SIX1: A Prognostic Biomarker in Uterine Corpus Endometrial Carcinoma.

BACKGROUND: Uterine Corpus Endometrial Carcinoma (UCEC) is a common malignancy of the female genital tract. The sine oculis homeobox homolog 1 (SIX1) protein has been documented to be important for tumor progression. However, little is known about the relationship between SIX1 and the pathogenesis of UCEC. OBJECTIVE: This study aimed to assess the prognostic value of biomarker SIX1 in UCEC by analyzing clinical traits, immune infiltration, and gene set enrichment analysis. METHODS: The Wilcoxon signed-rank test and logistic regression were used to analyze the relationship between clinicopathological characteristics and SIX1. The Kaplan-Meier method was used to assess the relationship between clinicopathological characteristics and prognosis verified by immunohistochemistry (IHC). Then gene set enrichment analysis (GSEA) was performed to explore signaling pathways correlated with SIX1 expression in UCEC. Finally, the TIMER2 database was used to analyze the correlation between SIX1 and immune infiltration, and the effect of SIX1 expression on immune cells was calculated with the CIBERSORT algorithm. RESULTS: We found that the expression of SIX1 in UCEC was up-regulated and correlated with a poor prognosis. Analysis showed that the expression of SIX1 was related to various clinical features and was an independent prognostic factor of UCEC. Enrichment analysis showed that SIX1 promoted the occurrence and development of UCEC by regulating multiple signaling pathways. The results of immune infiltration analysis showed that SIX1 has a complex correlation with immune infiltration. CONCLUSION: Our findings indicate that SIX1 is a promising biomarker for predicting the prognosis of UCEC and is a potential therapeutic target.

Long non-coding RNA RP11-379k17.4 derived microRNA-200c-3p modulates human endometrial cancer by targeting Noxa.

Objective: The research paid close attention to the function of lncRNA-related endogenous competitive RNAs (ceRNAs) network in endometrial cancer (EC). Methods: 45 primary endometrial cancer tissues (EC) and 45 normal endometrium (NE) were included in the research. The online software StarbaseV2.0 was made use of forecasting the lncRNA which most likely contained microRNA-200c-3p combining sites and could interact with microRNA-200c-3p. Subsequently, we chose lncRNAs which were consistent with the characteristics of polyadenylation of lncRNAs and lower expression in EC than that of NE. After that, lncRNAs, which were related with the microRNA-200c-3p-noxa network, were identified. Results: Rp11-379k17.4, a new gene related to endometrial cancer, was identified as noncoding RNA. It was a more effective ceRNA associated with the microRNA-200c-3p-noxa network. Conclusion: LncRNAs possess microRNA response elements (MREs) and give scope to significant roles in the post-transcriptional mechanism in EC.

A Time-Distributed Spatiotemporal Feature Learning Method for Machine Health Monitoring with Multi-Sensor Time Series.

Data-driven methods with multi-sensor time series data are the most promising approaches for monitoring machine health. Extracting fault-sensitive features from multi-sensor time series is a daunting task for both traditional data-driven methods and current deep learning models. A novel hybrid end-to-end deep learning framework named Time-distributed ConvLSTM model (TDConvLSTM) is proposed in the paper for machine health monitoring, which works directly on raw multi-sensor time series. In TDConvLSTM, the normalized multi-sensor data is first segmented into a collection of subsequences by a sliding window along the temporal dimension. Time-distributed local feature extractors are simultaneously applied to each subsequence to extract local spatiotemporal features. Then a holistic ConvLSTM layer is designed to extract holistic spatiotemporal features between subsequences. At last, a fully-connected layer and a supervised learning layer are stacked on the top of the model to obtain the target. TDConvLSTM can extract spatiotemporal features on different time scales without any handcrafted feature engineering. The proposed model can achieve better performance in both time series classification tasks and regression prediction tasks than some state-of-the-art models, which has been verified in the gearbox fault diagnosis experiment and the tool wear prediction experiment.

An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox.

A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment.

Increased Plasma Levels of Pentraxin 3 Are Associated with Poor Prognosis of Colorectal Carcinoma Patients.

Colorectal carcinoma (CRC) is one of the most common cancers globally. It is essential to identify a prognostic predictor for CRC. Pentraxin 3 (PTX3) is a glycoprotein that is secreted by a variety of human cells. It plays an important role in inflammation and immune regulation. Increasing evidence reveals that elevated PTX3 expression is related to poor prognosis in various cancers. The aim of the study was to determine the usefulness of plasma PTX3 level as a prognostic predictor in CRC. Total 184 CRC patients and 216 controls were included. Plasma levels of PTX3 were determined using Enzyme-linked immunosorbent assays. On admission, plasma PTX3 levels in CRC patients were higher than those in controls (11.8 ± 2.5 ng/ml vs. 3.1 ± 0.9 ng/ml, P < 0.001). After resection, plasma PTX3 levels in patients were decreased (6.0 ± 1.4 ng/ml, P = 0.007), and were elevated at the time of relapse (10.8 ± 2.8 ng/ml, P < 0.001). During the 60-month follow-up period, 108 patients suffered from relapse. Plasma PTX3 levels of ≥ 12 ng/ml on admission were associated with relapse (OR: 3.11, 95% CI: 1.74 ~ 6.29), and tumor-free survival rate in those patients with plasma PTX3 levels of ≥ 12 ng/ml was lower than that in other patients (P = 0.001). Furthermore, plasma PTX3 levels on admission showed positive linear correlations with plasma complement 3, 4 and 5b9 levels (P < 0.001, P < 0.001, P < 0.001). Therefore, we propose that PTX3 is an independent prognostic indicator in CRC.

Strain distribution in the intervertebral disc under unconfined compression and tension load by the optimized digital image correlation technique.

The unconfined compression and tension experiments of the intervertebral disc were conducted by applying an optimized digital image correlation technique, and the internal strain distribution was analysed for the disc. It was found that the axial strain values of different positions increased obviously with the increase in loads, while inner annulus fibrosus and posterior annulus fibrosus experienced higher axial strains than the outer annulus fibrosus and anterior annulus fibrosus. Deep annulus fibrosus exhibited higher compressive and tensile axial strains than superficial annulus fibrosus for the anterior region, while there was an opposite result for the posterior region. It was noted that all samples demonstrated a nonlinear stress-strain profile in the process of deforming, and an elastic region was shown once the sample was deformed beyond its toe region.

[Research on VOC concentration detection by photoelastic modulation infrared spectrum absorption method].

In order to ensure high stability and strong anti-interference ability in static interference system for qualitative and quantitative analysis of gas, a static scans interference detection system was designed based on photoelastic modulation infrared spectrum absorption system. The system consists of infrared laser, polarizer, photoelastic modulator, polarization analyzer and CCD components. By photoelastic modulator the principal refractive index of optical crystal will change cyclically by the modulation signal, producing cyclical changes in the optical path difference. With the calculation of modulation phase variation, the authors can get the function of the crystal length, the modulation cycle, and the range of optical path difference. Based on phase delay value and the energy distribution of interference pattern, the authors got the formula for the corresponding interference light intensity. The experiment used ZnSe crystal as the photoelastic modulation crystal, the polarizer uses the DOP3212 polarizer, and the detector uses the TCD5390AP array CCD. The five groups have different concentrations with three common VOC gases (formaldehyde, benzene and xylene) for detecting the concentrations of gases. The experimental results with the traditional infrared absorption were compared with the test results of photoelastic modulation infrared spectrum absorption method. The method of photoelastic modulation infrared spectrum absorption had high stability and real-time features, while the detection accuracy is better than the traditional infrared absorption method.