Specific autoantibody profiles and disease subgroups correlate with circulating micro-RNA in systemic sclerosis.

To evaluate the expression profiles of cell-free plasma miRNAs in SSc and to characterize their correlation with disease subgroups (lcSSc and dcSSc) and with autoantibody profiles

Diagnostic plasma miRNA-profiles for ovarian cancer in patients with pelvic mass

BackgroundOvarian cancer is the fifth most common cancer in women worldwide. Moreover, there are no reliable minimal invasive tests to secure the diagnosis of malignant pelvic masses. Cell-free, circulating microRNAs have the potential as diagnostic biomarkers in cancer. Here, we performed and validated a miRNA panel with the potential to distinguish OC from benign pelvic masses.MethodsThe profile of plasma microRNA was determined with a panel of 46 candidates in a discovery group and a validation group, each consisting of 190 pre-surgery plasma samples from age-matched patients with malignant (n = 95) and benign pelvic mass (n = 95), by real time RT-qPCR.ResultsFour up-regulated (miR-200c-3p, miR-221-3p, miR-21-5p, and miR-484) and two down-regulated (miR-195-5p and miR-451a) microRNAs were discovered. From those, miR-200c-3p and miR-221-3p were further confirmed in a validation cohort. A combination of these 2 microRNAs together with CA-125 yielded an overall diagnostic accuracy of AUC = 0.96.ConclusionsWe showed consistent plasma microRNA profiles that provide independent diagnostic information of late stage OC

The odontoblastic differentiation of dental mesenchymal stem cells: molecular regulation mechanism and related genetic syndromes

Dental mesenchymal stem cells (DMSCs) are multipotent progenitor cells that can differentiate into multiple lineages including odontoblasts, osteoblasts, chondrocytes, neural cells, myocytes, cardiomyocytes, adipocytes, endothelial cells, melanocytes, and hepatocytes. Odontoblastic differentiation of DMSCs is pivotal in dentinogenesis, a delicate and dynamic process regulated at the molecular level by signaling pathways, transcription factors, and posttranscriptional and epigenetic regulation. Mutations or dysregulation of related genes may contribute to genetic diseases with dentin defects caused by impaired odontoblastic differentiation, including tricho-dento-osseous (TDO) syndrome, X-linked hypophosphatemic rickets (XLH), Raine syndrome (RS), hypophosphatasia (HPP), Schimke immuno-osseous dysplasia (SIOD), and Elsahy-Waters syndrome (EWS). Herein, recent progress in the molecular regulation of the odontoblastic differentiation of DMSCs is summarized. In addition, genetic syndromes associated with disorders of odontoblastic differentiation of DMSCs are discussed. An improved understanding of the molecular regulation and related genetic syndromes may help clinicians better understand the etiology and pathogenesis of dentin lesions in systematic diseases and identify novel treatment targets

MicroRNAs in cardiac arrhythmia: DNA sequence variation of MiR-1 and MiR-133A in long QT syndrome.

Long QT syndrome (LQTS) is a genetic cardiac condition associated with prolonged ventricular repolarization, primarily a result of perturbations in cardiac ion channels, which predisposes individuals to life-threatening arrhythmias. Using DNA screening and sequencing methods, over 700 different LQTS-causing mutations have been identified in 13 genes worldwide. Despite this, the genetic cause of 30-50% of LQTS is presently unknown. MicroRNAs (miRNAs) are small (∼ 22 nucleotides) noncoding RNAs which post-transcriptionally regulate gene expression by binding complementary sequences within messenger RNAs (mRNAs). The human genome encodes over 1800 miRNAs, which target about 60% of human genes. Consequently, miRNAs are likely to regulate many complex processes in the body, indeed aberrant expression of various miRNA species has been implicated in numerous disease states, including cardiovascular diseases. MiR-1 and MiR-133A are the most abundant miRNAs in the heart and have both been reported to regulate cardiac ion channels. We hypothesized that, as a consequence of their role in regulating cardiac ion channels, genetic variation in the genes which encode MiR-1 and MiR-133A might explain some cases of LQTS. Four miRNA genes (miR-1-1, miR-1-2, miR-133a-1 and miR-133a-2), which encode MiR-1 and MiR-133A, were sequenced in 125 LQTS probands. No genetic variants were identified in miR-1-1 or miR-133a-1; but in miR-1-2 we identified a single substitution (n.100A> G) and in miR-133a-2 we identified two substitutions (n.-19G> A and n.98C> T). None of the variants affect the mature miRNA products. Our findings indicate that sequence variants of miR-1-1, miR-1-2, miR-133a-1 and miR-133a-2 are not a cause of LQTS in this cohort

A review of eco-friendly functional road materials

Extensive studies on traditional and novel engineering materials and the increasing demands by growing traffic have led to tremendous changes of the function of roads. Roads, as an important part of the human living environment, have evolved from structures that were designed and built for passing vehicles, to ecological assets with significant economic importance. In addition to structural stability and durability, functions such as noise reduction, urban heat island mitigation, de-icing and exhaust gas absorption, are also expected. This study focused on state-of-the-art research on the performance, applications and challenges of six environment-friendly functional road materials, namely the permeable asphalt concrete, noise-reducing pavement materials, low heat-absorbing pavement materials, exhaust gas-decomposing pavement materials, de-icing pavement materials, and energy harvesting pavement materials. With this study, we aim to provide references to the latest relevant literatures of the design and development of environment-friendly functional pavement, and promote innovation in materials science and pavement design principles. For this purpose, this review compiled extensive knowledge in modern road construction and related disciplines, in order to promote the development of modern pavement engineering technologies

Using UAV-Based Hyperspectral Imagery to Detect Winter Wheat Fusarium Head Blight

Fusarium head blight (FHB) is a major winter wheat disease in China. The accurate and timely detection of wheat FHB is vital to scientific field management. By combining three types of spectral features, namely, spectral bands (SBs), vegetation indices (VIs), and wavelet features (WFs), in this study, we explore the potential of using hyperspectral imagery obtained from an unmanned aerial vehicle (UAV), to detect wheat FHB. First, during the wheat filling period, two UAV-based hyperspectral images were acquired. SBs, VIs, and WFs that were sensitive to wheat FHB were extracted and optimized from the two images. Subsequently, a field-scale wheat FHB detection model was formulated, based on the optimal spectral feature combination of SBs, VIs, and WFs (SBs + VIs + WFs), using a support vector machine. Two commonly used data normalization algorithms were utilized before the construction of the model. The single WFs, and the spectral feature combination of optimal SBs and VIs (SBs + VIs), were respectively used to formulate models for comparison and testing. The results showed that the detection model based on the normalized SBs + VIs + WFs, using min–max normalization algorithm, achieved the highest R2 of 0.88 and the lowest RMSE of 2.68% among the three models. Our results suggest that UAV-based hyperspectral imaging technology is promising for the field-scale detection of wheat FHB. Combining traditional SBs and VIs with WFs can improve the detection accuracy of wheat FHB effectively

Combining Random Forest and XGBoost Methods in Detecting Early and Mid-Term Winter Wheat Stripe Rust Using Canopy Level Hyperspectral Measurements

Appropriate modeling methods and feature selection algorithms must be selected to improve the accuracy of early and mid-term remote sensing detection of wheat stripe rust. In the current study, we explored the effectiveness of the random forest (RF) algorithm combined with the extreme gradient boosting (XGboost) method for early and mid-term wheat stripe rust detection based on the vegetation indices extracted from canopy level hyperspectral measurements. Initially, 21 vegetation indices that were related to the early and mid-term winter wheat stripe rust were calculated on the basis of canopy level hyperspectral reflectance. Subsequently, the optimal vegetation index combination for disease detection was determined using correlation analysis (CA) combined with RF algorithms. Then, the disease severity detection model of early and mid-term winter wheat stripe rust was constructed using XGBoost method based on the optimal vegetation index combination. For the evaluation and comparison of the initial results, three commonly used classification methods, namely, RF, backpropagation neural network (BPNN), and support vector machine (SVM), were utilized. The vegetation index combinations determined by the single CA algorithm were also used to construct detection models. Compared with the detection models based on the vegetation index combination obtained using the single CA algorithm, the overall accuracy of the four detection models based on the optimal vegetation index combination based on CA combined with RF algorithms increased by 16.1% (XGBoost), 9.7% (RF), 8.1% (SVM), and 8.1% (BPNN). Among the eight models, the XGBoost detection model based on the optimal vegetation index combination using CA combined with RF algorithms, CA-RF-XGBoost, achieved the highest overall accuracy of 87.1% and the highest kappa coefficient of 0.798. Our results indicate that the RF combined with XGBoost can improve the detection accuracy of early and mid-term winter wheat stripe rust effectively at canopy scale

Identification of Key Modules, Hub Genes, and Noncoding RNAs in Chronic Rhinosinusitis with Nasal Polyps by Weighted Gene Coexpression Network Analysis

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a chronic inflammatory disease with relatively easy recurrence. However, the precise molecular mechanisms of this disease are poorly known. Based on gene sequencing data obtained from the Gene Expression Omnibus (GEO) database, we constructed coexpression networks by weighted gene coexpression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by the Database for Annotation, Visualization, and Integrated Discovery (DAVID). The core gene of pathogenesis, CRSwNP, was screened by protein-protein interaction data (PPI) from the HPRD database. Unsupervised clustering was applied to screen hub genes related to the phenotype of CRSwNP. Blue and turquoise modules were found to be most significantly related to the pathogenicity of CRSwNP. Functional enrichment analysis showed that cell proliferation in the blue modules, the apoptotic process in the turquoise module, and the cancer pathway in both modules were mostly significantly correlated with the development of CRSwNP. The noncoding RNAs (long noncoding RNA and microRNA) and the top 10 core genes in each module were found to be associated with the pathogenesis of CRSwNP. A total of nine hub genes were identified to be related to the CRSwNP phenotype. By qRT-PCR analysis, AKT1, CDH1, PIK3R1, CBL, LRP1, MALAT1, and XIST were proven to be associated with the pathogenesis of CRSwNP. AGR2, FAM3D, PIP, DSE, and TMC were identified to be related to the CRSwNP phenotype. Further exploration of these genes will reveal more important information about the mechanisms of CRSwNP