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Objective:To study the role of a novel brain-derived peptide hypoxic-ischemic brain damage associated peptide (HIBDAP) in regulating pyroptosis of oxygen-glucose deprived (OGD) microglia.Methods:The sequence of HIBDAP was coupled with the sequence of cell-penetrating peptide transactivator of transcription (TAT) to form TAT-HIBDAP. Fluorescein isothiocyanate (FITC) labeled TAT-HIBDAP was added to microglia cells and observed under fluorescence microscope. Microglia cells were treated with different concentrations of TAT-HIBDAP (1, 5, 10, 20 μmol/L) and then OGD process. Cell pyroptosis was analyzed using lactate dehydrogenase (LDH) assay. The concentration of TAT-HIBDAP with the most prominent inhibiting effects was determined and selected for subsequent experiments. The pyroptosis morphology of the control group, the OGD group and the HIBDAP group (5 μmol/L TAT-HIBDAP+OGD) was observed using transmission electron microscope. The mRNA and protein expression of NOD-like receptor family pyrin domain containing 3 (NLRP3) inflammasomes were examined using real-time quantitative PCR and Western Blot analysis.Results:Fluorescence microscope showed FITC-labeled TAT-HIBDAP could successfully enter microglia cells. Compared with the OGD group, low concentrations of TAT-HIBDAP (1, 5, 10 μmol/L) could significantly reduce microglia pyroptosis and the concentration of 5 μmol/L showed the most prominent effects. Compared with the control group, OGD group showed typical pyroptosis morphology and HIBDAP group showed significantly improved morphology. The mRNA and protein expression of NLRP3 inflammasomes in the OGD group were significantly higher than the control group and also the HIBDAP group.Conclusions:The novel brain-derived peptide HIBDAP may reduce the expression of NLRP3 inflammasomes and inhibit the pyroptosis of OGD microglia.
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Rectal cancer is one of the most common gastrointestinal malignancies in China. Accurate and reasonable assessment of the preoperative staging of rectal cancer can significantly enhance treatment outcomes and improve patient prognosis. Magnetic resonance imaging is the technique of choice for local staging of rectal cancer and has significant advantages in the diagnosis of rectal primary tumors (T) and peri-intestinal lymph nodes (N). In this review paper, the research ideas and progress of traditional radiomics and deep learning methods for preoperative TN staging prediction of rectal cancer were reviewed around multimodal magnetic resonance images, with the aim of providing new ideas for realizing fully automated TN staging algorithms for rectal cancer.
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Magnetic resonance imaging (MRI) is an important medical imaging method, whose major limitation is its long scan time due to the imaging mechanism, increasing patients' cost and waiting time for the examination. Currently, parallel imaging (PI) and compress sensing (CS) together with other reconstruction technologies have been proposed to accelerate image acquisition. However, the image quality of PI and CS depends on the image reconstruction algorithms, which is far from satisfying in respect to both the image quality and the reconstruction speed. In recent years, image reconstruction based on generative adversarial network (GAN) has become a research hotspot in the field of magnetic resonance imaging because of its excellent performance. In this review, we summarized the recent development of application of GAN in MRI reconstruction in both single- and multi-modality acceleration, hoping to provide a useful reference for interested researchers. In addition, we analyzed the characteristics and limitations of existing technologies and forecasted some development trends in this field.
Subject(s)
Humans , Acceleration , Algorithms , Magnetic Resonance Imaging , TechnologyABSTRACT
Accurate segmentation of ground glass nodule (GGN) is important in clinical. But it is a tough work to segment the GGN, as the GGN in the computed tomography images show blur boundary, irregular shape, and uneven intensity. This paper aims to segment GGN by proposing a fully convolutional residual network, i.e., residual network based on atrous spatial pyramid pooling structure and attention mechanism (ResAANet). The network uses atrous spatial pyramid pooling (ASPP) structure to expand the feature map receptive field and extract more sufficient features, and utilizes attention mechanism, residual connection, long skip connection to fully retain sensitive features, which is extracted by the convolutional layer. First, we employ 565 GGN provided by Shanghai Chest Hospital to train and validate ResAANet, so as to obtain a stable model. Then, two groups of data selected from clinical examinations (84 GGN) and lung image database consortium (LIDC) dataset (145 GGN) were employed to validate and evaluate the performance of the proposed method. Finally, we apply the best threshold method to remove false positive regions and obtain optimized results. The average dice similarity coefficient (DSC) of the proposed algorithm on the clinical dataset and LIDC dataset reached 83.46%, 83.26% respectively, the average Jaccard index (IoU) reached 72.39%, 71.56% respectively, and the speed of segmentation reached 0.1 seconds per image. Comparing with other reported methods, our new method could segment GGN accurately, quickly and robustly. It could provide doctors with important information such as nodule size or density, which assist doctors in subsequent diagnosis and treatment.
Subject(s)
Humans , Algorithms , China , Disease Progression , Multiple Pulmonary Nodules , Neural Networks, Computer , Tomography, X-Ray Computed/methodsABSTRACT
α-Amino acid ester acyltransferase (Aet) catalyzes the L-alanyl-L-glutamine forming reaction from L-alaine methylester hydrochloride and L-glutamine. In this study, the recombinant Escherichia coli saet-QC01 was used to express the α-amino acid acyltransferase, and its expression conditions were optimized. The recombinant protein was separated and purified by Ni-NTA affinity chromatography, and its enzymatic properties and catalytic applications were studied. The induction conditions suitable for enzyme production optimized were as follows: The temperature was 20 ℃, the induction stage (OD₆₀₀=2.0-2.5), IPTG concentration was 0.6 mmol/L, induction time was 12 h. The optimal reaction conditions of α-amino acid acyltransferase were 27 ℃, pH 8.5, it was most stable between pH 7.0 and 8.0 and relatively stable in an acidic environment, and low concentration of Co²⁺ or EDTA could promote the enzyme activity. Under optimal reaction conditions, 600 mmol/L of L-alaine methylester hydrochloride and 480 mmol/L of L-glutamine, the yield of L-alanyl-L-glutamine reached 78.2 g/L and productivity of 1.955 g/L/min, the conversion rate reached 75.0%. α-Amino acid ester acyltransferase has excellent acid-basei resistance, high catalytic efficiency. These characteristics suggest its application prospects in the industrial production.
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Objective@#To establish the method for determination of titanium dioxide in the air of workplace by inductivehy coupled plasma optical emission spectrometry (ICP-OES) .@*Methods@#The titanium dioxide was collected by filter membrane and then digested by microwave digestion apparatus in the mixed solvents (HNO3∶HF∶H2O=4∶1∶1) , dilutedto 25 ml and detected by ICP-OES.@*Results@#The sampling efficiency was higher than 95%; the linearity of ICP-OES was good at the range of 10-500 μg/ml, the minimum quantitation concentration was 0.72 mg/m3 (as collecting 150 L air sample) , the maximum quantitation concentration was 21.7 mg/m3 (as collecting 960 L air sample) , the recovery was ranged from 99.0%-102.0%, the RSD of intra- and inter-batch precision were 0.5%-3.2% and 1.7%-3.5%, respectively.@*Conclusion@#The sampling method and determination method meet the requirements of guide for establishing occupational health standards-part 4: determinatin methods of air chemicals in workplace (GBZ/T 210.4-2008) , and areapplys to the collection and determination of TiO2 in the air of workplace.