Minimal residual disease (MRD) detection in solid tumors using circulating tumor DNA: a systematic review

Minimal residual disease (MRD) refers to a very small number of residual tumor cells in the body during or after treatment, representing the persistence of the tumor and the possibility of clinical progress. Circulating tumor DNA (ctDNA) is a DNA fragment actively secreted by tumor cells or released into the circulatory system during the process of apoptosis or necrosis of tumor cells, which emerging as a non-invasive biomarker to dynamically monitor the therapeutic effect and prediction of recurrence. The feasibility of ctDNA as MRD detection and the revolution in ctDNA-based liquid biopsies provides a potential method for cancer monitoring. In this review, we summarized the main methods of ctDNA detection (PCR-based Sequencing and Next-Generation Sequencing) and their advantages and disadvantages. Additionally, we reviewed the significance of ctDNA analysis to guide the adjuvant therapy and predict the relapse of lung, breast and colon cancer et al. Finally, there are still many challenges of MRD detection, such as lack of standardization, false-negatives or false-positives results make misleading, and the requirement of validation using large independent cohorts to improve clinical outcomes

Aberrantly hydroxymethylated differentially expressed genes and the associated protein pathways in osteoarthritis

Background The elderly population is at risk of osteoarthritis (OA), a common, multifactorial, degenerative joint disease. Environmental, genetic, and epigenetic (such as DNA hydroxymethylation) factors may be involved in the etiology, development, and pathogenesis of OA. Here, comprehensive bioinformatic analyses were used to identify aberrantly hydroxymethylated differentially expressed genes and pathways in osteoarthritis to determine the underlying molecular mechanisms of osteoarthritis and susceptibility-related genes for osteoarthritis inheritance. Methods Gene expression microarray data, mRNA expression profile data, and a whole genome 5hmC dataset were obtained from the Gene Expression Omnibus repository. Differentially expressed genes with abnormal hydroxymethylation were identified by MATCH function. Gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the genes differentially expressed in OA were performed using Metascape and the KOBAS online tool, respectively. The protein–protein interaction network was built using STRING and visualized in Cytoscape, and the modular analysis of the network was performed using the Molecular Complex Detection app. Results In total, 104 hyperhydroxymethylated highly expressed genes and 14 hypohydroxymethylated genes with low expression were identified. Gene ontology analyses indicated that the biological functions of hyperhydroxymethylated highly expressed genes included skeletal system development, ossification, and bone development; KEGG pathway analysis showed enrichment in protein digestion and absorption, extracellular matrix–receptor interaction, and focal adhesion. The top 10 hub genes in the protein–protein interaction network were COL1A1, COL1A2, COL2A1, COL3A1, COL5A1, COL5A2, COL6A1, COL8A1, COL11A1, and COL24A1. All the aforementioned results are consistent with changes observed in OA. Conclusion After comprehensive bioinformatics analysis, we found aberrantly hydroxymethylated differentially expressed genes and pathways in OA. The top 10 hub genes may be useful hydroxymethylation analysis biomarkers to provide more accurate OA diagnoses and target genes for treatment of OA

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Dynamic bidding analysis in power market based on the supply function

AbstractThis paper presents a dynamic bidding model of the power market based on the Nash equilibrium and a supply function. The new model is composed of different dynamic systems and semismooth equations by means of the nonlinear complementarity method. Comparing with those existing bidding models, the remarkable characteristic of the new model is twofold: First, it adopts a dynamic bid so that the bidding limit point is the Nash equilibrium point of the market; Second, it considers the system requirement and the market property such as involving the transmission constraints in the network, and using a supply function which is suitable for the oligopolistic competitive power market. All of these imply that the new model is very close to the practical power market. The computation of the dynamic model is discussed by using the semismooth theory. A numerical simulation is presented to test the model behaviors in the uncogestion and the congestion cases, respectively. The numerical tests include the computing behavior of the dynamic model to reach Nash equilibrium points, the influence of the adjusted parameters and the system parameters to the Nash equilibrium, the local stability of the model, and the comparison of simulation effect between the proposed model and the Cournot model. The simulations show that the new bidding model is valid

Using Conditional Generative Adversarial 3-D Convolutional Neural Network for Precise Radar Extrapolation

Radar echo extrapolation is a basic but essential task in meteorological services. It could provide radar echo prediction results with high spatiotemporal resolution in a computationally efficient way, and effectively enhance the operational system's forecasting capability for meteorological hazards. Traditional methods perform extrapolation by estimating echo motions between contiguous radar data. This strategy is difficult to characterize complex nonlinear meteorological processes effectively, and it is difficult to benefit from large historical data. Recently, machine learning (ML) models have been used for radar echo extrapolation. These methods have effectively improved extrapolation quality in a data-driven way and from the statistical perspective. Although the ML-based methods show excellent performance, they usually produce blurry extrapolations. This leads to underestimating radar echo intensity and making echo lack small-scale details. Moreover, it makes models difficult to predict severe convective hazards. To solve this problem, a two-stage extrapolation model based on 3-D convolutional neural network and conditional generative adversarial network is proposed. These two models form the “pre-extrapolation” and “postprocessing” paradigm. The pre-extrapolation model is trained in the traditional way and performs rough extrapolation. The postprocessing model uses the pre-extrapolation result as input and is trained with the adversarial strategy. It could correct the echo intensity and increase the echo's details. In the experiment, our model could provide more precise radar echo extrapolations than other methods, especially for intense echoes and convective systems, in the data of North China from 2015 to 2016

Assessment of Wetland Ecosystem Health in the Yangtze and Amazon River Basins

As “kidneys of the earth”, wetlands play an important role in ameliorating weather conditions, flood storage, and the control and reduction of environmental pollution. With the development of local economies, the wetlands in both the Amazon and Yangtze River Basins have been affected and threatened by human activities, such as urban expansion, reclamation of land from lakes, land degradation, and large-scale agricultural development. It is necessary and important to develop a wetland ecosystem health evaluation model and to quantitatively evaluate the wetland ecosystem health in these two basins. In this paper, GlobeLand30 land cover maps and socio-economic and climate data from 2000 and 2010 were adopted to assess the wetland ecosystem health of the Yangtze and Amazon River Basins on the basis of a pressure-state-response (PSR) model. A total of 13 indicators were selected to build the wetland health assessment system. Weights of these indicators and PSR model components, as well as normalized wetland health scores, were assigned and calculated based on the analytic hierarchy process method. The results showed that from 2000 to 2010, the value of the mean wetland ecosystem health index in the Yangtze River Basin decreased from 0.482 to 0.481, while it increased from 0.582 to 0.593 in the Amazon River Basin. This indicated that the average status of wetland ecosystem health in the Amazon River Basin is better than that in the Yangtze River Basin, and that wetland health improved over time in the Amazon River Basin but worsened in the Yangtze River Basin

Prediction of Protein Subcellular Localization Based on Fusion of Multi-view Features

The prediction of protein subcellular localization is critical for inferring protein functions, gene regulations and protein-protein interactions. With the advances of high-throughput sequencing technologies and proteomic methods, the protein sequences of numerous yeasts have become publicly available, which enables us to computationally predict yeast protein subcellular localization. However, widely-used protein sequence representation techniques, such as amino acid composition and the Chou’s pseudo amino acid composition (PseAAC), are difficult in extracting adequate information about the interactions between residues and position distribution of each residue. Therefore, it is still urgent to develop novel sequence representations. In this study, we have presented two novel protein sequence representation techniques including Generalized Chaos Game Representation (GCGR) based on the frequency and distributions of the residues in the protein primary sequence, and novel statistics and information theory (NSI) reflecting local position information of the sequence. In the GCGR + NSI representation, a protein primary sequence is simply represented by a 5-dimensional feature vector, while other popular methods like PseAAC and dipeptide adopt features of more than hundreds of dimensions. In practice, the feature representation is highly efficient in predicting protein subcellular localization. Even without using machine learning-based classifiers, a simple model based on the feature vector can achieve prediction accuracies of 0.8825 and 0.7736 respectively for the CL317 and ZW225 datasets. To further evaluate the effectiveness of the proposed encoding schemes, we introduce a multi-view features-based method to combine the two above-mentioned features with other well-known features including PseAAC and dipeptide composition, and use support vector machine as the classifier to predict protein subcellular localization. This novel model achieves prediction accuracies of 0.927 and 0.871 respectively for the CL317 and ZW225 datasets, better than other existing methods in the jackknife tests. The results suggest that the GCGR and NSI features are useful complements to popular protein sequence representations in predicting yeast protein subcellular localization. Finally, we validate a few newly predicted protein subcellular localizations by evidences from some published articles in authority journals and books

Ursolic Acid Enhances the Sensitivity of MCF-7 and MDA-MB-231 Cells to Epirubicin by Modulating the Autophagy Pathway

Breast cancer is the leading cause of cancer death among women in the world, and its morbidity and mortality are increasing year by year. Epirubicin (EPI) is a commonly used drug for the treatment of breast cancer but unfortunately can cause cardiac toxicity in patients because of dose accumulation. Therefore, there is an urgent need for new therapies to enhance the sensitivity of breast cancer cells to EPI. In this study, we found ursolic acid (UA) can significantly improve the drug sensitivity of human breast cancer MCF-7/MDA-MB-231 cells to EPI. Next, we observed that the co-treatment of UA and EPI can up-regulate the expression of autophagy-related proteins Beclin-1, LC3-II/LC3-I, Atg5, and Atg7, and decrease the expression levels of PI3K and AKT, which indicates that the potential mechanism should be carried out by the regulating class III PI3K(VPS34)/Beclin-1 pathway and PI3K/AKT/mTOR pathway. Furthermore, we found the autophagy inhibitor 3-methyladenine (3-MA) could significantly reverse the inhibitory effect of co-treatment of UA and EPI on MCF-7 and MDA-MB-231 cells. These findings indicate that UA can dramatically enhance the sensitivity of MCF-7 and MDA-MB-231 cells to EPI by modulating the autophagy pathway. Our study may provide a new therapeutic strategy for combination therapy

Knee Joint Biomechanics in Physiological Conditions and How Pathologies Can Affect It: A Systematic Review

The knee joint, as the main lower limb motor joint, is the most vulnerable and susceptible joint. The knee injuries considerably impact the normal living ability and mental health of patients. Understanding the biomechanics of a normal and diseased knee joint is in urgent need for designing knee assistive devices and optimizing a rehabilitation exercise program. In this paper, we systematically searched electronic databases (from 2000 to November 2019) including ScienceDirect, Web of Science, PubMed, Google Scholar, and IEEE/IET Electronic Library for potentially relevant articles. After duplicates were removed and inclusion criteria applied to the titles, abstracts, and full text, 138 articles remained for review. The selected articles were divided into two groups to be analyzed. Firstly, the real movement of a normal knee joint and the normal knee biomechanics of four kinds of daily motions in the sagittal and coronal planes, which include normal walking, running, stair climbing, and sit-to-stand, were discussed and analyzed. Secondly, an overview of the current knowledge on the movement biomechanical effects of common knee musculoskeletal disorders and knee neurological disorders were provided. Finally, a discussion of the existing problems in the current studies and some recommendation for future research were presented. In general, this review reveals that there is no clear assessment about the biomechanics of normal and diseased knee joints at the current state of the art. The biomechanics properties could be significantly affected by knee musculoskeletal or neurological disorders. Deeper understanding of the biomechanics of the normal and diseased knee joint will still be an urgent need in the future

1,25-Dihydroxyvitamin D3 protects obese rats from metabolic syndrome via promoting regulatory T cell-mediated resolution of inflammation

Vitamin D3 has been found to produce therapeutic effects on obesity-associated insulin resistance and dyslipidemia through its potent anti-inflammatory activity, but the precise immunomodulatory mechanism remains poorly understood. In the present study we found that 1,25-dihydroxyvitamin D3 [1,25(OH)2D3], the biologically active form of vitamin D3, significantly attenuated monosodium glutamate (MSG)-induced obesity and insulin resistance as indicated by body weight reduction, oral glucose tolerance improvement, and a glucose infusion rate increase as detected with hyperinsulinemic-euglycemic clamp. Moreover, 1,25(OH)2D3 not only restored pancreatic islet functions but also improved lipid metabolism in insulin-targeted tissues. The protective effects of 1,25(OH)2D3 on glycolipid metabolism were attributed to its ability to inhibit an obesity-activated inflammatory response in insulin secretory and targeted tissues, as indicated by reduced infiltration of macrophages in pancreas islets and adipose tissue while enhancing the expression of Tgf-β1 in liver tissue, which was accompanied by increased infiltration of Treg cells in immune organs such as spleen and lymph node as well as in insulin-targeted tissues such as liver, adipose, and muscle. Together, our findings suggest that 1,25(OH)2D3 serves as a beneficial immunomodulator for the prevention and treatment of obesity or metabolic syndrome through its anti-inflammatory effects. KEY WORDS: Insulin resistance, Dyslipidemia, MSG-obese rat, Treg cell, Vitamin D3, 1,25-Dihydroxyvitamin D