The rapid development of antibiotic resistance in K. pneumonia has led to a major concern. In order to analyze the hotspots and develop trends in this field through visual the analysis, this study used CiteSpace software to summarize the available data in the literature to provide insights. A total of 9366 research articles were retrieved from the Web of Science Core Collection, and the number of published papers is increasing year by year. The country with the most articles was the USA, followed by China and India. The institution with the highest number of publications was LERU. The author with the highest number of articles was Li. The journal with the highest citation rate was Antimicrobial Agents and Chemotherapy. In addition, based on keyword coword analysis and cited literature prominence analysis by CiteSpace, the current research focus in the field was therapy, CRKP, and resistance genes. This paper provides a new quantitative visualization way for the development of the field in the recent ten years. The results show global trends that researchers can use to determine future directions.
Klebsiella pneumoniae is an important multidrug-resistant (MDR) pathogen that can cause a range of infections in hospitalized patients. With the growing use of antibiotics, MDR K. pneumoniae is more prevalent, posing additional difficulties and obstacles in clinical therapy. To provide a valuable reference to deeply understand K. pneumoniae, and also to provide the theoretical basis for clinical prevention of such bacteria infections, the antibiotic resistance and mechanism of K. pneumoniae are discussed in this article. We conducted a literature review on antibiotic resistance of K. pneumoniae. We ran a thorough literature search of PubMed, Web of Science, and Scopus, among other databases. We also thoroughly searched the literature listed in the papers. We searched all antibiotic resistance mechanisms and genes of seven important antibiotics used to treat K. pneumoniae infections. Antibiotics such as β-lactams, aminoglycosides, and quinolones are used in the treatment of K. pneumoniae infection. With both chromosomal and plasmid-encoded ARGs, this pathogen has diverse resistance genes. Carbapenem resistance genes, enlarged-spectrum β-lactamase genes, and AmpC genes are the most often β-lactamase resistance genes. K. pneumoniae is a major contributor to antibiotic resistance worldwide. Understanding K. pneumoniae antibiotic resistance mechanisms and molecular characteristics will be important for the design of targeted prevention and novel control strategies against this pathogen.
Nowadays, millions of patients suffer from physical disabilities, including lower-limb disabilities. Researchers have adopted a variety of physical therapies based on the lower-limb exoskeleton, in which it is difficult to adjust equipment parameters in a timely fashion. Therefore, intelligent control methods, for example, deep reinforcement learning (DRL), have been used to control the medical equipment used in human gait adjustment. In this study, based on the key-value attention mechanism, we reconstructed the agent’s observations by capturing the self-dependent feature information for decision-making in regard to each state sampled from the replay buffer. Moreover, based on Softmax Deep Double Deterministic policy gradients (SD3), a novel DRL-based framework, key-value attention-based SD3 (AT_SD3), has been proposed for gait adjustment. We demonstrated the effectiveness of our proposed framework in gait adjustment by comparing different gait trajectories, including the desired trajectory and the adjusted trajectory. The results showed that the simulated trajectories were closer to the desired trajectory, both in their shapes and values. Furthermore, by comparing the results of our experiments with those of other state-of-the-art methods, the results proved that our proposed framework exhibited better performance.
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