which generates yield and quality losses, as well as mycotoxin occurrence. The aims of this study were to determine (a) the incidence of the main species causing FHB in different locations of the barley-growing region of Argentina, (b) their ability to produce mycotoxins, and (c) the levels of deoxynivalenol (DON) and nivalenol (NIV) natural occurrence in grains at the harvest stage. Additionally, a strain of Bacillus velezensis was studied as a biocontrol agent in order to control F. graminearum sensu stricto and mycotoxin accumulation during the malting process, with the final objective being to reduce DON contamination in the beer manufacture chain. Fusarium graminearum ss was the most prevalent species causing FHB, with Fusarium poae being less distributed. Both species produced several mycotoxins, including NX-2 and NX-3, which is the first report of their production by strains isolated from barley in Argentina. Deoxynivalenol contamination was found in 95% of barley grains during the 2016 harvest season (mean: 0.4 mg/kg), while NIV contamination was present in 29% of samples (mean: 0.49 mg/kg). In the 2017 harvest season, 53.6% of grains were contaminated with DON (mean: 0.42 mg/kg), and 21% with NIV (mean: 0.8 mg/kg). Quantification of F. graminearum ss by real-time PCR during the micro-malting process showed that application of the biocontrol agent before the germination stage was the most effective treatment, with a 45% reduction in fungal DNA levels. Reduction in DON contamination (69.3–100%) in artificially infected grains with F. graminearum ss, was also observed. The present work contributes to the knowledge of FHB in Argentina and to the development of a strategy to control this disease and mycotoxin contamination in barley, promoting at the same time food security.

A study on the pharmacovigilance of various SGLT-2 inhibitors

BackgroundSodium-glucose co-transporter two inhibitors (SGLT2is) are widely used in clinical practice due to their proven cardiovascular and renal benefits. However, various adverse drug reactions (ADRs) have been reported. This study aims to systematically update the ADRs associated with SGLT2is and identify the differences among various SGLT2is acovigilance of various SGLT-2 inhibitors.MethodsData from the FAERS database covering Q1 2013 to Q2 2024 were selected for disproportionality analysis. ADRs were defined using the System Organ Classes (SOC) and Preferred Terms (PT) from the MedDRA 27.0 dictionary. Four signal detection metrics—reporting odds ratio (ROR), proportional reporting ratios (PRRs), Bayesian Confidence Propagation Neural Network (BCPNN), and empirical Bayesian geometric mean (EBGM)—were utilized to infer ADRs and assess differences among specific SGLT2i drugs through intersection analysis.ResultsExcept for canagliflozin, both dapagliflozin and empagliflozin showed a general increase in ADRs. Specifically, canagliflozin had 93 ADRs, dapagliflozin had 173, and empagliflozin had 214. Most of these were related to Infections and Infestations, Investigations, and Reproductive System and Breast Disorders, notably manifesting as inflammatory conditions of the urinary and reproductive systems, such as orchitis and testicular abscess, consistent with FDA labeling. Additionally, overlooked ADRs were identified, including bladder cancer, cholangiocarcinoma, and thrombotic strokes, none of which were reported for canagliflozin.DiscussionWhile shared ADRs for SGLT2is are noted in FDA labeling, monitoring for high-risk populations, such as those with cancers or strokes, remains crucial to prevent deterioration. Medication regimens may need adjustment, including selecting canagliflozin or non-SGLT2i alternatives when necessary.

Optimizing Exposure Measures in Large-Scale Household Air Pollution Studies: Results from the Multicountry HAPIN Trial.

Repeated measurements of household air pollution may provide better estimates of average exposure but can add to costs and participant burden. In a randomized trial of gas versus biomass cookstoves in four countries, we took supplemental personal 24-h measurements on a 10% subsample for mothers and infants, interspersed between protocol samples. Mothers had up to five postrandomization protocol measurements over 16 months, while infants had three measurements over one year. For the subsample, we added up to 6 supplemental postrandomization samples for mothers and 3 for infants, measuring PM2.5, black carbon (BC) (mothers only), and carbon monoxide (CO) at each visit. 310 mothers had both protocol (n = 1026) and supplemental (n = 1099) valid exposure measurements. For children, supplemental data sufficient for analysis were collected in only two countries; 94 infants had both protocol (n = 317) and supplemental (n = 234) samples. The geometric means for protocol and supplemental samples for mothers for PM2.5 were 37 μg/m3 and 38 μg/m3, respectively, while for infants, they were 42 μg/m3 and 46 μg/m3. Mixed models comparing supplemental to protocol samples, controlling for covariates, found few differences between protocol and supplemental samples. Supplemental analyses among control mothers with complete protocol measurements found that an average of three measurements explained 81% of the variance of the average of all six measurements.

Using causal diagrams and superpopulation models to correct geographic biases in biodiversity monitoring data

Abstract Biodiversity monitoring schemes periodically measure species' abundances and distributions at a sample of sites to understand how they have changed over time. Often, the aim is to infer change in an average sense across some wider landscape. Inference to the wider landscape is simple if the species' abundances and distributions are similar at sampled to non‐sampled locations. Otherwise, the data are geographically biased, and some form of correction is desirable. We combine causal diagrams with ‘superpopulation models’ to correct time‐varying geographic biases in biodiversity monitoring data. For a given time‐period, expert‐derived causal diagrams are used to deduce the set of variables that explain the geographic bias, and superpopulation models adjust for these variables to produce a corrected estimate of a landscape‐wide mean of for example abundance or occupancy. Estimating a time trend in the variable of interest is achieved by fitting models for multiple time‐periods and, if the drivers of bias are suspected to change over time, by constructing per period causal diagrams. We test the approach using simulated data then apply it to real data from the UK Butterfly Monitoring Scheme (UKBMS). If the variables that explain the geographic bias are known and measured without error, our method is unbiased. Introducing measurement error reduces the method's efficacy, but it is still an improvement on using the sample mean. When applied to data from the UKBMS, the approach gives different results to the scheme's current method, which assumes no geographic bias. Where the goal is to estimate change in some variable of interest at the landscape level (e.g. biodiversity indicators), models that do not adjust for geographic bias implicitly assume it does not exist. Our approach makes the weaker assumption that there is no geographic bias conditional on the adjustment variables, so it should yield more accurate estimates of time trends in many circumstances. The method does require assumptions about the drivers of bias, but these are codified explicitly in the causal diagrams. Operationalising our approach should be less costly than full probability sampling, which would be needed to satisfy the assumptions of conventional approaches.

A new method for estimating the tail index using truncated sample mean

A new method for estimating the tail index using truncated sample mean

Descriptive Study of Academic Procrastination in Students Doing Thesis at Padang State University

This research is a descriptive study that discusses academic procrastination among students working on their theses at Padang State University. Academic procrastination is irrational behavior carried out by students intentionally which in the end can affect the performance of a given assignment. This research uses quantitative research with a descriptive approach. In this research, the number of subjects used was 265 students who were working on their thesis at Padang State University, with details of 168 men and 97 women. Subjects were selected using cluster sampling techniques. The data collection technique uses a Likert scale. The research results show that there is a difference between the empirical mean and the hypothetical mean where the empirical mean shows a score of 88.27 while the hypothetical mean shows a score of 75. This means that the empirical mean score is greater than the hypothetical mean. This data shows that the academic procrastination of these students tends to be high.

Unveiling the safety profile of lecanemab: A comprehensive analysis of adverse events using FDA adverse event reporting system data.

Lecanemab, a novel monoclonal antibody targeting amyloid-β, has shown promise in treating Alzheimer's disease. Comprehensive post-marketing safety data analysis is crucial to understand its real-world risk profile. This study aimed to evaluate the safety profile of lecanemab using data from the FDA Adverse Event Reporting System (FAERS), with a focus on nervous system disorders and amyloid-related imaging abnormalities. We conducted a disproportionality analysis using the FAERS database to evaluate the safety signals associated with lecanemab. Reporting odds ratio (ROR), proportional reporting ratio, empirical Bayesian geometric mean, and information component were calculated at both system organ class (SOC) and preferred term (PT) levels. Additionally, we performed a time-to-onset analysis using Weibull shape parameter estimation. Analysis at the SOC level revealed significant signals for nervous system disorders (ROR: 7.32, 95% CI: 6.69-8.00). At the PT level, strong signals were observed for amyloid-related imaging abnormalities, particularly those associated with microhemorrhages and oedema (ROR: 4122.81 and 3922.78, respectively). Headache was the most frequently reported adverse event (200 cases), followed by chills (107 cases) and fatigue (97 cases). Time-to-onset analysis showed a median time of 33 days (range: 1-1283) for all adverse events, with neurological events occurring slightly later (median: 42 days, range: 1-1260). Our findings highlight a distinct safety profile for lecanemab, with a predominant impact on the nervous system and a notable association with imaging abnormalities. These results underscore the importance of vigilant monitoring and further research to optimize the risk-benefit profile of lecanemab in clinical practice.

Mapping Variability of Mycotoxins in Individual Oat Kernels from Batch Samples: Implications for Sampling and Food Safety

Oats are susceptible to contamination by Fusarium mycotoxins, including deoxynivalenol (DON), zearalenone (ZEN), and T-2/HT-2 toxins, posing food safety risks. This study analyses the variation in levels of 14 mycotoxins in 200 individual oat kernels from two DON-contaminated batch samples (mean = 3498 µg/kg) using LC-MS/MS. The samples also contained deoxynivalenol-3-glucoside (DON-3G), 3-acetyldeoxynivalenol (3-ADON), 15-acetyldeoxynivalenol (15-ADON), and ZEN. Contamination levels varied notably among individual kernels, with DON detected in 70% of them, followed by DON-3G (24.5%) and 3-ADON (20.5%). Importantly, 8% of kernels exceeded the EU legal limit for DON (1750 µg/kg), and some occasionally surpassed limits for ZEN and T-2/HT-2. Correlation analyses revealed strong associations between DON and its derivatives but weaker correlations with other toxins. Mycotoxin ratios varied widely, indicating that although they often co-occur, their concentrations differ between kernels. Contamination did not significantly impact kernel weight, though a slight trend toward lower weights in contaminated kernels was noted. Additionally, sampling statistics showed that as the percentage of selected kernels increased, the probability of batch sample rejection for DON contamination rose significantly. The study highlights the heterogeneity of mycotoxin contamination in oat batches, emphasising the importance of accurate detection and regulatory compliance to ensure safer oat-based products.

Driving Employee Digital Performance: Exploring The Mediating Role Of Employee Dynamic Capability In The Context Of Hi-Hrmp And Organizational Learning At Pt Astra Daihatsu Motor In Sunter & Karawang

This study investigates the relationship of High Involvement Human Resources Management Practice (HI-HRMP), Organizational Learning, Employee Dynamic Capability and Employee Digital Performance. The primary objective is aiming to explore how Employee Dynamic Capability mediates the effect of HI-HRMP and Organizational Learning on Employee Digital Performance. The research focuses on employees of PT Astra Daihatsu Motor in Sunter and Karawang, involving a sample of 185 respondents selected using purposive sampling from company’s workforce in both location. The research employs a quantitative approach, utilizing a questionnaire to collect cross-sectional data. Data analysis includes descriptive statistics to summarize the sample characteristics and mean, the Sobel test to examine the mediating effect of employee dynamic capability, and Structural Equation Modeling (SEM) to analyze the relationships between variables. The finding shows that HI-HRMP has a direct positive and significant effect on employee digital performance but does not influence on employee dynamic capability. Thus, dynamic capability fails to mediate the relationship between HI-HRMP and employee digital performance. Meanwhile, organizational learning significantly influence employee digital performance, both directly or indirect and through employee dynamic capability as a partially mediating variable.

Effects of interventions aimed at improving nurses’ work engagement in the workplace: a systematic review and meta-analysis protocol

IntroductionWork engagement enhances nurses’ physical and mental health, well-being, job performance and satisfaction. This reduces turnover rates and improves patient care quality, making work engagement a crucial factor in the...

Estimating Per-Class Statistics for Label Noise Learning.

Real-world data may contain a considerable amount of noisily labeled examples, which usually mislead the training algorithm and result in degraded classification performance on test data. Therefore, Label Noise Learning (LNL) was proposed, of which one popular research trend focused on estimating the critical statistics (e.g., sample mean and sample covariance), to recover the clean data distribution. However, existing methods may suffer from the unreliable sample selection process or can hardly be applied to multi-class cases. Inspired by the centroid estimation theory, we propose Per-Class Statistic Estimation (PCSE), which establishes the quantitative relationship between the clean (first-order and second-order) statistics and the corresponding noisy statistics for every class. This relationship is further utilized to induce a generative classifier for model inference. Unlike existing methods, our approach does not require sample selection from the instance level. Moreover, our PCSE can serve as a general post-processing strategy applicable to various popular networks pre-trained on the noisy dataset for boosting their classification performance. Theoretically, we prove that the estimated statistics converge to their ground-truth values as the sample size increases, even if the label transition matrix is biased. Empirically, we conducted intensive experiments on various binary and multi-class datasets, and the results demonstrate that PCSE achieves more precise statistic estimation as well as higher classification accuracy when compared with state-of-the-art methods in LNL.

Does the household clean energy transition benefit the mental health of the elderly in rural China? Evidence from China health and retirement longitudinal study.

Clean energy transition has become a key strategy in combating global air pollution. In 2017, China launched its "coal-to-gas" policy, which provided subsidies to households for their clean energy transition. While debates on this policy focus mainly on safety and cost concerns, few studies have examined the causal relationship between energy transition and its health benefits, particularly mental health. Using data from the China Health and Retirement Longitudinal Survey (CHARLS), this paper employs a difference-in-differences (DID) approach to estimate the impact of the "coal-to-gas" policy on the mental health of the rural elderly in China. The findings indicate that the policy significantly improved the cognitive status of the elderly, especially episodic memory, which saw a 10.4% increase relative to the sample mean. The policy also significantly boosted the proportion of households using clean cooking fuels by 5.8%. Further analysis suggests that the improved sleep quality from reduced indoor air pollution by household energy transition, as a mechanism, could explain these results. This study provides a timely assessment of the policy's effectiveness, highlighting the importance of indoor air pollution control by household energy transition.

Job Satisfaction of University Teachers: A Comparative Study of Gender, Age and Marital Status

This study's major aim was to compare university teachers' job satisfaction regarding gender, age, and marital status. The current study was descriptive in nature, and data was gathered by survey methods. 164 University of Kotli teachers made up the study population. The researcher chose 113 teachers to serve as the study's sample. The study's research tool was a standardized instrument. The researcher gathered the data through in-person visits to university instructors. The data was analyzed using SPSS, a statistical software for social science. In order to analyze the data, the researcher used the independent sample t-test, frequency, percentage, mean, and analysis of variance (ANOVA). It was found that teachers receive recognition from their immediate supervisors, and they give assistance when needed. Moreover, the immediate supervisor appreciates good teaching and assists in improving instruction. On the other hand, the immediate supervisor does not provide backup to teachers, and they are not willing to listen to suggestions from teachers. It is also recommended that immediate supervisors avoid giving meaningless instructions to the teachers so that they do not feel bored/ill.

Families of adults with idiopathic hypersomnia and narcolepsy: psychosocial impact and contribution to symptom management.

This study examined the impact of central disorders of hypersomnolence (CDH) on family members of adult patients, the ways family members assist with managing CDH, and family members' utilization and satisfaction with information and support. Participants were adults (N=100) with an adult family member diagnosed with idiopathic hypersomnia or narcolepsy. They completed a survey which included the Family Reported Outcome Measure (FROM-16), checklists, satisfaction ratings, and open-response questions. The FROM-16 sample mean (14.2, SD=6.8) corresponded to a moderate effect on quality of life. Compared to parents, partners reported a higher impact on the Personal and Social Life domain (p=.04, d=.44). The most frequently endorsed sources of support were family (60.0%) and friends (50.0%), whereas information was most commonly obtained from hypersomnia organizations (69.0%) and medical professionals (61.0%). Only 8.0% of participants were satisfied with support, and 9.0% were satisfied with information. Participants endorsed assisting with managing CDH, such as picking up prescriptions (61.0%), attending medical visits (50.0%), reminding to take medication (48.0%), and coordinating medical care (39.0%). Qualitative data indicated that relationships underwent a transformation from conflict and confusion (pre-diagnosis) to clarity (post-diagnosis), followed by adjusting expectations. Caregiving strain, effects on shared activities, and negative psychosocial impacts on family also emerged as themes. Family members play an important role in supporting adults with CDH in many ways, including tasks related to managing CDH. Family members experience many psychosocial impacts from CDH, and data from this study indicate unmet needs for support.

Integrating the maximum mean discrepancy metric with time–frequency enhanced convolutional neural networks for fault diagnosis

In the operation of rotating machinery, the condition monitoring of key components such as bearings and gears is very important. However, due to the complexity of the real environment and the diversity of equipment operating conditions, the collected mechanical vibration signals of bearings and gears often contain irrelevant noise, which brings challenges to signal processing. Traditional convolutional neural networks (CNNs), lacking denoising structures, have limited effectiveness in processing interfered vibration signals. In addressing these challenges, this paper introduces a novel approach, namely the Gramian-based CNN model and maximum mean square discrepancy (MMSD). For the model, we first propose a simple yet effective denoising strategy, namely Gramian noise reduction (GNR) based on the self-similarity of vibration signal cycles. Second, considering the limitations of traditional CNNs in handling interfered vibration signals, we develop a novel end-to-end GNR-based CNN model - Gramian Time–Frequency Enhanced Network (GTFE-Net), applied to bearing fault diagnosis. GTFE-Net consists of three parallel branches, each processing the original signal, the GNR-denosed signal, and spectral information. By integrating GNR into GTFE-Net, the network focuses more on feature extraction. Additionally, we introduce MMSD as a metric that comprehensively reflects differences in sample mean and variance information in the reproducing kernel Hilbert space, thereby enhancing domain confusion. The experimental results indicate that this method achieved a diagnostic accuracy of 77.19% for various transmission tasks in noisy environments. This finding provides reliable support for bearing fault diagnosis, demonstrating the method’s effectiveness and potential for application in complex environments.

Safety assessment of deutetrabenazine: real-world adverse event analysis from the FAERS database

BackgroundDeutetrabenazine is a widely used drug for the treatment of tardive dyskinesia (TD), and post-marketing testing is important. There is a lack of real-world, large-sample safety studies of deutetrabenazine. In this study, a pharmacovigilance analysis of deutetrabenazine was performed based on the FDA Adverse Event Reporting System (FAERS) database to evaluate its relevant safety signals for clinical reference.MethodsAdverse events (AEs) of FAERS with deutetrabenazine as the primary suspect drug were collected from the first quarter (Q1) of 2017 to Q1 of 2024. Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Empirical Bayesian Geometric Mean (EBGM) were used to mine AEs risk signals of deutetrabenazine. AEs were standardized and classified using the System Organ Class (SOC) and Preferred Terms (PTs) from Medical Dictionary for Regulatory Activities (MedDRA) version 23.0.ResultsA total of 3,583 AEs with deutetrabenazine as the primary suspect drug were collected in this study. We found that these AEs involved 23 SOCs, and the positive signals were mainly concentrated in systemic disease and various reactions at the site of administration (n = 1816, ROR = 1.23, PRR = 1.18, IC = 0.24, EBGM = 1.18), neurological disorders (n = 1736, ROR = 3.02, PRR = 2.60, IC = 1.38, EBGM = 2.60) and psychiatric disorders (n = 1,659, ROR = 4.15, PRR = 3.52, IC = 1.82, EBGM = 3.52). We eventually identified 100 valid PTs that met the criteria of the four algorithms. Drug ineffective, dyskinesia, depression, somnolence, suicidal ideation were considered to be the common PTs of deutetrabenazine. Tongue thrust (n = 4, ROR 253.47, PRR 253.35, IC 7.88, EBGM 235.95), grunting (n = 5, ROR 78.49, PRR 78.45, IC 6.26, EBGM 76.71) and drooling (n = 17, ROR 13.21, PRR 13.19, IC 3.72, EBGM 13.14) were not mentioned in the specification, but the high signal intensity suggested that they may be the potential adverse reactions.ConclusionThe efficacy of deutetrabenazine may be accompanied by some potential adverse effects in several systems. Adverse events in psychiatric, neurologic, gastrointestinal and respiratory need to be monitored in clinical practice.

Local Laws for Sparse Sample Covariance Matrices Without the Truncation Condition

Local Laws for Sparse Sample Covariance Matrices Without the Truncation Condition

Signal detection and analysis of sulfasalazine adverse reaction events based on the US FDA adverse event reporting database

ABSTRACT Background Based on real data from the FDA Adverse Event Reporting System (FAERS), we explored and evaluated the adverse reactions (ADRs) of sulfasalazine (SSZ) in the first quarter of 2004–2023 to provide a reference basis and early warning for the safe use of SSZ in the clinic. Research design and methods The reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN), and the empirical Bayesian geometric mean (EBGM) were used as measures of SSZ-associated adverse events (AEs) signals, mining for potential positive signals between SSZ and AE. Results In this study, a total of 5327 case reports related to SSZ in the last 20 years involving 25 system organ classes (SOCs) were collected in FAERS. The AEs of SSZ were mainly focused on Immune system disorders (n = 1621, IC025 = 2.86), Blood and lymphatic system disorders (n = 638, IC025 = 2.4), and others. In addition, we found 41 adverse reactions not mentioned in the specification, including oculomucocutaneous syndrome (n = 12, IC025 = 4.86), wegener’s granulomatosis(n = 6, IC025 = 4.05) and reproductive system aspects. Conclusion Significant new AEs signals were observed in this study and may provide important support for clinical monitoring and risk identification in SSZ.

Mining and analysis of adverse events profile of Guanfacine using FDA Adverse Event Reporting System (FAERS) database

ABSTRACT Objective This study aims to evaluate the Guanfacine-associated adverse events (AEs) in real-world using data mining in FAERS database, seeking to provide safety profile and guidance for the potential therapeutic practices. Methods We retrospectively extracted Guanfacine-associated AEs reports in the FAERS database from the first quarter of 2004 (Q1) to the fourth quarter of 2023 (Q4). All data were strictly collected and comprehensively analyzed. Disproportionality analyses, including the reporting odds ratio (ROR), the proportional reporting ratio (PRR), the Bayesian confidence propagation neural network (BCPNN), and the empirical Bayesian geometric mean (EBGM). Results Out of 16,985,532 reports collected from the FAERS database, 2,709 reports of Guanfacine as the ‘primary suspected (PS)’ and 8,464 AEs induced by Guanfacine were identified. Most AEs reports (67.45%) occurred within the first 30 days of treatment (excluding unreported or unknown onset time reports). A total of 158 significant PTs that meet all four algorithms were detected. Known neuropsychiatric events and cardiovascular events documented in the drug’s leaflet were found. Besides, unexpected significant AEs such as homicidal ideation, self-injurious ideation, growth failure, excessive eye blinking were also found. Conclusion Our study has unveiled novel Guanfacine-associated AEs signals and might provide guidance for its rational use.

Adverse events of nusinersen: a real-world drug safety surveillance study based on the FDA adverse event reporting system (FAERS) database

ABSTRACT Introduction Nusinersen, the initial FDA-approved medication, treats Spinal Muscular Atrophy. This study utilized the FDA’s adverse event reporting system (FAERS) database to examine, evaluate, and provide substantiation for the safety of Nusinersen to assist in clinical decision-making. Methods Nusinersen-related adverse reaction signals were mined using the reporting odds ratio (ROR), Proportional Reporting Ratio (PRR), and Bayesian Confidence Propagation Neural Network (BCPNN), Multi-item Gamma-Poisson Shrinker (MGPS) models with Empirical Bayesian Geometric Mean (EBGM). The rate and frequency of reported adverse reactions were investigated. Results Nusinersen-induced adverse events were observed in 25 system organ categories (SOCs). A total of 230 disproportionate preferred terms (PTs) were eliminated using four algorithms. Cardiac arrest, autism spectrum disorder, and epilepsy emerged as potential new side effects not previously listed, warranting further attention for drug safety. Analysis of the age distribution of the signals found that younger patients should be watched out for upper respiratory tract infections and increased CSF pressure; senior patients should be extensively checked for symptoms of post-lumbar puncture syndrome and protein urine present. Conclusions Our investigation discovered potential signals of novel adverse drug events that could support clinical monitoring and risk identification of Nusinersen. However, these results should be interpreted with caution.