Alantolactone exerts anti-proliferative and apoptotic effects on BGC823 and SGC7901 cells via activation of p38MAPK and inhibition of NF-κB signaling pathway

Purpose: To investigate the anti-proliferative and apoptotic influences of alantolactone on gastric carcinoma (GC) cell lines, and the mechanism(s) involved. Methods: Human gastric cancer cell line (BGC823) and gastric adenocarcinoma lymph node metastasis cell line (SGC7901) were maintained in Ham’s F12 medium supplemented with 10 % heatinactivated fetal bovine serum (FBS). In each group of cancer cell line, 5 groups of cells were used: control and four alantolactone groups which were treated with increasing concentrations of alantolactone (5 - 30 μM) for varying periods. Proliferation was determined using MTT assay, while realtime quantitative polymerase chain reaction (qRT-PCR) was used to assay the expressions of apoptosis- and metastasis-related genes. The expressions of p38MAPK and nuclear transcription factor-κB (NF-κB) in BGC823 and SGC7901 cells were measured with Western blotting. Results: Phosphorylated protein (p-p38 protein) expression was significantly higher in both groups of GC cells, relative to control (p < 0.05). The expressions of NF-κB in plasma protein were markedly higher in both groups of GC cells than in control group, but the corresponding expressions in nuclear protein were significantly lower in both groups of GC cells, relative to control (p < 0.05). Conclusion: Alantolactone exerts anti-proliferative and apoptotic effects on BGC823 and SGC7901 cells via mechanisms involving activation of the p38MAPK, and inhibition of the NF-κB signaling pathways. Thus, alantolactone may be a new and effective anti-gastric cancer drug

Improvement of CT Target Scanning Quality for Pulmonary Nodules by PDCA Management Method

High CT image quality is an important guarantee for doctors to correctly diagnose pulmonary nodules. The aim of this study was to explore the application value of PDCA management method in improving the quality of CT target scanning for pulmonary nodules. We identified 480 patients’ CT image with at least one pulmonary nodule admitted in Ninghai First hospital from September 1st, 2018, to April 30th, 2019. 240 CT images are carried out by the conventional target scanning method, and we analyzed the reasons for the low quality of some CT target scanning images of pulmonary nodules in the radiology department of our hospital. We established a new process of CT target scanning for pulmonary nodules based on the PDCA method and then tested 240 patients who were checked after January 1st, 2019. The excellent rate of CT target scanning image of pulmonary nodules in our department increased from 60.0% to more than 90.0%. The patients’ satisfaction with the examination was significantly higher than that without the implementation of PDCA management. The research result indicated that the process of CT target scanning image, postprocessing reconstruction, and numerical measurement of pulmonary nodules can be improved by standardized PDCA cycle, which benefits effectively improving the theoretical and operational skills of radiologists and significantly improving the image quality rate of CT target scanning of pulmonary nodules

Functional Synthetic Probes for Selective Targeting and Multi-analyte Detection and Imaging

In contrast to the classical design of a probe with one binding site to target one specific analyte, probes with multiple interaction sites or, alternatively, with single sites promoting tandem reactions to target one or multiple analytes, have been developed. They have been used in addressing the inherent challenges of selective targeting in the presence of structurally similar compounds and in complex matrices, as well as the visualization of the in vivo interaction or crosstalk between the analytes. Examples of analytes include reactive sulfur species, reactive oxygen species, nucleotides and enzymes. This review focuses on recent innovations in probe design, detection mechanisms and the investigation of biological processes. The vision is to promote the ongoing development of fluorescent probes to enable deeper insight into the physiology of bioactive analytes

Research of Multi-Fuel Burning Stability In A 300MW Coal-Fired Utility Boiler

AbstractWith China's economy growing rapidly, requirement of electricity is more and more. Now, 300MW coal-fired units are main units. With diversification of coal, there are big differences between actual coal and design coal, this affect on the safety of running. In this paper, a 300MW coal-fired unit was studied. A, B and C three kinds of mixed coal were chosen. Under the rated load, characteristics of coal were studied. Experiments of adaptabilities of mixed coal A, B and C with boiler were done. And results indicated that slag of boiler was related with distribution of fire box temperature, degree of flame-brush wall and degree of flue-gas turbidness. Slag of mixed coal C was serious, while mixed coal A was not easy lagging in superheater and water wall. Therefore, mixed coal A as fuel could meet the need of safe running of boiler

Effect of titanium addition on structure, corrosion resistance and mechanical properties of aluminum coatings on NdFeB by ion-beam-assisted magnetron sputtering

Titanium has been added in the Al coatings on sintered NdFeB magnets by ion-beam-assisted magnetron sputtering to improve the mechanical properties of coatings without corrosion resistance deterioration. The effect of Ti addition on structure, mechanical and electrochemical properties of Al coatings has been investigated, complemented by x-ray diffraction and transmission electron microscopy. The results show that the ion-beam-assisted magnetron co-sputtering of Al and Ti obtains a single-phase polycrystalline structure of Al-Ti solid solution. With the increasing Ti content, the coating structure becomes denser and the grain size is reduced. Both the mechanical and wear properties of the coatings are improved with the addition of Ti where the hardness of the Al-Ti coating increases with the increasing amount of Ti. Polarization and EIS testing results show that the titanium-doped coatings have an enhanced corrosion resistance by comparison with pure Al coatings when Ti content is lower than 4.3 at.%

Effect of the grain boundary Tb/Dy diffused microstructure on the magnetic properties of sintered Nd-Fe-B magnets

The effect of heavy rare earth element (HREE, Tb and Dy) coating thickness on the grain boundary diffusion process (GBDP) of sintered Nd-Fe-B magnets is investigated. The distribution of HREE concentration fits well with the distribution depicted by the dual-trend diffusion model for the magnets with thin HREE coatings. However, when the HREE coating is thicker than a critical value, two new types of structures of HREE distribution, namely, the anti-shell/core structure and the transitional structure, besides the well-reported shell/core structure, appear sequentially around the Nd-Fe-B grains near the close surface of the diffused magnets. The evolution of the microstructures and the magnetic properties of the diffused magnets are further studied on the basis of the dual-trend diffusion model. The coercivity of the diffused magnets with thick HREE coatings slowly improves when anti-shell/core and transitional structures form on the outer layer of the diffused magnets. The ultimate coercivity of the HREE-diffused magnets is the result of the combined action of the above three structures

Machine Learning Algorithms Identify Pathogen-Specific Biomarkers of Clinical and Metabolomic Characteristics in Septic Patients with Bacterial Infections

Sepsis is a high-mortality disease that is infected by bacteria, but pathogens in individual patients are difficult to diagnosis. Metabolomic changes triggered by microbial activity provide us with the possibility of accurately identifying infection. We adopted machine learning methods for training different classifiers with a clinical-metabolomic database from sepsis cases to identify the pathogen of sepsis. Records of clinical indicators and concentration of metabolites were obtained for each patient upon their arrival at the hospital. Machine learning algorithms were used in 100 patients with clear infection and corresponding 29 controls to select specific biosignatures to discriminate microorganism in septic patients. The sensitivity, specificity, and AUC value of clinical and metabolomic characteristics in predicting diagnostic outcomes were determined at admission. Our analyses demonstrate that the biosignatures selected by machine learning algorithms could have diagnostic value on the identification of infected patients and Gram-positive from Gram-negative; related AUC values were 0.94±0.054 and 0.80±0.085, respectively. Pathway and blood disease enrichment analyses of clinical and metabolomic biomarkers among infected patients showed that sepsis disease was accompanied by abnormal nitrogen metabolism, cell respiratory disorder, and renal or intestinal failure. The panel of selected clinical and metabolomic characteristics might be powerful biomarkers to discriminate patients with sepsis