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Background: Information on genotypic with comparison of phenotypic drug sensitivity test of anti-tuberculosis (TB) has been reported in several studies, which have variable results. The present study aimed to assess the Genotype MTBDRsl version 2.0/Line probe assay (LPA) for the detection of fluoroquinolones (FQ) and aminoglycosides (AMGs) resistance mutations among drug-resistant Mycobacterium TB (MTB) strains and also to compare the patterns of genotypic mutations of gyrA/B, rrs, and eis with mycobacteria growth indicator tube (MGIT 960). Methods: A total of 1416 samples were subjected to Genotype MTBDRsl version 2.0 assay. One hundred and twenty sputum smear positive MTB isolates and 37 sputum smear negative MTB isolates confirmed multiple drug resistance resistant to FQ and AMG by the Genotype MTBDRsl version 2.0 were subjected to phenotypic drug susceptibility testing (DST) were analyzed. Results: The association sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for the resistance detection between MGIT (DST) and the Genotype MTBDRsl version 2.0 assay was significant (P < 0.01) of moxifloxacin (MFX) concentration. Sensitivity and specificity value for kanamycin (KAN) resistance was 76% and 89%; 47% and 94% for capreomycin (CAP); and 60% and 76% for low-level KAN, respectively. Conclusion: Our results indicate that MFX (0.25and 1 μg/mL), KAN (2.5 μg/mL), and CAP (2.5 μg/mL) significantly (P < 0.01) and support the World Health Organization guidance to test FQ and AMG by genotypic test.

Data collection using local differential privacy (LDP) has mainly been studied for homogeneous data. Several data categories, including key-value pairs, must be estimated simultaneously in real-world applications, including the frequency of keys and the mean values within each key. It is challenging to achieve an acceptable utility-privacy tradeoff using LDP for key-value data collection since the data has two aspects, and a client could have multiple key-value pairs. Current LDP approaches are not scalable enough to handle large and small datasets. When the dataset is small, there is insufficient data to calculate statistical parameters; when the dataset is enormous, such as in streaming data, there is a risk of data leakage due to the high availability of too much information. The result is unsuitable for examination due to the substantial amount of randomization used in some methods. Existing LDP approaches are mostly restricted to basic data categories like category and numerical values. To address these difficulties, this research developed the DKVALP (Differentially private key-value pairs) algorithm, which ensures differential privacy in key-value pair data. This DKVALP is a lightweight, differentially private data algorithm that generates random noise using an updated Laplace algorithm to ensure differential privacy for the data. According to execution outputs on synthetic and real-world datasets, the proposed DKVALP framework offers improved usefulness for both frequency and mean predictions over the similar LDP security as conventional approaches. [ABSTRACT FROM AUTHOR]

An effective resource management is one of the critical success factors for precise virtualization process in cloud computing in presence of dynamic demands of the user. After reviewing the existing research work towards resource management in cloud, it was found that there is still a large scope of enhancement. The existing techniques are found not to completely utilize the potential features of virtual machine in order to perform resource allocation. This paper presents a framework called FDMC or Framework for Decision Making in Cloud that gives better capability for the VMs to perform resource allocation. The contribution of FDMC is a joint operation of VM to ensure faster processing of task and thereby withstand more number of increasing traffic. The study outcome was compared with some of the existing systems to find FDMC excels better performance in the scale of task allocation time, amount of core wasted, amount of storage wasted, and communication cost. [ABSTRACT FROM AUTHOR]

Background: Information on genotypic with comparison of phenotypic drug sensitivity test of anti-tuberculosis (TB) has been reported in several studies, which have variable results. The present study aimed to assess the Genotype MTBDRsl version 2.0/Line probe assay (LPA) for the detection of fluoroquinolones (FQ) and aminoglycosides (AMGs) resistance mutations among drug-resistant Mycobacterium TB (MTB) strains and also to compare the patterns of genotypic mutations of gyrA/B, rrs, and eis with mycobacteria growth indicator tube (MGIT 960)., Methods: A total of 1416 samples were subjected to Genotype MTBDRsl version 2.0 assay. One hundred and twenty sputum smear positive MTB isolates and 37 sputum smear negative MTB isolates confirmed multiple drug resistance resistant to FQ and AMG by the Genotype MTBDRsl version 2.0 were subjected to phenotypic drug susceptibility testing (DST) were analyzed., Results: The association sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for the resistance detection between MGIT (DST) and the Genotype MTBDRsl version 2.0 assay was significant (P < 0.01) of moxifloxacin (MFX) concentration. Sensitivity and specificity value for kanamycin (KAN) resistance was 76% and 89%; 47% and 94% for capreomycin (CAP); and 60% and 76% for low-level KAN, respectively., Conclusion: Our results indicate that MFX (0.25and 1 μg/mL), KAN (2.5 μg/mL), and CAP (2.5 μg/mL) significantly (P < 0.01) and support the World Health Organization guidance to test FQ and AMG by genotypic test., Competing Interests: None