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RASopathies are a group of genetic disorders that are caused by genes that affect the canonical Ras/mitogen-activated protein kinase (MAPK) signaling pathway. Despite tremendous progress in understanding the molecular consequences of these genetic anomalies, little movement has been made in translating these findings to the clinic. This year, the seventh International RASopathies Symposium focused on expanding the research knowledge that we have gained over the years to enhance new discoveries in the field, ones that we hope can lead to effective therapeutic treatments. Indeed, for the first time, research efforts are finally being translated to the clinic, with compassionate use of Ras/MAPK pathway inhibitors for the treatment of RASopathies. This biannual meeting, organized by the RASopathies Network, brought together basic scientists, clinicians, clinician scientists, patients, advocates, and their families, as well as representatives from pharmaceutical companies and the National Institutes of Health. A history of RASopathy gene discovery, identification of new disease genes, and the latest research, both at the bench and in the clinic, were discussed.

Neurofibromatosis 1 (NF1) is a single gene disorder associated with working Memory (WM) impairments. The aim of this study was to investigate P300 event-related potential (ERP) associated with WM in NF1. Sixteen adolescents with NF1 were compared with controls on measures of WM and EEG was recorded during a WM nback task. The NF1 group showed poorer performance on measures of WM as compared to the control group. No group differences were observed in P300 amplitude at Pz, but P300 latency was shorter in the NF1 group. Topographic analyses of P300 amplitude showed group differences indicating neural processing differences in the NF1 group relative to controls, which possibly contribute to the cognitive deficits seen in this population.

ImportanceRecent reports have demonstrated a higher incidence of autism spectrum disorder (ASD) and substantially elevated autistic trait burden in individuals with neurofibromatosis type 1 (NF1). However, important discrepancies regarding the distribution of autistic traits, sex predominance, and association between ASD symptoms and attentional problems have emerged, and critical features of the ASD phenotype within NF1 have never been adequately explored. Establishing NF1 as a monogenic cause for ASD has important implications for affected patients and for future research focused on establishing convergent pathogenic mechanisms relevant to the potential treatment targets for ASD.ObjectiveTo characterize the quantitative autistic trait (QAT) burden in a pooled NF1 data set.Design, setting, and participantsAnonymized, individual-level primary data were accumulated from 6 tertiary referral centers in the United States, Belgium, United Kingdom, and Australia. A total of 531 individuals recruited from NF1 clinical centers were included in the study.Main outcomes and measuresDistribution of ASD traits (Social Responsiveness Scale, second edition [SRS-2], with T scores of ≥75 associated with a categorical ASD diagnosis); attention-deficit/hyperactivity disorder (ADHD) traits (4 versions of Conners Rating Scale, with T scores of ≥65 indicating clinically significant ADHD symptoms); ASD symptom structure, latent structure, base rate derived from mixture modeling; and familiality.ResultsOf the 531 patients included in the analysis, 247 were male (46.5%); median age was 11 years (range, 2.5-83.9 years). QAT scores were continuously distributed and pathologically shifted; 13.2% (95% CI, 10.3%-16.1%) of individuals scored within the most severe range (ie, above the first percentile of the general population distribution) in which the male to female ratio was markedly attenuated (1.6:1) relative to idiopathic ASD. Autistic symptoms in this NF1 cohort demonstrated a robust unitary factor structure, with the first principal component explaining 30.9% of the variance in SRS-2 scores, and a strong association with ADHD symptoms (r = 0.61). Within-family correlation for QAT burden (intraclass correlation coefficient, 0.73 in NF1-affected first-degree relatives) exceeded that observed in the general population and ASD family samples.Conclusions and relevanceThis study provides confirmation that the diversity of mutations that give rise to NF1 function as quantitative trait loci for ASD. Moreover, the within-family correlation implicates a high degree of mutational specificity for this associated phenotype. Clinicians should be alerted to the increased frequency of this disabling comorbidity, and the scientific community should be aware of the potential for this monogenic disorder to help elucidate the biological features of idiopathic autism.

Brain variations are responsible for developmental impairments, including autism spectrum disorder (ASD). EEG signals efficiently detect neurological conditions by revealing crucial information about brain function abnormalities. This study aims to utilize EEG data collected from both autistic and typically developing children to investigate the potential of a Graph Convolutional Neural Network (GCNN) in predicting ASD based on neurological abnormalities revealed through EEG signals. In this study, EEG data were gathered from eight autistic children and eight typically developing children diagnosed using the Childhood Autism Rating Scale at the Central Institute of Psychiatry, Ranchi. EEG recording was done using a HydroCel GSN with 257 channels, and 71 channels with 10-10 international equivalents were utilized. Electrodes were divided into 12 brain regions. A GCNN was introduced for ASD prediction, preceded by autoregressive and spectral feature extraction. The anterior-frontal brain region, crucial for cognitive functions like emotion, memory, and social interaction, proved most predictive of ASD, achieving 87.07% accuracy. This underscores the suitability of the GCNN method for EEG-based ASD detection. The detailed dataset collected enhances understanding of the neurological basis of ASD, benefiting healthcare practitioners involved in ASD diagnosis. [ABSTRACT FROM AUTHOR]

The Children and Young People with Long COVID (CLoCk) study in England aims to understand the symptoms and prevalence of long COVID in children and young people. The study includes over 200,000 participants aged 11-17 who were tested for COVID-19 between September 2020 and March 2021. Participants completed follow-up questionnaires at different time points, providing information on their physical and mental health. The study provides valuable insights into the long-term effects of COVID-19 in children and young people. However, there were some limitations, such as a low response rate and potential selection bias. The dataset is available for further analysis and collaboration. [Extracted from the article]