Glycyrrhizin inhibits the invasion and metastasis of breast cancer cells via upregulation of expressions of miR-200c and e-cadherin

Purpose: To determine the inhibitory effect of glycyrrhizin (GLA) on cell invasion and metastasis in mammary carcinoma cells, and the mechanisms of actions involved.Methods: The effect of GLA at different concentrations on proliferation of breast cancer MDA-MB-231 and BT549 cells was assayed by MTT method. Transwell assay was used to determine the effect of GLA at different concentrations on invasiveness and metastasis of breast cancer MDA-MB-231 and BT549 cells. The influence of LGA on expressions of microRNA-200c and miR-200c was assayed by reverse transcriptase-polymerase chain reaction (RT-PCR).Results: There was no statistically significant difference in cell proliferation amongst cells treated with 5 and 20 μM GLA and untreated breast cancer cells. However, the proliferation of cells treated with 40 μM GLA was significantly reduced (p < 0.05). In the cell invasion and migration experiments, cell population transferred to the base of Transwell chamber in the two cell lines treated with GLA was markedly decreased, relative to cells without GLA treatment, while the number of cells decreased with increase in GLA concentration (p < 0.05). Results from image-pro-plus analysis revealed that the population of cells quantitatively crossing the Transwell compartment membrane decreased with increase in GLA concentration (p < 0.05). The expression of e-cadherin was increased by GLA treatment in a concentration-dependent manner. Moreover, GLA treatment led to significant changes in amounts of miR-200s a, b and c, with changes in miR-200c being the most significant (p < 0.05).Conclusion: GLA suppresses the invasiveness and metastasis of breast cancer MDA-MB-231 and BT549 cells via upregulation of the expressions of miR-200c and e-cadherin. These findings provide a theoretical basis for the development of new breast cancer drugs. Keywords: Glycyrrhiza, GLA, miR-200c, E-cadherin, Inhibition, Breast cancer cells, Invasion, Metastasi

Ginsenoside induces cell death in breast cancer cells via ROS/PI3K/Akt signaling pathway

Purpose: To study the influence of ginsenoside on breast carcinoma, and the mechanism of action involved.Methods: Different concentrations of ginsenoside were used to treat MCF-7 breast cancer cell line. Cell viability was measured by MTT assay, while protein expressions of p-Akt and p-PI3K were determined using Western blotting. The concentrations of reactive oxidative reactants and reactive oxygen species (ROS) were assessed using fluorescence immunoassay and immunofluorescence assay. The mechanism of action involved in ginsenoside-mediated apoptosis was determined based on ROS/PI3K/Akt signaling pathway.Results: There was no change in the inhibition of MCF-7 cell proliferation in control cells with time (p > 0.05). However, inhibition of MCF-7 cell proliferation in ginsenoside group was significantly higher than that in the control group (p < 0.05); furthermore, it increased with time and ginsenoside concentration. Apoptosis was markedly and concentration-dependently higher in ginsenoside-treated MCF-7 cells than in controls (p > 0.05). There were lower protein levels of p-PI3K and p-Akt in ginsenoside-exposed MCF-7 cells than in control group; the protein expressions  decreased with increase in ginsenoside concentration (p < 0.05). The expressions of ROS in ginsenoside-treated MCF-7 cells declined, relative to the untreated group; in addition, the expressions decreased with increase in ginsenoside concentration (p < 0.05).Conclusion: Ginsenoside suppresses proliferation of MCF-7 cell line, and exerts apoptotic effect on the cells via inhibition of the ROS/PI3K/Akt signal pathway. This provides a new approach to treat breast cancer. Keywords: Breast cancer cells, Ginsenoside, Apoptosis, ROS/PI3K/Akt signaling pathwa

Gray Matter Atrophy in Parkinson’s Disease and the Parkinsonian Variant of Multiple System Atrophy: A Combined ROI- and Voxel-Based Morphometric Study

OBJECTIVES: Parkinson’s disease (PD) and the parkinsonian variant of multiple system atrophy (MSA-P) are distinct neurodegenerative disorders that share similar clinical features of parkinsonism. The morphological alterations of these diseases have yet to be understood. The purpose of this study was to evaluate gray matter atrophy in PD and MSA-P using regions of interest (ROI)-based measurements and voxel-based morphometry (VBM). METHODS: We studied 41 patients with PD, 20 patients with MSA-P, and 39 controls matched for age, sex, and handedness using an improved T1-weighted sequence that eased gray matter segmentation. The gray matter volumes were measured using ROI and VBM. RESULTS: ROI volumetric measurements showed significantly reduced bilateral putamen volumes in MSA-P patients compared with those in PD patients and controls (po0.05), and the volumes of the bilateral caudate nucleus were significantly reduced in both MSA-P and PD patients compared with those in the controls (po0.05). VBM analysis revealed multifocal cortical and subcortical atrophy in both MSA-P and PD patients, and the volumes of the cerebellum and temporal lobes were remarkably reduced in MSA-P patients compared with the volumes in PD patients (po0.05). CONCLUSIONS: Both PD and MSA-P are associated with gray matter atrophy, which mainly involves the bilateral putamen, caudate nucleus, cerebellum, and temporal lobes. ROI and VBM can be used to identify these morphological alterations, and VBM is more sensitive and repeatable and less time-consuming, which may have potential diagnostic value

Distinct miRNAs associated with various clinical presentations of SARS-CoV-2 infection.

MicroRNAs (miRNAs) have been shown to play important roles in viral infections, but their associations with SARS-CoV-2 infection remain poorly understood. Here, we detected 85 differentially expressed miRNAs (DE-miRNAs) from 2,336 known and 361 novel miRNAs that were identified in 233 plasma samples from 61 healthy controls and 116 patients with COVID-19 using the high-throughput sequencing and computational analysis. These DE-miRNAs were associated with SASR-CoV-2 infection, disease severity, and viral persistence in the patients with COVID-19, respectively. Gene ontology and KEGG pathway analyses of the DE-miRNAs revealed their connections to viral infections, immune responses, and lung diseases. Finally, we established a machine learning model using the DE-miRNAs between various groups for classification of COVID-19 cases with different clinical presentations. Our findings may help understand the contribution of miRNAs to the pathogenesis of COVID-19 and identify potential biomarkers and molecular targets for diagnosis and treatment of SARS-CoV-2 infection

Combination of Decitabine and a Modified Regimen of Cisplatin, Cytarabine and Dexamethasone: A Potential Salvage Regimen for Relapsed or Refractory Diffuse Large B-Cell Lymphoma After Second-Line Treatment Failure

ObjectiveThe prognosis for patients with relapsed or refractory diffuse large B-cell lymphoma (R/R-DLBCL) after second-line treatment failure is extremely poor. This study prospectively observed the efficacy and safety of decitabine with a modified cisplatin, cytarabine, and dexamethasone (DHAP) regimen in R/R-DLBCL patients who failed second-line treatment.MethodsTwenty-one R/R-DLBCL patients were enrolled and treated with decitabine and a modified DHAP regimen. The primary endpoints were overall response rate (ORR) and safety. The secondary endpoints were progression-free survival (PFS) and overall survival (OS).ResultsORR reached 50% (complete response rate, 35%), five patients (25%) had stable disease (SD) with disease control rate (DCR) of 75%. Subgroup analysis revealed patients over fifty years old had a higher complete response rate compared to younger patients (P = 0.005), and relapsed patients had a better complete response rate than refractory patients (P = 0.031). Median PFS was 7 months (95% confidence interval, 5.1-8.9 months). Median OS was not achieved. One-year OS was 59.0% (95% CI, 35.5%-82.5%), and two-year OS was 51.6% (95% confidence interval, 26.9%-76.3%). The main adverse events (AEs) were grade 3/4 hematologic toxicities such as neutropenia (90%), anemia (50%), and thrombocytopenia (70%). Other main non-hematologic AEs were grade 1/2 nausea/vomiting (40%) and infection (50%). No renal toxicity or treatment-related death occurred.ConclusionDecitabine with a modified DHAP regimen can improve the treatment response and prognosis of R/R-DLBCL patients with good tolerance to AEs, suggesting this regimen has potential as a possible new treatment option for R/R-DLBCL patients after second-line treatment failure.Clinical Trial RegistrationClinicalTrials.gov, identifier: NCT03579082

Online Inertial Machine Learning for Sensor Array Long-Term Drift Compensation

The sensor drift problem is objective and inevitable, and drift compensation has essential research significance. For long-term drift, we propose a data preprocessing method, which is different from conventional research methods, and a machine learning framework that supports online self-training and data analysis without additional sensor production costs. The data preprocessing method proposed can effectively solve the problems of sign error, decimal point error, and outliers in data samples. The framework, which we call inertial machine learning, takes advantage of the recent inertia of high classification accuracy to extend the reliability of sensors. We establish a reasonable memory and forgetting mechanism for the framework, and the choice of base classifier is not limited. In this paper, we use a support vector machine as the base classifier and use the gas sensor array drift dataset in the UCI machine learning repository for experiments. By analyzing the experimental results, the classification accuracy is greatly improved, the effective time of the sensor array is extended by 4–10 months, and the time of single response and model adjustment is less than 300 ms, which is well in line with the actual application scenarios. The research ideas and results in this paper have a certain reference value for the research in related fields

Extracting Objects’ Spatial–Temporal Information Based on Surveillance Videos and the Digital Surface Model

Surveillance systems focus on the image itself, mainly from the perspective of computer vision, which lacks integration with geographic information. It is difficult to obtain the location, size, and other spatial information of moving objects from surveillance systems, which lack any ability to couple with the geographical environment. To overcome such limitations, we propose a fusion framework of 3D geographic information and moving objects in surveillance video, which provides ideas for related research. We propose a general framework that can extract objects’ spatial–temporal information and visualize object trajectories in a 3D model. The framework does not rely on specific algorithms for determining the camera model, object extraction, or the mapping model. In our experiment, we used the Zhang Zhengyou calibration method and the EPNP method to determine the camera model, YOLOv5 and deep SORT to extract objects from a video, and an imaging ray intersection with the digital surface model to locate objects in the 3D geographical scene. The experimental results show that when the bounding box can thoroughly outline the entire object, the maximum error and root mean square error of the planar position are within 31 cm and 10 cm, respectively, and within 10 cm and 3 cm, respectively, in elevation. The errors of the average width and height of moving objects are within 5 cm and 2 cm, respectively, which is consistent with reality. To our knowledge, we first proposed the general fusion framework. This paper offers a solution to integrate 3D geographic information and surveillance video, which will not only provide a spatial perspective for intelligent video analysis, but also provide a new approach for the multi-dimensional expression of geographic information, object statistics, and object measurement