Salmonella is one of the most dangerous and common food-borne pathogens. The overuse of antibiotics for disease prevention has led to the development of multidrug resistant Salmonella. Now, more than ever, there is a need for new antimicrobial drugs to combat these resistant bacteria. Aptamers have grown in popularity since their discovery, and their properties make them attractive candidates for therapeutic use. In this work, we describe the selection of highly specific DNA aptamers to S. enteritidis and S. typhimurium. To evolve species-specific aptamers, twelve rounds of selection to live S. enteritidis and S. typhimurium were performed, alternating with a negative selection against a mixture of related pathogens. Studies have shown that synthetic pools combined from individual aptamers have the capacity to inhibit growth of S. enteritidis and S. typhimurium in bacterial cultures; this was the result of a decrease in their membrane potential.
Apigenin, a natural plant flavone, has many beneficial effects, but there is no report about treatment of acetaminophen-induced liver injury. Our aim was to examine the protective effect of apigenin on acetaminophen-induced mouse acute liver injury and to investigate the potential mechanisms. A mouse model with acute liver injury was induced by intraperitoneally given acetaminophen 350 mg kg(-1) after oral administration of apigenin 100 and 200 mg kg(-1) for 7 days. The results showed that after treatment with apigenin, the levels of serum alanine aminotransferase and aspartate aminotransferase were gradually decreased, and the severity of liver injury was decreased. In particular, significant changes in liver necrosis were observed in the apigenin 200 mg kg(-1) group. Apigenin could gradually increase the hepatic glutathione reductase (GR) activity and reduced glutathione (GSH) content, and decrease the hepatic malondialdehyde content, but the activities of glutathione peroxidase and glutathione S-transferase in hepatic tissues between the model group and the apigenin-treated groups were not significantly different. It was concluded that apigenin could protect against acetaminophen-induced acute liver injury in mice, and the mechanisms might be associated with enhancing hepatic GSH content via increment of GR activity.
Antimicrobial peptides are the promising candidates for withstanding multidrug-resistant bacteria (MDRB) which were caused by the misuse and extensive use of antibiotics. In this research, in vitro activities of one antimicrobial cationic peptide, brevinin-2CE alone and in combination with five kinds of antibiotics were assessed against clinical isolates of extended-spectrum β-lactamase-producing Escherichia coli and methicillin-resistant Staphylococcus aureus. The results showed that most of the combination groups had synergistic effects. Also, it was obvious that brevinin-2CE had more rapid and severe action on the tested MDRBs which demonstrated that brevinin-2CE and the antibiotics had different antimicrobial mechanisms. Thus, it was presumed that the antimicrobial peptides destroyed the bacterial cells via pore formation mechanisms which lead to the increasing of membrane permeability; and then the other compounds like antibiotics might enter into the cells and accomplish the antimicrobial activities more rapidly and efficiently.
Perovskite oxides
are attractive candidates for various scientific
applications because of their outstanding structure flexibilities
and attractive physical and chemical properties. However, labor-intensive
and high-cost experimental and density functional theory calculation
approaches are normally used to screen candidate perovskites. Herein,
a machine learning method is employed to identify perovskites from
ABO
3
combinations formulated as constraint satisfaction
problems based on the restrictions of charge neutrality and Goldschmidt
tolerance factor. By eliminating five features based on their correlation
and importance, 16 features refined from 21 features are employed
to describe 343 known ABO
3
compounds for perovskite formability
and stability model training. It is found that the top three features
for predicting formability are structural features of the A–O
bond length, tolerance, and octahedral factors, whereas the top nine
features for predicting the stability are elemental and structural
features related to the B-site elements. The precision and recall
of the two models are 0.983, 1.00 and 0.971, 0.943, respectively.
The formability prediction model categorizes 2229 ABO
3
combinations
into 1373 perovskites and 856 nonperovskites, whereas the stability
prediction model distinguishes 430 stable perovskites from 1799 unstable
ones. Three hundred thirty-eight combinations are recognized as both
formable and stable perovskites for future investigation.
Triple optical response of a nano-composite facilitates discrimination of antibiotic-resistant Gram-negative bacteria from normal ones based on a sensing array technique.
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