Search

Background Ovarian cancer (OV) is one of the most common malignant tumors of gynecology oncology. The lack of effective early diagnosis methods and treatment strategies result in a low five-year survival rate. Also, immunotherapy plays an important auxiliary role in the treatment of advanced OV patient, so it is of great significance to find out effective immune-related tumor markers for the diagnosis and treatment of OV. Methods Based on the consensus clustering analysis of single-sample gene set enrichment analysis (ssGSEA) score transformed via The Cancer Genome Atlas (TCGA) mRNA profile, we obtained two groups with high and low levels of immune infiltration. Multiple machine learning methods were conducted to explore prognostic genes associated with immune infiltration. Simultaneously, the correlation between the expression of mark genes and immune cells components was explored. Results A prognostic classifier including 5 genes (CXCL11, S1PR4, TNFRSF17, FPR1 and DHRS95) was established and its robust efficacy for predicting overall survival was validated via 1129 OV samples. Some significant variations of copy number on gene loci were found between two risk groups and it showed that patients with fine chemosensitivity has lower risk score than patient with poor chemosensitivity (P = 0.013). The high and low-risk groups showed significantly different distribution (P Conclusion The present study identified five prognostic genes associated with immune infiltration of OV, which may provide some potential clinical implications for OV treatment.

Ovarian cancer (OV) is one of the most common malignant tumors of gynecology oncology. The lack of effective early diagnosis methods and treatment strategies result in a low five-year survival rate. Also, immunotherapy plays an important auxiliary role in the treatment of advanced OV patient, so it is of great significance to find out effective immune-related tumor markers for the diagnosis and treatment of OV.Based on the consensus clustering analysis of single-sample gene set enrichment analysis (ssGSEA) score transformed via The Cancer Genome Atlas (TCGA) mRNA profile, we obtained two groups with high and low levels of immune infiltration. Multiple machine learning methods were conducted to explore prognostic genes associated with immune infiltration. Simultaneously, the correlation between the expression of mark genes and immune cells components was explored.A prognostic classifier including 5 genes (CXCL11, S1PR4, TNFRSF17, FPR1 and DHRS95) was established and its robust efficacy for predicting overall survival was validated via 1129 OV samples. Some significant variations of copy number on gene loci were found between two risk groups and it showed that patients with fine chemosensitivity has lower risk score than patient with poor chemosensitivity (P = 0.013). The high and low-risk groups showed significantly different distribution (P 0.001) of five immune cells (Monocytes, Macrophages M1, Macrophages M2, T cells CD4 menory and T cells CD8).The present study identified five prognostic genes associated with immune infiltration of OV, which may provide some potential clinical implications for OV treatment.

A particle-in-cell simulation is conducted to investigate plasma acceleration process in a magnetically enhanced micro-cathode vacuum arc thruster. A co-axial electrode thruster structure with an external magnetic field configuration is used to study the influence of magnetic field distribution on the acceleration process of electrons and ions. The modeling results show that due to the small electron mass and the Larmor radius, electrons are magnetized and bound by the magnetic field lines to form a narrow electron channel. The heavy ions with large Larmor radius take a long time to keep up with the electron movement. The presence of magnetic field strengthens the charge separation phenomenon. The electric field caused by charge separation is mainly responsible for the ion acceleration at the downstream of computation. The effect of variations in the magnetic field distribution on acceleration of the plasma is also investigated in this study, it is found the position of magnetic coil relative to the thruster exit has a significant effect on the acceleration of ions. In order to increase the axial component of ion velocity, the design should be considered to reduce confinement of magnetic field on the electrons in the downstream divergent part of applied magnetic field.

The muzzle dynamic recoil device for guns was designed. The optimization of dynamic recoil device was studied in detail. Using the MATLAB optimization toolbox, a series of optimized parameters of dynamic recoil device were got. By compared the results of dynamic recoil test with live-fire of the gun, the rationality and practicability of the dynamic recoil device has been proved. It is also proved that the muzzle dynamic recoil device can meet the dynamic recoil test requirements of guns.

Pattern synchronization (PS) can capture one aspect of the dynamic interactions between bivariate physiological systems. It can be tested by several entropy-based measures, e.g., cross sample entropy (X-SampEn), cross fuzzy entropy (X-FuzzyEn), multivariate multiscale entropy (MMSE), etc. A comprehensive comparison on their distinguishability is currently missing. Besides, they are highly dependent on several pre-defined parameters, the threshold value r in particular. Thus, their consistency also needs further elucidation. Based on the well-accepted assumption that a tight coupling necessarily leads to a high PS, we performed a couple of evaluations over several simulated coupled models in this study. All measures were compared to each other with respect to their consistency and distinguishability, which were quantified by two pre-defined criteria-degree of crossing (DoC) and degree of monotonicity (DoM). Results indicated that X-SampEn and X-FuzzyEn could only work well over coupled stochastic systems with meticulously selected r. It is thus not recommended to apply them to the intrinsic complex physiological systems. However, MMSE was suitable for both, indicating by relatively higher DoC and DoM values. Final analysis on the cardiorespiratory coupling validated our results.

Background: Tranexamic acid (TXA) is an antifibrinolytic drug widely used to reduce blood loss during joint replacements, including total knee arthroplasty (TKA) and total hip arthroplasty (THA). However, there is no final consensus regarding the composition of an optimal administration of TXA regime between topical and systemic (intravenous). The purpose of our study was to compare the efficacy of topical and intravenous (IV) regimen of TXA during TKA and THA. Methods: Five relevant electronic online databases, PubMed, EMBASE, the Cochrane Central Register of Controlled Trials, Web of Science and Chinese Biomedical Database were systematically searched in November 2015. Randomized controlled trials (RCTs) that compared topical with intravenous TXA in patients with TKA or THA were included. The search terms included “topical,” “intravenous,” “tranexamic acid,” “knee arthroplasty” and “hip arthroplasty.” Two reviewers independently extracted data and assessed the risk of bias and study quality. Data were analyzed with Review Manager 5.3 software. Grades of Recommendation Assessment, Development and Evaluation (GRADE) were used to assess the quality of evidence. Results: Sixteen RCTs with 1250 patients undergoing TKA and 4 RCTs involving 550 patients undergoing THA were included. There were no significant differences in total blood loss (mean difference [MD]TKA = −28.72 mL, 95% confidence interval [CI] −195.97 to 138.54 mL, P = 0.74; MDTHA = 14.03 mL, 95% CI −35.53 to 63.59 mL; P = 0.78), total drain out (MDTKA = −3.09 mL, 95% CI −39.05 to 32.88 mL; P = 0.87; MDTHA −31.00 mL, 95% CI −66.56 to 4.66 mL; P = 0.09), and transfusion rates (ORTKA = 0.90, 95% CI 0.58–1.40, P = 0.64; ORTHA = 1.19, 95% CI 0.67–2.09; P = 0.63) between topical and intravenous (IV) TXA. Conclusions: The current evidence suggested that topical TXA was equally effective and safe compared with intravenous TXA in reducing blood loss and transfusion rate following TKA or THA. We recommended that either topically or systemically could be used in TKA and THA to decrease perioperative blood loss.

This study aimed to investigate the molecular mechanisms of osteoarthritis (OA) by microarray analysis. Three gene expression datasets GSE1919, 19664 and 55235 were downloaded from the Gene Expression Omnibus, and data of OA samples and healthy controls were used. After data preprocessing, differential expression analysis between the OA group and controls was performed using LIMMA (Linear Models for Microarray Data) package and genes with |log2FC (fold change)|>1 and P

Owing to a clerical error, the distance between two vectors was defined incorrectly for multivariate multiscale sample entropy (MMSE) in Table 1. The results and conclusion of our simulation was, however, unaffected because our simulation applied the correct definition, which is given below. For clarity, we give below the whole description of MMSE as in Table 1. The place where the mistake came in is shown in bold. We apologize for this oversight and for any confusion that it has caused. For p-variate normalized sequences