The prevalence of neurodevelopment disorders (NDDs) among children has been on the rise. This has affected the health and social life of children. This condition has also imposed a huge economic burden on families and health care systems. Currently, it is difficult to perform early diagnosis of NDDs, which results in delayed intervention. For this reason, patients with NDDs have a prognosis. In recent years, machine learning (ML) technology, which integrates artificial intelligence technology and medicine, has been applied in the early detection and prediction of diseases based on data mining. This paper reviews the progress made in the application of ML in the diagnosis and treatment of NDDs in children based on supervised and unsupervised learning tools. The data reviewed here provide new perspectives on early diagnosis and treatment of NDDs.

Alternative splicing (AS) is a vital and highly dynamic RNA regulatory mechanism that allows a single gene to generate multiple mRNA and protein isoforms. Dysregulation of AS has been identified as a key contributor to the pathogenesis of autism spectrum disorders (ASD). A comprehensive understanding of aberrant splicing mechanisms and their functional consequences in ASD can help uncover the molecular basis of the disorder and facilitate the development of therapeutic strategies. This review focuses on the major aberrant splicing events and key splicing regulators associated with ASD, highlighting their roles in linking defective splicing to ASD pathogenesis. In addition, a discussion of how emerging technologies, such as long-read sequencing, single-cell sequencing, spatial transcriptomics and CRISPR-Cas systems are offering novel insights into the role and mechanisms of AS in ASD is presented. Finally, the RNA splicing-based therapeutic strategies are evaluated, emphasizing their potential to address unmet clinical needs in ASD treatment.

The nutritional status of preschool children in economically underdeveloped multi-ethnic areas is a global concern. This study aimed to examine the effect of a 2.2-year cluster randomized clinical trial that provided customized nutritious breakfast and nutrition education to preschool children in Linxia County, China. A total of 578 children aged 3 to 6 years were enrolled. After the intervention, the incidence of undernourishment was significantly lower in the intervention group compared to the control group (8.73% vs. 9.92%, OR = 0.01 [95%CI 0.00, 0.39], p = 0.014). Additionally, children with non-Muslim dietary habits had a lower incidence of undernourishment compared to those with Muslim dietary habits (OR = 0.05 [95%CI 0.00, 0.88]; p = 0.010). The intervention group also had a lower prevalence rate of wasting (OR = 0.02 [95%CI 0.00, 0.40]; p = 0.011) and a higher mean BMI-for-age Z-score (β = 1.05 [95%CI 0.32, 1.77]; p = 0.005) compared to the control group. These findings suggest that providing nutritious breakfast and nutrition education is an effective strategy to improve the nutrition and health of preschool children, particularly in economically disadvantaged regions and among children with Muslim dietary habits.

Background and Objective: Neonatal encephalopathy (NE) can cause permanent neurological damage in newborns. This greatly increases the burden of care placed on families. It also places a tremendous economic strain on the social health system. Currently, NE is mostly diagnosed by imaging and blood gas analysis. However, current diagnostic methods mostly lag behind the disease, leading to a lag in medical interventions for NE. In recent years, machine learning (ML) techniques have been applied to medicine, including in the early diagnosis and screening of diseases. This study aimed to provide an overview of existing research on the application of ML to NE and to offer insights for future investigations. Methods: A full library search in fuzzy matching mode was performed to retrieve articles from the Web of Science database published before August 31, 2024 using the following search strategy: (neonatal encephalopathy * machine learning) (where NE comprised all the relevant diseases, and ML comprised the main algorithms), and the key information was filtered. Key Content and Findings: A total of 159 documents were retrieved, and 23 relevant documents were identified based on the topic, keywords and content. The relevant content showed that the included articles on NE and ML had issues in terms of study standardization, dichotomous study outcomes, and clinical usefulness. Conclusions: To date, most studies on the application of ML to NE have not comprehensively considered the aspects of experimental design, data processing, model building, and evaluation. It is hoped that such models will provide effective decision-making tools for clinical practice in the future, and thus improve the healthy life span of newborns.

There is a global increase in the prevalence of autism spectrum disorder (ASD). Early identification of ASD in children and intervention are key aspects in the management of ASD. However, early identification is partly dependent on knowledge on ASD among pediatricians. This study analyzed the extent of ASD knowledge and its underlying factors among pediatric residents in eastern China, to provide a reference for medical education reforms. The study employed the Knowledge about Childhood Autism among Health Workers questionnaire. A total of 138 pediatric residents participated in the survey. Descriptive statistics were used to describe demographic characteristics and the four domains of the questionnaire. Univariate analysis was employed to assess impacts of the demographic characteristics on the questionnaire scores. On the other hand, multivariate regression analysis was used to analyze the correlation between the participants’ demographic characteristics and the questionnaire scores. The average ASD cognitive score of 138 respondents was 13.38 ± 4.48. The ASD cognitive scores in female pediatric residents were higher compared to that in males (p < 0.05). Residents who had obtained professional doctor qualification certificate were more than those without professional doctor qualification certificate (p < 0.05). The ASD knowledge in the group which did not have rotation in both departments was lower than in the group which had rotation in both departments (p < 0.05) as well as the group that had rotation in developmental and behavioral pediatrics department only (p < 0.05). Our multivariate linear regression model demonstrated significant statistical differences (p < 0.05), and showed that gender and systematic exposure to ASD knowledge had significant effects on cognitive scores (p < 0.05). Most participants had relatively low levels of awareness and knowledge about ASD, especially on ASD comorbidities and age of onset. Women, systematic learning of ASD knowledge in medical school, successful passing of the physician examination, and rotation in the developmental and behavioral pediatrics (DBP) department significantly influence the levels of ASD awareness and knowledge. It is, therefore, important to strengthen ASD education in medical students at the university level and make rotation in the DBP department a requisite for pediatric trainees.

Background: Neurodevelopmental disorders (NDDs)—including autism spectrum disorder, attention deficit hyperactivity disorder (ADHD), intellectual disability, learning disability, speech disorder, and Tourette disorder—impact brain development and impair social, learning, and occupational functioning. We performed a Mendelian randomization (MR) analysis using summary data from global genome‐wide association studies to investigate the potential causal relationship between leisure screen time (LST) and NDDs risk. Methods: Our dataset comprised 703,901 participants of European ancestry from 51 studies, with 256,725 individuals in the LST‐valid sample. We investigated causal associations with six types of NDDs using five MR methods: inverse‐variance weighted (IVW), MR Egger, weighted median, simple mode, and weighted mode. IVW was the primary method due to its efficiency and precision. Heterogeneity and horizontal pleiotropy were assessed using IVW and MR Egger, while the other methods served as sensitivity analyses to confirm robustness. Results: The IVW method revealed that each standard deviation increase in LST was associated with a reduced risk of ADHD (OR = 0.68; 95% CI: 0.52–0.89) and an elevated risk of intellectual disability (OR = 1.66; 95% CI: 1.26–2.18). These associations were consistent with the weighted median analysis (ADHD: OR = 0.68; 95% CI: 0.47–0.98; intellectual disability: OR = 1.51; 95% CI: 1.06–2.14). Conclusions: Our findings suggest that genetic predisposition to increased LST is causally associated with a lower risk of ADHD but a higher risk of intellectual disability, with no evidence for a causal relatawdionship with the other four NDDs. Larger or longitudinal studies are needed for further validation.