Early identification of neonatal jaundice (NJ) appears to be essential to avoid bilirubin encephalopathy and neurological sequelae. The interaction between gut microbiota and metabolites plays an important role in early life. It is unclear whether the composition of the gut microbiota and metabolites can be used as an early indicator of NJ or to aid clinical decision-making. This study involved a total of 196 neonates and conducted two rounds of "discovery-validation" research on the gut microbiome-metabolome. It utilized methods of machine learning, causal inference, and clinical prediction model evaluation to assess the significance of gut microbiota and metabolites in classifying neonatal jaundice (NJ), as well as the potential causal relationships between corresponding clinical variables and NJ. In the discovery stage, NJ-associated gut microbiota, network modules, and metabolite composition were identified by gut microbiome-metabolome association analysis. The NJ-associated gut microbiota was closely related to bile acid metabolites. By Lasso machine learning assessment, we found that the gut bacteria were associated with abnormal bile acid metabolism. The machine learning-causal inference approach revealed that gut bacteria affected serum total bilirubin and NJ by influencing bile acid metabolism. NJ-associated gut bile acids are potential biomarkers of NJ, and clinical prediction models constructed based on these biomarkers have some clinical effects and the model may be used for disease risk prediction. In the validation stage, it was found that intestinal metabolites can predict NJ, and the machine learning-causal inference approach revealed that bile acid metabolites affected NJ itself by affecting the total bilirubin content. Intestinal bile acid metabolites are potential biomarkers of NJ. By applying machine learning-causal inference methods to gut microbiome-metabolome association studies, we found NJ-associated intestinal bacteria and their network modules and bile acid metabolite composition. The important role of intestinal bacteria and bile acid metabolites in NJ was determined, which can predict the risk of NJ.

Background and purpose: Neonatal hyperbilirubinemia, also known as neonatal jaundice, is a common and frequent clinical condition with a complex etiology that can lead to brain damage in severe cases. Early recognition of hyperbilirubinemia and timely intervention and treatment can help reduce the occurrence of sequelae. This study was conducted to identify whether the gut microbiota composition can distinguish neonates with hyperbilirubinemia. Methods: Meconium samples were collected from 69 neonates with neonatal jaundice (NJ) and 69 age- and sex-matched neonates without clinically significant jaundice (healthy controls; HCs) for 16S rRNA gene sequencing and microbiome analysis. Results: Compared with HCs, the Chao 1 richness index of the gut microbiota was significantly decreased in the NJ group. The relative abundance of the probiotic gut bacterium, Lactobacillus, was significantly lower in the NJ group than in the HC group, whereas the abundances of potentially harmful gut bacteria, such as Escherichia coli and Staphylococcus, were significantly higher in the NJ group than in HCs. Correlation of the gut microbiota and clinical indicators revealed a positive correlation between Escherichia coli/Staphylococcus and serum total bilirubin levels. Finally, the results of a random forest machine-learning method to evaluate the possibility of using NJ-associated gut microbiota compositions as potential NJ biomarkers revealed an area under the curve of 96.88%. Conclusions: The abundances of Escherichia coli and Staphylococcus were positively correlated with serum total bilirubin levels. Hence, the gut microbiota composition is a potential biomarker of NJ.

Background: Bronchopulmonary dysplasia (BPD) is the most prevalent chronic lung disease in preterm infants. Studies have shown that Ureaplasma urealyticum (UU) infection is linked to its pathogenesis. However, it remains controversial whether UU colonization in preterm infants increases the risk of developing BPD.Objective: This study aimed to conduct a systematic review and meta-analysis to summarize the correlation between UU and BPD.Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, Wanfang Database, Chinese National Knowledge Infrastructure Database, Chinese Science and Technique Journal Database, and the China Biology Medicine disc from their inception to March 15, 2024. We included cohort and case-control studies investigating the association between UU infections and BPD in preterm infants, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The Newcastle-Ottawa Scale was used for quality assessment. The outcome was defined as the continued need for oxygen or respiratory support at 28 days after birth (BPD28) or at 36 weeks postmenstrual age (BPD36). Considering the potential publication bias in observational studies, we used a random-effects meta-analysis model, assessed heterogeneity (I²), performed subgroup analyses, evaluated publication bias, and graded the quality of evidence.Results: The meta-analysis included 36 cohort studies encompassing 5,991 participants. Among these, 20 reported on BPD28, 13 on BPD36, and 3 on both. The results indicated a significant association between UU colonization and BPD28 (odds ratio (OR): 2.26, 95% confidence interval (CI): 1.78–2.85, P < 0.00001, 23 studies, very low certainty of evidence) and BPD36 (OR: 2.13, 95% CI: 1.47–3.07, P < 0.0001, 16 studies, very low certainty of evidence).Conclusion: There is a correlation between UU colonization and the development of BPD in preterm infants. Future research should prioritize well-designed, large-scale, high-quality randomized controlled trials (RCTs) to comprehensively assess the risk of BPD in neonates following UU infection and to provide stronger evidence for clinical screening and prevention strategies to improve the prognosis of affected newborns.Systematic Review Registration: https://www.crd.york.ac.uk/, identifier (CRD42024524846).

Autism spectrum disorder (ASD) affects more than 1% of children, and there is no viable pharmacotherapeutic agent to treat the core symptoms of ASD. Studies have shown that children with ASD show changes in their levels of immune response molecules. Our previous studies have shown that ASD is more common in children with folate receptor autoantibodies. We also found that children with ASD have abnormal gut immune function, which was characterized by a significant increase in the content of immunoglobulin A and an increase in gut-microbiota-associated epitope diversity. These studies suggest that the immune mechanism plays an important role in the occurrence of ASD. The present study aims to systematically assess gene mutations in immune mediators in patients with ASD. We collected genetic samples from 72 children with ASD (2–12 years old) and 107 healthy controls without ASD (20–78 years old). We used our previously-designed immune gene panel, which can capture cytokine and receptor genes, the coding regions of MHC genes, and genes of innate immunity. Target region sequencing (500×) and bioinformatics analytical methods were used to identify variants in immune response genes associated with patients with ASD. A total of 4 rare variants were found to be associated with ASD, including HLA-B: p.A93G, HLA-DQB1: p.S229N, LILRB2: p.R322H, and LILRB2: c.956-4C>T. These variants were present in 44.44% (32/72) of the ASD patients and were detected in 3.74% (4/107) of the healthy controls. We expect these genetic variants will serve as new targets for the clinical genetic assessment of ASD, and our findings suggest that immune abnormalities in children with ASD may have a genetic basis.

Introduction Necrotising enterocolitis (NEC) of the intestine of preterm infants leads to the risk of abdominal surgery, short bowel syndrome, neurodevelopmental disorders and death. Although the risks of NEC and its complications have been recognised in many countries, few countries have established NEC-specific registries to continuously monitor its aetiology and prognosis. In China, the understanding of risk factors and prognosis of NEC is incomplete, characterised by a lack of evidence from prospective and multicentre studies. Therefore, we designed a multicentre, prospective, open observational cohort study with the aim of investigating the risk factors and prognosis of NEC in a real-world setting in Shenzhen, Guangdong Province, by constructing an online registry of children with NEC and a bank of biospecimens. Methods This is a prospective, multicentre, open observational cohort study. From June 2024 to June 2028, more than 600 patients with NEC from 15 tertiary hospitals in Shenzhen, Guangdong Province, will be enrolled in the study. By constructing an online registry for NEC, clinical data will be collected during the prenatal and hospitalisation periods. Prospectively, biospecimens will be collected during the period of suspected NEC, at the time of confirmed NEC, and at the time of confirmed severe NEC, and filed in the online registry system. Follow-up data will include postdischarge healthcare needs, growth patterns measures, eye or vision examinations, cranial MRI findings, brainstem auditory evoked potentials or automated auditory brainstem responses, and the Chinese Griffith Developmental Scale at corrected age 18–24 months. Follow-up results were likewise recorded in an online registry system. Hospitalisation outcomes, including severe NEC, somatic growth and survival status, will be collected at discharge. Follow-up outcomes will include loss to visit, survival status, somatic growth measures and severe neurodevelopmental deficits at corrected age 18–24 months. This study will enhance our overall understanding of the risk factors and outcomes of NEC, ultimately helping to reduce the incidence of neonatal NEC and its poor prognosis. Ethics and dissemination Our programme has received approval from the Ethics Committee for Scientific Research Projects of the Longgang District Maternity & Child Healthcare Hospital in Shenzhen City (ethics approval number: LGFYKYXMLL-2024-47-01). We anticipate presenting our findings at various national conferences and submitting them to peer-reviewed paediatrics journals. Trial registration number ChiCTR2400085043.

Background: while most gut microbiota research has focused on term infants, the health outcomes of preterm infants are equally important. Very-low-birth-weight (VLBW) or extremely-low-birth-weight (ELBW) preterm infants have a unique gut microbiota structure, and probiotics have been reported to somewhat accelerate the maturation of the gut microbiota and reduce intestinal inflammation in very-low preterm infants, thereby improving their long-term outcomes. The aim of this study was to investigate the structure of gut microbiota in ELBW neonates to facilitate the early identification of different types of low-birth-weight (LBW) preterm infants. Methods: a total of 98 fecal samples from 39 low-birth-weight preterm infants were included in this study. Three groups were categorized according to different birth weights: ELBW (n = 39), VLBW (n = 39), and LBW (n = 20). The gut microbiota structure of neonates was obtained by 16S rRNA gene sequencing, and microbiome analysis was conducted. The community state type (CST) of the microbiota was predicted, and correlation analysis was conducted with clinical indicators. Differences in the gut microbiota composition among ELBW, VLBW, and LBW were compared. The value of gut microbiota composition in the diagnosis of extremely low birth weight was assessed via a random forest-machine learning approach. Results: we briefly analyzed the structure of the gut microbiota of preterm infants with low birth weight and found that the ELBW, VLBW, and LBW groups exhibited gut microbiota with heterogeneous compositions. Low-birth-weight preterm infants showed five CSTs dominated by Enterococcus, Staphylococcus, Klebsiella, Streptococcus, Pseudescherichia, and Acinetobacter. The birth weight and clinical indicators related to prematurity were associated with the CST. We found the composition of the gut microbiota was specific to the different types of low-birth-weight premature infants, namely, ELBW, VLBW, and LBW. The ELBW group exhibited significantly more of the potentially harmful intestinal bacteria Acinetobacter relative to the VLBW and LBW groups, as well as a significantly lower abundance of the intestinal probiotic Bifidobacterium. Based on the gut microbiota’s composition and its correlation with low weight, we constructed random forest model classifiers to distinguish ELBW and VLBW/LBW infants. The area under the curve of the classifiers constructed with Enterococcus, Klebsiella, and Acinetobacter was found to reach 0.836 by machine learning evaluation, suggesting that gut microbiota composition may be a potential biomarker for ELBW preterm infants. Conclusions: the gut bacteria of preterm infants showed a CST with Enterococcus, Klebsiella, and Acinetobacter as the dominant genera. ELBW preterm infants exhibit an increase in the abundance of potentially harmful bacteria in the gut and a decrease in beneficial bacteria. These potentially harmful bacteria may be potential biomarkers for ELBW preterm infants.