Sequencing and Genetic Variation of Multidrug Resistance Plasmids in Klebsiella pneumoniae

BACKGROUND: The development of multidrug resistance is a major problem in the treatment of pathogenic microorganisms by distinct antimicrobial agents. Characterizing the genetic variation among plasmids from different bacterial species or strains is a key step towards understanding the mechanism of virulence and their evolution. RESULTS: We applied a deep sequencing approach to 206 clinical strains of Klebsiella pneumoniae collected from 2002 to 2008 to understand the genetic variation of multidrug resistance plasmids, and to reveal the dynamic change of drug resistance over time. First, we sequenced three plasmids (70 Kb, 94 Kb, and 147 Kb) from a clonal strain of K. pneumoniae using Sanger sequencing. Using the Illumina sequencing technology, we obtained more than 17 million of short reads from two pooled plasmid samples. We mapped these short reads to the three reference plasmid sequences, and identified a large number of single nucleotide polymorphisms (SNPs) in these pooled plasmids. Many of these SNPs are present in drug-resistance genes. We also found that a significant fraction of short reads could not be mapped to the reference sequences, indicating a high degree of genetic variation among the collection of K. pneumoniae isolates. Moreover, we identified that plasmid conjugative transfer genes and antibiotic resistance genes are more likely to suffer from positive selection, as indicated by the elevated rates of nonsynonymous substitution. CONCLUSION: These data represent the first large-scale study of genetic variation in multidrug resistance plasmids and provide insight into the mechanisms of plasmid diversification and the genetic basis of antibiotic resistance

Small molecular inhibitors reverse cancer metastasis by blockading oncogenic PITPNM3

Most cancer‐related deaths are a result of metastasis. The development of small molecular inhibitors reversing cancer metastasis represents a promising therapeutic opportunity for cancer patients. This pan‐cancer analysis identifies oncogenic roles of membrane‐associated phosphatidylinositol transfer protein 3 (PITPNM3), which is crucial for cancer metastasis. Small molecules targeting PITPNM3 must be explored further. Here, PITPNM3‐selective small molecular inhibitors are reported. These compounds exhibit target‐specific inhibition of PITPNM3 signaling, thereby reducing metastasis of breast cancer cells. Besides, by using nanoparticle‐based delivery systems, these PITPNM3‐selective compounds loaded nanoparticles significantly repress metastasis of breast cancer in mouse xenograft models and organoid models. Notably, the results establish an important metastatic‐promoting role for PITPNM3 and offer PITPNM3 inhibition as a therapeutic strategy in metastatic breast cancer

Sirtuin 1 and Autophagy Attenuate Cisplatin-Induced Hair Cell Death in the Mouse Cochlea and Zebrafish Lateral Line

Cisplatin-induced ototoxicity is one of the major adverse effects in cisplatin chemotherapy, and hearing protective approaches are unavailable in clinical practice. Recent work unveiled a critical role of autophagy in cell survival in various types of hearing loss. Since the excessive activation of autophagy can contribute to apoptotic cell death, whether the activation of autophagy increases or decreases the rate of cell death in CDDP ototoxicity is still being debated. In this study, we showed that CDDP induced activation of autophagy in the auditory cell HEI-OC1 at the early stage. We then used rapamycin, an autophagy activator, to increase the autophagy activity, and found that the cell death significantly decreased after CDDP injury. In contrast, treatment with the autophagy inhibitor 3-methyladenine (3-MA) significantly increased cell death. In accordance with in vitro results, rapamycin alleviated CDDP-induced death of hair cells in zebrafish lateral line and cochlear hair cells in mice. Notably, we found that CDDP-induced increase of Sirtuin 1 (SIRT1) in the HEI-OC1 cells modulated the autophagy function. The specific SIRT1 activator SRT1720 could successfully protect against CDDP-induced cell loss in HEI-OC1 cells, zebrafish lateral line, and mice cochlea. These findings suggest that SIRT1 and autophagy activation can be suggested as potential therapeutic strategies for the treatment of CDDP-induced ototoxicity

Aggregation-Induced Emission (AIE), Life and Health

Light has profoundly impacted modern medicine and healthcare, with numerous luminescent agents and imaging techniques currently being used to assess health and treat diseases. As an emerging concept in luminescence, aggregation-induced emission (AIE) has shown great potential in biological applications due to its advantages in terms of brightness, biocompatibility, photostability, and positive correlation with concentration. This review provides a comprehensive summary of AIE luminogens applied in imaging of biological structure and dynamic physiological processes, disease diagnosis and treatment, and detection and monitoring of specific analytes, followed by representative works. Discussions on critical issues and perspectives on future directions are also included. This review aims to stimulate the interest of researchers from different fields, including chemistry, biology, materials science, medicine, etc., thus promoting the development of AIE in the fields of life and health

Cation Valences and Multiferroic Properties of EuTiO3 Co-Doped with Ba and Transition Metals of Co/Ni

Eu1−xBaxTi1−yMyO3 (M = Co or Ni) was sintered at 1400 °C under a reduction atmosphere. X-ray photoelectron spectroscopy revealed the mixed valences of Eu2+/Eu3+ and Ti4+/Ti3+ in EuTiO3 and Eu0.7Ba0.3TiO3, as well as some oxygen vacancies required to keep the charge neutrality. The co-doping of Co2+/Ni2+ in Eu0.7Ba0.3TiO3 resulted in the disappearance of oxygen vacancies, as a result of a reduction in Ti3+ numbers and an increase in Eu3+ numbers. On the other hand, Ba2+ doping led to an increased lattice parameter due to its larger ionic size than Eu2+, whereas the Co2+/Ni2+ co-doping resulted in smaller lattice parameters because of the combined effects of ionic size and variation in the oxygen-vacancy numbers. Eu0.7Ba0.3TiO3 exhibited a clear ferroelectricity, which persisted in the Co2+/Ni2+ co-doped samples until the doping levels of y = 0.05 and 0.10, respectively. Eu0.7Ba0.3TiO3 remained to be antiferromagnetic with a reduced transition temperature of 3.1 K, but co-doping of Co2+/Ni2+ turned the samples from antiferromagnetic to ferromagnetic with transition temperatures of 2.98 K and 2.72 K, respectively. The cause for such a transition could not be explained by the larger lattice volume, oxygen vacancies and mixed valences of Eu2+/Eu3+, which were proposed in previous works. Instead, it was more likely to arise from a large asymmetric distortion of the Eu–O polyhedron introduced by the aliovalent doping, which promotes the admixture of Eu 5d and 4f states

Privacy-enhanced federated learning scheme based on generative adversarial networks

Federated learning, a distributed machine learning paradigm, has gained a lot of attention due to its inherent privacy protection capability and heterogeneous collaboration.However, recent studies have revealed a potential privacy risk known as “gradient leakage”, where the gradients can be used to determine whether a data record with a specific property is included in another participant’s batch, thereby exposing the participant’s training data.Current privacy-enhanced federated learning methods may have drawbacks such as reduced accuracy, computational overhead, or new insecurity factors.To address this issue, a differential privacy-enhanced generative adversarial network model was proposed, which introduced an identifier into vanilla GAN, thus enabling the input data to be approached while satisfying differential privacy constraints.Then this model was applied to the federated learning framework, to improve the privacy protection capability without compromising model accuracy.The proposed method was verified through simulations under the client/server (C/S) federated learning architecture and was found to balance data privacy and practicality effectively compared with the DP-SGD method.Besides, the usability of the proposed model was theoretically analyzed under a peer-to-peer (P2P) architecture, and future research work was discussed

An online medical image management system

This paper proposed an under development online medical imaging management system with advanced web-based tools at the front-end that can perform functions, in real-time to load and process images, extract important features at front-end, and save the information into the back-end database server. The modern laptops and smart phones are very powerful and the internet speed is much faster than 10 years ago. The goal of this research is to study and develop a client-server system to utilize browsers on laptops or mobile device to process images and store the images and images\u27 information on a centralized server. The online system and architecture prototype has been developed and several functions and results will be discussed in the paper

Learning-based IoT Data Aggregation for Disaster Scenarios

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Experimental Investigation on Nonlinear Flow Anisotropy Behavior in Fracture Media

A series of flow experiments were performed on matched fractures to study the problem of non-Darcy flow in fractured media. Five rock fractures of different roughness were generated using indirect tensile tests, and their surface topographies were measured using a stereo topometric scanning system. The fracture was assumed to be a self-affine surface, and its roughness and anisotropy were quantified by the fractal dimension. According to the flow tortuosity effect, the nonlinear flow was characterized by hydraulic tortuosity and surface tortuosity power law relationships based on Forchheimer’s law. Fracture seepage experiments conducted with two injection directions (0° and 90°) showed that Forchheimer’s law described the nonlinear flow well. Both the proposed model and Chen’s double-parameter model gave similar results to the experiment, but the match was closer with the proposed model. On this basis, a new formula for the critical Reynolds number is proposed, which serves to distinguish linear flow and Forchheimer flow