Coronavirus Disease 2019 (COVID‐19) is a newly emerging infectious disease caused by a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). After its first occurrence in Wuhan of China from December 2019, COVID‐19 rapidly spread around the world. According to the World Health Organization statement on 13 March 2020, there had been over 132 500 confirmed cases globally. Nevertheless, the case reports of children are rare, which results in the lack of evidence for preventing and controlling of children's infection. Here, we report three cases of SARS‐CoV‐2 infected children diagnosed from 3 February to 17 February 2020 in Tianjin, China. All of these three cases experienced mild illness and recovered soon after the treatment, with the nucleic acid of throat swab turning negative within 14, 11, and 7 days after diagnosis, respectively. However, after been discharged, all three cases were tested SARS‐CoV‐2 positive in the stool samples within 10 days, in spite of their remained negative nucleic acid in throat swab specimens. Therefore, it is necessary to be aware of the possibility of fecal‐oral transmission of SARS‐CoV‐2 infection, especially for children cases.

Background: Acute myelogenous leukemia (AML) is a common pediatric malignancy in children younger than 15 years old. Although the overall survival (OS) has been improved in recent years, the mechanisms of AML remain largely unknown. Hence, the purpose of this study is to explore the differentially methylated genes and to investigate the underlying mechanism in AML initiation and progression based on the bioinformatic analysis. Methods: Methylation array data and gene expression data were obtained from TARGET Data Matrix. The consensus clustering analysis was performed using ConsensusClusterPlus R package. The global DNA methylation was analyzed using methylationArrayAnalysis R package and differentially methylated genes (DMGs), and differentially expressed genes (DEGs) were identified using Limma R package. Besides, the biological function was analyzed using clusterProfiler R package. The correlation between DMGs and DEGs was determined using psych R package. Moreover, the correlation between DMGs and AML was assessed using varElect online tool. And the overall survival and progression-free survival were analyzed using survival R package. Results: All AML samples in this study were divided into three clusters at k = 3. Based on consensus clustering, we identified 1,146 CpGs, including 40 hypermethylated and 1,106 hypomethylated CpGs in AML. Besides, a total 529 DEGs were identified, including 270 upregulated and 259 downregulated DEGs in AML. The function analysis showed that DEGs significantly enriched in AML related biological process. Moreover, the correlation between DMGs and DEGs indicated that seven DMGs directly interacted with AML. CD34, HOXA7, and CD96 showed the strongest correlation with AML. Further, we explored three CpG sites cg03583857, cg26511321, cg04039397 of CD34, HOXA7, and CD96 which acted as the clinical prognostic biomarkers. Conclusion: Our study identified three novel methylated genes in AML and also explored the mechanism of methylated genes in AML. Our finding may provide novel potential prognostic markers for AML.

Background: In children, refractory Mycoplasma pneumoniae pneumonia (RMPP) may result in severe complications and high medical costs. There is research on a simple and easy-to-use nomogram for early prediction and timely treatment of RMPP.Methods: From December 2018 to June 2021, we retrospectively reviewed medical records of 299 children with Mycoplasma pneumoniae pneumonia (MPP) hospitalized in Tianjin Children's Hospital. According to their clinical manifestations, patients were divided into the RMPP group and the general Mycoplasma pneumoniae pneumonia (GMPP) group. The clinical manifestations, laboratory indicators, and radiological data of the two groups were obtained. Stepwise regression was employed for variable selection of RMPP. The predictive factors selected were used to construct a prediction model which presented with a nomogram. The performance of the prediction model was evaluated by C statistics, calibration curve, and receiver operating characteristic (ROC) curve.Results: The RMPP group significantly showed a higher proportion of females, longer fever duration, and longer hospital stay than the GMPP group (P < 0.05). Additionally, the RMPP group revealed severe clinical characteristics, including higher incidences of extrapulmonary complications, decreased breath sounds, unilateral pulmonary consolidation >2/3, and plastic bronchitis than the GMPP group (P < 0.05). The RMPP group had higher neutrophil ratio (N%), C-reactive protein (CRP), interleukin-6 (IL-6), lactic dehydrogenase (LDH), and D-dimer than the GMPP group (P < 0.05). Stepwise regression demonstrated that CRP [OR = 1.075 (95% CI: 1.020–1.133), P < 0.001], LDH [OR = 1.015 (95% CI: 1.010–1.020), P < 0.001], and D-dimer [OR = 70.94 (95% CI: 23.861–210.904), P < 0.001] were predictive factors for RMPP, and developed a prediction model of RMPP, which can be visualized and accurately quantified using a nomogram. The nomogram showed good discrimination and calibration. The area under the ROC curve of the nomogram was 0.881, 95% CI (0.843, 0.918) in training cohorts and 0.777, 95% CI (0.661, 0.893) in validation cohorts, respectively.Conclusion: C-reactive protein, LDH, and D-dimer were predictive factors for RMPP. The simple and easy-to-use nomogram assisted us in quantifying the risk for predicting RMPP, and more accurately and conveniently guiding clinicians to recognize RMPP, and contribute to a rational therapeutic choice.

Background: Early identification of plastic bronchitis (PB) is of great importance and may aid in delivering appropriate treatment. This study aimed to develop and validate a nomogram for predicting PB in patients with refractory Mycoplasma pneumoniae pneumonia (RMPP). Methods: A total of 547 consecutive children with RMPP who underwent fiberoptic bronchoscopy (FOB) intervention from January 2016 to June 2021 were enrolled in this study. Subsequently, 374 RMPP children (PB: 137, without PB: 237) from January 2016 to December 2019 were assigned to the development dataset to construct the nomogram to predict PB and 173 RMPP children from January 2020 to June 2021 were assigned to the validation dataset. The clinical, laboratory and radiological findings were screened using Least Absolute Shrinkage and Selection Operator (LASSO) regression and logistic regression was applied to construct a nomogram. The performance of the nomogram was evaluated by discrimination, calibration and clinical utility. Comparsion of ROC analysis and decision curve analysis (DCA) between nomogram and other models was performed to evaluate the discrimination ability and clinical utility. Results: The development dataset included 374 patients with a mean age of 6.6 years and 185(49.5%) were men. The validation dataset included 173 patients and the mean age of the dataset was 6.7 years and 86 (49.7%) were men. From 26 potential predictors, LASSO regression identified 6 variables as significant predictive factors to construct the nomogram for predicting PB, including peak body temperature, neutrophil ratio (N%), platelet counts (PLT), interleukin-6 (IL-6), actic dehydrogenase (LDH) and pulmonary atelectasis. The nomogram showed good discrimination, calibration and clinical value. The mean AUC of the nomogram was 0.813 (95% CI 0.769–0.856) in the development dataset and 0.895 (95% CI 0.847–0.943) in the validation dataset. Through calibration plot and Hosmer–Lemeshow test, the predicted probability had a good consistency with actual probability both in the development dataset (P = 0.217) and validation dataset (P = 0.183), and DCA showed good clinical utility. ROC analysis indicated that the nomogram showed better discrimination ability compared with model of peak body temperature + pulmonary atelactsis and another model of N% + PLT + IL-6 + LDH, both in development dataset (AUC 0.813 vs 0.757 vs 0.754) and validation dataset (AUC 0.895 vs 0.789 vs 0.842). Conclusions: In this study, a nomogram for predicting PB among RMPP patients was developed and validated. It performs well on discrimination ability, calibration ability and clinical value and may have the potential for the early identification of PB that will help physicians take timely intervention and appropriate management.

Chest radiography (CXR) is commonly used for diagnosing childhood pneumonia, but concerns about radiation exposure have raised interest in using radiation‐free lung ultrasound (LUS) as an alternative imaging modality. Therefore, we designed this meta‐analysis to compare the accuracy of LUS and CXR for diagnosing childhood pneumonia. We searched 8 databases and 1 clinical trial registry for studies published from inception to March 2023. Studies assessing lung ultrasound and chest radiography for diagnosing childhood pneumonia were included. Two reviewers independently screened literature, extracted data, and assessed the risk of bias using the QUADAS‐2 tool for each study. Meta‐analysis was conducted using a random‐effects model, and pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and summary receiver operating characteristic (SROC) curve were assessed. Statistical analyses were performed using Meta‐Disc 1.4, RevMan 5.4, and Stata 17.0 software. Heterogeneity was examined, and subgroup analysis was conducted to explore the accuracy of lung ultrasound in diagnosing childhood pneumonia. Out of the 4089 screened articles, 30 studies were included, encompassing a total of 4546 children. Of those, 3257 were diagnosed with pneumonia, 3190 through LUS, and 2925 via CXR. The meta‐analysis showed that the sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio of LUS were 0.940 (95% CI 0.930–0.949), 0.855 (95% CI 0.835–0.873), 7.561 (95% CI 4.956–11.536), 0.08 (95% CI 0.056–0.113), and 110.77 (95% CI 62.156–197.40), respectively. The combined area under the SROC curve was 0.9712, Q index = 0.9218. For CXR, the sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio were 0.893 (95% CI 0.881–0.905), 0.906 (95% CI 0.889–0.921), 18.742 (95% CI 7.551–46.520), 0.105 (95% CI 0.062–0.180), and 237.43 (95% CI 74.080–760.99), respectively. The combined area under the SROC curve was 0.9810, Q index = 0.9391. Subgroup analysis showed that the implementation location, interval between lung ultrasound and chest radiography, and operator experience had no impact on the accuracy of lung ultrasound in diagnosing childhood pneumonia. Existing evidence suggests that lung ultrasound has high accuracy for diagnosing childhood community‐acquired pneumonia. Compared with chest radiography, lung ultrasound has higher sensitivity, similar specificity, and advantages such as radiation‐free, lower cost, simplicity of operation, and ease of follow‐up, making it an important imaging modality for diagnosing childhood pneumonia.