Giant cell tumor of bone (GCTB) is benign tumor that can cause significant osteolysis and bone destruction at the epiphysis of long bones. Osteoclasts are thought to be highly associated with osteolysis in GCTB. However, the migration of osteoclasts in GCTB remains unclear. A deeper understanding of the complex tumor microenvironment is required in order to delineate the migration of osteoclasts in GCTB. In this study, samples were isolated from one patient diagnosed with GCTB. Single-cell RNA sequencing (scRNA-seq) was used to detect the heterogeneity of GCTB. Multiplex immunofluorescence staining was used to evaluate the cell subtypes identified by scRNA-seq. A total of 8,033 cells were obtained from one patient diagnosed with GCTB, which were divided into eight major cell types as depicted by a single-cell transcriptional map. The osteoclasts were divided into three subsets, and their differentiation trajectory and migration status were further analyzed. Osteoclast migration may be regulated via a series of genes associated with cell migration. Furthermore, four signaling pathways (RANKL, PARs, CD137 and SMEA3 signaling pathway) were found to be highly associated with osteoclast migration. This comprehensive single-cell transcriptome analysis of GCTB identified a series of genes associated with cell migration as well as four major signaling pathways that were highly related to the migration of osteoclasts in GCTB. Our findings broaden the understanding of GCTB bionetworks and provides a theoretical basis for anti-osteolysis therapy against GCTB in the future.

IntroductionThe diagnosis and treatment of ankylosing spondylitis (AS) is a difficult task, especially in less developed countries without access to experts. To address this issue, a comprehensive artificial intelligence (AI) tool was created to help diagnose and predict the course of AS.MethodsIn this retrospective study, a dataset of 5389 pelvic radiographs (PXRs) from patients treated at a single medical center between March 2014 and April 2022 was used to create an ensemble deep learning (DL) model for diagnosing AS. The model was then tested on an additional 583 images from three other medical centers, and its performance was evaluated using the area under the receiver operating characteristic curve analysis, accuracy, precision, recall, and F1 scores. Furthermore, clinical prediction models for identifying high-risk patients and triaging patients were developed and validated using clinical data from 356 patients.ResultsThe ensemble DL model demonstrated impressive performance in a multicenter external test set, with precision, recall, and area under the receiver operating characteristic curve values of 0.90, 0.89, and 0.96, respectively. This performance surpassed that of human experts, and the model also significantly improved the experts' diagnostic accuracy. Furthermore, the model's diagnosis results based on smartphone-captured images were comparable to those of human experts. Additionally, a clinical prediction model was established that accurately categorizes patients with AS into high-and low-risk groups with distinct clinical trajectories. This provides a strong foundation for individualized care.DiscussionIn this study, an exceptionally comprehensive AI tool was developed for the diagnosis and management of AS in complex clinical scenarios, especially in underdeveloped or rural areas that lack access to experts. This tool is highly beneficial in providing an efficient and effective system of diagnosis and management.

The present study was designed to investigate the protective effect of moracin on primary culture of nucleus pulposus cells in intervertebral disc and explore the underlying mechanism. Moracin treatment significantly inhibited the LPS-induced inflammatory cytokine accumulation (IL-1β, IL-6 and TNF-α) in nucleus pulposus cells. And moracin also dramatically decreased MDA activity, and increased the levels of SOD and CAT induced by LPS challenge. Moreover, the expressions of Nrf-2 and HO-1 were decreased and the protein levels of p-NF-κBp65, p-IκBα, p-smad-3 and TGF-β were increased by LPS challenge, which were significantly reversed after moracin treatments. Moracin treatments also decreased the levels of matrix degradation enzymes (MMP-3, MMP-13) as indicated by RT-PCR analysis. However, Nrf-2 knockdown abolished these protective effects of moracin. Together, our results demonstrated the ability of moracin to antagonize LPS-mediated inflammation in primary culture of nucleus pulposus in intervertebral disc by partly regulating the Nrf2/HO-1 and NF-κB/TGF-β pathway in nucleus pulposus cells.

Cancer remains as the leading cause of death all over the world due to the lack of efficient diagnostic techniques and therapeutic methods. Many studies have reported the potential diagnostic value of microRNA-17 (miRNA-17, miR-17) family members as biomarkers for cancer detection. However, inconsistent results were revealed from a wide range of studies. As a result of this, a meta-analysis based on 19 studies was conducted to assess the diagnostic performance of miR-17 family for cancer detection. A total of 1772 patients with certain types of cancer and 1320 healthy controls were involved in these studies. The overall diagnostic accuracy was measured by the following: sensitivity, 0.67 (95 % confidence interval (CI) 0.60-0.74); specificity, 0.83 (95 % CI 0.74-0.85); positive likelihood ratio (PLR), 3.9 (95 % CI 2.6-5.9); negative likelihood ratio (NLR), 0.40 (95 % CI 0.34-0.48); and diagnostic odds ratio (DOR), 10 (95 % CI 6-16), respectively. Additionally, the pooled area under the summary receiver operator characteristic (SROC) curve (area under the curve (AUC)) was 0.79 (95 % CI 0.75-0.82), indicating a relatively low accuracy of miR-17 family as biomarkers for cancer detection. Subgroup analysis further showed that miR-17 family had more reliable performance in cancer diagnosis for Asian than that for Caucasian. Moreover, multiple miRNAs containing miR-17, -20a/b, and -93 reflected higher diagnostic accuracy than both miR-106a/b (single miRNA) and the overall miR-17 family assay. Therefore, appropriate combinations of miR-17 family may be used as non-invasive screening biomarkers for cancer, and it is necessary to carry out a large-scale population-based study to further assess the potential diagnostic value of miR-17 family.

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