scholarly journals CPO Complete, a novel test for fast, accurate phenotypic detection and classification of carbapenemases

2019 ◽  
Author(s):  
Gina K. Thomson ◽  
Sameh AbdelGhani ◽  
Kenneth S. Thomson
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Acinetobacter Baumannii ◽  
Sensitivity And Specificity ◽  
Classification Accuracy ◽  
Limited Resource ◽  
Complete Test ◽  
Classification Evaluation ◽  
Automated Instruments ◽  
Lactamase Inhibitor

AbstractRapid, accurate detection of carbapenemase-producing organisms (CPOs) and the classification of their carbapenemases are valuable tools for reducing the mortality of the CPO-associated infections, preventing the spread of CPOs, and optimizing use of new β-lactamase inhibitor combinations such as ceftazidime/avibactam and meropenem/vaborbactam. The current study evaluated the performance of CPO Complete, a novel, manual, phenotypic carbapenemase detection and classification test. The test was evaluated for sensitivity and specificity against 262 CPO isolates of Enterobacteriaceae, Pseudomonas aeruginosa and Acinetobacter baumannii and 67 non-CPO isolates. It was also evaluated for carbapenemase classification accuracy against 205 CPOs that produced a single carbapenemase class. The test exhibited 100% sensitivity 98.5% specificity for carbapenemase detection within 90 minutes and detected 74.1% of carbapenemases within 10 minutes. In the classification evaluation, 99.0% of carbapenemases were correctly classified. The test is technically simple and has potential for adaptation to automated instruments. With lyophilized kit storage at temperatures up to 38°C the CPO Complete test has the potential to provide rapid, accurate carbapenemase detection and classification in both limited resource and technologically advanced laboratories.

scholarly journals Artificial Intelligence in Fractured Dental Implant Detection and Classification: Evaluation Using Dataset from Two Dental Hospitals

Diagnostics ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 233
Author(s):  
Dong-Woon Lee ◽  
Sung-Yong Kim ◽  
Seong-Nyum Jeong ◽  
Jae-Hong Lee
Keyword(s):  
Dental Implant ◽  
Classification Accuracy ◽  
Reliability And Validity ◽  
Mechanical Complication ◽  
Radiographic Images ◽  
Acceptable Accuracy ◽  
Reliable Detection ◽  
Classification Evaluation ◽  
Accuracy Performance

Fracture of a dental implant (DI) is a rare mechanical complication that is a critical cause of DI failure and explantation. The purpose of this study was to evaluate the reliability and validity of a three different deep convolutional neural network (DCNN) architectures (VGGNet-19, GoogLeNet Inception-v3, and automated DCNN) for the detection and classification of fractured DI using panoramic and periapical radiographic images. A total of 21,398 DIs were reviewed at two dental hospitals, and 251 intact and 194 fractured DI radiographic images were identified and included as the dataset in this study. All three DCNN architectures achieved a fractured DI detection and classification accuracy of over 0.80 AUC. In particular, automated DCNN architecture using periapical images showed the highest and most reliable detection (AUC = 0.984, 95% CI = 0.900–1.000) and classification (AUC = 0.869, 95% CI = 0.778–0.929) accuracy performance compared to fine-tuned and pre-trained VGGNet-19 and GoogLeNet Inception-v3 architectures. The three DCNN architectures showed acceptable accuracy in the detection and classification of fractured DIs, with the best accuracy performance achieved by the automated DCNN architecture using only periapical images.

scholarly journals Rapidec Carba NP Test for Rapid Detection of Carbapenemase Producers

2015 ◽  
Vol 53 (9) ◽  
pp. 3003-3008 ◽  
Author(s):  
Laurent Poirel ◽  
Patrice Nordmann
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Sample Preparation ◽  
Acinetobacter Baumannii ◽  
Sensitivity And Specificity ◽  
Rapid Detection ◽  
Content Type

Performances of the Rapidec Carba NP test (bioMérieux) were evaluated for detection of all types of carbapenemases inEnterobacteriaceae,Acinetobacter baumannii, andPseudomonas aeruginosa. In less than 2 h after sample preparation, it showed a sensitivity and specificity of 96%. This ready-to-use test is well adapted to the daily need for detection of carbapenemase producers in any laboratory worldwide.

scholarly journals CLASSIFICATION OF GRAM-NEGATIVE BACILLI ACCORDING TO THEIR RESISTANCE TO ANTIMICROBIALS IN AN INFECTIOUS DISEASE CLINIC

2021 ◽  
Vol 24 (2) ◽  
pp. 83-86
Author(s):  
Lucian Giubelan ◽  
Keyword(s):  
Escherichia Coli ◽  
Pseudomonas Aeruginosa ◽  
Antibiotic Resistance ◽  
Klebsiella Pneumoniae ◽  
Acinetobacter Baumannii ◽  
Proteus Mirabilis ◽  
Multiple Criteria ◽  
Automated System ◽  
Gram Negative

Objectives. Classification on multiple criteria of Gram-negative bacilli (GNBs) according to antibiotic resistance. Material and method. Retrospective study (January 2017-December 2018) carried out in the Infectious Diseases Clinic from Craiova; GNBs were identified using the Vitek 2 automated system, which subsequently established their sensitivity to antimicrobials; GNBs were classified based on an arbitrary score from 1 (minimum) to 5 (maximum) based on the multiple antibiotic resistance index (MAR), the percentage of multidrug resistant strains (MDR) and the percentage of extended resistance strains (XDR). The final classification represents the sum of the points awarded for each category considered. Results. The following GNBs were considered: Escherichia coli (n = 720), Klebsiella pneumoniae (n = 335), Pseudomonas aeruginosa (n = 139), Proteus mirabilis (n = 60) and Acinetobacter baumannii (n = 29). MAR values are: Acinetobacter baumannii (Ab) – 0.6, Proteus mirabilis (Pm) – 0.52, Pseudomonas aeruginosa (Pa) – 0.51, Klebsiella pneumoniae (Kp) - 0.37 and Escherichia coli (Ec) – 0.23. The percentage of MDR strains is: Pm – 76.67%, Kp – 68.86%, Pa - 58.71%, Ec – 51.94% and Ab – 51.72%; XDR strains were identified for Ab - 17.24% and Pa – 6.47%. The final classification of GNBs is as follows: Pa – 12p, Ab - 11 p, Pm – 7p, Kp – 6p, Ec – 3p. Conclusions. Depending on the resistance profile on multiple criteria, the classification of the studied Gram-negative bacteria is as follows: Pa, Ab, Pm, Kp, Ec.

scholarly journals Multicenter Evaluation of the Modified Carbapenem Inactivation Method and the Carba NP for Detection of Carbapenemase-Producing Pseudomonas aeruginosa and Acinetobacter baumannii

2017 ◽  
Vol 56 (1) ◽  
Author(s):  
Patricia J. Simner ◽  
J. Kristie Johnson ◽  
William B. Brasso ◽  
Karen Anderson ◽  
David R. Lonsway ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Acinetobacter Baumannii ◽  
Sensitivity And Specificity ◽  
Content Type ◽  
Class A ◽  
Carbapenem Resistant ◽  
Reliable Detection ◽  
Initial Comparison ◽  
The Mean ◽  
Mean Sensitivity

ABSTRACT The purpose of this study was to develop the modified carbapenem inactivation method (mCIM) for the detection of carbapenemase-producing Pseudomonas aeruginosa (CP-PA) and carbapenemase-producing Acinetobacter baumannii (CP-AB) and perform a multicenter evaluation of the mCIM and Carba NP tests for these nonfermenters. Thirty P. aeruginosa and 30 A. baumannii isolates previously characterized by whole-genome sequencing from the CDC-FDA Antibiotic Resistance Isolate Bank were evaluated, including CP isolates (Ambler class A, B, and D), non-carbapenemase-producing (non-CP) carbapenem-resistant isolates, and carbapenem-susceptible isolates. Initial comparison of a 1-μl versus 10-μl loop inoculum for the mCIM was performed by two testing sites and showed that 10 μl was required for reliable detection of carbapenemase production among P. aeruginosa and A. baumannii. Ten testing sites then evaluated the mCIM using a 10-μl loop inoculum. Overall, the mean sensitivity and specificity of the mCIM for detection of CP-PA across all 10 sites were 98.0% (95% confidence interval [CI], 94.3 to 99.6; range, 86.7 to 100) and 95% (95% CI, 89.8 to 97.7; range, 93.3 to 100), whereas the mean sensitivity and specificity among CP-AB were 79.8% (95% CI, 74.0 to 84.9; range, 36.3 to 95.7) and 52.9% (95% CI, 40.6 to 64.9; range, 28.6 to 100), respectively. At three sites that evaluated the performance of the Carba NP test using the same set of isolates, the mean sensitivity and specificity of the Carba NP test were 97.8% (95% CI, 88.2 to 99.9; range, 93.3 to 100) and 97.8% (95% CI, 88.2 to 99.9; range, 93.3 to 100) for P. aeruginosa and 18.8% (95% CI, 10.4 to 30.1; range, 8.7 to 26.1) and 100% (95% CI, 83.9 to 100; range, 100) for A. baumannii. Overall, we found both the mCIM and the Carba NP test to be accurate for detection of carbapenemase production among P. aeruginosa isolates and less reliable for use with A. baumannii isolates.

scholarly journals Impact of Intrinsic Resistance Mechanisms on Potency of QPX7728, a New Ultrabroad-Spectrum Beta-Lactamase Inhibitor of Serine and Metallo-Beta-Lactamases in Enterobacteriaceae, Pseudomonas aeruginosa, and Acinetobacter baumannii

2020 ◽  
Vol 64 (6) ◽  
Author(s):  
Olga Lomovskaya ◽  
Kirk Nelson ◽  
Debora Rubio-Aparicio ◽  
Ruslan Tsivkovski ◽  
Dongxu Sun ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Acinetobacter Baumannii ◽  
Resistance Mechanisms ◽  
Beta Lactamase ◽  
Beta Lactam ◽  
Intrinsic Resistance ◽  
Content Type ◽  
Beta Lactamases ◽  
Beta Lactam Antibiotics ◽  
Lactamase Inhibitor

ABSTRACT QPX7728 is an ultrabroad-spectrum boronic acid beta-lactamase inhibitor that demonstrates inhibition of key serine and metallo-beta-lactamases at a nanomolar concentration range in biochemical assays with purified enzymes. The broad-spectrum inhibitory activity of QPX7728 observed in biochemical experiments translates into enhancement of the potency of many beta-lactams against strains of target pathogens producing beta-lactamases. The impacts of bacterial efflux and permeability on inhibitory potency were determined using isogenic panels of KPC-3-producing isogenic strains of Klebsiella pneumoniae and Pseudomonas aeruginosa and OXA-23-producing strains of Acinetobacter baumannii with various combinations of efflux and porin mutations. QPX7728 was minimally affected by multidrug resistance efflux pumps either in Enterobacteriaceae or in nonfermenters, such as P. aeruginosa or A. baumannii. Against P. aeruginosa, the potency of QPX7728 was further enhanced when the outer membrane was permeabilized. The potency of QPX7728 against P. aeruginosa was not affected by inactivation of the carbapenem porin OprD. While changes in OmpK36 (but not OmpK35) reduced the potency of QPX7728 (8- to 16-fold), QPX7728 (4 μg/ml) nevertheless completely reversed the KPC-mediated meropenem resistance in strains with porin mutations, consistent with the lesser effect of these mutations on the potency of QPX7728 compared to that of other agents. The ultrabroad-spectrum beta-lactamase inhibition profile, combined with enhancement of the activity of multiple beta-lactam antibiotics with various sensitivities to the intrinsic resistance mechanisms of efflux and permeability, indicates that QPX7728 is a useful inhibitor for use with multiple beta-lactam antibiotics.

scholarly journals Multicenter Evaluation of the BD Phoenix CPO Detect Test for Detection and Classification of Carbapenemase-Producing Organisms in Clinical Isolates

2020 ◽  
Vol 58 (5) ◽  
Author(s):  
Vicki Whitley ◽  
Susan Kircher ◽  
Tracey Gill ◽  
Janet A. Hindler ◽  
Susan O’Rourke ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Acinetobacter Baumannii ◽  
Bacterial Infections ◽  
Clinical Performance ◽  
Treatment Options ◽  
Reference Method ◽  
Patient Treatment ◽  
High Morbidity ◽  
Content Type

ABSTRACT Limited treatment options contribute to high morbidity/mortality rates with carbapenem-resistant, Gram-negative bacterial infections. New approaches for carbapenemase-producing organism (CPO) detection may help inform clinician decision-making on patient treatment and infection control. BD Phoenix CPO detect (CPO detect) detects and classifies carbapenemases in Enterobacterales, Acinetobacter baumannii, and Pseudomonas aeruginosa during susceptibility testing. The clinical performance of CPO detect is reported here. Enterobacterales, Acinetobacter baumannii, and Pseudomonas aeruginosa isolates were evaluated across three sites using CPO detect and a composite reference method (RM); the latter was comprised of the modified carbapenem inactivation method and a MIC screen for ertapenem, imipenem, and meropenem. Multiplex PCR testing was also utilized for Ambler class determination. Positive and negative percentages of agreement (PPA and NPA, respectively) between CPO detect and the RM were determined. The PPA and NPA for Enterobacterales were 98.5% (confidence intervals, 96.6%, 99.4%) and 97.2% (95.8%, 98.2%), respectively. The A. baumannii PPA and NPA, respectively, were 97.1% (90.2%, 99.2%) and 97.1% (89.9%, 99.2%). The P. aeruginosa PPA and NPA, respectively, were 95.9% (88.6%, 98.6%) and 92.3% (86.7%, 95.6%). The PPA values for carbapenemase class designations for all organisms combined and Enterobacterales alone, respectively, were 95.3% (90.2%, 97.8%) and 94.6% (88.8%, 97.5%) for class A, 94.0% (88.7%, 96.6%) and 96.4% (90.0%, 98.8%) for class B, and 95.0% (90.1%, 97.6%) and 99.0% (94.4%, 99.8%) for class D carbapenemases. NPA values for all organisms and Enterobacterales alone ranged from 98.5% to 100%. CPO detect provided accurate detection and classification of CPOs for the majority of isolates of Enterobacterales, Acinetobacter baumannii, and Pseudomonas aeruginosa tested.

Optimization of K Value in KNN Algorithm for Spam and Ham Email Classification

2020 ◽  
Vol 4 (2) ◽  
pp. 377-383
Author(s):  
Eko Laksono ◽  
Achmad Basuki ◽  
Fitra Bachtiar
Keyword(s):  
Frequency Distribution ◽  
Confusion Matrix ◽  
High Accuracy ◽  
K Value ◽  
A Value ◽  
Optimal Value ◽  
Classification Evaluation ◽  
Email Spam ◽  
Email Classification

There are many cases of email abuse that have the potential to harm others. This email abuse is commonly known as spam, which contains advertisements, phishing scams, and even malware. This study purpose to know the classification of email spam with ham using the KNN method as an effort to reduce the amount of spam. KNN can classify spam or ham in an email by checking it using a different K value approach. The results of the classification evaluation using confusion matrix resulted in the KNN method with a value of K = 1 having the highest accuracy value of 91.4%. From the results of the study, it is known that the optimization of the K value in KNN using frequency distribution clustering can produce high accuracy of 100%, while k-means clustering produces an accuracy of 99%. So based on the results of the existing accuracy values, the frequency distribution clustering and k-means clustering can be used to optimize the K-optimal value of the KNN in the classification of existing spam emails.

Exploiting Quorum Sensing Inhibition for the Control of Pseudomonas aeruginosa and Acinetobacter baumannii Biofilms

2017 ◽  
Vol 17 (17) ◽  
pp. 1915-1927 ◽  
Author(s):  
Israel Castillo-Juarez ◽  
Luis Esau Lopez-Jacome ◽  
Gloria Soberon-Chavez ◽  
Maria Tomas ◽  
Jintae Lee ◽  
...  
Keyword(s):  
Pseudomonas Aeruginosa ◽  
Quorum Sensing ◽  
Acinetobacter Baumannii ◽  
Quorum Sensing Inhibition

scholarly journals An Imbalanced Image Classification Method for the Cell Cycle Phase

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 249
Author(s):  
Xin Jin ◽  
Yuanwen Zou ◽  
Zhongbing Huang
Keyword(s):  
Cell Cycle ◽  
Deep Learning ◽  
Image Classification ◽  
Classification Accuracy ◽  
Data Augmentation ◽  
Cycle Phase ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Cellular Life

The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

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