Long non-coding RNA (lncRNA) X-inactive specific transcript (XIST) is oncogenic in multiple cancers. Herein, the present study is aimed at delving into how XIST functions in retinoblastoma (RB) and ...investigating its underlying mechanism. In this study, XIST, miR-191-5p, BDNF mRNA, and BDNF expression levels in RB tissues or cell lines were examined by quantitative real-time polymerase chain reaction (qRT-PCR) or Western blot. The models of gain-of-function and loss-of-function were established by the transfection of pcDNA3.1-XIST, XIST siRNA, and miR-191-5p mimics and inhibitors into SO-Rb50 and Y79 cells, respectively. RB cell proliferation, migration, invasion, and apoptosis were detected employing cell counting kit-8 (CCK-8), Transwell, and terminal deoxynucleotidyl transferased UTP nick end labeling (TUNEL) assays. The regulatory relationships among XIST, miR-191-5p, and BDNF were affirmed utilizing bioinformatics analysis, luciferase reporter assay, qRT-PCR, as well as Western blot. We reported that, XIST expression was markedly elevated in RB tissue and RB cells. XIST overexpression accelerated RB cell proliferation, migration, and invasion, and attenuated RB cell apoptosis but miR-191-5p exerted the opposite effects. Besides, BDNF expression was inhibited by miR-191-5p in both mRNA and protein levels. XIST indirectly improved BDNF expression by repressing miR-191-5p expression as a competitive endogenous RNA. In conclusion, XIST expression is abnormally elevated in RB tissues and XIST can modulate proliferation, migration, invasion, and apoptosis of RB cells by regulating miR-191-5p/BDNF axis.
Abstract
The availability of high-throughput sequencing data creates opportunities to comprehensively understand human diseases as well as challenges to train machine learning models using such high ...dimensions of data. Here, we propose a denoised multi-omics integration framework, which contains a distribution-based feature denoising algorithm, Feature Selection with Distribution (FSD), for dimension reduction and a multi-omics integration framework, Attention Multi-Omics Integration (AttentionMOI) to predict cancer prognosis and identify cancer subtypes. We demonstrated that FSD improved model performance either using single omic data or multi-omics data in 15 The Cancer Genome Atlas Program (TCGA) cancers for survival prediction and kidney cancer subtype identification. And our integration framework AttentionMOI outperformed machine learning models and current multi-omics integration algorithms with high dimensions of features. Furthermore, FSD identified features that were associated to cancer prognosis and could be considered as biomarkers.
Abstract
In view of the current operation and maintenance mode for railway station equipment, the method of traditional manual and post-diagnosis for the equipment faults is still used, which cannot ...meet the reliability requirements of equipment operation and maintenance management. A health diagnosis system based on cloud-edge collaboration for railway station equipment is proposed. Firstly, it collects online operation information of multi-source perception equipment, which realized through edge intelligent gateway by componentized micro- service and Docker technology. Moreover, it realizes data storage and publishing, edge detection, monitoring online and early warning through the intellectual server models. Secondly, a life prediction model is established based on HSMM to calculate the health status and service life of the equipment. Finally, the cloud-edge collaboration model is designed based on KubeEdge to realize the cloud-edge communication and collaborative service of resources. It effectively solves the problems of remote condition monitoring, health diagnosis and predictive maintenance for railway station equipment, and it provides technical support for the safe operation of railway station.
Sustainable development has received more attention in recent years due to growing ecological and environmental concerns. Thus, eco-innovation becomes a topic of increasing interest and generates a ...large amount of publications. This paper uses extensive data from Web of Science and Scopus to examine the evolution of eco-innovation research and also uses meta-analysis to delve deeper into the determinants. The findings reveal that (1) the number of publications has increased steadily over three stages—slow budding, steady development, and rapid growth—with an overall average growth rate of 16.0%; (2) increasing countries/regions are studying eco-innovation, primarily in developed countries, but the contribution from developing countries is also growing; (3) the most published journals are Journal of Cleaner Production, Sustainability, Business Strategy and the Environment, Technological Forecasting and Social Change, and Ecological Economics; (4) keyword analysis reveals determinants of eco-innovation is a long-term hot topic; (5) meta-analysis concludes that innovation capability and environmental regulations can significantly affect eco-innovation; and (6) high economic development level can effectively enhance eco-innovation by improving R&D, knowledge, and innovation capability. Compared to large firms, eco-innovation by small and medium-sized firms is more influenced by cooperation and government. This paper suggests the government should construct more financial institutions to relieve firms’ investment pressures, as well as a property right protection mechanism and corresponding innovative knowledge reward.
Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a minimally invasive technique. A meta-analysis was performed to assess the efficacy and safety of EBUS-TBNA in ...intrathoracic tuberculosis (TB).
We searched PubMed, the Cochrane Library, and the Web of Science for suitable studies. The pooled sensitivity, specificity, likelihood ratios (LRs), and diagnostic odds ratio were calculated. A summary receiver operating characteristic (ROC) curve was constructed to calculate the area under the summary ROC curve and Qpoint value (Q*).
A total of 8 studies with 809 patients were included. The pooled sensitivity and specificity of EBUS-TBNA for diagnosis of intrathoracic TB were 0.80 (95% confidence interval CI 0.74-0.85) and 1.00 (95% CI, 0.99-1.00), respectively. The positive LR was 38.25 (95% CI, 13.59-107.65); the negative LR was 0.24 (95% CI, 0.17-0.33); and the diagnostic odds ratio was 186.35 (95% CI, 63.57-546.28). The area under the summary ROC curve was 0.935, and the Q*was 0.871. The pooled sensitivity of EBUS-TBNA for diagnosis of intrathoracic tuberculous lymphadenopathy was 0.87 (95% CI, 0.80-0.95). Only 1 serious complication was reported.
Endobronchial US-guided TBNA is an effective and safe diagnostic tool for intrathoracic TB, especially intrathoracic tuberculous lymphadenopathy.
Difficulties of identification for multivariable controlled autoregressive moving average (ARMA) systems lie in that there exist unknown noise terms in the information vector, and the iterative ...identification can be used for the system with unknown terms in the information vector. By means of the hierarchical identification principle, those noise terms in the information vector are replaced with the estimated residuals and a least squares based iterative algorithm is proposed for multivariable controlled ARMA systems. The simulation results indicate that the proposed algorithm is effective.
Despite revascularisation, a large proportion of acute coronary syndrome (ACS) patients continue to experience major adverse cardiovascular events (MACEs), which are worsened by diabetes mellitus ...(DM). Fibrinogen (FIB) is a risk factor for MACEs in coronary artery disease and often elevated in DM. However, the relationships between FIB, glucose metabolism (haemoglobin A1c HbA1c and fasting blood glucose FBG) and MACEs following percutaneous coronary intervention (PCI) in DM, non-DM or whole patients with ACS remains unknown.
A total of 411 ACS patients undergoing PCI were enrolled in this study. We compared baseline FIB levels between DM (n = 103) and non-DM (n = 308) patients and divided participants into three groups according to FIB level, i.e. FIB-L, FIB-M and FIB-H, to compare baseline characteristics and MACEs. Linear regression analysis of the relationship between glucose metabolism and FIB, Cox regression, survival and landmark analyses of MACEs were also performed over a median of 27.55 months of follow-up.
Patients with DM had higher FIB levels than non-DM patients (3.56 ± 0.99 mg/dL vs. 3.34 ± 0.80 mg/dL, P < 0.05). HbA1c and FBG were significantly positively correlated with FIB in whole and DM patients but not in non-DM patients (all P < 0.05). Compared with the FIB-L group, the FIB-M (hazard ratio HR 1.797, 95% CI 1.117-2.892, P = 0.016) and FIB-H (HR 1.664, 95% CI 1.002-2.763, P = 0.049) groups were associated with higher MACEs in whole; the FIB-M (HR 7.783, 95% CI 1.012-59.854, P = 0.049) was associated with higher MACEs in DM patients. FIB was not associated with MACEs in non-DM patients. During landmark analysis, FIB showed better predictive value for MACEs after PCI in the first 30 months of follow up than in the subsequent period.
In this study from China, FIB was positively associated with glucose metabolism (HbA1c and FBG) in whole and DM populations with ACS. Moreover, elevated baseline FIB levels may be an important and independent predictor of MACEs following PCI, especially amongst those with DM. However, as the follow-up period increased, the baseline FIB levels lost their ability to predict MACEs.
Background
It is uncertain how various degree of glycemic status affect left ventricular (LV) myocardial strain in ST‐segment elevation myocardial infarction (STEMI) patients undergoing primary ...percutaneous coronary intervention (PPCI).
Purpose
To investigate the relationship of glycemic status and myocardial strain in STEMI patients.
Study Type
Prospective cohort study.
Population
282 STEMI patients with cardiac magnetic resonance imaging 5 ± 2 days post‐PPCI. Patients were divided into three groups based on the level of glycated hemoglobin A1c (HbA1c) (group 1: HbA1c < 5.7%; group 2: 5.7% ≤ HbA1c < 6.5%; group 3: HbA1c ≥ 6.5%).
Field Strength/Sequence
3.0‐T; late gadolinium enhancement, balanced steady‐state free precession cine sequence, black blood fat‐suppressed T2‐weighted.
Assessment
LV function, myocardial strain, and infarct characteristics (infarct size, microvascular obstruction, and intramyocardial hemorrhage) were compared among the three groups by one‐way analysis of variance (ANOVA) or Wilcoxon rank sum test. Intraobserver and interobserver reproducibility of LV myocardial strain was evaluated.
Statistical Tests
ANOVA or Wilcoxon rank sum test, Pearson chi‐square or Fisher's exact test, Spearman's correlation analyses and multivariable linear regression analysis. A two‐tailed P value <0.05 was considered statistically significant.
Results
Infarct characteristics were similar among the three groups (P = 0.934, P = 0.097, P = 0.533, respectively). Patients with HbA1c ≥ 6.5% had decreased LV myocardial strain compared with HbA1c 5.7%–6.4%, as evidenced by global radial (GRS), global circumferential (GCS), and global longitudinal (GLS) strain. However, no significant differences in myocardial strain were observed between patients with HbA1c 5.7%–6.4% and HbA1c < 5.7% (P = 0.716; P = 0.294; P = 0.883, respectively). After adjustment for confounders, HbA1c as a continuous variable (beta coefficient β = −0.676; β = 0.172; β = 0.205, respectively) and HbA1c ≥ 6.5% (β = −3.682; β = 0.552; β = 0.681, respectively) were both independently associated with decreased GRS, GCS, and GLS.
Data Conclusion
Patients with uncontrolled blood glucose (categorized in group HbA1c ≥ 6.5%) had worse myocardial strain. The level of HbA1c appeared to be independently associated with decreased myocardial strain in STEMI patients.
Level of Evidence
2
Technical Efficacy Stage
2
Cyber-Physical Production Systems (CPPS) often use wireless sensor networks (WSNs) for monitoring purposes. However, data from WSNs may be inaccurate and unreliable due to power exhaustion, noise and ...other issues. In order to achieve a reliable and accurate data acquisition while ensuring low energy consumption and long lifetime of WSNs, data cleansing algorithms for energy-saving are proposed in this research. The cleansing algorithms are computationally lightweight in local sensors and energy-efficient due to low energy consumption in communications. Dynamic voltage scaling and dynamic power management are adopted for reducing energy consumption, without compromising the performance at system level. A low-power protocol for sink node communication is proposed at network level. A health monitoring system for a Cyber-Physical Machine Tool (a typical example of CPPS) is designed. Experiment results show that the proposed energy-saving data cleansing algorithm yields high-performance and effective monitoring.
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