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Intussusception is a common cause of acute abdominal pain in children and the most frequent cause of intestinal obstruction in infants. Although often idiopathic, it can stem from conditions like lymphoma. This study delves into lymphoma-related intussusception in children, aiming to enhance early detection and management. A retrospective review encompassed children admitted from 2012 to 2023 with intussusception due to intestinal lymphoma. Demographic, clinical, and imaging data were meticulously extracted and analyzed. The study included 31 children in the lymphoma-related intussusception group. Contrasted with non-lymphoma-related cases, the patients of lymphoma-related intussusception were notably older (median age: 87 months vs. 18.5 months), predominantly male, and demonstrated protracted abdominal pain. Ultrasound unveiled mesenteric lymph node enlargement and distinct intra-abdominal masses; enema reduction success rates were notably diminished. Detecting lymphoma-related intussusception remains intricate. Age, prolonged symptoms, and distinctive ultrasound findings can arouse suspicion. Timely surgical intervention, based on preoperative imaging, proves pivotal for accurate diagnosis. Conclusion: Swift identification of lymphoma-related intussusception, distinguished by unique clinical and ultrasound features, is imperative for timely intervention and treatment. Further research is warranted to refine diagnostic approaches. What is Known: • Intussusception in pediatric patients can be caused by a wide spectrum of underlying diseases including lymphoma. • Early Identifying the exact underlying cause of intussusception is crucial for tailored therapy, however often challenging and time-consuming. What is New: • Lymphoma-related intussusception may present with increased abdominal fluid accumulation, intestinal obstruction, and a higher likelihood of failed reduction during enema procedures. • For high-risk children, repeated ultrasound examinations or further investigations may be necessary to confirm the diagnosis. [ABSTRACT FROM AUTHOR]

Multi-agent technologies have been widely employed for the development of web-based systems, including web-based e-markets, web-based grid computing, e-governments and service-oriented software systems. In these service-oriented systems, service provider agents and service consumer agents are autonomous and rational entities, which can enter and leave the environments freely. For simplicity, we use terms 'provider' and 'consumer' to represent this two types of agents. How to select the most suitable service providers according to a service request from a consumer in such an open environment is a very challenging issue. In this paper, we propose an innovative trust model, called the GTrust model, for service group selection in general service-oriented environments. In the GTrust model, the trust evaluation for a service group is based on the functionality coverage of the group, the dependency relationships among individual services in the group, the ratings of individual services on the attributes of the service request and a similarity measurement of the extent to which reference reports can reflect the service request in terms of the priority distribution of attributes. The experimental results and analysis demonstrate the good performance of the GTrust model on the service group selection in service-oriented environments. [ABSTRACT FROM AUTHOR]

The present study aims to explore the etiology of Diabetic osteoporosis (DOP), a chronic complication associated with diabetes mellitus. Specifically, the research seeks to identify potential miRNA biomarkers of DOP and investigated role in regulating osteoblasts. To achieve this, an animal model of DOP was established through the administration of a high-sugar and high-fat diet, and then injection of streptozotocin. Bone microarchitecture and histopathology analysis were analyzed. Rat calvarial osteoblasts (ROBs) were stimulated with high glucose (HG). MiRNA profiles of the stimulated osteoblasts were compared to control osteoblasts using sequencing. Proliferation and mineralization abilities were assessed using MTT assay, alkaline phosphatase, and alizarin red staining. Expression levels of OGN, Runx2, and ALP were determined through qRT-PCR and Western blot. MiRNA-sequencing results revealed increased miRNA-702-5p levels. Luciferase reporter gene was utilized to study the correlation between miR-702-5p and OGN. High glucose impaired cell proliferation and mineralization in vitro by inhibiting OGN, Runx2, and ALP expressions. Interference with miR-702-5p decreased OGN, Runx2, and ALP levels, which were restored by OGN overexpression. Additionally, downregulation of OGN and Runx2 in DOP rat femurs was confirmed. Therefore, the miRNA-702-5p/OGN/Runx2 signaling axis may play a role in DOP, and could be diagnostic biomarker and therapeutic target for not only DOP but also other forms of osteoporosis. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate AI biases and errors in estimating bone age (BA) by comparing AI and radiologists' clinical determinations of BA. Methods: We established three deep learning models from a Chinese private dataset (CHNm), an American public dataset (USAm), and a joint dataset combining the above two datasets (JOIm). The test data CHNt (n = 1246) were labeled by ten senior pediatric radiologists. The effects of data site differences, interpretation bias, and interobserver variability on BA assessment were evaluated. The differences between the AI models' and radiologists' clinical determinations of BA (normal, advanced, and delayed BA groups by using the Brush data) were evaluated by the chi-square test and Kappa values. The heatmaps of CHNm-CHNt were generated by using Grad-CAM. Results: We obtained an MAD value of 0.42 years on CHNm-CHNt; this result indicated an appropriate accuracy for the whole group but did not indicate an accurate estimation of individual BA because with a kappa value of 0.714, the agreement between AI and human clinical determinations of BA was significantly different. The features of the heatmaps were not fully consistent with the human vision on the X-ray films. Variable performance in BA estimation by different AI models and the disagreement between AI and radiologists' clinical determinations of BA may be caused by data biases, including patients' sex and age, institutions, and radiologists. Conclusions: The deep learning models outperform external validation in predicting BA on both internal and joint datasets. However, the biases and errors in the models' clinical determinations of child development should be carefully considered. Key Points: • With a kappa value of 0.714, clinical determinations of bone age by using AI did not accord well with clinical determinations by radiologists. • Several biases, including patients' sex and age, institutions, and radiologists, may cause variable performance by AI bone age models and disagreement between AI and radiologists' clinical determinations of bone age. • AI heatmaps of bone age were not fully consistent with human vision on X-ray films. [ABSTRACT FROM AUTHOR]

In online discussion platforms, participants gather to discuss the effective approaches to solve the common issues they face. To facilitate these discussions proceeding smoothly and reaching consensus efficiently, human facilitators are introduced into these discussions. However, human facilitator-related problems such as human bias and scalability arise with the increasing sophistication of these online discussions. As a result, it becomes critical to find approaches that support facilitation in these online discussions. Towards this end, we propose a novel case-based reasoning framework to support online discussion facilitation. In the proposed framework (CBR), each discussion thread is styled using an issue-based information system (IBIS), where complex problems are modeled as argumentation processes among several stack holders. Upon identifying a new discussion case, facilitation suggestions are generated by retrieving the most similar facilitated case from the discussion case base. For the sake of experimental evaluation, we build an online facilitation dataset that contains the facilitation information from annotated real-world discussion data. The experimental results show the validity of the generated facilitation suggestions. [ABSTRACT FROM AUTHOR]