Function of PD-L1 in antitumor immunity of glioma cells

Lou, Yongli; Shi, Jin; Guo, Dewei; Qureshi, Ahmad Kaleem; Song, Laijun

doi:10.1016/j.sjbs.2015.06.025

Cited by 49 publications

(30 citation statements)

References 25 publications

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“…In the cosmetics industry, it is currently being used as facial masks or ointments and also in food products, accompanied with other components to treat skin disease as well as help in activation of blood circulation [3]. Presently, the replacement of synthetic antioxidant with naturally occurring antioxidant is increasing due to the side effects associated with synthetic antioxidant such as increased risk of cancer [4][5][6][7][8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Physicochemical and DPPH� Cleaning Activity of Ultrasonic Assisted Extraction of Polysaccharide from Leonurus japonicus

Yang¹,

Mercy²,

Efehi³

et al. 2020

Rev. Chim.

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Leonurus japonicus is rich in bioactive compounds especially polysaccharide. Single factors test and response surface methodology were used to study the optimum conditions for ultrasonic - assisted extraction of polysaccharide from L. japonicus, and its physiochemical and DPPH scavenging activities also were evaluated. The optimal conditions as underpinned by this paper is as follow: extraction temperature of 70 �C, extraction time of 50 min and extraction power of 210W. The polysaccharide of L. japonicus was shown to be a non-reducing sugar and to exhibit DPPH scavenging activity to an extent. The polysaccharide structures were studied using SEM, AFM, and FT-IR. It showed L. japonicus polysaccharide had been having more than one chains which were branched and entangled with each other. and existed a broad band wavelength of 2925.64, 1635.06, 1539.7, 1046.73, and 477.86 cm-1. The study indicated that L. japonicus may have a beneficial use to the medical and food industries.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Physicochemical and DPPH� Cleaning Activity of Ultrasonic Assisted Extraction of Polysaccharide from Leonurus japonicus

Yang¹,

Mercy²,

Efehi³

et al. 2020

Rev. Chim.

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show abstract

“…In the observation group, the expression level of CRP after treatment was significantly lower than that before treatment (P< 0.05), which may be because the antibacterial effect of antibiotics reduced patients' inflammations, weakened the infiltration of inflammatory cells and reduced the expression level of CRP. This indicates that CRP may be a potential diagnostic indicator for the efficacy of severe pneumonia (29)(30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40).…”

Section: Discussionmentioning

confidence: 93%

Expression levels of blood platelet and C-reactive protein in patients with severely pneumonic and their predictive values for efficacy

Ren²,

Guo

et al. 2020

Cell Mol Biol (Noisy-le-grand)

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This paper aims to detect the expression levels of blood platelet (PLT) and C-reactive protein (CRP) in severely pneumonic patients and analyze their correlation. For this purpose, eighty-one severely pneumonic patients were retrospectively selected as an observation group and 106 healthy people as a control group. Pretreatment and post-treatment expression levels of PLT and CRP, their predictive values for efficacy, and correlation of PLT, CRP, and PSI scores in observation group after treatment were analyzed. Before treatment, the expression level of PLT in the observation group was higher than the control group (P< 0.05). In the observation group, the expression level of PLT after treatment was significantly lower than that before treatment (P< 0.05). Before treatment, the expression level of CRP in the observation group was higher than the control group (P< 0.05). In the observation group 1) the pretreatment PLT expression level was higher than that in the control group; 2) the post-treatment PLT expression level was significantly lower than that in the pretreatment one; 3) the pretreatment CRP expression level was higher than that in the control group; and 4) the post-treatment CRP expression level was significantly lower than the pretreatment one (All P-values< 0.05). Based upon the efficacy, the observation group was divided into an effective group and an invalid group. The post-treatment expression levels of PLT and CRP in the effective group were lower than those in the invalid group (P< 0.05). Based upon the ROC curve, the area under curves (AUC) of PLT, CRP, and joint detection were 0.843, 0.864, and 0.886, respectively. When the cut-off point was > 0.579, the best specificity and sensitivity were 98.44 and 70.59%, respectively. According to the Pearson test, positive correlations existed between PLT and CRP, between PLT and PSI scores, and between CRP and PSI scores. In conclusion, the expression levels of PLT and CRP in severely pneumonic patients might be used to evaluate the efficacy and conducive to detection of the disease, which have high application values in clinic.

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“…However, the confusion matrix can not see the classification accuracy at a glance. Therefore, a variety of classification accuracy indicators are derived from the confusion matrix, among which the overall classification accuracy (OA) and kappa coefficient (Kappa) are most widely used [39], [40].…”

Section: B Results and Analysismentioning

confidence: 99%

Research on Medical Image Classification Based on Machine Learning

Tang

2020

IEEE Access

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In this paper, we propose a new method for CT pathological image analysis of brain and chest to extract image features and classify images. Because the deep neural network needs a large number of labeled samples to complete the training, and the cost of medical image labeling is very high, the training samples needed to train the deep neural network are insufficient. In this paper, a semi supervised learning based image classification method is proposed, which uses a small amount of labeled pathological image data to train the network model, and then integrates the features extracted by the network to classify the image. The results show that the classification effect of the neural network is better than convolution neural network and other traditional image classification models. To some extent, it can reduce the dependence of neural network on a large number of training samples, and effectively reduce the over fitting phenomenon of the network. Through the analysis of the overall classification accuracy and kappa coefficient of different classification methods under different sample numbers, it is found that the overall classification accuracy and kappa coefficient are increasing with the increasing number of training samples. Especially in the case of a small number of training samples, compared with other deep neural networks and traditional classification methods, the classification accuracy of the counter neural network is about 10% higher than that of other neural networks and traditional classification methods, and the advantages are more obvious. INDEX TERMS Generative adversarial network, deep learning, feature extraction, image classification.

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Function of PD-L1 in antitumor immunity of glioma cells

Cited by 49 publications

References 25 publications

Evaluation of Physicochemical and DPPH� Cleaning Activity of Ultrasonic Assisted Extraction of Polysaccharide from Leonurus japonicus

Evaluation of Physicochemical and DPPH� Cleaning Activity of Ultrasonic Assisted Extraction of Polysaccharide from Leonurus japonicus

Expression levels of blood platelet and C-reactive protein in patients with severely pneumonic and their predictive values for efficacy

Research on Medical Image Classification Based on Machine Learning

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