Oxalate as a potent promoter of kidney stone formation

Kidney stones are among the most prevalent urological diseases, with a high incidence and recurrence rate. Treating kidney stones has been greatly improved by the development of various minimally invasive techniques. Currently, stone treatment is relatively mature. However, most current treatment methods are limited to stones and cannot effectively reduce their incidence and recurrence. Therefore, preventing disease occurrence, development, and recurrence after treatment, has become an urgent issue. The etiology and pathogenesis of stone formation are key factors in resolving this issue. More than 80% of kidney stones are calcium oxalate stones. Several studies have studied the formation mechanism of stones from the metabolism of urinary calcium, but there are few studies on oxalate, which plays an equally important role in stone formation. Oxalate and calcium play equally important roles in calcium oxalate stones, whereas the metabolism and excretion disorders of oxalate play a crucial role in their occurrence. Therefore, starting from the relationship between renal calculi and oxalate metabolism, this work reviews the occurrence of renal calculi, oxalate absorption, metabolism, and excretion mechanisms, focusing on the key role of SLC26A6 in oxalate excretion and the regulatory mechanism of SLC26A6 in oxalate transport. This review provides some new clues for the mechanism of kidney stones from the perspective of oxalate to improve the understanding of the role of oxalate in the formation of kidney stones and to provide suggestions for reducing the incidence and recurrence rate of kidney stones

Critical roles of lncRNA-mediated autophagy in urologic malignancies

Urologic oncology is a significant public health concern on a global scale. Recent research indicates that long chain non-coding RNAs (lncRNAs) and autophagy play crucial roles in various cancers, including urologic malignancies. This article provides a summary of the latest research findings, suggesting that lncRNA-mediated autophagy could either suppress or promote tumors in prostate, kidney, and bladder cancers. The intricate network involving different lncRNAs, target genes, and mediated signaling pathways plays a crucial role in urological malignancies by modulating the autophagic process. Dysregulated expression of lncRNAs can disrupt autophagy, leading to tumorigenesis, progression, and enhanced resistance to therapy. Consequently, targeting particular lncRNAs that control autophagy could serve as a dependable diagnostic tool and a promising prognostic biomarker in urologic oncology, while also holding potential as an effective therapeutic approach

Novel Yersinia enterocolitica Prophages and a Comparative Analysis of Genomic Diversity

Yersinia enterocolitica is a major agent of foodborne diseases worldwide. Prophage plays an important role in the genetic evolution of the bacterial genome. Little is known about the genetic information about prophages in the genome of Y. enterocolitica, and no pathogenic Y. enterocolitica prophages have been described. In this study, we induced and described the genomes of six prophages from pathogenic Y. enterocolitica for the first time. Phylogenetic analysis based on whole genome sequencing revealed that these novel Yersinia phages are genetically distinct from the previously reported phages, showing considerable genetic diversity. Interestingly, the prophages induced from O:3 and O:9 Y. enterocolitica showed different genomic sequences and morphology but highly conserved among the same serotype strains, which classified into two diverse clusters. The three long-tailed Myoviridae prophages induced from serotype O:3 Y. enterocolitica were highly conserved, shared ≥99.99% identity and forming genotypic cluster A; the three Podoviridae prophages induced from the serotype O:9 strains formed cluster B, also shared more than 99.90% identity with one another. Cluster A was most closely related to O:5 non-pathogenic Y. enterocolitica prophage PY54 (61.72% identity). The genetic polymorphism of these two kinds of prophages and highly conserved among the same serotype strains, suggested a possible shared evolutionary past for these phages: originated from distinct ancestors, and entered pathogenic Y. enterocolitica as extrachromosomal genetic components during evolution when facing selective pressure. These results are critically important for further understanding of phage roles in host physiology and the pathology of disease

Early Second-Trimester Serum MiRNA Profiling Predicts Gestational Diabetes Mellitus

BACKGROUND: Gestational diabetes mellitus (GDM) is one type of diabetes that presents during pregnancy and significantly increases the risk of a number of adverse consequences for the fetus and mother. The microRNAs (miRNA) have recently been demonstrated to abundantly and stably exist in serum and to be potentially disease-specific. However, no reported study investigates the associations between serum miRNA and GDM. METHODOLOGY/PRINCIPAL FINDINGS: We systematically used the TaqMan Low Density Array followed by individual quantitative reverse transcription polymerase chain reaction assays to screen miRNAs in serum collected at 16-19 gestational weeks. The expression levels of three miRNAs (miR-132, miR-29a and miR-222) were significantly decreased in GDM women with respect to the controls in similar gestational weeks in our discovery evaluation and internal validation, and two miRNAs (miR-29a and miR-222) were also consistently validated in two-centric external validation sample sets. In addition, the knockdown of miR-29a could increase Insulin-induced gene 1 (Insig1) expression level and subsequently the level of Phosphoenolpyruvate Carboxy Kinase2 (PCK2) in HepG2 cell lines. CONCLUSIONS/SIGNIFICANCE: Serum miRNAs are differentially expressed between GDM women and controls and could be candidate biomarkers for predicting GDM. The utility of miR-29a, miR-222 and miR-132 as serum-based non-invasive biomarkers warrants further evaluation and optimization

Reliability analysis for a large and complex landslide in the three gorges reservoir area (China) based on incomplete information

The soil parameters for large, complex landslides are typically derived from incomplete information based on a small sample set due to budgetary constraints. This informational incompleteness results in large statistical uncertainty in landslide reliability analyses. In this article, the bootstrap technique is proposed to quantify the statistical uncertainties associated with a small sample set, and a practice-oriented reliability analysis is performed. The results suggest that the obtained reliability indices are characterized by a long tail, in which the worst-case scenario has a local extreme value and a small population. The statistical uncertainties are quantified and characterized by a confidence interval at a specified confidence level. The confidence interval of the reliability index and identification of the worst-case scenario enable engineers to make more informed decisions

Thermal Infrared Imagery Integrated with Terrestrial Laser Scanning and Particle Tracking Velocimetry for Characterization of Landslide Model Failure

A laboratory model test is an effective method for studying landslide risk mitigation. In this study, thermal infrared (TIR) imagery, a modern no-contact technique, was introduced and integrated with terrestrial laser scanning (TLS) and particle tracking velocimetry (PTV) to characterize the failure of a landslide model. The characteristics of the failure initiation, motion, and region of interest, including landslide volume, deformation, velocity, surface temperature changes, and anomalies, were detected using the integrated monitoring system. The laboratory test results indicate that the integrated monitoring system is expected to be useful for characterizing the failure of landslide models. The preliminary results of this study suggest that a change in the relative TIR signal (ΔTIR) can be a useful index for landslide detection, and a decrease in the average value of the temperature change ( Δ T I R ¯ ) can be selected as a precursor to landslide failure

Displacement Prediction of a Complex Landslide in the Three Gorges Reservoir Area (China) Using a Hybrid Computational Intelligence Approach

Displacement prediction of reservoir landslide remains inherently uncertain since a complete understanding of the complex nonlinear, dynamic landslide system is still lacking. An appropriate quantification of predictive uncertainties is a key underpinning of displacement prediction and mitigation of reservoir landslide. A density prediction, offering a full estimation of the probability density for future outputs, is promising for quantification of the uncertainty of landslide displacement. In the present study, a hybrid computational intelligence approach is proposed to build a density prediction model of landslide displacement and quantify the associated predictive uncertainties. The hybrid computational intelligence approach consists of two steps: first, the input variables are selected through copula analysis; second, kernel-based support vector machine quantile regression (KSVMQR) is employed to perform density prediction. The copula-KSVMQR approach is demonstrated through a complex landslide in the Three Gorges Reservoir Area (TGRA), China. The experimental study suggests that the copula-KSVMQR approach is capable of construction density prediction by providing full probability density distributions of the prediction with perfect performance. In addition, different types of predictions, including interval prediction and point prediction, can be derived from the obtained density predictions with excellent performance. The results show that the mean prediction interval widths of the proposed approach at ZG287 and ZG289 are 27.30 and 33.04, respectively, which are approximately 60 percent lower than that obtained using the traditional bootstrap-extreme learning machine-artificial neural network (Bootstrap-ELM-ANN). Moreover, the obtained point predictions show great consistency with the observations, with correlation coefficients of 0.9998. Given the satisfactory performance, the presented copula-KSVMQR approach shows a great ability to predict landslide displacement

Response Extremes of Floating Offshore Wind Turbine Based on Inverse Reliability and Environmental Contour Method

Floating structures are subject to complex marine conditions. To ensure their safety, reliability analysis needs to be conducted during the design phase. However, because of the complexity of traditional full long-term analysis, the environmental contour method (ECM) based on the inverse reliability method, which can combine accuracy and efficiency, is extensively used. Due to the unique environment in the South China Sea, the probabilistic characteristics of three-dimensional (3D) environmental parameters of wind, wave and current are investigated. The ECs of the target sea are established via the ECM based on both the inverse first-order reliability method (IFORM) and inverse second-order reliability method (ISORM). It is found that the sea state forecasted by ISORM is more extreme and may lead to a more conservative design than IFORM. Furthermore, the wind–wave–current combination coefficient matrixes developed using the 3D ECs are proposed for the design of FOWTs in the South China Sea. The validity and practicality of the contours and matrixes are tested by using a floating offshore wind turbine (FOWT) as a numerical example. Then, the short-term response of the structure under the combined wind, wave and current conditions is calculated, providing a theoretical reference for the design of FOWTs

Forecasting of Landslide Displacement Using a Probability-Scheme Combination Ensemble Prediction Technique

Data-driven models have been extensively employed in landslide displacement prediction. However, predictive uncertainty, which consists of input uncertainty, parameter uncertainty, and model uncertainty, is usually disregarded in deterministic data-driven modeling, and point estimates are separately presented. In this study, a probability-scheme combination ensemble prediction that employs quantile regression neural networks and kernel density estimation (QRNNs-KDE) is proposed for robust and accurate prediction and uncertainty quantification of landslide displacement. In the ensemble model, QRNNs serve as base learning algorithms to generate multiple base learners. Final ensemble prediction is obtained by integration of all base learners through a probability combination scheme based on KDE. The Fanjiaping landslide in the Three Gorges Reservoir area (TGRA) was selected as a case study to explore the performance of the ensemble prediction. Based on long-term (2006–2018) and near real-time monitoring data, a comprehensive analysis of the deformation characteristics was conducted for fully understanding the triggering factors. The experimental results indicate that the QRNNs-KDE approach can perform predictions with perfect performance and outperform the traditional backpropagation (BP), radial basis function (RBF), extreme learning machine (ELM), support vector machine (SVM) methods, bootstrap-extreme learning machine-artificial neural network (bootstrap-ELM-ANN), and Copula-kernel-based support vector machine quantile regression (Copula-KSVMQR). The proposed QRNNs-KDE approach has significant potential in medium-term to long-term horizon forecasting and quantification of uncertainty