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Introduction: Owing to the poor prognosis of Ewing's sarcoma, reliable prognostic biomarkers are highly warranted for clinical diagnosis of the disease. Materials and Methods: A combination of the weighted correlation network analysis and differentially expression analysis was used for initial screening; glycolysis-related genes were extracted and subjected to univariate Cox, LASSO regression, and multivariate Cox analyses to construct prognostic models. The immune cell composition of each sample was analysed using CIBERSORT software. Immunohistochemical analysis was performed for assessing the differential expression of modelled genes in Ewing's sarcoma and paraneoplastic tissues. Results: A logistic regression model constructed for the prognosis of Ewing's sarcoma exhibited that the patient survival rate in the high-risk group is much lower than in the low-risk group. CIBERSORT analysis exhibited a strong correlation of Ewing's sarcoma with naive B cells, CD8+ T cells, activated NK cells, and M0 macrophages (P < 0.05). Immunohistochemical analysis confirmed the study findings. Conclusions: GLCE and TPI1 can be used as prognostic biomarkers to predict the prognosis of Ewing's sarcoma, and a close association of Ewing's sarcoma with naive B cells, CD8+ T cells, activated NK cells, and M0 macrophages provides a novel approach to the disease immunotherapy.

Background There have been few literature reports on the use of perioperative parameters to predict the risk of albumin transfusion after spinal tuberculosis surgery based on the application of nomogram and propensity score matching (PSM) analysis. Objective The purpose was to predict the risk of albumin transfusion after spinal tuberculosis surgery based on a combination of PSM and nomogram. Methods The clinical data of the patients were collected in our hospital, including preoperative clinical data, preoperative laboratory tests, and postoperative clinical data. All data were divided into 2 groups, including the albumin transfusion group and the non–albumin transfusion group. The PSM analysis was used to adjust the baseline data of the 2 groups. The nomogram was further constructed. The practicability and predictive ability of the model were evaluated. Results A total of 494 cases were collected in this article; 102 pairs by PSM analysis were used to construct the nomogram. There were statistical differences in surgical approach, aspartate aminotransferase/alanine aminotransferase levels, drainage, and kyphosis by logistic analysis, and these parameters were included in the construction of the nomogram. The C-index of the prediction model was 0.734. The area under the curve was 0.73 and the net benefit was between 0.13 and 0.99. The calculated C-index was 0.71 by the internal verification method. Conclusions The PSM analysis had a good matching effect and the nomogram had a good predictive ability. Surgical approach, aspartate aminotransferase/alanine aminotransferase levels, drainage, and kyphosis might be predictors of albumin transfusion after spinal tuberculosis surgery.

Ankylosing spondylitis (AS) is a chronic inflammation of the joints and spine. Its pathogenesis has not been elucidated, especially involving hip joint disease. The purpose of this study was to analyze the proteome of diseased hip in AS and to identify key protein biomarkers. We used label-free quantification combined with liquid chromatography mass spectrometry (LC–MS/MS) to screen for differentially expressed proteins in hip ligament samples between AS and No-AS groups. Key protein was screened by Bioinformatics methods and verified by in vitro experiments. There were 3755 identified proteins, of which 92.916% were quantified. 193 DEPs (49 up-regulated proteins and 144 down-regulated proteins) were identified according to P < 0.01 and Log|FC| > 1. DEPs were mainly involved in cell compartment, including the vacuolar lumen, azurophil granule,primary lysosome, etc. The main KEGG pathway included Phagosome, Glycerophospholipid metabolism, Lysine degradation, Pentose phosphate pathway. Myeloperoxidase (MPO) was identified as a key protein participated in Phagosome pathway, which induces hip lesions in ankylosing spondylitis. Further experiments confirmed that MPO promoted the inflammatory response of fibroblasts in AS-affected hip joint.

The Beiyun river flows through a hot spot region of Beijing-Tianjin-Hebei in China that serves a majority of occupants. However, the region experiences severe nitrate pollution, posing a threat to human health due to inadequate self-purification capacity. In that context, there is an urgent need to assess nitrate levels in this region. Herein, we used δ15N-NO3, δ18O-NO3 isotopes analysis, and stable isotope analysis model to evaluate the nitrate source apportionment in the Beiyun river. A meta-analysis was then used to compare the potential similarity of nitrate sources among the Beiyun riverine watershed and other watersheds. Results of nitrate source apportionment revealed that nitrate originated from the manure & sewage (contribution rate: 89.6%), soil nitrogen (5.9%), and nitrogen fertilizer (3.9%) in the wet season. While in the dry season, nitrate mainly originated from manure & sewage (91.6%). Further, different land-use types exhibited distinct nitrate compositions. Nitrate in urban and suburban areas mostly was traced from manure & sewage (90.5% and 78.8%, respectively). Notably, the different nitrate contribution in the rural-urban fringe and plant-covered areas were manure & sewage (44.3% and 32.8%), soil nitrogen (26.9% and 35.7%), nitrogen fertilizer (23.5% and 29.4%), and atmospheric deposition (5.3% and 2.0%). Through a meta-analysis, we found nitrogen fertilizer, soil nitrogen, and manure & sewage as the main nitrate sources in the Beiyun riverine watershed or the other similar complex watersheds in the temperate. Thus, this study provides a scientific basis for nitrate source apportionment and nitrate preventive management in watersheds with complex land-use types in temperate regions.

IntroductionOsteosarcoma is among the most common orthopedic neoplasms, and currently, there are no adequate biomarkers to predict its prognosis. Therefore, the present study was aimed to identify the prognostic biomarkers for autophagy-and immune-related osteosarcoma using bioinformatics tools for guiding the clinical diagnosis and treatment of this disease.Materials and MethodsThe gene expression and clinical information data were downloaded from the Public database. The genes associated with autophagy were extracted, followed by the development of a logistic regression model for predicting the prognosis of osteosarcoma using univariate and multivariate COX regression analysis and LASSO regression analysis. The accuracy of the constructed model was verified through the ROC curves, calibration plots, and Nomogram plots. Next, immune cell typing was performed using CIBERSORT to analyze the expression of the immune cells in each sample. For the results obtained from the analysis, we used qRT-PCR validation in two strains of human osteosarcoma cells.ResultsThe screening process identified a total of three genes that fulfilled all the screening criteria. The survival curves of the constructed prognostic model revealed that patients with the high risk presented significantly lower survival than the patients with low risk. Finally, the immune cell component analysis revealed that all three genes were significantly associated with the immune cells. The expressions of TRIM68, PIKFYVE, and DYNLL2 were higher in the osteosarcoma cells compared to the control cells. Finally, we used human pathological tissue sections to validate the expression of the genes modeled in osteosarcoma and paracancerous tissue.ConclusionThe TRIM68, PIKFYVE, and DYNLL2 genes can be used as biomarkers for predicting the prognosis of osteosarcoma.

The current study is aimed at developing and validating a nomogram of the risk of failure of internal fixation devices in Chinese patients undergoing spinal internal fixation. We collected data from a total of 1139 patients admitted for spinal internal fixation surgery at the First Affiliated Hospital of Guangxi Medical University from May 2012 to February 2019. Of these, 1050 patients were included in the spinal internal fixation group and 89 patients in the spinal internal fixation device failure group. Patients were divided into training and validation tests. The risk assessment of the failure of the spinal internal fixation device used 14 characteristics. In the training test, the feature selection of the failure model of the spinal internal fixation device was optimized using the least absolute shrinkage and selection operator (LASSO) regression model. Based on the characteristics selected in the LASSO regression model, multivariate logistic regression analysis was used for constructing the model. Identification, calibration, and clinical usefulness of predictive models were assessed using C-index, calibration curve, and decision curve analysis. A validation test was used to validate the constructed model. In the training test, the risk prediction nomogram included gender, age, presence or absence of scoliosis, and unilateral or bilateral fixation. The model demonstrated moderate predictive power with a C-index of 0.722 (95% confidence interval: 0.644–0.800) and the area under the curve (AUC) of 0.722. Decision curve analysis depicted that the failure risk nomogram was clinically useful when the probability threshold for internal fixation device failure was 3%. The C-index of the validation test was 0.761. This novel nomogram of failure risk for spinal instrumentation includes gender, age, presence or absence of scoliosis, and unilateral or bilateral fixation. It can be used for evaluating the risk of instrumentation failure in patients undergoing spinal instrumentation surgery.

Background The aim of this study is to explore the use of preoperative imaging stability studies of adult lumbar spondylolisthesis and it's effects on intraoperative correction, postoperative observation, and clinical efficacy. Methods We retrospectively analyzed a total of 104 patients diagnosed with lumbar spondylolisthesis who underwent Posterior lumbar interbody fusion surgery between 2011 and 2014. The qualified patients were divided into two groups; group A (study/unstable group: 52 cases) and group B (control/stable group: 52 cases). Group A was further divided into group A1 (slip instability: 27 cases) and group A2 (angular instability: 25 cases). The preoperative lumbar sagittal plane translation distances and segmental angle of the preoperative lateral X-rays, hyperflexion X-rays, and hyperextension X-rays were measured and compared with their third-day postoperative lateral X-ray, and slip rate. The occurrences of intraoperative or postoperative complications, one-month postoperative follow-up data, including JOA, and VAS scores were access to evaluate the clinical efficacy of the treatment. Results Satisfactory postoperative radiographic correction was recorded in all groups. Better correction and correction was observed in group A1 patients and group B patients who used puller screws compared to those who used normal screws (P

BACKGROUND This retrospective study aimed to investigate the risk factors associated with the recurrence of L5-S1 disc herniation after percutaneous endoscopic transforaminal discectomy (PETD). MATERIAL AND METHODS There were 484 patients L5-S1 disc herniation who underwent PETD who were divided into the recurrence group (n=46) and the non-recurrence group (n=438). Transforaminal endoscopic approaches included modifications of the Yeung endoscopy spine system (YESS) (the intraforaminal intradiscal approach) and the transforaminal endoscopic spine system (TESSYS) (intraforaminal extradiscal approach). Demographic and clinical characteristics and imaging data were analyzed. The two study groups were compared to determine the factors associated with the recurrence of L5-S1 disc herniation. The patients underwent postoperative follow-up for between one and four years. RESULTS At follow-up, 9.504% of patients (46/484) with the recurrence of L5-S1 disc herniation following PETD when compared with the non-recurrence group showed no significant difference for time to return to work, gender, history of diabetes mellitus, trauma, duration of symptoms, smoking and alcohol history, hypertension, location of disc herniation, transverse process length, intervertebral space height, and pelvic incidence angle (P>0.05). However, age, body mass index (BMI), the degree of disc degeneration, sagittal range of motion, lumbar lordosis angle, and sacral slope were significantly associated with the recurrence of L5-S1 disc herniation following PETD (P

Background: Autophagy is closely related to skin cutaneous melanoma (SKCM), but the mechanism involved is unclear. Therefore, exploration of the role of autophagy-related genes (ARGs) in SKCM is necessary. Materials and methods: Differential expression autophagy-related genes (DEARGs) were first analysed. Univariate and multivariate Cox regression analyses were used to evaluate the expression of DEARGs and prognosis of SKCM. Further, the expression levels of prognosis-related DEARGs were verified by immunohistochemical (IHC) staining. Finally, gene set enrichment analysis (GSEA) was used to explore the underlying molecular mechanisms of SKCM. Results: Five ARGs (APOL1, BIRC5, EGFR, TP63, and SPNS1) were positively correlated with the prognosis of SKCM. IHC verified the results of the differential expression of these 5 ARGs in the bioinformatics analysis. According to the receiver operating characteristic curve, the signature had a good performance at predicting overall survival in SKCM. The signature could classify SKCM patients into high-risk or low-risk groups according to distinct overall survival. The nomogram confirmed that the risk score has a particularly large impact on the prognosis of SKCM. Calibration plot displayed excellent agreement between nomogram predictions and actual observations. Principal component analysis indicated that patients in the high-risk group could be distinguished from those in low-risk group. Results of GSEA indicated that the low-risk group is enriched with aggressiveness-related pathways such as phosphatidylinositol-3-kinase/protein kinase B and mitogen-activated protein kinase signalling pathways. Conclusion: Our study identified a 5-gene signature. It revealed the mechanisms of autophagy that lead to the progression of SKCM and established a prognostic nomogram that can predict overall survival of patients with SKCM. The findings of this study provide novel insights into the relationship between ARGs and prognosis of SKCM.

BACKGROUND This retrospective study aimed to identify the factors associated with successful surgical correction of thoracic kyphosis (TK) in 43 patients with adolescent idiopathic scoliosis (AIS) with Lenke type 1 curvature, in which the major curve with the largest Cobb angle was mainly in the thoracic region. MATERIAL AND METHODS We collected data from patients with Lenke 1 AIS. The following parameters were measured: Cobb angle, side-bending Cobb angle, cervical lordosis (CL), TK, lumbar lordosis (LL), pelvic incidence (PI), sacral slope (SS), pelvic tilt (PT), the sagittal vertical axis (SVA), the center of a C7 plumb line to the center sacral vertical line (C7-CSVL), correction rate, Ponte osteotomy, flexibility, and screw density. Univariate analysis and multivariate logistic regression analyses were performed. RESULTS Among the 43 cases analyzed, the mean postoperative Cobb angle at the last follow-up, C7-CSVL, SVA, CL, TK, LL, PI, SS, and PT were respectively 21.33±9.47°, 10.41±8.45 mm, 19.68±14.33 mm, 16.19±7.45°, 23.12±7.45°, 50.33±11.37°, 49.70±9.83°, 39.42±8.11°, and 10.16±6.63°. Univariate analysis suggested that preoperative TK, preoperative LL, and Ponte osteotomy were statistically significant (P

Background: Ewing sarcoma (ES), the second most prevalent bone malignant tumor has no widely known prognostic biomarker. Earlier studies have suggested that chaperonin containing TCP1 complex 6A (CCT6A), which encodes a molecular protein chaperone, is involved in the pathogenesis of many cancers. However, there are no known reports providing clear evidence of its role in ES pathogenesis. Methods: We performed a bioinformatic analysis of 32 ES specimens from the GSE17618 dataset concentrating on the differences in gene expression, OS, event-free survival (EFS) in the different subgroups. Immunohistochemical studies were also performed to identify the expression levels of selected genes in ES and immediate paracancerous tissues. Results: After 3 screenings, CCT6A was identified to be highly correlated with ES prognosis. Our survival analysis revealed a low overall survival (OS) for high CCT6A expression (P-value = .024). Our Cox regression analysis identified CCT6A expression, lEFS, and age were strongly associated with prognosis of ES. Our multivariate Cox regression analysis shows that CCT6A (P-value = .015), age (P-value = .026), and EFS (P-value = .002) were independent poor prognostic biomarkers. Our immunohistochemical analysis showed that the expression levels of CCT6A were significantly higher in ES tissues compared to the paracancerous tissues. Conclusion: From the results of our study, we identified the expression levels of CCT6A to be strongly associated with prognosis of ES. Thus, the expression levels of the CCT6A gene could serve as a biomarker for the prediction of ES prognosis.

Supplemental Digital Content is available in the text, Background: In pediatric tumors, immunotherapy exhibits less toxicity than chemotherapy and radiation. The current study aims to identify potential immune targets in immune-related genes of C-C motif chemokine ligand genes (CCLs) and C-C motif chemokine receptors (CCRs) in childhood osteosarcoma (OS) and to explore the underlying molecular mechanisms of childhood OS. Methods: Firstly, we identified immune-related genes in CCLs and CCRs, these genes were used for functional annotation and interaction analysis. Then, the prognostic value of these genes was evaluated using Kaplan-Meier analysis and multivariate COX regression model. And the potential relationship between risk score and immune infiltrating cells was identified. Finally, gene set enrichment analysis was used to determine the underlying molecular mechanism of OS. Immune-related genes in CCLs and CCRs are inextricably linked. Results: The results of survival analysis of these genes show that CCL5, CCL8, CCR4, and CCR5 are significantly associated with the prognosis of childhood OS. The combined effect survival analysis shows that the co-high expression of these 4 genes has a good prognosis for childhood OS. A prognostic signature model was constructed based on the 4 genes mentioned above, and the result of time-dependent receiver operating characteristic curves showed that this model was a good predictor of childhood OS 3- and 5-year prognosis. In addition, the risk score of the constructed prognostic signature model was closely related to immune infiltration. We also found that CCL5, CCL8, and CCR5 may affect the prognosis of OS through complex regulation among Toll-like receptor signaling pathway, mitogen-activated protein kinase (MAPK) family signaling cascade, and nuclear factor-kappaB pathway, whereas CCR4 affects the prognosis of OS by regulating eukaryotic translation. Conclusion: CCL5, CCL8, CCR4, and CCR5 are potential prognostic markers for the prognosis of childhood OS, and the underlying molecular mechanisms of childhood OS have been identified.

Background: The incidence of triple negative breast cancer (TNBC) is at a relatively high level, and our study aimed to identify differentially expressed genes (DEGs) in TNBC and explore the key pathways and genes of TNBC. Methods: The gene expression profiling (GSE86945, GSE86946 and GSE102088) data were obtained from Gene Expression Omnibus Datasets, DEGs were identified by using R software, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed by the Database for Annotation, Visualization and Integrated Discovery (DAVID) tools, and the protein-protein interaction (PPI) network of the DEGs was constructed by the STRING database and visualized by Cytoscape software. Finally, the survival value of hub DEGs in breast cancer patients were performed by the Kaplan–Meier plotter online tool. Results: A total of 2998 DEGs were identified between TNBC and health breast tissue, including 411 up-regulated DEGs and 2587 down-regulated DEGs. GO analysis results showed that down-regulated DEGs were enriched in gene expression (BP), extracellular exosome (CC), and nucleic acid binding, and up-regulated were enriched in chromatin assembly (BP), nucleosome (CC), and DNA binding (MF). KEGG pathway results showed that DEGs were mainly enriched in Pathways in cancer and Systemic lupus erythematosus and so on. Top 10 hub genes were picked out from PPI network by connective degree, and 7 of top 10 hub genes were significantly related with adverse overall survival in breast cancer patients (P

Aiming at improving the video transmission service in Wireless Mesh Network (WMN), this paper presents a scalable video transmission system over WMN with Software Defined Networking (SDN) architecture. By integrating SDN, the control plane of WMN is separated from the data plane and been managed by a centralized controller which conduct network optimization using global network information. Following this architecture, we formulate the scalable video bitrate adaptation problem as maximizing the average video quality while keeping the link congestion probability below certain threshold. In order to solve this problem, we derive an online adaptation strategy based on large deviation principle (LDP) to estimate congestion probability and adjust the video's bitrate accordingly. To verify the performance of the proposed strategy, we implement our experiments in Mininet-wifi and the results validate the effectiveness of our algorithm.

We propose a method for automatic evolutionary clustering of multi neuronal spike trains on the basis of community detection in complex networks. We use a genetic algorithm for optimization to maximize the modularity for community partitioning and then automatically determine the number of clusters hidden in the multi neuronal spike trains. The number of clusters does not need to be specified in advance. Compared with the traditional graph partitioning method, the genetic evolutionary modularity optimization clustering algorithm can obtain the maximum value of modularity and, determine the number of communities. We evaluate the performance of this method on surrogate spike train datasets with ground truth. The results obtained showed improvement. We then apply this proposed method to raw real spike trains. We obtain a larger value for modularity and the results. This finding suggests that the proposed method can be used to detect the hidden firing pattern.

Multi-threshold image segmentation have achieved good results, but multi-threshold searching can be very time consuming, in order to improve the efficiency of multi-threshold searching, in this paper, the Firefly Algorithm is applied to solve multi-threshold searching. With maximum entropy as the optimization objective function, more information of images can be reserved, then using the Firefly Algorithm's heuristic searching, the optimal thresholds of image segmentation can be found. Experimental results show that compared to Genetic Algorithm, our proposed Firefly Algorithm has a higher convergence speed and produces a better segmentation effect when applied to image segmentation.