ObjectiveTo explore the latent profiles of sleep patterns and their association with gastrointestinal heat accumulation syndrome in preschool children in Beijing. MethodsA cross-sectional study design was adopted， and preschool children were recruited from 57 kindergartens in Beijing using convenience sampling. Sleep characteristics and gastrointestinal heat accumulation syndrome was assessed using the Chinese version of the Sleep Disturbance Scale for Children （SDSC） and Self-rating Diagnostic Scale for Gastrointestinal Heat Retention Syndrome in children （GHRS）. Latent profile analysis （LPA） was applied to identify distinct sleep-related subgroups， and multivariate logistic regression was used to analyze the association between different sleep profiles and gastrointestinal heat accumulation syndrome. ResultsA total of 6878 preschool children were included in the study. Among them， 3.46% of the children （238/6878） had sleep disorders， while 54.42% （3743/6878） had gastrointestinal heat accumulation. Compared to children without gastrointestinal heat accumulation syndrome， those with this syndrome exhibited a higher prevalence of sleep disorders， higher SDSC scores， higher T-transformed total scores， and higher scores across all dimensions， including disorders of initiating sleep， disorders of maintaining sleep， sleep hyperhidrosis， sleep breathing disorders， parasomnias， nonrestorative sleep and excessive somnolence （P＜0.01）. Latent profile analysis identified four distinct sleep characteristic profiles， which were the mixed high-symptom group （32.39%， 2228/6878）， sleep initiation and maintenance difficulty symptom group （49.07%， 3375/6878）， low-symptom group （14.19%， 976/6878）， and the group with no significant symptoms （4.35%， 299/6878）. Multivariate logistic regression analysis demonstrated that the mixed high-symptom group， sleep initiation and maintenance difficulty symptom group， and low-symptom group were significantly associated with gastrointestinal heat accumulation syndrome. Among them， the mixed high-symptom group had the most significant association with gastrointestinal heat accumulation （OR=12.657， P＜0.001）， followed by the sleep initiation and maintenance difficulty symptom group （OR=4.964， P＜0.001）. ConclusionSleep characteristics in preschool children mainly present as composite patterns with different symptom combinations. Moreover， the sleep latent profiles of the mixed high-symptom group， the sleep initiation and maintenance difficulty symptom group， and the low-symptom group were significantly associated with gastrointestinal heat accumulation syndrome.

Objective To study the multifractal features of fetal heart rate(FHR)signals,and to investigate the multiscale energy distribution features of FHR variability.Methods The study focuses on FHR signals from an open database released by Czech Technical University-University Hospital in Brno(CTU-UHB).Daubechies second-order wavelets db2 was applied to decompose FHR signals into level d1-d7 detail components and approximation components.R/S analysis was performed on the decomposed components,and Hurst index was used to study the multifractal features of the decomposed signals.Hurst index＜0.5 indicated that the signal had anti-persistence,Hurst index＞0.5 indicated that the signal had positive persistence,and the decomposed signal was reconstructed by using fractal features.The distribution law of energy was analyzed.Results The Hurst index of d1-d7 approximation components was＞0.5,which indicates that the approximation components of FHR signal have positive persistent single fractal characteristics.The Hurst index of d1-d3 detail components was＞0.5,the Hurst index of d7 detail components was＜0.5,and the Hurst index of d4-d6 detail components contained both＞0.5 and＜0.5,showing the bi-fractal feature.Based on the above multi-fractal analysis results,the FHR decomposed signals were reconstructed into three different subsequences,i.e.microscale sequence,mesoscale sequence,and macroscale sequence.The statistical results showed that the energy of these three sequences accounts for approximately 5％,70％,and 25％of the total energy,respectively(P＜0.05).Conclusion The FHR signal was characterized by complex multifractal features,and the energy of the fetal heart rate signal was mainly concentrated in the mesoscale.

Objective Based on the knowledge graph to analyze the research status and application trends of artificial intelligence(AI)in the field of fetal health status assessment,and to explore the development context and research hotspots.Methods The relevant literature in the field of fetal health status assessment was retrieved in the Web of Science,PubMed,IEEE Xplore,CNKI and Wanfang Data Knowledge Service Platform,and the search time period was from January 2015 to December 2024.The CiteSpace software was used to generate visual maps of authors,institutions and key words,and bibliometric analysis was conducted on institutions,authors and key words,etc.Results Globally,the number of annual publications of research related to the application of AI to the assessment of fetal health status had been on the rise in the past 10 years.The research hotspots focused on influencing factors,prenatal diagnosis,feature extraction,diagnostic models,machine learning,deep learning,fetal heart rate,etc,indicating that AI had promoted the transformation of fetal condition monitoring and clinical decision-making towards digital diagnosis and treatment driven by deep learning through technological innovation.Conclusion At present,research on AI assisted intelligent monitoring of fetal health mainly focuses on signal processing,feature extraction,and innovative applications.The combination of medical and engineering is an important way to carry out related research and achieve application implementation.In the future,the intelligent,digital,and precise processes in this field will continue to accelerate.

Objective: To evaluate the platelet transfusion requirements in children with high-risk stage Ⅳ neuroblastoma undergoing autologous hematopoietic stem cell transplantation (ASCT), and to identify risk factors for increased transfusion needs and prolonged time to platelet transfusion independence. Methods: This single-center retrospective clinical study included 96 children with high-risk stage Ⅳ neuroblastoma who underwent ASCT from January 2019 to May 2024 in our hospital. Relevant clinical data were collected and analyzed, including age, gender, body surface area, platelet count (PLT) on stem cell infusion day (day 0), conditioning regimen, CD34 stem cell dose, platelet transfusion requirements during transplantation, and time to platelet transfusion independence post-transplant. Results: All 96 (100%) children received transfusion after ASCT. From day 0 to transfusion independence, the median number of platelet transfusion was 3 (2, 4.50), and the median volume of platelet transfused was 3 (2, 4.25) units. Platelet transfusion was required in almost all children in pseudo-healing stage (day 4 to day 6) and polar stage (day 7 to day 14), with transfusion rates as high as 83.33%(n=80) and 100%(n=96), respectively. The median time to platelet transfusion independence post-transplant was 13(11,17) days. Multivariate analysis showed that PLT<100×10 /L on day 0, platelet transfusion within one week before ASCT, the use of “busulfan+ melphalan” conditioning regimen, and CD34 stem cell dose<4.0×10 /kg were associated with significantly increased platelet requirements and numbers of transfusion (P<0.05). PLT<100×10 /L on day 0, platelet transfusion within one week before ASCT, and CD34 stem cell dose<4.0×10 /kg were associated with significantly delayed platelet transfusion independence (P<0.05). Age, sex, and blood type showed no statistically significant association (P>0.05) with post-transplant platelet transfusion requirements or time to transfusion independence in neuroblastoma patients. Conclusion: This study provided quantitative data for platelet transfusion after ASCT in children with high-risk stage Ⅳ neuroblastoma, and identified PLT<100×10 /L on day 0, platelet transfusion within one week before ASCT, CD34 stem cell dose<4.0×10 /kg were risk factors for increased platelet transfusions and delayed transfusion independence. Furthermore, the use of the BuMel (busulfan-melphalan) conditioning regimen was also found to contribute to increased transfusion requirements.

Objective: To evaluate the platelet transfusion requirements in children with high-risk stage Ⅳ neuroblastoma undergoing autologous hematopoietic stem cell transplantation (ASCT), and to identify risk factors for increased transfusion needs and prolonged time to platelet transfusion independence. Methods: This single-center retrospective clinical study included 96 children with high-risk stage Ⅳ neuroblastoma who underwent ASCT from January 2019 to May 2024 in our hospital. Relevant clinical data were collected and analyzed, including age, gender, body surface area, platelet count (PLT) on stem cell infusion day (day 0), conditioning regimen, CD34 stem cell dose, platelet transfusion requirements during transplantation, and time to platelet transfusion independence post-transplant. Results: All 96 (100%) children received transfusion after ASCT. From day 0 to transfusion independence, the median number of platelet transfusion was 3 (2, 4.50), and the median volume of platelet transfused was 3 (2, 4.25) units. Platelet transfusion was required in almost all children in pseudo-healing stage (day 4 to day 6) and polar stage (day 7 to day 14), with transfusion rates as high as 83.33%(n=80) and 100%(n=96), respectively. The median time to platelet transfusion independence post-transplant was 13(11,17) days. Multivariate analysis showed that PLT<100×10 /L on day 0, platelet transfusion within one week before ASCT, the use of “busulfan+ melphalan” conditioning regimen, and CD34 stem cell dose<4.0×10 /kg were associated with significantly increased platelet requirements and numbers of transfusion (P<0.05). PLT<100×10 /L on day 0, platelet transfusion within one week before ASCT, and CD34 stem cell dose<4.0×10 /kg were associated with significantly delayed platelet transfusion independence (P<0.05). Age, sex, and blood type showed no statistically significant association (P>0.05) with post-transplant platelet transfusion requirements or time to transfusion independence in neuroblastoma patients. Conclusion: This study provided quantitative data for platelet transfusion after ASCT in children with high-risk stage Ⅳ neuroblastoma, and identified PLT<100×10 /L on day 0, platelet transfusion within one week before ASCT, CD34 stem cell dose<4.0×10 /kg were risk factors for increased platelet transfusions and delayed transfusion independence. Furthermore, the use of the BuMel (busulfan-melphalan) conditioning regimen was also found to contribute to increased transfusion requirements.

Objective To study the multifractal features of fetal heart rate(FHR)signals,and to investigate the multiscale energy distribution features of FHR variability.Methods The study focuses on FHR signals from an open database released by Czech Technical University-University Hospital in Brno(CTU-UHB).Daubechies second-order wavelets db2 was applied to decompose FHR signals into level d1-d7 detail components and approximation components.R/S analysis was performed on the decomposed components,and Hurst index was used to study the multifractal features of the decomposed signals.Hurst index＜0.5 indicated that the signal had anti-persistence,Hurst index＞0.5 indicated that the signal had positive persistence,and the decomposed signal was reconstructed by using fractal features.The distribution law of energy was analyzed.Results The Hurst index of d1-d7 approximation components was＞0.5,which indicates that the approximation components of FHR signal have positive persistent single fractal characteristics.The Hurst index of d1-d3 detail components was＞0.5,the Hurst index of d7 detail components was＜0.5,and the Hurst index of d4-d6 detail components contained both＞0.5 and＜0.5,showing the bi-fractal feature.Based on the above multi-fractal analysis results,the FHR decomposed signals were reconstructed into three different subsequences,i.e.microscale sequence,mesoscale sequence,and macroscale sequence.The statistical results showed that the energy of these three sequences accounts for approximately 5％,70％,and 25％of the total energy,respectively(P＜0.05).Conclusion The FHR signal was characterized by complex multifractal features,and the energy of the fetal heart rate signal was mainly concentrated in the mesoscale.

Objective Based on the knowledge graph to analyze the research status and application trends of artificial intelligence(AI)in the field of fetal health status assessment,and to explore the development context and research hotspots.Methods The relevant literature in the field of fetal health status assessment was retrieved in the Web of Science,PubMed,IEEE Xplore,CNKI and Wanfang Data Knowledge Service Platform,and the search time period was from January 2015 to December 2024.The CiteSpace software was used to generate visual maps of authors,institutions and key words,and bibliometric analysis was conducted on institutions,authors and key words,etc.Results Globally,the number of annual publications of research related to the application of AI to the assessment of fetal health status had been on the rise in the past 10 years.The research hotspots focused on influencing factors,prenatal diagnosis,feature extraction,diagnostic models,machine learning,deep learning,fetal heart rate,etc,indicating that AI had promoted the transformation of fetal condition monitoring and clinical decision-making towards digital diagnosis and treatment driven by deep learning through technological innovation.Conclusion At present,research on AI assisted intelligent monitoring of fetal health mainly focuses on signal processing,feature extraction,and innovative applications.The combination of medical and engineering is an important way to carry out related research and achieve application implementation.In the future,the intelligent,digital,and precise processes in this field will continue to accelerate.

[Abstract] [Objective] To analyze the production of platelet antibodies in children with congenital heart disease, identify the types of antibodies, and explore their effects on platelet count, coagulation function and platelet function. [Methods] A retrospective analysis was conducted on 3 504 congenital heart disease patients without a history of blood transfusion who were treated at Beijing Children's Hospital between January 2019 and June 2024 to study the positive rate of platelet antibodies. Platelet antibody types were detected using the solid-phase agglutination method, and the platelet count and coagulation function of the children were analyzed. The impact of platelet antibodies on platelet function was evaluated using a coagulation and platelet function analyzer. [Results] The positive rate of platelet antibody in children with congenital heart disease with no history of blood transfusion was 9.7% (341/3 504), higher than the overall positive rate of 6.6% (2 657/40 311) in the general pediatric population. The platelet antibodies in congenital heart disease cases with positive platelet antibodies were mainly autoantibodies. There was no significant difference in platelet count between antibody-positive children and antibody-negative children. However, the prothrombin time (s) of antibody-positive children was significantly longer than that of antibody-negative children[(12.19±1.07) vs (11.32±0.77)]. Platelets sensitized by antibodies showed a significant reduction in function compared to non-sensitized platelets. [Conclusion] Children with congenital heart disease have a high rate of positivity for autoantibodies, which are associated with abnormalities in coagulation function and can lead to reduced platelet function.

In recent years,radiomics has been developed rapidly in the field of clinical medicine,and the artificial intelligence(AI)technology has been utilized to assist diagnosis.This paper introduced the background of radiomics,analyzed the basic research process of radiomics,and looked forward to its application in the identification of bone and joint injuries in the field of forensic medicine.Reviewing the three aspects is expected to provide a theoretical foundation of radiomics,which will be helpful to develop its application in forensic medicine.

Objective To explore the distribution and types of platelet antibodies in children with positive platelet anti-body in initial screening.Methods Blood samples of 80 pediatric patients who applied for platelet transfusion in our hospi-tal from September 2021 to May 2022 and tested positive for platelet antibodies were identified using the PAKPLUS kit for antibody identification,and the distribution of HLA and HPA antibodies were analyzed.Results Among the 80 reactive samples in initial screening,9 were negative,71 were positive.Among the 71 positive cases,1 was HLA-Ⅰantibody positive(1.41％,1/71),21 were HPA antibody positive(29.58％,21/71),and 49 were both HLA-Ⅰantibody and HPA antibody positive(69.01％,49/71).Among the70 HPA positive cases,23.95％(17/71)had a single HPA antibody,with18.31％(13/71)of anti GP Ⅱb/Ⅲa,2.82％(2/71)of anti GP Ⅰa/Ⅱa,2.82％(2/71)of anti GP Ⅳ and 0％(0/71)of anti GP Ⅰb/Ⅸ,while74.65％(53/71)presented multiple HPA antibodies.No statistically significant difference was found in antibody distribution among age,gender,transfusion history and disease types.Conclusion HLA-Ⅰ antibody combined with HPA antibody are the main types of platelet antibodies among children with positive platelet antibodies.Anti-GPⅡb/Ⅲa accounted for the largest proportion of HPA antibodies.Antibody distribution is not releted to age,gender,history of blood transfusion and disease types.