GST-π was purified from human placenta and its antiserum was raised in rabbits. The antibody IgC was purified and degraded into Fab' fragment which was conjugated with horseradish peroxidase (HRP) using N-succini...GST-π was purified from human placenta and its antiserum was raised in rabbits. The antibody IgC was purified and degraded into Fab' fragment which was conjugated with horseradish peroxidase (HRP) using N-succinimidyl-4-(N-maleimido-methyl) cyclo-hexane-1-carboxylate (SMCC) as crosslinking reagent to produce Fab'-HRP conjugate. A sandwich ELISA was established for the microquantitative determination of GST-π. The sensitivity was 11 pg/tube, which was far more sensitive than the radioimmunoassay so far reported. Using this method, the serum GST-π of 41 cases normal adult was found to be 1.06±0.94 ng/ml. The upper limit of the normal value was 2.6 ng/ml. In 30 cases of primary hepatocarcinoma, the level of serum GST-π was 24.4± 17.4 ng/ml, which was 23 times higher than the normal average value (P<0.01). The positive rate was 90%. In contrast, serum GST-π in 25 cases of chronic hepatitis was determined to be 1.74±1.16 ng/ml, which was not significantly different from the normal value (P>0.05). The pseudo-positive rate was 12.0%.展开更多
Corona Virus Disease-2019(COVID-19)continues to spread rapidly in the world.It has dramatically affected daily lives,public health,and the world economy.This paper presents a segmentation and classification framework ...Corona Virus Disease-2019(COVID-19)continues to spread rapidly in the world.It has dramatically affected daily lives,public health,and the world economy.This paper presents a segmentation and classification framework of COVID-19 images based on deep learning.Firstly,the classification process is employed to discriminate between COVID-19,non-COVID,and pneumonia by Convolutional Neural Network(CNN).Then,the segmentation process is applied for COVID-19 and pneumonia CT images.Finally,the resulting segmented images are used to identify the infected region,whether COVID-19 or pneumonia.The proposed CNN consists of four Convolutional(Conv)layers,four batch normalization layers,and four Rectified Linear Units(ReLUs).The sizes of Conv layer used filters are 8,16,32,and 64.Four maxpooling layers are employed with a stride of 2 and a 2×2 window.The classification layer comprises a Fully-Connected(FC)layer and a soft-max activation function used to take the classification decision.A novel saliencybased region detection algorithm and an active contour segmentation strategy are applied to segment COVID-19 and pneumonia CT images.The acquired findings substantiate the efficacy of the proposed framework for helping the specialists in automated diagnosis applications.展开更多
Helicobacter pylori (H. pylori) neutrophil-activating protein (HP-NAP) was originally identified as a virulence factor of H. pylori for its ability to activate neutrophils to generate respiratory burst by releasing re...Helicobacter pylori (H. pylori) neutrophil-activating protein (HP-NAP) was originally identified as a virulence factor of H. pylori for its ability to activate neutrophils to generate respiratory burst by releasing reactive oxygen species. Later on, HP-NAP was also found to be involved in the protection of H. pylori from DNA damage, supporting the survival of H. pylori under oxidative stress. This protein is highly conserved and expressed by virtually all clinical isolates of H. pylori. The majority of patients infected with H. pylori produced antibodies specific for HP-NAP, suggesting its important role in immunity. In addition to acting as a pathogenic factor by activating the innate immunity through a wide range of human leukocytes, including neutrophils, monocytes, and mast cells, HP-NAP also mediates adaptive immunity through the induction of T helper cell type I responses. The pro-inflammatory and immunomodulatory properties of HP-NAP not only make it play an important role in disease pathogenesis but also make it a potential candidate for clinical use. Even though there is no convincing evidence to link HP-NAP to a disease outcome, recent findings supporting the pathogenic role of HP-NAP will be reviewed. In addition, the potential clinical applications of HP-NAP in vaccine development, clinical diagnosis, and drug development will be discussed.展开更多
文摘GST-π was purified from human placenta and its antiserum was raised in rabbits. The antibody IgC was purified and degraded into Fab' fragment which was conjugated with horseradish peroxidase (HRP) using N-succinimidyl-4-(N-maleimido-methyl) cyclo-hexane-1-carboxylate (SMCC) as crosslinking reagent to produce Fab'-HRP conjugate. A sandwich ELISA was established for the microquantitative determination of GST-π. The sensitivity was 11 pg/tube, which was far more sensitive than the radioimmunoassay so far reported. Using this method, the serum GST-π of 41 cases normal adult was found to be 1.06±0.94 ng/ml. The upper limit of the normal value was 2.6 ng/ml. In 30 cases of primary hepatocarcinoma, the level of serum GST-π was 24.4± 17.4 ng/ml, which was 23 times higher than the normal average value (P<0.01). The positive rate was 90%. In contrast, serum GST-π in 25 cases of chronic hepatitis was determined to be 1.74±1.16 ng/ml, which was not significantly different from the normal value (P>0.05). The pseudo-positive rate was 12.0%.
基金This research was funded by the Deanship of Scientific Research at Princess Nourah Bint Abdulrahman University through the Fast-track Research Funding Program.
文摘Corona Virus Disease-2019(COVID-19)continues to spread rapidly in the world.It has dramatically affected daily lives,public health,and the world economy.This paper presents a segmentation and classification framework of COVID-19 images based on deep learning.Firstly,the classification process is employed to discriminate between COVID-19,non-COVID,and pneumonia by Convolutional Neural Network(CNN).Then,the segmentation process is applied for COVID-19 and pneumonia CT images.Finally,the resulting segmented images are used to identify the infected region,whether COVID-19 or pneumonia.The proposed CNN consists of four Convolutional(Conv)layers,four batch normalization layers,and four Rectified Linear Units(ReLUs).The sizes of Conv layer used filters are 8,16,32,and 64.Four maxpooling layers are employed with a stride of 2 and a 2×2 window.The classification layer comprises a Fully-Connected(FC)layer and a soft-max activation function used to take the classification decision.A novel saliencybased region detection algorithm and an active contour segmentation strategy are applied to segment COVID-19 and pneumonia CT images.The acquired findings substantiate the efficacy of the proposed framework for helping the specialists in automated diagnosis applications.
基金Supported by National Science Council of Taiwan,No.NSC101-2311-B-007-007
文摘Helicobacter pylori (H. pylori) neutrophil-activating protein (HP-NAP) was originally identified as a virulence factor of H. pylori for its ability to activate neutrophils to generate respiratory burst by releasing reactive oxygen species. Later on, HP-NAP was also found to be involved in the protection of H. pylori from DNA damage, supporting the survival of H. pylori under oxidative stress. This protein is highly conserved and expressed by virtually all clinical isolates of H. pylori. The majority of patients infected with H. pylori produced antibodies specific for HP-NAP, suggesting its important role in immunity. In addition to acting as a pathogenic factor by activating the innate immunity through a wide range of human leukocytes, including neutrophils, monocytes, and mast cells, HP-NAP also mediates adaptive immunity through the induction of T helper cell type I responses. The pro-inflammatory and immunomodulatory properties of HP-NAP not only make it play an important role in disease pathogenesis but also make it a potential candidate for clinical use. Even though there is no convincing evidence to link HP-NAP to a disease outcome, recent findings supporting the pathogenic role of HP-NAP will be reviewed. In addition, the potential clinical applications of HP-NAP in vaccine development, clinical diagnosis, and drug development will be discussed.
