Objective To investigate the recurrence of immune thrombocytopenia(ITP)in children and to establish a predictive model.Methods A total of 288 children with ITP admitted to Children's Hospital of Wujiang District and Children's Hospital Affiliated to Suzhou University from January 2018 to April 2022 were collected.The factors potentially related to the recurrence of ITP in children were screened.The children in the model group were divided into 2 groups according to the presence or absence of recurrence and the indicators of the 2 groups were compared.After screening the potential influencing factors by LASSO regression and the independent influencing factors of relapse in children with ITP patients by Logstic regression analysis,we constructed a column-line graph model by using R language and validated it.Results A total of 37(18.47%)of 201 patients in the model group experienced relapse.The age,blood type,duration of disease before treatment,antecedent infections,bleeding,initial treatment regimen,antinuclear antibody titer,initial count and mean platelet volume,initial platelet distri-bution width,initial peripheral blood lymphocyte count and time length to effective platelet count after treatment were found in the recurrence group versus the non-recurrence group The difference was statistically significant(P＜0.05).The results of multifactorial logistic regression analysis performed on the basis of LASSO regression showed that blood type,duration of illness before treatment,antecedent infection,initial treatment regimen,ini-tial peripheral blood lymphocyte count,and time to effective platelet count after treatment were independent influ-ences on the conversion of cough variant asthma to classic asthma in children.Based on the results of the multifac-torial analysis,a column chart model for predicting ITP recurrence in children was developed in R.The results of the receiver operating characteristic(ROC)analysis showed that the area under curve(AUC)of the column chart model for predicting ITP recurrence in children in the modeling group was 0.867[95%CI(0.796,0.938)]with a sensitivity of 84.2%and a specificity of 73.1%,and that in the validation group,the AUC was 0.838[95%CI(0.765),0.911]with a sensitivity of 82.3%and a specificity of 78.4%,0.911)]sensitivity was 82.3%and specificity was 78.4%.The Bootstrap method was used to repeat the sampling 1000 times,and the validation group was used for validation.The results of the calibration curve showed that the prediction curves of the model group and the validation group were basically fitted with the standard curve,suggesting that the model prediction accuracy was high.The results of the decision curve analysis of the model group showed that the net benefit rate of patients was greater than zero when the probability threshold of the column line graph model of pre-dicting ITP recurrence in children was 0.15-0.75.Conclusions ITP recurrence in children is mainly affected by the patient's age,blood type,and pre-treatment course of the disease,and the column-line diagram model based on these factors has a high accuracy and differentiation for ITP recurrence in parenting children.

Objective : To explore the diagnostic value of lymphocyte subpopulations combined with chemokines in children with immunologic thrombocytopenic purpura ( ITP) . Methods : 132 children with proposed diagnosis of ITP were collected , and the children were divided into ITP and non ITP groups according to the diagnostic results of ITP related clinical diagnostic criteria. 6 ml of peripheral venous blood was drawn , the levels of CD4 + CD8 + and CD3 + were detected using flow cytometry , and the levels of chemokine (C-C motif) ligand 5 (CCL5) , Recombi nant Chemokine (C-X-C Motif) Ligand 1 (CXCL11) , and monocyte chemotactic protein 1 (MCP-1) were detec ted using enzyme linked immunosorbent assay , the blood platelet (PLT) was measured by a fully automated cell an alyzer. The children were divided into ITP and non ITP groups according to the clinical diagnostic criteria related to ITP. The lymphocyte subpopulations and chemokine levels of the two groups of children were compared , and the correlation between lymphocyte subpopulations and chemokine levels and PLT was analyzed . The ROC method was used to evaluate the diagnostic efficacy of individual and combined detection of each indicator for ITP. Results: The levels of CD4 + and CD3 + in the ITP group were lower than those in the non ITP group (P < 0.05) , while the levels of CD8 + were higher than those in the non ITP group (P < 0.05) . The levels of CCL5 , CXCL11 , and MCP-1 in the ITP group were higher than those in the non ITP group (P < 0.05) . The correlation analysis results showed that CD4 + , CD3 + and platelet count were positively correlated in the ITP group(P < 0.05) , while CD8 + , CCL5 , CXCL11 , MCP-1 were negatively correlated with PLT (P < 0.05) . The ROC analysis results showed that the cut off values of CD4 + , CD8 + , CD3 + , CCL5 , CXCL11 , and MCP-1 for the diagnosis of ITP in children were 27.13% , 24.02% , 59.88% , 41.02 ng/L , 30.18 ng/L , and 188.27 ng/L , respectively. The AUC values were 0.893 , 0.880 , 0.629 , 0.801 , 0.892 , and 0.751 , respectively , The AUC of the parallel diagnosis ( meaning that one or more of CD4 + , CD3 + was below the cut off value and/or one or more of CD8 + , CCL5 , CXCL11 , MCP-1 was above the cut off value at the time of parallel testing) was 0.967 , indicating that one or more of them was lower than the cut off value and/or one or more of them was higher than the cut off value when tested separately. Its diag nostic efficacy was higher than that of each indicator tested separately (P < 0.05) . Conclusion There are signifi cant differences in lymphocyte subpopulations and chemokines between pediatric ITP patients and non ITP patients . CD4 + , CD8 + , CD3 + , CCL5 , CXCL11 , and MCP-1 can be used for the diagnosis of pediatric ITP. Combined de tection of various indicators can improve detection efficiency.

Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental disorder associated with both genetic and environmental risks. Neuroimaging approaches have been widely employed to parse the neurophysiological mechanisms underlying ASD, and provide critical insights into the anatomical, functional, and neurochemical changes. We reviewed recent advances in neuroimaging studies that focused on ASD by using magnetic resonance imaging (MRI), positron emission tomography (PET), or single-positron emission tomography (SPECT). Longitudinal structural MRI has delineated an abnormal developmental trajectory of ASD that is associated with cascading neurobiological processes, and functional MRI has pointed to disrupted functional neural networks. Meanwhile, PET and SPECT imaging have revealed that metabolic and neurotransmitter abnormalities may contribute to shaping the aberrant neural circuits of ASD. Future large-scale, multi-center, multimodal investigations are essential to elucidate the neurophysiological underpinnings of ASD, and facilitate the development of novel diagnostic biomarkers and better-targeted therapy.