Translational pediatrics最新文献

Background: Cupriavidus gilardii is a multidrug-resistant (MDR) pathogen found in soil and water. Human infection is extremely rare, with only two pediatric cases reported to date, and its clinical features and the antimicrobial strategies remain unclear. The pathogen's MDR nature often poses a therapeutic challenge. This case provides valuable clinical evidence regarding the management of C. gilardii infection in immunocompromised pediatric patients.

Case description: A 4-year-old boy with acute lymphoblastic leukemia developed severe immunosuppression after reinduction cyclophosphamide, cytarabine (Ara-C), mercaptopurine (CAM) chemotherapy according to the SCCCG-ALL-2023 protocol. The patient initially presented with recurrent fever, septic shock, and progressive respiratory distress. Despite an aggressive empirical antibiotic regimen including meropenem, imipenem, piperacillin-tazobactam, linezolid, and levofloxacin, his condition continued to worsen. Blood cultures and metagenomic next-generation sequencing (mNGS) subsequently confirmed C. gilardii infection. Based on susceptibility testing, therapy was adjusted to a combination of ceftazidime-avibactam (CAZ-AVI) and tigecycline. After this adjustment, the patient showed marked clinical improvement, with decreased inflammatory indicators and nearly completed clearance of the pathogen. Unfortunately, on hospital day 26, he developed sudden massive hemoptysis due to Aspergillus pulmonary artery invasion and died despite emergency treatment.

Conclusions: This case demonstrates that CAZ-AVI may offer effective antimicrobial control for C. gilardii infection in immunocompromised pediatric patients. Although the patient succumbed to secondary fungal complications, successful microbiological control demonstrates that CAZ-AVI may serve as a potential salvage therapy for rare MDR Gram-negative bacteria and provides clinical insight into the management of uncommon pediatric infections.

Background: Early identification of individuals at high risk for autism spectrum disorder (ASD) is crucial for optimizing intervention strategies and improving outcomes. This study aims to develop a risk prediction model integrating biopsychosocial factors through a systematic review with multicenter validation.

Methods: A comprehensive search was conducted across PubMed, Cochrane Library, and Embase for articles on biopsychosocial ASD risk factors during 2010-2023. Two reviewers independently extracted data. Meta-regression analysis of 37 systematic reviews/meta-analyses identified 18 potential risk factors by Stata 16.0. Four core variables were included in the prediction model, while 14 were excluded due to low-quality evidence or insufficient data after screening. Multivariate logistic regression with least absolute shrinkage and selection operator (LASSO) variable selection derived model weights. External validation was performed in a Chinese cohort (n=1,175) from two tertiary hospitals. Model discrimination was assessed via receiver operating characteristic (ROC) curves and clinical utility by decision curve analysis (DCA).

Results: Analysis of 37 systematic reviews identified four independent predictors of ASD risk: adverse childhood experiences (ACEs) [odds ratio (OR) =2.11; 95% confidence interval (CI): 1.61-2.77], preterm birth (OR =3.3; 95% CI: 1.24-7.60), antidepressant exposure during pregnancy (OR =1.17; 95% CI: 1.08-1.21), and perinatal antibiotic exposure (OR =1.52; 95% CI: 1.09-2.12). The risk model formula was: 0.82 × (ACEs) + 1.19 × (preterm birth) + 0.42 × (antidepressant exposure) + 0.21 × (perinatal antibiotic exposure). External validation showed excellent discrimination [area under the curve (AUC) =0.78; 95% CI: 0.75-0.81]. DCA confirmed significantly higher net clinical benefit compared to universal intervention strategies.

Conclusions: This study developed a risk prediction model integrating biopsychosocial factors, providing an evidence-based tool for early identification of individuals at high risk for ASD.

Background: Mycoplasma pneumoniae (MP), characterized by periodic outbreaks, causes community-acquired pneumonia in Chinese children. Non-pharmaceutical interventions (NPIs) during the coronavirus disease 2019 (COVID-19) pandemic significantly altered MP transmission. Following the relaxation of NPIs, MP infections rebounded globally, with trends toward younger individuals and increased severity. In this study, we aimed to identify new changes in the epidemiological characteristics, clinical phenotypes, and treatment strategies of MP infection in the post-pandemic era in China.

Methods: In this retrospective study, we analyzed the clinical data of 541 pediatric patients with MP nucleic acid samples who were treated at a general hospital in Chongqing, China between January 2018 and December 2024. Participants were divided into an observation group [2023-2024] and a control group [2018-2022] based on their hospitalization dates. Differences in the group characteristics were compared.

Results: After the relaxation of NPIs, the 2023 hospitalization rate was 5.1 times higher than that before the pandemic (11.16% vs. 2.29%). The peak age of onset shifted from 5-15 to 3-7 years. The observation group had a significantly higher proportion of severe cases (74.34% vs. 40.00%), more intense fever and cough symptoms, multilobar involvement (66.89%), and a higher post-discharge residual cough rate (64.69% vs. 20.00%). In the observation group, tetracycline usage increased (57.38% in children <8 years), glucocorticoid use increased (63.82% vs. 23.53%), and treatment duration was extended.

Conclusions: The post-pandemic era witnessed a surge in MP infections in younger patients, with more severe cases requiring adjusted treatment strategies. This study provides critical evidence for the clinical management and public health decision-making for MP pneumonia.

Background: Angle Class II Division 1 malocclusion in adolescents can impair oral health and facial aesthetics, yet evidence on clear aligner therapy is limited. This study aimed to evaluate the clinical efficacy of clear aligner therapy combined with Class II intermaxillary traction for treating Angle Class II Division 1 malocclusion in adolescents during the early permanent dentition stage.

Methods: A total of 30 adolescents (14 males and 16 females; age range, 12-15 years; mean age: 12.6 years) diagnosed with Angle Class II Division 1 malocclusion in early permanent dentition were enrolled. All patients received non-extraction treatment using clear aligners combined with Class II intermaxillary traction. Treatment outcomes were assessed by comparing pre- and post-treatment cephalometric measurements using statistical analysis.

Results: The mean treatment duration was 28.6 months. All patients achieved satisfactory occlusal outcomes, including improved overjet, overbite, and Class I molar relationship, along with notable enhancement in facial profile aesthetics. Post-treatment cephalometric analysis revealed statistically significant improvements (P<0.05) in most parameters, including sella-nasion-point B angle (SNB), point A-nasion-point B angle (ANB), gonion-pogonion distance (Go-Pog) distance, U1-SN angle, U1-NA angle, U1-L1 angle, U1-NA distance, L1-MP angle, L1-NB angle, L1-NB distance, overjet, overbite, U1-PP distance, L1-MP distance, PTM-U6 distance, upper lip to E-plane (UL-EP), lower lip to E-plane (LL-EP), nasolabial angle (NLA), and facial convexity angle (FCA). No significant changes were observed in sella-nasion-point A angle (SNA), MP-SN, or MP-FH angles (P>0.05).

Conclusions: Clear aligner therapy, when combined with Class II intermaxillary traction, is an effective approach for correcting Angle Class II Division 1 Malocclusion in adolescents. It not only improves occlusal relationships but also enhances facial soft tissue profile.

Background: Acute kidney injury (AKI) is a serious postoperative complication in hospitalized neonates. We aimed to develop and evaluate a machine learning (ML) model for predicting the risk of postoperative AKI in neonates.

Methods: The clinical records of 2,025 neonates were collected, and the patients were randomly divided into training and test sets. The outcome variable was the occurrence of postoperative AKI, and the models incorporated 25 predictive variables, including demographics, intraoperative infusions, and postoperative indicators. ML models were developed using six different algorithms on the training set, and their performance was assessed on the test set using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. The model with the best AUC was selected for validation in the test set. The association between the risk factors and postoperative AKI was interpreted using the SHapley Additive exPlanations (SHAP) method.

Results: A total of 110 neonatal patients (5.43%) developed AKI following surgery. Patient age, operation duration, and urine output were the three most important predictors of AKI. Among the tested models, the logistic regression (LR) algorithm was the best predictor of postoperative AKI, achieving the highest AUC [median, 0.807; 95% confidence interval (CI): 0.701-0.897] and the highest sensitivity (median, 0.733; 95% CI: 0.5-0.938). The SHAP method was used to illustrate the prediction process of the LR model for neonatal postoperative AKI at the level of individual patients.

Conclusions: The ML model that uses the LR algorithm with eight commonly measured variables could serve as a tool to predict postoperative AKI in neonates.

Background: Febrile seizures (FSs) are the most common seizure disorder in children aged 6 months to 5 years, influencing approximately 2-5% of the global population, with a higher prevalence in men and those with a family history. Their complex pathogenesis involves fever-induced neuroinflammation, imbalances in neuronal excitation and inhibition, and genetic predispositions. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), are key regulators of gene expression and have been implicated in various neurological disorders. However, their specific roles in FSs remain underexplored. This systematic review aimed to summarize the epidemiology and pathophysiological mechanisms of FSs, elucidate the emerging roles of ncRNAs, and evaluate their potential as biomarkers and therapeutic targets.

Methods: A systematic literature search was conducted following the PRISMA guidelines. Electronic databases, including PubMed, Web of Science, and Embase, were searched using keyword combinations related to "Febrile Seizures" and "non-coding RNA". The screening process involved initial title/abstract review, full-text assessment, and final inclusion based on predefined criteria, with exclusions documented at each stage to ensure comprehensive coverage and minimize bias.

Results: The review highlighted that FS pathophysiology could be influenced by developmental characteristics of the nervous system (e.g., heightened neuronal excitability), immune responses (e.g., pro-inflammatory cytokines regulated by ncRNAs, such as miR-146a and miR-155), and genetic factors (e.g., ion channel genes post-transcriptionally controlled by ncRNAs, such as miR-134). Key ncRNAs, including miR-134 (associated with neuronal hyperexcitability), miR-146a (modulating neuroinflammation), and circHIPK2 (involving astrocyte activation), were identified as critical in seizure mechanisms based on animal models and mechanistic studies. NcRNAs demonstrate promise as biomarkers due to their stability in biological fluids, while challenges in sensitivity and specificity should be addressed. Therapeutically, targeting ncRNAs through strategies, such as antisense oligonucleotides for miR-134 or mimics for miR-146a shows promise in preclinical models. However, efficient delivery to the central nervous system remains a significant challenge.

Conclusions: NcRNAs serve as dynamic regulators in the pathogenesis of FSs, providing valuable insights for diagnosis and treatment. They hold remarkable potential as non-invasive biomarkers and therapeutic targets. However, future research should prioritize validating the findings in clinical cohorts, elucidating causal mechanisms, and addressing translational challenges, such as standardization and delivery systems, to advance personalized medicine for pediatric FSs.

Background: Neonates who are small for their gestational age (SGA) often experience poor immediate and long-term outcomes. However, more information is required to understand the prevalence of SGA in regional Australia and the associated risk factors. This study investigated the prevalence of SGA infants and examined its known risk factors from the literature from 2017 to 2019 among infants admitted to the postnatal ward (PNW) and special care nursery (SCN) in a regional centre providing care for infants born at or after 32 weeks gestation unless unavoidable deliveries of lesser gestation.

Methods: SGA infants were determined based on the birth weight below the 10th percentile using the Fenton Growth Chart. This retrospective observational study included eleven risk factors documented in the literature: maternal age (age <18 and >35 years), multiple pregnancies (>1), obstetric complications, chronic maternal complications, maternal infections, smoking, substance use, alcohol use, maternal body mass index (BMI) (<18.5 and >24 kg/m²), low socioeconomic status determined by postcode in Index of Relative Socio-economic Advantage and Disadvantage (IRSAD) map-2021, and prematurity (32 weeks gestation or later). Descriptive statistics and bivariate correlation were used for statistical analysis.

Results: Between 2017 and 2019, a total of 2,546 live births were recorded, and 100 infants were SGA born at or after 32 weeks in a regional centre. Of eleven known risk factors studied, the results showed socioeconomic status (99, 99.0%), smoking (63, 64.3%), and high maternal BMI (51, 52.6%) were the three most prevalent risk factors among SGA infants. There was a significant but minimal negative relationship between the birth weight of SGA infants and the number of associated risk factors [r(98)=-0.209, P=0.04]. All SGA infants had at least one associated risk factor, and more than half (N=54, 54.0%, Mode =5) presented with five and more risk factors (N=96, 96.0%, Mode =5).

Conclusions: The study found that 4% of infants were SGA at a regional centre, predominantly caring for infants born at or after 32 weeks gestation. SGA infants were more common in mothers with low maternal socioeconomic status, smoking and elevated BMI and the majority had two or more risk factors. Further research is required to compare the prevalence of SGA throughout regional Australia and to explore the associated risk factors further.

Background: Allergic rhinitis (AR) in children is a common condition with rising prevalence globally, causing a substantial negative impact on patient quality of life and an economic burden. While Western medicine provides symptom relief, recurrence rates and side effects remain concerns. Traditional Chinese medicine (TCM), through syndrome differentiation, offers an effective, affordable alternative. However, clinical diagnosis in TCM often relies on subjective judgment. Digital tongue image analysis, combined with clinical symptoms and medical history, may enhance the accuracy and objectivity of syndrome differentiation, offering a promising approach to more effective treatment for pediatric AR. This study aimed to assist clinicians in accurately distinguishing between cold and heat syndromes in pediatric patients with AR.

Methods: A total of 391 children with AR were included in this study. Patients were classified with cold syndrome (n=92) or heat syndrome (n=299). Patients were randomly divided into a training set (n=176) and a test set (n=215). A multimodal deep learning model was developed with three stages. First, a hybrid Dense Convolutional Network model with a Squeeze-and-Excitation (SE-DenseNet) module was used to extract features from tongue images. Second, the independent sample t-test was used to screen and select relevant features from patient demographic and clinical information and patient and family medical history. Third, a transformer model was used to integrate the features for cold and heat syndrome classification. Model performance was evaluated using area under the curve (AUC), accuracy, precision, recall, and F1 scores.

Results: The multimodal model outperformed other models when classifying children with AR as cold syndrome or heat syndrome. It had the best AUC, accuracy, precision, recall, and F1 score. In the training set, the AUC, accuracy, precision, recall, and F1 score were 0.931, 0.875, 0.949, 0.869, and 0.920, respectively. In the test set, the AUC, accuracy, precision, recall, and F1 score were 0.877, 0.856, 0.863, 0.829, and 0.910, respectively.

Conclusions: The multimodal model integrating clinical features and features from tongue images demonstrated high accuracy, with potential to assist pediatricians in syndrome differentiation and treatment decision-making for children with AR. The multimodal model may enable objective and quantifiable diagnostic results, improving efficiency and accuracy.