bThe Abbott RealTime MTB (RT MTB) assay is a new automated nucleic acid amplification test for the detection of Mycobacterium tuberculosis complex (MTBC) in clinical specimens. In combination with the RealTime MTB INH/RIF (RT MTB INH/RIF) resistance assay, which can be applied to RT MTB-positive specimens as an add-on assay, the tests also indicate the genetic markers of resistance to isoniazid (INH) and rifampin (RIF). We aimed to evaluate the diagnostic sensitivity and specificity of RT MTB using different types of respiratory and extrapulmonary specimens and to compare performance characteristics directly with those of the FluoroType MTB assay. The resistance results obtained by RT MTB INH/RIF were compared to those from the GenoType MTBDRplus and from phenotypic drug susceptibility testing. A total of 715 clinical specimens were analyzed. Compared to culture, the overall sensitivity of RT MTB was 92.1%; the sensitivity rates for smear-positive and smear-negative samples were 100% and 76.2%, respectively. The sensitivities of smear-negative specimens were almost identical for respiratory (76.3%) and extrapulmonary (76%) specimens. Specificity rates were 100% and 95.8% for culturenegative specimens and those that grew nontuberculous mycobacteria, respectively. RT MTB INH/RIF was applied to 233 RT MTB-positive samples and identified resistance markers in 7.7% of samples. Agreement with phenotypic and genotypic drug susceptibility testing was 99.5%. In conclusion, RT MTB and RT MTB INH/RIF allow for the rapid and accurate diagnosis of tuberculosis (TB) in different types of specimens and reliably indicate resistance markers. The strengths of this system are the comparably high sensitivity with paucibacillary specimens, its ability to detect INH and RIF resistance, and its high-throughput capacities. R apid and accurate diagnosis of tuberculosis (TB) and fast detection of drug resistance are essential to ensure early initiation of appropriate antituberculotic treatment, adequately manage the disease, and control further transmission. Worldwide, one-third of all TB cases and almost three-quarters of the 480,000 cases of multidrug-resistant (MDR; defined as resistance toward rifampin [RIF] and isoniazid [INH]) TB are not reported, with the vast majority of them occurring in high-burden countries (1). Molecular tests are the most promising tools to close this diagnostic gap. Consequently, nucleic acid amplification tests (NAATs), such as PCR assays that allow for the fast and accurate detection of Mycobacterium tuberculosis complex (MTBC) DNA directly in clinical specimens, have become an indispensable tool in TB diagnostics over the last several decades. Most commercial tests show excellent specificity and sensitivity rates with smear-positive specimens while sensitivity rates range from 49% to 78% with smearnegative samples (2-7).Particularly in regions with high prevalences of MDR-TB, the molecular detection of genetic markers of resistance directly in the clinical specimen is playing a pivotal role in early not...

Motivation Resistance co-occurrence within first-line anti-tuberculosis (TB) drugs is a common phenomenon. Existing methods based on genetic data analysis of Mycobacterium tuberculosis (MTB) have been able to predict resistance of MTB to individual drugs, but have not considered the resistance co-occurrence and cannot capture latent structure of genomic data that corresponds to lineages. Results We used a large cohort of TB patients from 16 countries across six continents where whole-genome sequences for each isolate and associated phenotype to anti-TB drugs were obtained using drug susceptibility testing recommended by the World Health Organization. We then proposed an end-to-end multi-task model with deep denoising auto-encoder (DeepAMR) for multiple drug classification and developed DeepAMR_cluster, a clustering variant based on DeepAMR, for learning clusters in latent space of the data. The results showed that DeepAMR outperformed baseline model and four machine learning models with mean AUROC from 94.4% to 98.7% for predicting resistance to four first-line drugs [i.e. isoniazid (INH), ethambutol (EMB), rifampicin (RIF), pyrazinamide (PZA)], multi-drug resistant TB (MDR-TB) and pan-susceptible TB (PANS-TB: MTB that is susceptible to all four first-line anti-TB drugs). In the case of INH, EMB, PZA and MDR-TB, DeepAMR achieved its best mean sensitivity of 94.3%, 91.5%, 87.3% and 96.3%, respectively. While in the case of RIF and PANS-TB, it generated 94.2% and 92.2% sensitivity, which were lower than baseline model by 0.7% and 1.9%, respectively. t-SNE visualization shows that DeepAMR_cluster captures lineage-related clusters in the latent space. Availability and implementation The details of source code are provided at http://www.robots.ox.ac.uk/∼davidc/code.php. Supplementary information Supplementary data are available at Bioinformatics online.

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У статті розглядаються стан і проблеми, зв’язані з підтриманням льотної придатності існуючого паркуповітряних суден України радянського та іноземного виробництва, шляхом створення центрів по їх технічномуобслуговуванню із залученням молодих спеціалістів. На території України відсутні сертифіковані організації з технічного обслуговування іноземної авіаційноїтехніки. Організація подібних центрів дозволить істотно скоротити час, кошти, підвищить ефективність використання повітряних суден і знизити вартість перевезень. Матеріали статті будуть корисні для експлуатантів, Міністерства освіти і науки, Державній авіаційнійслужбі України.

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