World Journal of Pediatrics最新文献

Background: Type 1 diabetes mellitus is a heterogeneous autoimmune disease with diverse characteristics between ethnicities and ages. Understanding this heterogeneity is essential for optimizing management and developing precision treatments. This study investigates the clinical, metabolic and immunological heterogeneity of type 1 diabetes mellitus in children across different ages of onset.

Methods: A retrospective analysis of 401 children newly diagnosed with type 1 diabetes mellitus at a single center from January 2009 to August 2024 was conducted. We compared the clinical characteristics of our cohort with others from different countries. Patients were categorized into three age groups: 6 months-5 years, 5-10 years and ≥ 10 years at diagnosis. Clinical, metabolic, and immunological features were compared among groups.

Results: The median cohort age was 8.58 years; 48.9% were male. Diabetic ketoacidosis occurred in 56.1% of patients, higher than in the Finnish and American cohorts. Most clinical characteristics are not significantly different among age groups. The 6 months-5 years group had a lower area under the curve (AUC) for C-peptide compared to the other age groups. The ≥ 10 years group was more likely to be thyroid antibody positive and have vitamin D deficiency. Immunologically, type 1 diabetes mellitus patients showed significantly increased counts of T lymphocytes, CD3 + CD8 + T cells and B lymphocytes, along with decreased interleukin-2 and increased interleukin-6 levels compared to healthy controls. Of note, the 6 months-5 years group had a higher CD4/CD8 ratio, which was negatively correlated with C-peptide AUC.

Conclusions: Significant heterogeneity in type 1 diabetes mellitus features exists across age groups. Early-onset patients showed poorer islet function and late-onset patients were more prone to metabolic complications. Collectively our study emphasizes the need for age-specific management strategies.

Background: MKRN3 gene loss-of-function mutations cause central precocious puberty (CPP), whereas its deletion in Prader-Willi syndrome (PWS) paradoxically leads to hypogonadism. The mechanistic basis for these opposing reproductive phenotypes remains largely unclear.

Methods: We performed whole-exome sequencing in 98 Chinese CPP patients along with a systematic review of previously reported MKRN3 pathogenic and likely pathogenic variants to summarize genotype-phenotype correlations. Subsequently, genome-wide DNA methylation profiling was performed in CPP patients with the MKRN3 pathogenic variant, and the results were compared with those of patients with PWS, idiopathic CPP, and healthy controls.

Results: A pathogenic frameshift MKRN3 variant [c.476dupC (p.Ala159fs*15)], representing the first frameshift mutation reported within the inter-C3H1 hotspot region in an Asian cohort, was identified. Patients with severe MKRN3 variants exhibited significantly earlier pubertal onset (5.80 vs. 7.50 years, P = 0.029) and higher GnRH-stimulated peak LH levels (34.55 vs. 11.00 IU/L, P = 0.047) than those with missense mutations. Methylation analysis revealed no differences in MKRN3 but identified 18,609 differentially methylated positions between MKRN3-CPP and PWS. Key findings included hypermethylation of IGSF10 (Δβ = 0.37), ZC3H18 (Δβ = 0.27), SH3RF3 (Δβ = 0.36), and PTH1R (Δβ = 0.28), alongside hypomethylation of MAGEL2 (Δβ = - 0.19), and PTPA (Δβ = - 0.23), where Δβ represents the difference in DNA methylation β values between groups.

Conclusions: We identified a first frameshift pathogenic variant localized to the inter-C3H1 region in Asia, further confirming its functional significance. Our study suggests an epigenetic framework that could potentially explain how divergent pubertal phenotypes in MKRN3 deficiency might arise from dysregulated epigenetic programming of downstream neuroendocrine pathways.

Background: Bronchial asthma is a common chronic respiratory disease in children. For many years, concerns about exercise-induced bronchoconstriction have limited physical activity in this population, with negative consequences for both physical and mental health. Recent evidence indicates that exercise should be incorporated into the daily routine of children with asthma, with appropriately prescribed programs shown to improve disease control, lung function, and quality of life. This study aims to systematically describe the safety, benefits and key factors of exercise for children with asthma.

Data sources: Initiated by the National Clinical Research Center for Child Health, this set of recommendations was developed by a multidisciplinary team of 17 experts. A comprehensive Literature search was conducted across PubMed, Embase, Cochrane and other databases, yielding 64 studies that met inclusion criteria up to May 2025. The Oxford Centre for Evidence-Based Medicine 2011 levels of evidence were used to evaluate evidence quality. Two rounds of expert voting were conducted using Delphi methodology to formulate final recommendations on key clinical topics.

Results: Recommendations were formulated across nine core domains: exercise safety, exercise-related benefits, pre-exercise screening, exercise prescription design, plan adjustment and progression, pre-exercise preparation, exercise monitoring, outcome assessment and the management of exercise-induced bronchoconstriction. Specific guidance is offered on individualized exercise planning based on asthma control status, physical fitness, exercise habits and environmental factors. Recommendations also address appropriate modalities of aerobic, resistance and flexibility training, strategies for monitoring intensity and progression and both pharmacologic and non-pharmacologic approaches to exercise-induced bronchoconstriction prevention and management.

Conclusions: These recommendations provide scientific and practical guidance for the development and implementation of individualized exercise prescriptions in children with asthma. Moreover, they highlight the importance of multidisciplinary collaboration and reinforce exercise as an integral component of asthma management. Further high-quality clinical research is needed to optimize exercise protocols and evaluate long-term outcomes.

Background: Undifferentiated autoinflammatory diseases (uAIDs) represent a newly recognized category of AIDs that fail to meet the diagnostic criteria for established monogenic or polygenic AIDs. This systematic review aims to clarify the prevalence of uAIDs and summarize the clinical features and treatment responses of affected patients.

Methods: A systematic literature review of PubMed, Web of Science, EMBASE, Cochrane and Scopus was performed in accordance with the 2020 PRISMA guidelines. English-language studies mentioning uAIDs were included for analysis. The classification criteria used for the diagnosis of uAIDs, as well as the demographic data, clinical manifestations and treatment responses of patients, were extracted and analyzed. Study quality was assessed via the tool developed by Hoy et al. The primary outcome, the pooled prevalence of uAIDs, was calculated via common- or random-effects meta-analyses, as appropriate.

Results: Thirty-five articles were included. Although termed variably, the definition of uAIDs consistently includes the following key points: patients with clinical signs and symptoms of systemic inflammation; patients who fail to meet the criteria for any well-defined polygenic AIDs; next-generation sequencing is negative or inconclusive; and other confounding conditions are excluded. The pooled prevalence of uAIDs among patients with AIDs was 21% (95% confidence interval: 14-29). Fever, arthralgia and abdominal pain were the most common clinical manifestations. Colchicine was the most frequently reported immunosuppressant in the literature, while interleukin (IL)-1-targeted biologics achieved remission in 70% of treated patients.

Conclusions: A diagnosis of uAIDs should be considered in patients who present with autoinflammatory features, such as fever, arthralgia and abdominal pain and who do not meet the diagnostic criteria for monogenic or polygenic AIDs or other confounding conditions. The responsiveness to IL-1-targeted biologics implies that the IL-1 pathway may be a potential gateway in uAIDs. Future prospective studies with standardized criteria are needed to overcome current limitations of exclusion-based diagnosis and study heterogeneity in uAIDs research.

Background: Despite the rise of adolescent video gaming, evidence-based parenting guidelines and research on its specific behavioral impacts remain limited. This study evaluated whether media parenting practices are prospectively associated with video game use in adolescents 1 and 2 years later.

Methods: We analyzed 7407 adolescents (51.6% male, age: 12.9 ± 0.6 years) from the Adolescent Brain Cognitive Development Study (year 3: 2019-2021 to year 5: 2021-2023). Multiple mixed-effects ordinal logistic regression and generalized linear models assessed the associations between parent media practices (screen time modeling, mealtime screen use, bedroom screen use, use to control behavior, monitoring and limiting) and video game behaviors (mature-rated games, problematic use and weekend video game time) 1 and 2 years later, adjusting for covariates.

Results: Higher parental screen time modeling, mealtime screen use and bedroom screen use were associated with higher odds of playing mature-rated video games, whereas higher parental monitoring of screen time and limiting screen time were associated with lower odds of playing mature-rated video games and less total video game use 1 and 2 years later. Higher mealtime screen use, bedroom screen use and use of screens to control behavior were associated with greater total video game use 1 and 2 years later.

Conclusions: This study demonstrates that certain media parenting practices can reduce adolescent video game use, while low parental involvement is linked to more problematic video game use behaviors. This study shows that parenting practices, including screen modeling, may influence adolescents' video game behaviors.

Background: Transcranial Doppler (TCD) detects characteristic waveforms indicating cerebral circulatory arrest and has been widely applied as an ancillary test for adult brain death or death by neurologic criteria. However, its application in children remains controversial due to anatomical differences and limited evidence. This review outlines the current role of transcranial Doppler in confirming pediatric brain death or death by neurologic criteria, offering practical insights and directions for future research.

Methods: A literature review was conducted on the use of transcranial Doppler to confirm pediatric brain death or death by neurologic criteria. This included original studies, meta-analyses, reviews, clinical guidelines, consensus statements, position papers, and legislation. Databases searched included PubMed, Embase, Cochrane Library, Web of Science, Google Scholar, China National Knowledge Infrastructure and Wanfang, covering records from inception to July 5, 2025. Search terms included "transcranial Doppler", "TCD", "cerebral circulatory arrest", "death by neurologic criteria", and "brain death".

Results: The use of transcranial Doppler varies across countries for confirming pediatric brain death or death by neurologic criteria. Some recommend transcranial Doppler, while others do not. Diagnostic criteria, including vessel selection, interpretation of absent blood flow signals and the number of tests, also vary. Pediatric studies support the clinical value of transcranial Doppler in the determination of brain death or death by neurologic criteria, but small sample sizes, methodological inconsistencies, and false results due to hemodynamic instability, open fontanelles or skull defects limit firm conclusions. Transcranial color-coded Doppler may improve diagnostic accuracy in certain cases.

Conclusions: Transcranial Doppler demonstrates considerable potential in confirming pediatric brain death or death by neurologic criteria, particularly in resource-limited or bedside settings. Given the current variability in clinical practice and the limitations of existing evidence, further large-scale, prospective studies are warranted to validate its role in this indication.

Background: Neurodevelopmental disorders are a group of conditions that affect key areas of development and may significantly impact a child's quality of life. This underscores the importance of accurate diagnostic tools to improve outcomes. Artificial intelligence (AI) has shown measurable effectiveness for enhancing the diagnosis and monitoring of neurodevelopmental disorders. This scoping review aims to summarize the current evidence on the use of AI technologies, including deep learning, supervised machine learning, decision support systems, and biosignal analysis, in improving diagnostic accuracy for pediatric neurodevelopmental disorders.

Data sources: A systematic search was conducted across PubMed, LILACS, MEDLINE, Google Scholar, and psychology-indexed journals, covering publications from 2000 to January 2025. Keywords and Medical Subject Headings terms were used to search for and select studies, applying specific inclusion and exclusion criteria. Selection followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews guidelines and included clinical studies, reviews, and validation research. The data were extracted and synthesized descriptively.

Results: Twenty-two studies were included. Deep learning models achieved diagnostic accuracies exceeding 85% in most studies in neuroimaging interpretation, whereas supervised machine learning improved the subtype classification of autism spectrum disorder and attention deficit hyperactivity disorder. Decision support systems have increased diagnostic efficiency, and biosignal-based AI has shown potential in identifying physiological markers related to neurodevelopmental disorders.

Conclusions: AI technologies may significantly contribute to improving early diagnosis and clinical decision-making in pediatric neurodevelopment. However, variability in study design, population, and algorithm standardization remains a challenge. AI technologies are also facing ethical concerns such as data privacy and security, interpretability, equity and access, and algorithmic bias. Further multicenter validation and regulatory frameworks are essential for clinical translation.