scholarly journals Prediction of Minimal Inhibitory Concentration of Meropenem Against Klebsiella pneumoniae Using Metagenomic Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Rundong Tan ◽  
Anqi Yu ◽  
Ziming Liu ◽  
Ziqi Liu ◽  
Rongfeng Jiang ◽  
...  
Keyword(s):  
Klebsiella Pneumoniae ◽  
Regression Model ◽  
Minimal Inhibitory Concentration ◽  
Inhibitory Concentration ◽  
Classification Model ◽  
Metagenomic Data ◽  
Single Nucleotide ◽  
Genome Data ◽  
Average Accuracy ◽  
Visible Growth

Minimal inhibitory concentration (MIC) is defined as the lowest concentration of an antimicrobial agent that can inhibit the visible growth of a particular microorganism after overnight incubation. Clinically, antibiotic doses for specific infections are determined according to the fraction of MIC. Therefore, credible assessment of MICs will provide a physician valuable information on the choice of therapeutic strategy. Early and precise usage of antibiotics is the key to an infection therapy. Compared with the traditional culture-based method, the approach of whole genome sequencing to identify MICs can shorten the experimental time, thereby improving clinical efficacy. Klebsiella pneumoniae is one of the most significant members of the genus Klebsiella in the Enterobacteriaceae family and also a common non-social pathogen. Meropenem is a broad-spectrum antibacterial agent of the carbapenem family, which can produce antibacterial effects of most Gram-positive and -negative bacteria. In this study, we used single-nucleotide polymorphism (SNP) information and nucleotide k-mers count based on metagenomic data to predict MICs of meropenem against K. pneumoniae. Then, features of 110 sequenced K. pneumoniae genome data were combined and modeled with XGBoost algorithm and deep neural network (DNN) algorithm to predict MICs. We first use the XGBoost classification model and the XGBoost regression model. After five runs, the average accuracy of the test set was calculated. The accuracy of using nucleotide k-mers to predict MICs of the XGBoost classification model and XGBoost regression model was 84.5 and 89.1%. The accuracy of SNP in predicting MIC was 80 and 81.8%, respectively. The results show that XGBoost regression is better than XGBoost classification in both nucleotide k-mers and SNPs to predict MICs. We further selected 40 nucleotide k-mers and 40 SNPs with the highest correlation with MIC values as features to retrain the XGBoost regression model and DNN regression model. After 100 and 1,000 runs, the results show that the accuracy of the two models was improved. The accuracy of the XGBoost regression model for k-mers, SNPs, and k-mers & SNPs was 91.1, 85.2, and 91.3%, respectively. The accuracy of the DNN regression model was 91.9, 87.1, and 91.8%, respectively. Through external verification, some of the selected features were found to be related to drug resistance.

scholarly journals Hypervirulent Klebsiella pneumoniae of Lineage ST66-K2 Caused Tonsillopharyngitis in a German Patient

2021 ◽  
Vol 9 (1) ◽  
pp. 133
Author(s):  
Kathleen Klaper ◽  
Sebastian Wendt ◽  
Christoph Lübbert ◽  
Norman Lippmann ◽  
Yvonne Pfeifer ◽  
...  
Keyword(s):  
Klebsiella Pneumoniae ◽  
Genomic Analysis ◽  
Comparative Genomic ◽  
Snp Analysis ◽  
Whole Genome ◽  
Single Nucleotide ◽  
German Patient ◽  
Genome Data ◽  
Genome Analyses ◽  
Clinical Awareness

Hypervirulent Klebsiella pneumoniae (hvKp) is a novel pathotype that has been rarely described in Europe. This study characterizes a hvKp isolate that caused a community-acquired infection. The hypermucoviscous Klebsiella pneumoniae (K. pneumoniae) strain 18-0005 was obtained from a German patient with tonsillopharyngitis in 2017. Antibiotic susceptibility testing was performed and the genome was sequenced by Illumina and Nanopore technology. Whole genome data were analyzed by conducting core genome multilocus sequence typing (cgMLST) and single nucleotide polymorphism (SNP) analysis. Virulence genes were predicted by applying Kleborate. Phenotypic and whole genome analyses revealed a high similarity of the study isolate 18-0005 to the recently reported antibiotic-susceptible hvKp isolate SB5881 from France and the “ancestral” strain Kp52.145; both were assigned to the ST66-K2 lineage. Comparative genomic analysis of the three plasmids showed that the 18-0005 plasmid II differs from SB5881 plasmid II by an additional 3 kb integrated fragment of plasmid I. Our findings demonstrate the genetic flexibility of hvKp and the occurrence of a strain of the clonal group CG66-K2 in Germany. Hence, it emphasizes the need to improve clinical awareness and infection monitoring of hvKp.

Nồng độ ức chế tối thiểu (Minimal Inhibitory Concentration - MIC) của một số loại kháng sinh đến vi khuẩn phân lập được từ cá dìa thương phẩm mắc bệnh lở loét (Siganus guttatus, Bloch, 1787)

2015 ◽  
Vol 104 (5) ◽  
Author(s):  
Lê Văn Bảo Duy ◽  
Dương Thị Thủy ◽  
Nguyễn Ngọc Phước ◽  
Trương Thị Hoa ◽  
Nguyễn Đức Quỳnh Anh
Keyword(s):  
Minimal Inhibitory Concentration ◽  
Vibrio Parahaemolyticus ◽  
Inhibitory Concentration ◽  
Vibrio Tubiashii

Nghiên cứu được tiến hành nhằm xác định nồng độ ức chế tối thiểu (Minimal Inhibitory Concentration - MIC) của một số loại kháng sinh đến vi khuẩn phân lập được từ cá dìa thương phẩm mắc bệnh lở loét (Siganus guttatus). Từ kết quả phân lập định danh cho thấy 2 chủng Vibrio parahaemolyticus VPMP22 và Vibrio tubiashii ATCC 19109 có mặt trên các vết lở loét ở cá dìa thương phẩm. Kết quả thử nghiệm MIC cho thấy các loại kháng sinh Cefuroxim, Cefotaxim, Tetracycline, Erythromicin, Rifamicin có nồng độ ức chế vi khuẩn Vibrio parahaemolyticus VPMP22 tốt nhất dưới 0.21 µg/ml. Các kháng sinh có Cefuroxim, Cefotaxim, Oxytetraciline, Erythromicin, Trimethoprim nồng độ ức chế vi khuẩn Vibrio tubiashii ATCC 19109 tốt nhất dưới 1.25 µg/ml. Penicillin có nồng độ ức chế tối thiểu cao nhất đối với cả 2 chủng vi khuẩn trên (80 µg/ml), cho thấy 2 chủng vi khuẩn trên đã có sự kháng thuốc đối với loại kháng sinh này. Do đó, trong phòng trị bệnh lở loét trên cá dìa nên sử dụng Cefuroxim và Cefotaxim để có hiệu quả cao nhất trong phòng trị bệnh.

scholarly journals Enhanced Convolutional-Neural-Network Architecture for Crop Classification

2021 ◽  
Vol 11 (9) ◽  
pp. 4292
Author(s):  
Mónica Y. Moreno-Revelo ◽  
Lorena Guachi-Guachi ◽  
Juan Bernardo Gómez-Mendoza ◽  
Javier Revelo-Fuelagán ◽  
Diego H. Peluffo-Ordóñez
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Network Architecture ◽  
Classification Model ◽  
Classification Error ◽  
Small Scale ◽  
Post Processing ◽  
Average Accuracy ◽  
Processing Step ◽  
Crop Classification

Automatic crop identification and monitoring is a key element in enhancing food production processes as well as diminishing the related environmental impact. Although several efficient deep learning techniques have emerged in the field of multispectral imagery analysis, the crop classification problem still needs more accurate solutions. This work introduces a competitive methodology for crop classification from multispectral satellite imagery mainly using an enhanced 2D convolutional neural network (2D-CNN) designed at a smaller-scale architecture, as well as a novel post-processing step. The proposed methodology contains four steps: image stacking, patch extraction, classification model design (based on a 2D-CNN architecture), and post-processing. First, the images are stacked to increase the number of features. Second, the input images are split into patches and fed into the 2D-CNN model. Then, the 2D-CNN model is constructed within a small-scale framework, and properly trained to recognize 10 different types of crops. Finally, a post-processing step is performed in order to reduce the classification error caused by lower-spatial-resolution images. Experiments were carried over the so-named Campo Verde database, which consists of a set of satellite images captured by Landsat and Sentinel satellites from the municipality of Campo Verde, Brazil. In contrast to the maximum accuracy values reached by remarkable works reported in the literature (amounting to an overall accuracy of about 81%, a f1 score of 75.89%, and average accuracy of 73.35%), the proposed methodology achieves a competitive overall accuracy of 81.20%, a f1 score of 75.89%, and an average accuracy of 88.72% when classifying 10 different crops, while ensuring an adequate trade-off between the number of multiply-accumulate operations (MACs) and accuracy. Furthermore, given its ability to effectively classify patches from two image sequences, this methodology may result appealing for other real-world applications, such as the classification of urban materials.

Toxicity of the First Ten MEIC Chemicals to Bacteria

1993 ◽  
Vol 21 (2) ◽  
pp. 151-155
Author(s):  
Gustaw Kerszman
Keyword(s):  
Escherichia Coli ◽  
Bacillus Subtilis ◽  
Linear Regression ◽  
Minimal Inhibitory Concentration ◽  
Blood Concentration ◽  
Inhibitory Concentration ◽  
Human Lymphocytes ◽  
Bacterial Species ◽  
E Coli ◽  
Mild Toxicity

The toxicity of the first ten MEIC chemicals to Escherichia coli and Bacillus subtilis was examined. Nine of the chemicals were toxic to the bacteria, with the minimal inhibitory concentration (MIC) ranging from 10-3 to 4.4M. The sensitivities of both organisms were similar, but the effect on E. coli was often bactericidal, while it was bacteriostatic for B. subtilis. Digoxin was not detectably toxic to either bacterial species. Amitriptyline and FeSO4 were relatively less toxic to the bacteria than to human cells. For seven chemicals, a highly significant linear regression was established between log MIC in bacteria and log of blood concentration, giving lethal and moderate/mild toxicity in humans, as well as with toxicity to human lymphocytes.

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