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Novel Self-assembled Organic Nanoprobe for Molecular Imaging and Treatment of Gram-positive Bacterial Infection.

ABSTRACT: Background: Increasing bacterial infections as well as a rise in bacterial resistance call for the development of novel and safe antimicrobial agents without inducing bacterial resistance. Nanoparticles (NPs) present some advantages in treating bacterial infections and provide an alternative strategy to discover new antibiotics. Here, we report the development of novel self-assembled fluorescent organic nanoparticles (FONs) with excellent antibacterial efficacy and good biocompatibility. Methods: Self-assembly of 1-(12-(pyridin-1-ium-1-yl)dodecyl)-4-(1,4,5-triphenyl-1H-imidazol-2-yl)pyridin-1-ium (TPIP) in aqueous solution was investigated using dynamic light scattering (DLS) and transmission electron microscopy (TEM). The bacteria were imaged under a laser scanning confocal microscope. We evaluated the antibacterial efficacy of TPIP-FONsin vitro using sugar plate test. The antimicrobial mechanism was explored by SEM. The biocompatibility of the nanoparticles was examined using cytotoxicity test, hemolysis assay, and histological staining. We further tested the antibacterial efficacy of TPIP-FONsin vivo using the S. aureus-infected rats. Results: In aqueous solution, TPIP could self-assemble into nanoparticles (TPIP-FONs) with characteristic aggregation-induced emission (AIE). TPIP-FONs could simultaneously image gram-positive bacteria without the washing process. In vitro antimicrobial activity suggested that TPIP-FONs had excellent antibacterial activity against S. aureus (MIC = 2.0 µg mL-1). Furthermore, TPIP-FONs exhibited intrinsic biocompatibility with mammalian cells, in particular, red blood cells. In vivo studies further demonstrated that TPIP-FONs had excellent antibacterial efficacy and significantly reduced bacterial load in the infectious sites. Conclusion: The integrated design of bacterial imaging and antibacterial functions in the self-assembled small molecules provides a promising strategy for the development of novel antimicrobial nanomaterials.

SUBMITTER: Gao T

PROVIDER: S-EPMC5858508 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Novel Self-assembled Organic Nanoprobe for Molecular Imaging and Treatment of Gram-positive Bacterial Infection.

Gao Tang T Zeng Hongliang H Xu Huan H Gao Feng F Li Wei W Zhang Shengwang S Liu Yi Y Luo Guifang G Li Mingdan M Jiang Dejian D Chen Zhigao Z Wu Yong Y Wang Wei W Zeng Wenbin W

Theranostics 20180214 7

Background: Increasing bacterial infections as well as a rise in bacterial resistance call for the development of novel and safe antimicrobial agents without inducing bacterial resistance. Nanoparticles (NPs) present some advantages in treating bacterial infections and provide an alternative strategy to discover new antibiotics. Here, we report the development of novel self-assembled fluorescent organic nanoparticles (FONs) with excellent antibacterial efficacy and good bioc ...[more]

PMID: 29556364

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Project description:BackgroundTo investigate the difference in the severity of illness, organ dysfunction, and prognosis of acute cholangitis due to different pathogenic bacterial infection types.MethodsA retrospective observational study was performed. Patients who met the selection criteria according to blood culture and bile culture results of different pathogenic bacterial were divided into groups. The severity of illness, organ dysfunction, and prognosis of the groups were analyzed and compared comprehensively.ResultsA total of 424 patients were included, and no bacterial growth developed in 111 patients (26.2%). Among the 313 patients (73.8%) with bacterial growth, 155 patients had only Gram-negative bacteria cultured (49.5%), 48 patients had only Gram-positive bacteria cultured (15.3%), and 110 patients had both Gram-negative and Gram-positive bacteria cultured (35.1%). The proportion of Grade III patients and the APACHE II and SOFA scores of the mixed Gram-negative and positive group were the highest (p < 0.05); the intensive care unit admission day and hospital stay were longer, and the mortality rate were also higher 20/110 (18.2%) than the other two groups. Regression analysis showed that bacterial growth was an independent risk factor for organ dysfunction. The risks of an increased septic shock, neurological dysfunction, hepatic dysfunction, hematological dysfunction, and respiratory dysfunction in the mixed Gram-negative and positive group were higher than the Gram-negative group (P < 0.05). The Cox proportional hazards regression prompt showed that different culture results were independent risk factors for death. The mixed Gram-negative and positive group had increased hazard ratios and 95% CI of 7.30 (95% CI 1.55 to 34.38) compared with the Gram-negative group. There was no difference between the Gram-negative group and the Gram-positive group in the severity of illness, organ dysfunction, intensive care unit admission day, hospital stay, mortality rate, and risk of death (P > 0.05).ConclusionsIn acute cholangitis, mixed infection with Gram-negative and Gram-positive bacteria was more severe and was associated with a higher risk of death. There were no apparent differences between Gram-negative and Gram-positive bacterial infections.

Project description:BackgroundBacterial bloodstream infection is responsible for the majority of cases of sepsis and septic shock. Early recognition of the causative pathogen is pivotal for administration of adequate empiric antibiotic therapy and for the survival of the patients. In this study, we developed a feasible machine learning (ML) model to predict gram-positive and gram-negative bacteremia based on routine laboratory parameters.MethodsData for 2118 patients with bacteremia were obtained from the Medical Information Mart for Intensive Care dataset. Patients were randomly split into the training set and test set by stratified sampling, and 374 routine laboratory blood test variables were retrieved. Variables with missing values in more than 40% of the patients were excluded. Pearson correlation test was employed to eliminate redundant features. Five ML algorithms were used to build the model based on the selected features. Additionally, 132 patients with bacteremia who were treated at Qilu Hospital of Shandong University were included in an independent test cohort to evaluate the model.ResultsAfter feature selection, 32 variables remained. All the five ML algorithms performed well in terms of discriminating between gram-positive and gram-negative bacteremia, but the performance of convolutional neural network (CNN) and random forest (RF) were better than other three algorithms. Consider of the interpretability of models, RF was chosen for further test (ROC-AUC = 0.768; 95%CI = 0.715-0.798, with a sensitivity of 75.20% and a specificity of 63.79%). To expand the application of the model, a decision tree (DT) was built utilizing the major variables, and it achieved an AUC of 0.679 (95%CI = 0.632-0.723), a sensitivity of 66%, and a specificity of 67.82% in the test cohort. When tested in the Qilu Hospital cohort, the ROC-AUC of the RF and DT models were 0.666 (95%CI = 0.579-0.746) and 0.615 (95%CI = 0.526-0.698), respectively. Finally, a software was developed to make the RF- and DT-based prediction models easily accessible.ConclusionThe present ML-based models could effectively discriminate between gram-positive and gram-negative bacteremia based on routine laboratory blood test results. This simple model would be beneficial in terms of guiding timely antibiotic selection and administration in critically ill patients with bacteremia before their pathogen test results are available.

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Novel Self-assembled Organic Nanoprobe for Molecular Imaging and Treatment of Gram-positive Bacterial Infection.

Publications

Novel Self-assembled Organic Nanoprobe for Molecular Imaging and Treatment of Gram-positive Bacterial Infection.

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