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.

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.

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.

[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.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective:To explore the feasibility of using ultrasonic convex array probe compressing abdominal wall to increase success rate of external cephalic version (ECV) without anesthesia in full-term and near-term pregnancy.Methods:Totally 190 singleton and non‐cephalic presentation pregnant women in 36-39 +4 weeks of gestation performed ECV from April 2019 to August 2023 in the First Affiliated Hospital of Nanjing Medical University were analyzed. According to whether use the ultrasound probe compressing fetal breech or not, the pregnant women were divided into two groups: 81 cases in the probe-compressing group (including primipara 61 cases and multipara 20 cases) and 109 cases in the non-probe-compressing group(including primipara 72 cases and multipara 37 cases). Clinical data, ECV related factors and complications were analyzed and compared between the two groups. Results:(1) The overall success rate of ECV was 64.2% (122/190). There was no significant difference in the success rate of ECV between probe-compressing group and non-probe-compressing group [69.1% (56/81) vs 60.6% (66/109), χ2=1.490, P=0.222]. The total vaginal delivery rate after successful ECV was 81.1% (99/122), while 71.1% (54/76) in primipara and 97.8% (45/46) in multipara, respectively. (2) Compare to the non-probe-compressing group, the success rate of ECV in primipara was significantly higher in the probe-compressing group [45.8% (33/72) vs 70.5% (43/61)], but the gestational age was shorter and the height was higher in the probe-compressing group (all P<0.05). The success rate of ECV of multipara in the probe-compressing group (65.0%, 13/20) was lower than that in the non-probe-compressing group (89.2%, 33/37), but there was no significant difference between the two groups ( P>0.05). (3) Multivariate logistic regression analysis showed that abdominal wall compressed by ultrasound probe ( OR=2.601, 95% CI: 1.113-6.075; P=0.027) and amniotic fluid index (AFI; OR=1.010, 95% CI: 1.001-1.020; P=0.028) were positive factors for the successful rate of ECV in primipara pregnant women. (4) The main complication of ECV was transient fetal heart rate reduction (8.9%,17/190), the incidence in the probe-compressing group was significantly higher than that in the non-probe-compressing group [14.8% (12/81) vs 4.6% (5/109); χ2=5.967, P=0.015]. No statistical differences were found in rates of complications between the ECV successful and unsuccessful pregnant women, and between probe-compressing and non-probe-compressing groups (all P>0.05). No adverse maternal and neonatal outcomes related to ECV were observed. Conclusions:The ultrasonic convex array probe compressing could significantly improve the success rate of ECV in primipara without increasing the incidence of adverse maternal and fetal outcomes. The success rate of ECV in primipara is influenced by AFI and operation mode.