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The objectives were to identify the key features of malignant and benign peripheral nerve sheath tumors (PNSTs) and determine a strategy for differentiating them using sonography.Forty-six malignant peripheral nerve sheath tumors (MPNSTs) and 83 benign peripheral nerve sheath tumors (BPNSTs) confirmed by pathology from April 2010 to July 2021 were included. The general data and grayscale and color Doppler ultrasonic manifestations were compared between the two groups. We used single factor, multifactor, and area under the receiver operating characteristic (ROC) curve analyses to extract significant malignant risk factors and then established a scoring system with these factors.The significant variables identified in univariate analysis (P .05) were maximum diameter, location, shape, boundary, encapsulation, echogenicity, texture pattern, calcification, entering or exiting nerve, and vascularity. Shape, boundary and vascularity were significant risk factors, and a scoring system was established. The area under the ROC curve (0.925) confirmed the usefulness of the scoring system for differentiating MPNSTs and BPNSTs.Ultrasonography is an effective method for differentiating MPNSTs from BPNSTs.

Accurately identifying the health status of train running gear bearings is crucial to en-sure the quality of operation. As the early fault information of bearings is weak and submerged in the complex noise environment, which is difficult to diagnose. Therefore, a new weak fault diagnosis approach for train running gear bearings based on variational mode decomposition (VMD) with improved performance and refined weighted kurtosis (RWK) index is proposed to solve this problem. First, an improved grey wolf optimizer (IGWO) based on a variety of strategies is proposed. Secondly, the VMD performance is improved using the IGWO algorithm, and the improved VMD is used to process the early weak signals of bearings. A new fault-sensitive index called the RWK is proposed to detect the mode with the most fault information. Finally, the envelope analysis of the characteristic signals is performed to achieve the early weak fault diagnosis of bearings. Compared with the other nine optimization algorithms, the IGWO algorithm has strong optimization ability, stable performance and a fast convergence speed. Four cases verify that the RWK index has the highest sensitivity to fault information and can more effec-tively filter out modal components containing rich fault information than the compari-son methods.

Remaining useful life (RUL) prediction for bearing is a significant part of the maintenance of urban rail transit trains. Bearing RUL is closely linked to the reliability and safety of train running, but the current prediction accuracy is difficult to meet the requirements of high reliability operation. Aiming at the problem, a prediction model based on improved long short-term memory(ILSTM) network is proposed. Firstly, the variational mode decomposition is used to process the signal, and the intrinsic mode function with stronger representation ability is determined according to energy entropy, and the degradation feature data is constructed combined with the time domain characteristics. Then, to improve learning ability, rectified linear unit (ReLU) is applied to activate a fully connected layer lying after LSTM, the hidden state outputs of the layer are weighted by attention mechanism. Harris hawks optimization algorithm is introduced to adaptively set the hyperparameters to improve the performance of LSTM. Finally, the ILSTM is applied to predict bearing RUL. Through experimental cases, the better performance in bearing RUL prediction and the effectiveness of each improving measures of the model are validated, and its superiority of hyperparameters setting is demonstrated.

The effect of early fault vibration signals from rotating machinery is weak and easily drowned out by intensive noise. Thus, it is still a great challenge to perform early fault diagnosis. An intelligent early fault diagnosis method for rotating machines is proposed based on Variational Mode Decomposition (VMD) parameter optimization and Deep Multi-Kernel Extreme Learning Machine (DMKELM). First, the iterative chaotic mapping, nonlinear convergence factor and inertia weight are introduced into the whale optimization algorithm (WOA). Then, an improved WOA (IWOA) is presented and utilized to optimize VMD parameters. The optimized VMD (OVMD) combined with the sampling entropy is used to denoise and reconstruct the signals. Finally, the mish function is served as the activation function. Meanwhile, the radial basis kernel function (RBF) and polynomial kernel (PK) are introduced to construct the mixed kernel function. The mixed kernel assignment is applied to replace the random assignment, and the DMKELM is presented to enhance the classification performance and generalization capability of the model, and DMKELM is used for early intelligent fault diagnosis. Two experiments on bearings and gears demonstrated that the fault diagnosis accuracy is 99% and 98.5%, respectively, and the fault diagnosis accuracy is high. It shows that this method has great superiority in early fault diagnosis of rotating machinery.

With the gradual expansion of the scale of urban rail transit, its safety and cost issues have aroused widespread concern. Aiming at the common phenomena of "over-maintenance" or "under-maintenance" in urban rail trains, the imperfect preventive maintenance model with a two-level inspection strategy based on a three-stage failure process for train bogie system is proposed. Firstly, three parameters of Weibull distribution of components are obtained by analyzing historical fault data. According to the reliability curve and failure rate obtained by Weibull three parameters, the single components are divided into three stages: normal driving stage, initial deterioration stage and severe deterioration stage, and the three stages are identified by a two-level inspection. Among them, the normal driving stage and the severe deterioration stage are identified by the low-level inspection, and at the same time, it is possible to identify the initial deterioration stage, while all stages are identified accurately by the high-level inspection. Secondly, maintenance economy and train availability are taken as the optimization condition of the imperfect maintenance strategy, and the obtained optimal imperfect maintenance interval is taken as the high-level inspection time in the two-level inspection. Then an inspection threshold is proposed to determine the functional relationship between the high-level inspection and the low-level inspection. With the minimum expected cost as the goal, the expected cost of single components is calculated through different low-level inspection time, inspection threshold and preventive repair threshold. Finally, taking certain city urban rail train bogie system as an example, it is found that the minimum expected cost of axle box subassembly is CNY 43.28 a day, the maintenance period is 428 days. The minimum expected cost of gear box subassembly is CNY 27.26 a day, and the maintenance period is 651 days. Compared with the similar models, the maintenance period is prolonged and the expected cost is reduced, which verifies the effectiveness and superiority of the model and provides a theoretical basis for scientific maintenance.

As a critical component of rotating machinery, the health status of bearings is of great significance to ensure the safety of machine operation. Since the vibration signals are interfered by noise during the machine operation, the traditional bearing fault diagnosis model has insufficient feature extraction ability, low diagnostic accuracy, and slow convergence speed. An intelligent bearing fault diagnosis method is proposed based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and inverted residual convolution neural network integrated with Ghost module (Ghost-IRCNN). Firstly, ICEEMDAN is used to decompose the signals, and the kurtosis-correlation coefficient-margin factor index is proposed to filter and reconstruct the important modal components. Then, the multi-scale sample entropy is used to construct the feature vectors. Finally, the Ghost module, convolution block attention module, and inverted residual bottleneck are combined to build Ghost-IRCNN to realize bearing fault classification and diagnosis. The experimental results show that the proposed method can extract bearing fault features effectively, and the diagnosis accuracy can reach more than 98.80% under complex conditions.

Background: PM exposure can lead to myocardial hypertrophy, with a potential contribution via DNA methylation. Myocardial lipotoxicity is closely related to myocardial hypertrophy. But, myocardial lipotoxicity caused by PM has not been reported. PDGFRβ, a platelet-derived growth factor receptor, is also essential for normal cardiovascular development. However, It is unclear the role of PM-induced PDGFRβ methylation in myocardial hypertrophy and myocardial lipotoxicity. We investigated the effect of PDGFRβ methylation induced by PM on myocardial hypertrophy and myocardial lipotoxicity. Results: PDGFRβ methylation caused by PM decreased PDGFRβ mRNA and protein expression in C57BL/6J mouse hearts. Thus inhibiting gene expression in its downstream pathway, ultimately leading to cardiac hypertrophy. Disturbances of myocardial lipid metabolism caused by PM in AC16 cells and C57BL/6J mouse hearts were also observed. High expression of PDGFRβ in neonatal rat primary cardiomyocytes was found to activate its downstream pathway and ameliorate the effects of PM-induced cardiac hypertrophic activity. At the same time, adenovirus was used to induce high expression of PDGFRβ in C57BL/6J mice. It was found that PDGFRβ not only improved PM-induced cardiac hypertrophy, but also alleviated PM-induced myocardial lipotoxicity. Conclusions: PDGFRβ gene methylation may be one of the potential biomarkers of myocardial hypertrophy induced by PM exposure. And high expression of PDGFRβ may be a potential way to prevent myocardial hypertrophy and cardiac lipid metabolism disorder caused by PM exposure in mice.

Objectives To explore the associations of environmental endocrine disruptors on precocious puberty in girls. Methods This was a case-control study in which 30 girls with precocious puberty and 46 age- and race-matched prepubertal females were enrolled. The concentrations of 10 environment endocrine disruptors (bisphenol A, bisphenol B, butylparaben, propylparaben, ethvlparaben, methylparaben, mono-butyl phthalate, mono-2-ethylhexyl phthalate, monoethyl phthalate, and monomethyl phthalate) in urine and 10 steroid hormones (dihydrotestosterone, corticosterone, hydrocortisone, 11-deoxycortisol, 17α-hydroxy progesterone, 4-androstene-3,17-dione, estrone, deoxycorticosterone, pregnenolone, and dehydroepiandrosterone) in serum were detected with the liquid chromatography-mass spectrometry (LC-MS). Results According to the Mann–Whitney U test, urinary levels of bisphenol A, monobutyl phthalate, and monomethyl phthalate were significantly higher in the precocious group than in the prepubertal group, and blood levels of hydrocortisone, 11-deoxycortisol, corticosterone, deoxycorticosterone, and pregnenolone were significantly lower in the precocious group than in the prepubertal group (p1). Conclusions Our findings confirm the association between phthalate exposure and the incidence of precocious puberty in girls. Control and reduction of children exposure to phthalate esters should be considered as a health priority.

Rotating machinery is widely used in various systems of trains, and its health status is directly related to the reliability of train operation. Therefore, the fault diagnosis of train rotating machinery plays a promoting role in the safe operation and maintenance of trains. Due to the influence of track excitation and other equipment vibration, the vibration signal of rotating machinery is non-stationary and nonlinear, which brings challenges to the fault diagnosis of rotating machinery. To solve this problem, an intelligent fault diagnosis method for rotating machinery based on multi-objective variational mode decomposition (VMD) optimization and ensemble learning is proposed. Firstly, an improved multi-verse optimizer (MVO) algorithm is proposed by introducing Tent chaos theory and Levy flight theory, and a multi-objective VMD parameter optimization model is established. The improved MVO algorithm is utilized to optimize the parameters and reconstruct the signal. Then, a multi-domain feature extraction method is proposed for multi-domain fault feature extraction and feature selection. Finally, an adaptive mutation particle swarm optimization random forest (AMPSO-RF) pattern recognition algorithm is proposed for fault pattern recognition. Through the verification of bearing and gear fault data, the results showed that the raised method can accurately realize fault diagnosis of train rotating parts, and the accuracy rate reaches 100%, which is better than the compared methods.

Background Recently, the role of electroacupuncture (EA) in chronic neuropathic pain has been widely reported. However, its specific mechanisms and ability to mitigate depression-like behaviors induced by chronic pain remains unclear. This study aims to determine the analgesic and antidepressant effect of EA. Methods The mechanical threshold sensory and hot plate tests were employed to measure mechanical hyperalgesia and thermal allodynia. The open filed test (OFT) and tail suspension test (TST) were used to observe depressive behavior in chronic constrictive injury (CCI) mice. In addition, the 5-hydroxytryptamine (5-HT) and brain-derived neuropathic factor (BDNF) levels in the anterior cingulate cortex (ACC) and spinal cord were assessed using enzyme-linked immunosorbent assay (ELISA). The protein levels of cAMP-response element-binding protein (CREB) and BDNF in the ACC were analyzed by western blotting. Results Our results demonstrated that EA treatment could increase the mechanical withdrawal threshold (MWT) and thermal withdrawal latency (TWL) values. Also, EA improved the CCI-induced depression-like behaviors, and significantly reversed the down-regulation of BDNF and 5-HT expression in the ACC and spinal cord after CCI. Furthermore, EA regulated the level of CREB in the ACCs and spinal cords of mice. Conclusions These results suggested that the analgesic and antidepressant effect of EA is achieved through regulating CREB-5-HT/BDNF signaling pathway in the ACCs and spinal cords of mice.

Flywheel energy storage system is widely used in train braking energy recovery, and has achieved excellent energy-saving effect. As a key component of the flywheel energy storage system, the health of the bearing is greatly significant to realize the effective recovery of train braking energy. The vibration signal of the bearing presents complex nonlinear and non-stationary characteristics, which makes it difficult to diagnose the fault of the bearing. To solve this problem, a fault diagnosis method for bearing of flywheel energy storage system based on parameter optimization Variational Mode Decomposition (VMD) energy entropy is proposed. Firstly, the improved Sparrow Search Algorithm is used to optimize VMD parameters with the dispersion entropy as the fitness value. Then, the original signal is decomposed into a series of intrinsic mode components by using the optimized VMD algorithm, and the energy entropy of each component is calculated to construct the feature vector. Finally, an Inverted Residual Convolutional Neural Network (IRCNN) is used as feature vector input model for fault diagnosis. The experimental results show that the proposed method can effectively extract the bearing fault characteristics and realize accurate fault diagnosis, and the recognition rate reaches 97.5%, which is better than the comparison method.

The effect of doping anions on the degradation of electrochemical activity of polypyrrole (PPy)/A (A: SO42−, pTS−, PSS−, and GO) films in NaOH is investigated by various electrochemical methods and microscopic analyses. The degradation rates of PPy/A films follow first-order kinetics. The PPy/SO4 and PPy/GO films have two degradation stages with higher rate constants (k), while the PPy/pTS and PPy/PSS films just show one degradation stage with much lower k values. The exchange ability of doping anions with OH− and the penetration of OH− into the inner film are two crucial factors for the degradation of PPy in NaOH.

The purpose of the study is to identify the Cancer Stem Cells (CSCs) and to determine their expression profiles in different pathological stages of liver cancer by using multiple markers Methods: In this study, the expression profiles of CD133 and CD13, along with those of stem cell markers Oct4 and SOX2, were analyzed using immunohistochemistry and immunoblotting to clarify the character of CSCs in different stages of liver cancer.CD133 liver cancer cells were injected into mice, and the tissues were processed for histology. The histological data revealed the progression of liver cancer. Immunohistochemical analysis showed the strong expression of CD133 in metastatic cancer. In contrast, the expression of CD13 in both primary and metastatic liver cancer was found to be very strong. Interestingly, the expression levels of Oct4 and SOX2 were found to be upregulated in primary tumors, but, in the metastatic stage, their expression was downregulated. The immunoblot analysis also confirmed the same result.Our findings demonstrate that tumor-suppressor proteins Oct4 and SOX2 have a prominent expression profile in the primary stage of cancer, but, in the metastatic stage, their expression is downregulated, leading to the failure to prevent metastatic cancer.

A kind of electroactive nanoparticles (NPs) was prepared in solution by self-assembling amphiphilic 3-thiophenecarboxylic acid-modified starch (TPCA-St), which was synthesized through esterification reaction. By casting TPCA-St NPs on the surface of gold electrode and subsequent electropolymerization of the thiophene moieties in NP film, the electroconductive TPCA-St NP-based film was formed. After horseradish peroxidase and Nafion were sequentially cast on the film surface, a biosensor was successfully prepared. The prepared biosensor showed high sensitivity for H2O2 detection. The linear range from 1 × 10−10 to 1 × 10−5 mol L−1 with a detection limit of 3 × 10−11 mol L−1 was obtained for H2O2 sensing. The biosensor also showed good repeatability and stability, and it has been successfully used to sense H2O2 in commercial disinfector with satisfactory results.

Homeobox (HOX) transcript antisense RNA (HOTAIR) is a long intergenic non-coding RNA (lncRNA) that has been reported to be highly upregulated in several types of cancers. However, the role of HOTAIR in human cervical cancer is still unclear. We therefore investigated the expression and probable function of HOTAIR in cervical cancer cells. The expression of HOTAIR was examined in (HeLa, CaSki, ME-180, HT-3) and Human Cervical Epithelial Cells (HCerEpiC) by qRT-PCR. Transfection of si-NC, si-HOTAIR or si-STAT3 was carried out with the help of Lipofectamine 2000. The cell viability was assessed by CCK-8 assay. The cell migration and invasion was examined by wound healing and Boyden chamber assays. Protein expression was determined by western blotting. Our results showed that expression of HOTAIR was significantly upregulated in cervical cancer cells and inhibition of the expression of HOTAIR in HeLa cervical cancer cells resulted in suppression of cell proliferation, migration and invasion. Further, analysis of the promoter of HOTAIR, revealed that STAT3 could potentially regulate the activity of the HOTAIR in cervical cancer cells and inhibition of STAT3 had similar effects on the proliferation, migration and invasion of the cervical cancer cells as that of HOTAIR. Further, the suppression of STAT3 expression was associated with concomitant downregulation of IncRNA HOTAIR as indicated by the qRT-PCR. To unveil if STAT3 and HOTAIR have synergistic effects on the cell migration and invasion, si-STAT3 and si-HOTAIR were co-transformed into cervical HeLa cancer cells and it was observed that STAT3 and HOTAIR could synergistically inhibit the proliferation, migration and invasion of the cervical cancer cells. Taken together we conclude that HOTAIR and STAT3 synergistically regulate the proliferation, migration and invasion of cervical cancer cells.

Dopamine modified γ-polyglutamic acid (γ-PGA-DA) copolymer and melamine (Mel) can self-assemble into Mel/γ-PGA-DA micelles via weak intermolecular interactions in aqueous solution containing small amount of methanol. When Mel/γ-PGA-DA micellar solution was cast on the surface of formvar stabilized with carbon support films, the lotus-like nano-architectures were formed on the surface of substrate via hierarchical assembly of micelles. The size of lotus-like nanoarchitectures was approximately 200 nm×500 nm. The possible driving forces for hierarchical assembly of micelles were the solution fluid and interaction between micelles. The hierarchical assembly of micelles was similar to the fractal aggregation of inorganic particles and a possible reason was discussed.

Convection-enhanced delivery (CED, the infusion of drugs under controlled pressure to the brain parenchyma via targeted micro-catheters, allows accurate anatomical targeting and delivery of higher (therapeutic) drug concentrations through clinically relevant volumes of brain tissue or tumor. Histone deacetylase inhibitors have been found in vitro to be the most active agents against Diffuse Midline Gliomas (DMGs) Using a novel device (implantable subcutaneous pump connected with catheter directly implanted into the pons/thalamus) we are performing a Phase I safety study of repeated infusions of MTX110 (MTX110, Midatech) in a dose escalation manner. Eligible patients include 3–18 years of age with newly diagnosed DMGs following radiation therapy without evidence of hemorrhage or cysts with intact organ function. Patients undergo a tumor biopsy and a single catheter (Spetzler lumbar shunt catheter, Integra, Plainsboro, NJ) is placed stereotactically into the geometric center of the tumor. A second catheter is inserted subcutaneously with the distal tubing connected to the infusion pump, (SynchroMed II (Medtronic)), also inserted subcutaneously. The infusion pump is prefilled with MTX110 and administered using wireless N’Vison Clinical programmer into two 24-hour infusions, consisting of 20 hours of drug infusions at 0.2mL/hr. The pulse is completed 7 days later. This is a dose escalation study with the infusate consisting of gadolinium and MTX110 (30, 60, or 90 microM). The study describing the first use in children of this device for direct-to-tumor drug delivery is open to recruitment (January 2020) and the preliminary data will be available for presentation by June 2020.

Rotating machinery is widely used in industrial systems, and its operation state is directly related to the working performance of the system. Research on equipment condition monitoring and fault diagnosis based on vibration signal analysis is of great significance to ensure the safe and stable operation of equipment. In recent years, the field of deep learning has developed rapidly, and many researchers have applied it to the vibration signal analysis of rotating machinery equipment. Firstly, the development history of deep learning is reviewed. Then, the principles of deep learning models such as convolutional neural network, deep belief network, stacked auto-encoder and their applications in vibration signal analysis of rotating machinery are introduced. Finally, the future development trend of deep learning is discussed.

Aiming at the problem that the vibration signal of train rolling bearing presents nonlinear and non-stationary characteristics, which leads to the difficulty of fault feature extraction, a fault diagnosis method of train bogie rolling bearing based on variational mode decomposition (VMD) and bat algorithm optimization support vector machine (BA-SVM) is proposed. Firstly, the center frequency method is used to determine the K value of VMD algorithm. Then, the original signal is decomposed into a series of intrinsic mode components and the distribution entropy of each component is calculated as the feature vector, and the bat algorithm is used to optimize the model parameters of support vector machine. Finally, the BA-SVM model is used for fault pattern recognition of train rolling bearing. The experimental results show that this method can effectively extract the fault characteristics of train rolling bearings and realize fault diagnosis, and the recognition rate is better than that of the comparison method.

Ovarian cancer is one of the most common types of gynecological malignancy worldwide, and is the fourth leading cause of cancer-associated mortality among women. Despite improvements in therapeutic treatments, the prognosis for epithelial ovarian cancer (EOC) remains poor, mainly due to the rapid growth and metastasis of ovarian cancer tumors. An increasing number of studies have indicated that microRNAs (miRNAs) are involved in the carcinogenesis and progression of human cancer, suggesting that miRNAs may be used in clinical prognosis and as a therapeutic target in EOC. The aim of the present study was to investigate the expression levels of miRNA-494 in EOC tissues and cell lines. The clinical significance of miRNA-494 in patients with EOC was also evaluated. The results demonstrated that miRNA-494 was significantly downregulated in EOC tissues and cell lines. Low expression levels of miRNA-494 were associated with poor prognostic features, including International Federation of Gynecology and Obstetrics stage, tumor size and lymph node metastasis. In vitro functional studies demonstrated that overexpression of miRNA-494 inhibited proliferation, migration and invasion in EOC cells. By contrast, knockdown of miRNA-494 enhanced cell growth, migration and invasion in EOC cells. Notably, sirtuin 1 (SIRT1) was identified as a direct target of miRNA-494 in EOC. Furthermore, MTT, cell migration and invasion assays verified that EOC cell proliferation, migration and invasion were completely restored with forced miRNA-494 expression and SIRT1 restoration. Together, these findings suggest that miRNA-494 is a potential prognostic marker, and may provide novel therapeutic regimens of targeted therapy for EOC.

// Zhenzhen Jin 1, * , Huanfu Niu 1, * , Xuenan Wang 1 , Lei Zhang 2 , Qin Wang 1 and Aijun Yang 1 1 Center for Reproductive Medicine, Affiliated Hospital of Jining Medical University, Jining, China 2 Department of Pathology and Laboratory Medicine, Clinical Microarray Core, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, USA * Co-first author Correspondence to: Aijun Yang, email: yajjiningmd@163.com , yajlws2017@163.com Keywords: ovarian cancer, mTOR, CC223, signaling Received: April 12, 2017 Accepted: April 25, 2017 Published: May 10, 2017 ABSTRACT Aberrant activation of mTOR contributes to ovarian cancer progression. CC223 is a novel and potent mTOR kinase inhibitor. The current study tested its activity against human ovarian cancer cells. We showed that CC223, at nM concentrations, inhibited survival and proliferation of established/primary human ovarian cancer cells. Further, significant apoptosis activation was observed in CC223-treated ovarian cancer cells. CC223 disrupted assembly of mTOR complex 1 (mTORC1) and mTORC2 in SKOV3 cells. Meanwhile, activation of mTORC1 and mTORC2 was almost completely blocked by CC223. Intriguingly, restoring mTOR activation by introduction of a constitutively-active Akt1 only partially inhibited CC223-induced cytotoxicity in SKOV3 cells. Further studies showed that CC223 inhibited sphingosine kinase 1 (SphK1) activity and induced reactive oxygen species (ROS) production in SKOV3 cells. At last, oral administration of CC223 potently inhibited SKOV3 xenografted tumor growth in nude mice. The results of this study imply that CC223 could be further studied as a potential anti-ovarian cancer agent.