The prevalence of carbapenem-resistant Klebsiella pneumoniae (CRKP) is rapidly increasing worldwide in recent decades and poses a challenge for today’s clinical practice. Rapid detection of CRKP can avoid inappropriate antimicrobial therapy and save lives. Traditional detection methods for CRKP are extremely time-consuming; PCR and other sequencing methods are too expensive and technologically demanding, making it hard to meet the clinical demands. Nanopore assay has been used for screening biomarkers of diseases recently because of its high sensitivity, real-time detection, and low cost. In this study, we distinguished CRKP from carbapenem-sensitive K. pneumoniae (CSKP) by the detection of increasing amount of extracted 16S ribosomal RNA (16S rRNA) from bacterial culture with antibiotics imipenem, indicating the uninhibited growth of CRKP by the imipenem. Specific signals from single channel recording of 16S rRNA bound with probes by MspA nanopore allowed the ultra-sensitive and fast quantitative detection of 16S rRNA. We proved that only 4 h of CRKP culture time was needed for nanopore assay to distinguish the CRKP and CSKP. The time-cost of the assay is only about 5% of disk diffusion method while reaching the similar accuracy. This new method has the potential application in the fast screening of drug resistance in clinical microorganism samples.
The detection of biomarkers requires not only high sensitivity but also different signal reading methods depending on the actual situation. Herein, the luminescent properties of CdTe quantum dots (QDs) were exploited, where CdTe QDs were used as shared signal molecules. Combining multiple types of nucleic acid and chemical signal amplification techniques, and various signal detection techniques, a magnetic nanoparticle (NP) and filter-assisted separation multimode sensing strategy has been developed. In this work, miRNA-141 was selected as a representative target, which can trigger the catalyzed hairpin assembly and hybrid chain reaction enzyme-free nucleic acid signal amplification that generates long double-stranded DNA. Subsequently, the chemical amplification of silver NPs (Ag NPs) that release a large amount of Ag + was introduced into the system. Finally, the cationexchange reaction between CdTe QDs and Ag + was utilized to quench the fluorescence (FL) of the CdTe QDs, releasing free Cd 2+ . The visual/FL/chemical vapor generation-atomic fluorescence spectrometry (CVG-AFS)/inductively coupled plasma mass spectrometry (ICP-MS) method could then be performed for the analysis of miRNA. After investigating its experimental performance, it has been found that 10 fM can be differentiated from the blank solution with the naked eye. In addition, FL/CVG-AFS/ICP-MS methods all displayed good analytical capability for target detection, and the limits of detection (LODs) are as low as fM, which show high target sequence selectivity. This platform was applied to investigate miRNA-141 expression in various cancer cells, which can accurately detect in the range of 100−100 000 MDA-MB-231 cells (breast cancer cell lines), with an LOD of 15 cells. Therefore, the multimode sensing strategy based on a single signal molecule and multiple signal amplification strategies is an applicable and versatile detection method of biomarkers; it can even achieve point-of-care testing, improving the accuracy and efficiency of medical diagnosis.
: Antibiotic resistance is currently a world health crisis that urges the development of new antibacterial substances. To this end, natural products, including flavonoids, alkaloids, terpenoids, steroids, peptides and organic acids that play a vital role in the development of medicines and thus constitute a rich source in clinical practices, provide an important source of drugs directly or for the screen of lead compounds for new antibiotic development. Because membrane proteins, which comprise more than 60% of the current clinical drug targets, play crucial roles in signal transduction, transport, bacterial pathogenicity and drug resistance, as well as immunogenicity, it is our aim to summarize those natural products with different structures that target bacterial membrane proteins, such as efflux pumps and enzymes, to provide an overview for the development of new antibiotics to deal with antibiotic resistance.
BACKGROUND Solving a mechanistic model of simulated moving bed (SMB) is extremely time consuming, therefore, it is inefficient for online control using the optimization method based on such a mechanistic model. To this end, the machine learning model can be regarded as a potential substitution to accelerate the optimization process. RESULTS Several machine learning algorithms were applied to predict the product purities under various operation conditions using two SMB processes as case studies: i.e., a sugar separation of rebaudioside A and stevioside, as well as an enantioseparation of 1,1′‐bi‐2‐naphthol racemate. The results indicate that the random forest (RF) model and the deep neural network (DNN) model provide a satisfactory accuracy with a mean absolute error (MAE) lower than 0.19% (RF) and 0.08% (DNN), respectively. During the optimization process to maximize the feed flowrate under specific purity requirements, among the two selected models, DNN model showed a better generalization ability than RF model and gave a feed flowrate 10% higher than the highest value in the training dataset, which was consistent with the result obtained by using the mechanistic model. The optimized operation conditions for sugar separation were verified experimentally and the achieved purities of rebaudioside A and stevioside were 99.2% and 98.8%, respectively. CONCLUSION The DNN model was successfully used to substitute the mechanistic model to reach a rapid optimization of SMB processes with an improved efficiency of 103 ~ 104 times. © 2021 Society of Chemical Industry (SCI).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.