Search

The measurement error evaluation of Power Metering Equipment (PME) is significant for the instrument design and accurate metering of electric energy, especially in extreme environmental stresses. However, actual measurement error prediction is often disturbed by environmental noise and insufficient feature information. To address this problem, an Improved Local Outlier Factor (ILOF) method is first proposed to detect potential outliers. And an optimized distance function and adaptive threshold constraint method based on boxplot are used to improve the outlier detection performance of ILOF. Next, an error prediction method, namely Kernel Support Vector Regression (KSVR), is presented to fuse measurement error and multiple extreme environmental stresses by using the proposed kernel approach. Integrating the ILOF and KSVR, examples from the extreme environmental region demonstrate that the proposed evaluation framework has a higher assessment performance. Compared with several state-of-art prediction methods, our framework has profound outlier identification and error prediction performance under small sample conditions.

Objectives To evaluate the performance of a dual-energy computed tomography (DECT) virtual non-calcium (VNCa) technique in the detection of edema-like marrow signal intensity (ELMSI) in patients with knee joint osteoarthritis (OA) compared to magnetic resonance imaging (MRI). Methods The study received local ethics board approval, and written informed consent was obtained. DECT and MRI were used to examine 28 knees in 24 patients with OA. VNCa images were generated by dual-energy subtraction of calcium. The knee joint was divided into 15 regions for ELMSI grading, performed independently by two musculoskeletal radiologists, with MRI as the reference standard. We also analyzed CT numbers through receiver operating characteristics and calculated cut-off values. Results For the qualitative analysis, we obtained CT sensitivity (Readers 1, 2 = 83.7%, 89.8%), specificity (Readers 1, 2 = 99.5%, 99.5%), positive predictive value (Readers 1, 2 = 95.3%, 95.7%), and negative predictive value (Readers 1, 2 = 97.9%, 98.7%) for ELMSI. The interobserver agreement was excellent (κ = 0.92). The area under the curve for Reader 1 and Reader 2 was 0.961 (95% CI 0.93, 0.99) and 0.992 (95% CI 0.98, 1.00), respectively. CT numbers obtained from the VNCa images were significantly different between regions with and without ELMSI (p Conclusions VNCa images have good diagnostic performance for the qualitative and quantitative analysis of knee osteoarthritis-related ELMSI.

Contribution: This article presents a teaching experiment platform for an undergraduate laboratory program designed to fulfill the needs of control engineering classroom experiment teaching. The teaching experiment platform is a cost-effective teaching tool that can be promoted on a large scale in colleges and universities. The teaching experiment platform is a portable device that breaks through the limitations of the time and place of experiments. The platform can be used for many courses, which helps students combine theory with practice. Background: 1) Undergraduate programs usually follow traditional educational methods, which lead to students often unable to connect knowledge learned in class to actual situations;2) the price of existing experimental devices is far beyond the affordability of students and teachers;and 3) most undergraduate students have to learn at home due to the COVID-19 epidemic. Therefore, students need a kind of cost-effective take-home teaching experiment platform, which can enable them to do experiments at home and improved the learning efficiency. Intended Outcomes: The teaching experimental platform gives the students out of the classroom more time to analyze the results of the experiment and to relate the concepts presented in the lecture course to the results of the experiment. With the aid of the platform, students can achieve the learning objectives, significantly improve interactivity in the classroom and fulfill the ABET requirement. Application Design: The course of Principle of Automatic Control taught at the Hunan University is given as a case study in which the evaluation process was based on the corresponding student outcomes and ABET criteria. Findings: The analysis based on positive student feedback as well as their academic grades indicates that the teaching platform presented in this article is an effective learning tool during the learning process of undergraduate students. IEEE

For the purpose of fault monitoring of dynamic processes, this study proposes a hybrid framework of canonical variate analysis (CVA) and global-local preserving projection (GLPP) and refer to it as canonical GLPP analysis. The construction of Laplacian matrix based on the Hankel matrix provides a novel technology for preserving projection analysis. The optimal projection matrix solved by the canonical GLPP analysis framework can simultaneously preserves time series correlations and spatial distribution information in dimension reduction. This method inherits the advantages of GLPP in handling high-dimensional data with manifold learning and the strength of CVA in self-correlation analysis with state-space model. The solution to the proposed method is formulated as a generalized eigenvalue problem. The effectiveness of the canonical GLPP analysis method in fault detection and identification of dynamic processes is verified with a case study on the Tennessee Eastman process. Compared with the Fisher discriminant analysis based CVA, dynamic PCA, dynamic GLPP and the GLPP method, the proposed canonical GLPP analysis framework provides a more robust and accurate paradigm for dynamic process monitoring.

The nearest neighbor selection of multivariate statistical projection analysis methods assumes locally constant probabilities. However, ignoring the non-uniform distributed characteristic of data causes information redundancy in data-intensive regions and insufficient information in data-sparse regions, leading to detection performance decline. In this study, a new weighted distance named Cam weighted distance is used to reselect the neighbors and consequently overcome the aforementioned limitation. An nonlinear industrial fault detection method based on KGLPP-Cam is developed. The proposed method can preserve not only global and local information but also orientation and adaptive scale to obtain the information of neighbors according to different surroundings. T 2 and S P E statistics are calculated for fault detection. A change ratio function is constructed to select sensitive principal components adaptively and better describe the sensitivity of different projection directions for processing change information. The proposed method is examined through a numerical example and TE process.

The current study was designed to unveil the anticancer effects of naringenin against breast cancer MDA-MB-231 cells. Cytotoxic effects were estimated via MTT viability assay. Clonogenic assay was performed to assess clonogenic potential of MDA-MB-231 cells. Apoptosis was examined via AO/EB staining, quantified via annexin V/PI staining and western blotting was performed to monitor apoptosis allied protein expressions. Cell cycle was analyzed through flow cytometric analysis. Transwell chambers assay was executed for determination of cell migration and cell invasion tendency of MDA-MB-231 breast cancer cells. Results indicated significant anticancer potential of naringenin drug against MDA-MB-231 cells. On evaluation of cell proliferation rate of breast cancer cells by MTT assay, it was observed that naringenin inhibited proliferation rate in dose as well as time dependent manner. AO/EB staining assay revealed potential morphological changes indicating apoptotic cell death. Annexin V/PI staining assay revealed increased apoptotic cell percentage with increased drug doses. The apoptosis inducing potential of naringenin drug was observed to be mediated via caspase activation. Flow cytometric analysis predicted cell cycle arrest at G2/M phase of cell cycle. Further cell migration as well as cell invasion tendency of MDA-MB-231 cells was reduced to minimum upon application of naringenin drug.

Objective. The purpose of this study was to evaluate the accuracy of brachytherapy (BT) planning structures derived from Deep learning (DL) based auto-segmentation compared with standard manual delineation for postoperative cervical cancer. Approach. We introduced a convolutional neural networks (CNN) which was developed and presented for auto-segmentation in cervical cancer radiotherapy. The dataset of 60 patients received BT of postoperative cervical cancer was used to train and test this model for delineation of high-risk clinical target volume (HRCTV) and organs at risk (OARs). Dice similarity coefficient (DSC), 95% Hausdorff distance (95%HD), Jaccard coefficient (JC) and dose-volume index (DVI) were used to evaluate the accuracy. The correlation between geometric metrics and dosimetric difference was performed by Spearman’s correlation analysis. The radiation oncologists scored the auto-segmented contours by rating the lever of satisfaction (no edits, minor edits, major edits). Main results. The mean DSC values of DL based model were 0.87, 0.94, 0.86, 0.79 and 0.92 for HRCTV, bladder, rectum, sigmoid and small intestine, respectively. The Bland-Altman test obtained dose agreement for HRCTV_D90%, HRCTV_Dmean, bladder_D2cc, sigmoid_D2cc and small intestine_D2cc. Wilcoxon’s signed-rank test indicated significant dosimetric differences in bladder_D0.1cc, rectum_D0.1cc and rectum_D2cc (P < 0.05). A strong correlation between HRCTV_D90% with its DSC (R = −0.842, P = 0.002) and JC (R = −0.818, P = 0.004) were found in Spearman’s correlation analysis. From the physician review, 80% of HRCTVs and 72.5% of OARs in the test dataset were shown satisfaction (no edits). Significance. The proposed DL based model achieved a satisfied agreement between the auto-segmented and manually defined contours of HRCTV and OARs, although the clinical acceptance of small volume dose of OARs around the target was a concern. DL based auto-segmentation was an essential component in cervical cancer workflow which would generate the accurate contouring.

With the rapid development of sensor and computer technology, deep learning has received extensive attention in the field of fault detection with powerful nonlinear feature extraction capabilities. However, the feature extracted by deep learning contains different fault information volume. Fault detection performance would be affected by features with less fault information. Motivated by this, the deep high-dimensional features extracted from the deep belief network are analyzed, and an index for measuring fault information volume is proposed to select the deep highly-sensitive feature (DHSF) with a large amount of fault information volume as the feature to be detected. Based on DHSFs, Euclidean distance is used for fault detection, and the moving average window function is used to reduce burst noise interference and improve detection performance. Finally, a numerical case and Tennessee Eastman process demonstrate the advantage of the proposed method in fault detection results compared with other methods.

Cyber spoofing with distributed synchrophasor adversely affects the decision-making and situational awareness of the power grid. To detect the spoofing trail, this letter proposes a composite signature-based cyber spoofing detection methodology. The intrinsic principal modes are first extracted from the distributed synchrophasor data. Then, multiple signatures of different intrinsic model components are derived to quantify the spoofing. Thereafter, the dynamic dual-kernel support vector machine is proposed to identify cyber spoofing using multiple signatures. Multiple experimental results using six spoofing methods have verified the validity of the methodology.

This article proposes a novel approach for the detection of interharmonics using sparse signal decomposition (SSD) based on matching pursuit (MP) on the overcomplete dictionary. First, an overcomplete dictionary is designed according to the characteristics of harmonics and interharmonics. Then, use the MP algorithm to obtain the sparse decomposition of the signal. Thus, the interharmonic components are extracted from the signal, and the estimation of frequency, amplitude, and start and end time can be computed from the parameter set of the atoms that best match signal residues. An imperialist competitive algorithm (ICA) is employed to search for the best matching atoms in the continuous parameter space, which can avoid the limitation of the step sizes on the detection accuracy and greatly reduce the computational complexity. Search function and differential evolution operator are introduced in the assimilation stage to solve the issue that ICA is likely to get stuck at a local optimum. The proposed method is tested using simulated and realistic signals. The experimental results demonstrate the effectiveness and robustness of the proposed method using the SSD technique in detection and location of interharmonics, even under noisy conditions.

The synchrophasor data recorded by Phasor Measurement Units (PMUs) plays an increasingly critical role in the regulation and situational awareness of power systems. However, the widely installed PMUs are vulnerable to multiple malicious attacks from cyber hackers during data transmission and storage. To address this problem, a Modified Ensemble Empirical Mode Decomposition (MEEMD) is proposed first to extract the intrinsic mode functions of each Synchrophasor Data Attacks (SDA). The frequency-based adaptive screening criterion embedded in MEEMD is used to eliminate the false intrinsic mode functions. Next, a Multivariate Convolutional Neural Network (MCNN) is proposed to identify multiple SDA by utilizing the extracted intrinsic mode functions and original SDA as input vectors. A fusion block as the main structure of MCNN is also leveraged to increase the diversity of features and compress the model parameters. Integrating MEEMD and MCNN, a framework with automatic feature extraction and multi-source information fusion capability, referred to as Feature Interactive Network (FIN), is proposed to detect multiple SDA. Based on the proposed FIN framework, six types of SDA are explored for the first time using actual synchrophasor data in FNET/Grideye that was collected from different locations in the U.S. Eastern Interconnection. Finally, a large quantity of experiments with different attack strengths are used to evaluate the adaptability and classification performance of the proposed FIN.

Objective:Deep learning (DL) based approach aims to construct a full workflow solution for cervical cancer with external radiotherapy (EBRT) and brachytherapy (BT).The purpose of this study was to evaluate the accuracy of EBRT planning structures derived from DL based auto-segmentation which would be a part implemented in the workflow of cervical cancer.Methods:Clinical target volumes (CTVs) and organs at risk (OARs) of 75 cervical cancer patients were manually delineated by senior radiation oncologists and auto-segmented by DL based method.The accuracy of DL based auto-segmented contours were evaluated using geometric and dosimetric metrics including dice similarity coefficient (DSC),95%hausdorf distance (95%HD),jaccard coefficient (JC) and dose-volume index (DVI).The correlation between geometric metrics and dosimetric difference was performed by Spearman’s correlation analysis. Results:The DL based auto-segmentation generated similar geometric performance in right kidney,left kidney,bladder,right femoral head and left femoral head with mean DSC of 0.88-0.93,95%HD of 1.03-2.96mm and JC of 0.78-0.88. Wilcoxon’s signed-rank test indicated significant dosimetric differences between manual and DL based auto-segmentation in CTV,spinal cord and pelvic bone (PConclusion:Auto-segmentation achieved a satisfied agreement for most EBRT planning structures,although the dosimetric consistency of CTV was a concern. DL based auto-segmentation was an essential component in cervical cancer workflow which would generate the accurate contouring.

Purpose: To seek optimal keV settings for imaging carpal tunnel in adults by dual-energy computed tomography (DECT) monoenergetic technique; to describe anatomic characteristics of carpal tunnel and to observe correlation between carpal bony and soft tissue structures. Methods: DECT images of 20 wrists (11 left and 9 right wrists; 14 men, mean age 26.93 ± 1.38 years, range 23 to 28, and 6 women, mean age 24.17 ± 0.98 years, range 23 to 26) were evaluated. Monoenergetic images were reconstructed at 42, 62, 82, 102, 122, and 142 keV. Image quality was assessed along a 5-point Likert scale, and the highest-quality images were chosen for quantitative analysis. Two musculoskeletal radiologists performed both analyses independently. Results: The optimal energy spectrum with the best contrast-to-noise ratio (CNR) for monoenergetic images were at 62 keV (19 wrists, 95%) and 61 keV (1 wrist, 5%). There was substantial interobserver agreement between the readers in the 5-point Likert scale analysis of image quality (? = 0.793). Bland-Altman plots also indicated good agreement between observers in quantitative analysis. Intra-category 1 and 2 correlation was mostly discovered at hamate hook level and middle level of pisiform (P < 0.05), while bony and soft tissue structures partly reached correlation (P < 0.05). Conclusion: The optimal energy spectrum for monoenergetic DECT imaging of carpal tunnel structures was 62 keV. DECT monoenergetic imaging could predict changes in soft tissue structures and demonstrate carpal tunnel anatomic structures. Keywords: DECT, monoenergetic, keV, carpal tunnel, Continuous...

Accurate power quality disturbance (PQD) classification is significantly important for power grid pollution control. However, the use of nonlinear loads makes power system signals complex and distorted and, thus, increases the difficulty of detecting and classifying PQD signals. To address this issue, this article first proposes an optimized S-transform (OST). It optimizes different window parameters to improve time-frequency resolution using maximum energy concentration. A kernel SVM (KSVM) classifier is proposed to classify multiple features using a combination of kernels. Integrating OST and KSVM, a classification framework is further proposed to detect and classify various PQD signals. Extensive experiments on computer simulations and experimental signals demonstrate that the proposed classification framework shows better performance than several state-of-art methods in classifying not only single and multiple PQD signals but also PQD signals with different noise levels. More importantly, our framework has superior performance in detecting nonlinearly mixed PQD signals.

Security of Distribution Synchrophasors Data (DSD) is of paramount importance as the data is used for critical smart grid applications including situational awareness, advanced protection, and dynamic control. Unfortunately, the DSD are attractive targets for malicious attackers aiming to damage grid. Data spoofing is a new class of deceiving attack, where the DSD of one Phasor Measurement Units (PMUs) is tampered by other PMUs thereby spoiling measurement based applications. To address this issue, a source authentication based data spoofing attack detection method is proposed using Multi-view Convolutional Neural Network (MCNN). First, common components embedded in raw frequency measurements from DSD are removed by Savitzky-Golay (SG) filter. Second, fast S transform (FST) is utilized to extract representative spatial fingerprints via time frequency analysis. Third, the spatial fingerprint is fed to MCNN, which combines dilated and standard convolutions for automatic feather extraction and source identification. Finally, according to the output of MCNN, spoofing attack detection is performed via threshold criterion. Extensive experiments with actual DSD from multiple locations in FNET/Grideye are conducted to verify the effectiveness of the proposed method.

Accurate reliability and residual life analysis is paramount during the designing of reliability requirements and rotation of power measuring equipment (PME). However, the sample dataset of failure is usually sparse and contains inevitable pollution data, which has an adverse effect on the reliability analysis. To tackle this issue, this paper first applies nonlinear regression to fuse the failure rate and environmental features of PME collected from various locations. Then, a novel binary hierarchical Bayesian probability method is proposed to model the failure trend and identify outliers, in which the outlier identification structure is embedded into hierarchical Bayesian. Integrating binary hierarchical Bayesian and the bagging method, a binary hierarchical Bayesian with bagging (BHBB) framework is further introduced to improve predictive performance in a small sample dataset by resampling. Last, the influence of typical environmental features, failure rate, and reliability are obtained by the BHBB under the real sample dataset from multiple typical locations. Experiments show that our framework has superior performance and interpretability comparing with other typical data-based approaches.

This study investigates the role of circular RNA (circRNA) hsa_circ_0000515 in cervical cancer and the underlying mechanism associated with microRNA-326 (miR-326). hsa_circ_0000515 and ETS transcription factor ELK1 (ELK1) were initially over-expressed and miR-326 was down-regulated in cervical cancer tissues and cells. Low hsa_circ_0000515 expression was found to be associated with favorable prognosis of patients with cervical cancer. A series of mimics, inhibitors, over-expression plasmids or siRNAs were introduced into cervical cancer cells to alter the expression of hsa_circ_0000515, miR-326 and ELK1. In vitro experiments exhibited that silencing of hsa_circ_0000515 or upregulation of miR-326 resulted in suppressed proliferation and invasion, along with induced apoptosis and autophagy of cervical cancer cells. Dual-luciferase reporter assay, RNA pull-down and RIP assays highlighted that hsa_circ_0000515 was able to act as a ceRNA of miR-326 to increase ELK1. Furthermore, enhancement of ELK1 expression resulted in enhanced proliferation and invasion but repressed apoptosis and autophagy of cervical cancer cells. In vivo experiments further confirmed the suppressed tumor growth by hsa_circ_0000515 silencing. Our findings demonstrated that hsa_circ_0000515 acts as a tumor promoter in cervical cancer. The study provides evidence for targeting hsa_circ_0000515 for therapeutic purposes in treating cervical cancer.

A novel data-driven process monitoring method named Fisher discriminant global-local preserving projection (FDGLPP) is proposed and applied to diagnosis fault in industrial process. Integrating Fisher discriminant analysis with global-local preserving projection to solve the optimal projection direction, then transforming original data set with the projection matrix can not only preserve global manifold structure and local neighborhood structure of the data set but also promise the discrimination of the projection subspace, which can afford better fault identification performance. On the other hand, Kernel density estimation is introduced to calculate a more accurate control limit of monitoring indexes, which improves process monitoring performance with less false alarm and detection delay. A numerical simulation is introduced to validate the better dimension reduction performance of FDGLPP algorithm. And a case study on Tennessee Eastman process demonstrates the advantages of proposed method in fault detection and identification contrast with GLPP, LPP and PCA method. Then, t-SNE is applied to visualization the discrimination of FDGLPP.

Objective: To determine the predictive value of the 2018 International Federation of Gynecology and Obstetrics (FIGO) staging system for cervical cancer patients with surgical risk factors. Methods: Data of 662 cervical cancer patients (stages IB and IIA) with surgical risk factors treated at Zhejiang Cancer Hospital between 2008 and 2011 were retrospectively reviewed. Univariate log-rank test and multivariate Cox regression models were adopted to evaluate the relationship between 2018 FIGO stage and survival. Results: On re-staging of patients, 17.3%, 44.5%, 25.4%, and 37.1% of the patients with FIGO 2009 stage IB1, IB2, IIA1, and IIA2, respectively, were upgraded to FIGO 2018 IIIC1P stage, and 2.1%, 3.0%, 3.1%, and 2.1% patients, respectively, were upgraded to IIIC2P stage. The 5-year overall survival (OS) rates of patients with FIGO 2018 stage IB1, IB2, IB3, IIA1, IIA2, IIIC1P, and IIIC2P were 95.3%, 95.1%, 90.4%, 92.4%, 86.4%, 81.9%, and 56.3%, respectively. The 5-year progression-free survival (PFS) rates were 94.0%, 91.0%, 88.5%, 91.4%, 86.4%, 79.5%, and 43.8%, respectively. The 5-year OS rates of patients with 1-2 positive pelvic lymph nodes (PLNs) and those with >2 positive PLNs were 86.0% and 73.7%, respectively, and the 5-year PFS rates were 84.2% and 70.2%, respectively. OS and PFS of patients with 1-2 positive PLNs in stage IIIC1P were similar to those of patients in stage IIA2 without lymph node metastasis, but significantly better than those of patients with >2 positive PLNs. Multivariate analysis showed FIGO 2018 stage to be an independent prognostic factor for OS and PFS. Conclusion: The 2018 FIGO staging system for cervical cancer appears to be useful for predicting prognosis of patients with risk factors after radical surgery. Survival of stage IIA1 patients is better than that of stage IB3 patients. Stage IIIC1 is not homogenous; survival in stage IIIC1P depends on the number of positive PLNs.

Objective: To evaluate the factors associated with positive pelvic lymph nodes (LNs) on the survival of patients with 2018 FIGO stage IIIC1p cervical cancer. Methods: We retrospectively analyzed 155 patients with pelvic lymph node metastasis (LNM) confirmed by pathology after radical resection of cervical cancer treated at Zhejiang Cancer Hospital, China, between March 2008 and October 2011. We analyzed the influence of the factors associated with positive pelvic LNs on the survival of patients. Results: The 5-year progress-free survival (PFS) and overall survival (OS) of patients were 78.1% and 81.9%, respectively. The 5-year PFS and OS of patients with more than 2 LNM were worse compared with patients with 1 or 2 LNM (68.4% vs 83.7%, p=0.013; 72.4% vs 87.6%, p=0.017, respectively). The 5-year PFS and OS of patients with more than 2 LNM sites were worse than that of patients with 1 or 2 LNM sites (60.0% vs 82.4%, p=0.008; 70.0% vs 84.8%, p=0.045, respectively). The 5-year PFS and OS of patients with common iliac LNM was poorer than that of patients without common iliac LNM (60.7% vs 81.9%, p=0.008; 67.9% vs 85.0%, p=0.020, respectively). Compared with other patients, the survival of patients with these three factors (more than 2 LNM, more than 2 LNM sites, and common iliac LNM) was the worst (p

S transform and its improved algorithms are limited by the Gaussian window, which cannot realize high time-frequency energy concentration and exact identification of harmonic time-frequency parameters. A significant improvement has done in this paper: K-S decomposition is built based on the idea of FFT + Kaiser window + IFFT. The Each column vector of the two-dimensional time-frequency complex matrix obtained by K-S decomposition represents the frequency of the signal and each row is instantaneous magnitude in each selected frequency in time domain. Thus the parameter of amplitude, spectrum, time and phase can be achieved and adaptive time-frequency analysis is realized. The simulation experiments of the complex power system harmonic verify the feasibility and adaptive ability of K-S decomposition.

Background: Atherosclerosis is an inflammatory disease involving activation of adaptive and innate immune responses to antigens, including oxidized low-density lipoprotein (oxLDL) and phosphorylcholine (PC). Dendritic cells (DCs), which are antigen-presenting cells that activate T cells, are present in atherosclerotic lesions and are activated in immune organs. However, the mechanism by which PC promotes atherosclerosis is unclear. Methods and Results: To evaluate whether PC promotes atherosclerosis via DCs, 2×105 DCs activated by PC-keyhole limpet hemocyanin (DCs+PC-KLH) were injected into ApoE−/− mice and the features of the plaques and the effects of the DCs on cellular and humoral immunity against PC-KLH were determined. Mice injected with DCs+PC-KLH had significantly larger atherosclerotic lesions than controls, with increased inflammation in the lesions and plaque instability. Furthermore, DCs+PC-KLH were characterized using flow cytometry after coculture of bone marrow-derived DCs and naïve T cells. DCs+PC-KLH showed an inflammatory phenotype, with increased CD86, CD40, and major histocompatibility complex Class II molecules (MHC-II), which promoted PC-specific T helper (Th) 1 and Th17 cell differentiation in vivo and in vitro. Moreover, 2 weeks after the administration of DCs+PC-KLH to mice, these mice produced PC- and oxLDL-specific IgG2a, compared with no production in the controls. Conclusions: These findings suggest that DCs presenting PC promote specific immunity to PC, increase lesion inflammation, and accelerate atherosclerosis, which may explain how PC promotes atherosclerosis.

BACKGROUD Due to the emergency nature of ST-elevation myocardial infarction (STEMI), the estimation of the risk of contrast-induced nephropathy (CIN) is always limited and the hydration prior to primary percutaneous coronary interventions (PCI) is difficult. Minimizing contrast volume (CV) might be the most desirable option for preventing the incidence of CIN in these patients. The improvement of myocardial perfusion due to thrombus aspiration (TA) may be accompanied by a reduction in CV.OBJECTIVES This study aimed to determine the effect of TA prior to angioplasty on CV in patients with STEMI.METHODS Consecutive 380 STEMI patients undergoing primary PCI with high thrombus burden (HTB) were randomly assigned to receive either TA before primary PCI (TA group, n = 190) or primary PCI alone (control group, n = 190). Serum creatinine was detected at the time of admission, and 48 and 72 hours after primary PCI.RESULTS Baseline characteristic were well matched. Although the CV in TA group was significantly lower than that in control group (71.5 ± 15.7 mL vs. 82.3 ± 17.5 mL, p = 0.000), the incidence of CIN was comparable (9.5% vs. 13.3%, p = 0.249). During hospitalization, no significant differences between groups was observed in major bleeding, urgent dialysis, stroke, reoperation after 72 hours, death after 72 hours and total incidence of adverse clinical events.CONCLUSIONS The study demonstrated that TA reduces CV but not CIN or adverse clinical events during hospitalization in STEMI patients undergoing primary PCI with HTB.

Background: Various inflammatory biomarkers, such as the neutrophil lymphocyte ratio (NLR) and platelet lymphocyte ratio (PLR), have been well authenticated to predict clinical outcomes in numerous types of cancer. The optimal treatment for patients with locally advanced esophageal squamous cell carcinoma (ESCC) located in middle or upper region is still inconclusive. The aim of the study was to examine pretreatment NLR and PLR to select from radical surgery or definitive chemoradiotherapy (dCRT) for these patients. The linkage between pretreatment NLR / PLR and prognosis was also analyzed.Methods: NLR and PLR were calculated in 113 locally advanced ESCC located in middle or upper esophagus of patients who underwent radical surgery or dCRT between January 2014 and December 2019. A receiver operating characteristic curve was plotted to select the best cut-off value of NLR and PLR for predicting survival. A survival curve was plotted using the Kaplan–Meier method. Univariate and multivariate Cox regression analyses were applied to assess predictors for survival. Results: NLR and PLR was both associated with the extent of lymph node metastasis (NLR: P = 0.045; PLR: P = 0.002). Additionally, high PLR and recurrence with distant organs metastasis were closely related (P = 0.014), and NLR was related to the tumor stage (P = 0.043). The results of multivariate analysis revealed that NLR (> 2.07) and PLR (> 183.06) were independently associated with poor prognosis. It is noteworthy that surgery was associated with a superior OS compared with dCRT in the low NLR population (P = 0.045). Conclusions: Low pretreatment NLR patients made a decision to undergo radical surgery with a substantial therapeutic benefit. Pretreatment NLR and PLR are independent predictors for patients with locally advanced ESCC located in the middle and upper esophagus who underwent radical surgery or dCRT.