Suchergebnisse

Abstract Background Zambia continues to grapple with a high tuberculosis (TB) burden despite a long running Directly Observed Treatment Short course programme. Understanding issues that affect patient adherence to treatment programme is an important component in implementation of a successful TB control programme. We set out to investigate pulmonary TB patient's attitudes to seek health care, assess the care received from government health care centres based on TB patients' reports, and to seek associations with patient adherence to TB treatment programme. Methods This was a cross-sectional study of 105 respondents who had been registered as pulmonary TB patients (new and retreatment cases) in Ndola District between January 2006 and July 2007. We administered a structured questionnaire, bearing questions to obtain individual data on socio-demographics, health seeking behaviour, knowledge on TB, reported adherence to TB treatment, and health centre care received during treatment to consenting respondents. Results We identified that respondents delayed to seek treatment (68%) even when knowledge of TB symptoms was high (78%) or when they suspected that they had TB (73%). Respondent adherence to taking medication was high (77%) but low adherence to submitting follow-up sputum (47%) was observed in this group. Similarly, caregivers educate their patients more often on the treatment of the disease (98%) and drug taking (100%), than on submitting sputum during treatment (53%) and its importance (54%). Respondent adherence to treatment was significantly associated with respondent's knowledge about the disease and its treatment (p < 0.0001), and with caregiver's adherence to treatment guidelines (p = 0.0027). Conclusions There is a need to emphasise the importance of submitting follow-up sputum during patient education and counselling in order to enhance patient adherence and ultimately treatment outcome.

Open science aims at sharing scientific output in order to maximize the impact of research. This allows follow-on studies, facilitates new discoveries, improves reproducibility of experiments and favours transparency of results. Although open data is becoming a well-known concept, less attention is given to the availability of research materials. In life sciences, public microbial collections represent an historical example of open science, thanks to their longstanding experience in the preservation of living microbial strains and their distribution for further scientific investigations or development. These microbial resource centres provide well-characterized, quality-controlled and authenticated strains and associated data (1). In microbiology, the diversity of bacteria, fungi and algae is an invaluable source of applications for the bio-industry. It needs to be secured following (inter)national legislations for future utilizations and research questions. The responsibility to make microorganisms available is shared by researchers, funding agencies and publishers (1). Microbiologists need to be more aware towards strain conservation. Governmental funding policies should request the deposit of strains isolated during financed projects. Regarding publishers, most journals encourage authors to deposit their datasets (codes, sequences, etc) in public repositories but very few specifically require deposit of biological material and cultivated strains in scientific collections. However, this is a key prerequisite to "make it possible to repeat the experiments and perform future research"(2). Editors should therefore implement mechanisms for active agreement by authors to deposit strains when submitting an article. Such mechanisms could follow Transparency and Openness Promotion guidelines (3) for journals that include standards for research materials.

The surveillance of drug resistance among tuberculosis (TB) patients is central to preventing the spread of antimicrobial resistance. The Democratic Republic of the Congo (DR Congo) is classified by the World Health Organization (WHO) as a country with a high burden of TB and multidrug-resistant TB (MDR-TB), but there are no nationally representative data on drug resistance. In 2016–2017, a national survey of TB patients was conducted in 108 microscopy centres across all 11 provinces of the country using innovative molecular approaches. Sputum samples were collected from 1,545 new and 163 previously treated patients. These were tested by the Xpert MTB/RIF assay, followed by targeted next-generation sequencing performed directly on sputum. The prevalence of rifampicin resistance was low, at 1.8% (95% CI: 1.0–3.2) among new and 17.3% (95% CI: 11.9–24.4) among previously treated patients. Resistance to pyrazinamide, fluoroquinolones and second-line injectables was also low. The prevalence of resistance to isoniazid among rifampicin-susceptible patients was higher, at 6.6% (95% CI: 4.4–9.8) among new and 8.7% (95% : 3.2–21.2) among previously treated patients. Diagnosing and treating isoniazid-resistant patients remains a challenge, given that many will be missed by the current national diagnostic algorithm that is driven by detecting rifampicin resistance by Xpert MTB/RIF. This is the first nationwide survey incorporating targeted sequencing directly on sputum. It serves as a proof-of-concept for other settings that do yet have rapid specimen transport networks or capacity to conduct culture.

The surveillance of drug resistance among tuberculosis (TB) patients is central to preventing the spread of antimicrobial resistance. The Democratic Republic of the Congo (DR Congo) is classified by the World Health Organization (WHO) as a country with a high burden of TB and multidrug-resistant TB (MDR-TB), but there are no nationally representative data on drug resistance. In 2016-2017, a national survey of TB patients was conducted in 108 microscopy centres across all 11 provinces of the country using innovative molecular approaches. Sputum samples were collected from 1,545 new and 163 previously treated patients. These were tested by the Xpert MTB/RIF assay, followed by targeted next-generation sequencing performed directly on sputum. The prevalence of rifampicin resistance was low, at 1.8% (95% CI: 1.0-3.2) among new and 17.3% (95% CI: 11.9-24.4) among previously treated patients. Resistance to pyrazinamide, fluoroquinolones and second-line injectables was also low. The prevalence of resistance to isoniazid among rifampicin-susceptible patients was higher, at 6.6% (95% CI: 4.4-9.8) among new and 8.7% (95% : 3.2-21.2) among previously treated patients. Diagnosing and treating isoniazid-resistant patients remains a challenge, given that many will be missed by the current national diagnostic algorithm that is driven by detecting rifampicin resistance by Xpert MTB/RIF. This is the first nationwide survey incorporating targeted sequencing directly on sputum. It serves as a proof-of-concept for other settings that do yet have rapid specimen transport networks or capacity to conduct culture.

The surveillance of drug resistance among tuberculosis (TB) patients is central to preventing the spread of antimicrobial resistance. The Democratic Republic of the Congo (DR Congo) is classified by the World Health Organization (WHO) as a country with a high burden of TB and multidrug-resistant TB (MDR-TB), but there are no nationally representative data on drug resistance. In 2016-2017, a national survey of TB patients was conducted in 108 microscopy centres across all 11 provinces of the country using innovative molecular approaches. Sputum samples were collected from 1,545 new and 163 previously treated patients. These were tested by the Xpert MTB/RIF assay, followed by targeted next-generation sequencing performed directly on sputum. The prevalence of rifampicin resistance was low, at 1.8% (95% CI: 1.0-3.2) among new and 17.3% (95% CI: 11.9-24.4) among previously treated patients. Resistance to pyrazinamide, fluoroquinolones and second-line injectables was also low. The prevalence of resistance to isoniazid among rifampicin-susceptible patients was higher, at 6.6% (95% CI: 4.4-9.8) among new and 8.7% (95% : 3.2-21.2) among previously treated patients. Diagnosing and treating isoniazid-resistant patients remains a challenge, given that many will be missed by the current national diagnostic algorithm that is driven by detecting rifampicin resistance by Xpert MTB/RIF. This is the first nationwide survey incorporating targeted sequencing directly on sputum. It serves as a proof-of-concept for other settings that do yet have rapid specimen transport networks or capacity to conduct culture.

International audience ; Conventional molecular tests for detecting Mycobacterium tuberculosis complex (MTBC) drug resistance on clinical samples cover a limited set of mutations. Whole genome sequencing (WGS) typically requires culture. Here, we evaluated the Deeplex Myc-TB targeted deep sequencing assay for prediction of resistance to 13 anti-tuberculous drugs/drug classes, directly applicable on sputum. With MTBC DNA tests, the limit of detection was 100–1000 genome copies for fixed resistance mutations. Deeplex Myc-TB captured in silico 97.1–99.3% of resistance phenotypes correctly predicted by WGS from 3651 MTBC genomes. On 429 isolates, the assay predicted 92.2% of 2369 first- and second-line phenotypes, with a sensitivity of 95.3% and specificity of 97.4%. Fifty-six of 69 (81.2%) residual discrepancies with phenotypic results involved pyrazinamide, ethambutol, and ethionamide, and low-level rifampicin- or isoniazid-resistance mutations, all notoriously prone to phenotypic testing variability. Only 2 of 91 (2.2%) resistance phenotypes undetected by Deeplex Myc-TB had known resistance-associated mutations by WGS analysis outside Deeplex Myc-TB targets. Phenotype predictions from Deeplex Myc-TB analysis directly on 109 sputa from a Djibouti survey matched those of MTBSeq/PhyResSE/Mykrobe, fed with WGS data from subsequent cultures, with a sensitivity of 93.5/98.5/93.1% and specificity of 98.5/97.2/95.3%. Most residual discordances involved gene deletions/indels and 3–12% heteroresistant calls undetected by WGS analysis, or natural pyrazinamide resistance of globally rare "M. canettii" strains then unreported by Deeplex Myc-TB. On 1494 arduous sputa from a Democratic Republic of the Congo survey, 14 902 of 19 422 (76.7%) possible susceptible or resistance phenotypes could be predicted culture-free. Deeplex Myc-TB may enable fast, tailored tuberculosis treatment.

International audience ; Conventional molecular tests for detecting Mycobacterium tuberculosis complex (MTBC) drug resistance on clinical samples cover a limited set of mutations. Whole genome sequencing (WGS) typically requires culture. Here, we evaluated the Deeplex Myc-TB targeted deep sequencing assay for prediction of resistance to 13 anti-tuberculous drugs/drug classes, directly applicable on sputum. With MTBC DNA tests, the limit of detection was 100–1000 genome copies for fixed resistance mutations. Deeplex Myc-TB captured in silico 97.1–99.3% of resistance phenotypes correctly predicted by WGS from 3651 MTBC genomes. On 429 isolates, the assay predicted 92.2% of 2369 first- and second-line phenotypes, with a sensitivity of 95.3% and specificity of 97.4%. Fifty-six of 69 (81.2%) residual discrepancies with phenotypic results involved pyrazinamide, ethambutol, and ethionamide, and low-level rifampicin- or isoniazid-resistance mutations, all notoriously prone to phenotypic testing variability. Only 2 of 91 (2.2%) resistance phenotypes undetected by Deeplex Myc-TB had known resistance-associated mutations by WGS analysis outside Deeplex Myc-TB targets. Phenotype predictions from Deeplex Myc-TB analysis directly on 109 sputa from a Djibouti survey matched those of MTBSeq/PhyResSE/Mykrobe, fed with WGS data from subsequent cultures, with a sensitivity of 93.5/98.5/93.1% and specificity of 98.5/97.2/95.3%. Most residual discordances involved gene deletions/indels and 3–12% heteroresistant calls undetected by WGS analysis, or natural pyrazinamide resistance of globally rare "M. canettii" strains then unreported by Deeplex Myc-TB. On 1494 arduous sputa from a Democratic Republic of the Congo survey, 14 902 of 19 422 (76.7%) possible susceptible or resistance phenotypes could be predicted culture-free. Deeplex Myc-TB may enable fast, tailored tuberculosis treatment.

International audience ; Conventional molecular tests for detecting Mycobacterium tuberculosis complex (MTBC) drug resistance on clinical samples cover a limited set of mutations. Whole genome sequencing (WGS) typically requires culture. Here, we evaluated the Deeplex Myc-TB targeted deep sequencing assay for prediction of resistance to 13 anti-tuberculous drugs/drug classes, directly applicable on sputum. With MTBC DNA tests, the limit of detection was 100–1000 genome copies for fixed resistance mutations. Deeplex Myc-TB captured in silico 97.1–99.3% of resistance phenotypes correctly predicted by WGS from 3651 MTBC genomes. On 429 isolates, the assay predicted 92.2% of 2369 first- and second-line phenotypes, with a sensitivity of 95.3% and specificity of 97.4%. Fifty-six of 69 (81.2%) residual discrepancies with phenotypic results involved pyrazinamide, ethambutol, and ethionamide, and low-level rifampicin- or isoniazid-resistance mutations, all notoriously prone to phenotypic testing variability. Only 2 of 91 (2.2%) resistance phenotypes undetected by Deeplex Myc-TB had known resistance-associated mutations by WGS analysis outside Deeplex Myc-TB targets. Phenotype predictions from Deeplex Myc-TB analysis directly on 109 sputa from a Djibouti survey matched those of MTBSeq/PhyResSE/Mykrobe, fed with WGS data from subsequent cultures, with a sensitivity of 93.5/98.5/93.1% and specificity of 98.5/97.2/95.3%. Most residual discordances involved gene deletions/indels and 3–12% heteroresistant calls undetected by WGS analysis, or natural pyrazinamide resistance of globally rare "M. canettii" strains then unreported by Deeplex Myc-TB. On 1494 arduous sputa from a Democratic Republic of the Congo survey, 14 902 of 19 422 (76.7%) possible susceptible or resistance phenotypes could be predicted culture-free. Deeplex Myc-TB may enable fast, tailored tuberculosis treatment.

International audience ; Conventional molecular tests for detecting Mycobacterium tuberculosis complex (MTBC) drug resistance on clinical samples cover a limited set of mutations. Whole genome sequencing (WGS) typically requires culture. Here, we evaluated the Deeplex Myc-TB targeted deep sequencing assay for prediction of resistance to 13 anti-tuberculous drugs/drug classes, directly applicable on sputum. With MTBC DNA tests, the limit of detection was 100–1000 genome copies for fixed resistance mutations. Deeplex Myc-TB captured in silico 97.1–99.3% of resistance phenotypes correctly predicted by WGS from 3651 MTBC genomes. On 429 isolates, the assay predicted 92.2% of 2369 first- and second-line phenotypes, with a sensitivity of 95.3% and specificity of 97.4%. Fifty-six of 69 (81.2%) residual discrepancies with phenotypic results involved pyrazinamide, ethambutol, and ethionamide, and low-level rifampicin- or isoniazid-resistance mutations, all notoriously prone to phenotypic testing variability. Only 2 of 91 (2.2%) resistance phenotypes undetected by Deeplex Myc-TB had known resistance-associated mutations by WGS analysis outside Deeplex Myc-TB targets. Phenotype predictions from Deeplex Myc-TB analysis directly on 109 sputa from a Djibouti survey matched those of MTBSeq/PhyResSE/Mykrobe, fed with WGS data from subsequent cultures, with a sensitivity of 93.5/98.5/93.1% and specificity of 98.5/97.2/95.3%. Most residual discordances involved gene deletions/indels and 3–12% heteroresistant calls undetected by WGS analysis, or natural pyrazinamide resistance of globally rare "M. canettii" strains then unreported by Deeplex Myc-TB. On 1494 arduous sputa from a Democratic Republic of the Congo survey, 14 902 of 19 422 (76.7%) possible susceptible or resistance phenotypes could be predicted culture-free. Deeplex Myc-TB may enable fast, tailored tuberculosis treatment.

International audience ; Conventional molecular tests for detecting Mycobacterium tuberculosis complex (MTBC) drug resistance on clinical samples cover a limited set of mutations. Whole genome sequencing (WGS) typically requires culture. Here, we evaluated the Deeplex Myc-TB targeted deep sequencing assay for prediction of resistance to 13 anti-tuberculous drugs/drug classes, directly applicable on sputum. With MTBC DNA tests, the limit of detection was 100–1000 genome copies for fixed resistance mutations. Deeplex Myc-TB captured in silico 97.1–99.3% of resistance phenotypes correctly predicted by WGS from 3651 MTBC genomes. On 429 isolates, the assay predicted 92.2% of 2369 first- and second-line phenotypes, with a sensitivity of 95.3% and specificity of 97.4%. Fifty-six of 69 (81.2%) residual discrepancies with phenotypic results involved pyrazinamide, ethambutol, and ethionamide, and low-level rifampicin- or isoniazid-resistance mutations, all notoriously prone to phenotypic testing variability. Only 2 of 91 (2.2%) resistance phenotypes undetected by Deeplex Myc-TB had known resistance-associated mutations by WGS analysis outside Deeplex Myc-TB targets. Phenotype predictions from Deeplex Myc-TB analysis directly on 109 sputa from a Djibouti survey matched those of MTBSeq/PhyResSE/Mykrobe, fed with WGS data from subsequent cultures, with a sensitivity of 93.5/98.5/93.1% and specificity of 98.5/97.2/95.3%. Most residual discordances involved gene deletions/indels and 3–12% heteroresistant calls undetected by WGS analysis, or natural pyrazinamide resistance of globally rare "M. canettii" strains then unreported by Deeplex Myc-TB. On 1494 arduous sputa from a Democratic Republic of the Congo survey, 14 902 of 19 422 (76.7%) possible susceptible or resistance phenotypes could be predicted culture-free. Deeplex Myc-TB may enable fast, tailored tuberculosis treatment.

Conventional molecular tests for detecting Mycobacterium tuberculosis complex (MTBC) drug resistance on clinical samples cover a limited set of mutations. Whole-genome sequencing (WGS) typically requires culture. Here, we evaluated the Deeplex Myc-TB targeted deep-sequencing assay for prediction of resistance to 13 anti-tuberculous drugs/drug classes, directly applicable on sputum. With MTBC DNA tests, the limit of detection was 100–1000 genome copies for fixed resistance mutations. Deeplex Myc-TB captured in silico 97.1–99.3% of resistance phenotypes correctly predicted by WGS from 3651 MTBC genomes. On 429 isolates, the assay predicted 92.2% of 2369 first- and second-line phenotypes, with a sensitivity of 95.3% and a specificity of 97.4%. 56 out of 69 (81.2%) residual discrepancies with phenotypic results involved pyrazinamide, ethambutol and ethionamide, and low-level rifampicin or isoniazid resistance mutations, all notoriously prone to phenotypic testing variability. Only two out of 91 (2.2%) resistance phenotypes undetected by Deeplex Myc-TB had known resistance-associated mutations by WGS analysis outside Deeplex Myc-TB targets. Phenotype predictions from Deeplex Myc-TB analysis directly on 109 sputa from a Djibouti survey matched those of MTBSeq/PhyResSE/Mykrobe, fed with WGS data from subsequent cultures, with a sensitivity of 93.5/98.5/93.1% and a specificity of 98.5/97.2/95.3%, respectively. Most residual discordances involved gene deletions/indels and 3–12% heteroresistant calls undetected by WGS analysis or natural pyrazinamide resistance of globally rare "Mycobacterium canettii" strains then unreported by Deeplex Myc-TB. On 1494 arduous sputa from a Democratic Republic of the Congo survey, 14 902 out of 19 422 (76.7%) possible susceptible or resistance phenotypes could be predicted culture-free. Deeplex Myc-TB may enable fast, tailored tuberculosis treatment.

10 Pages, 1 Figure, 3 Tables. Supplementary information: http://dx.doi.org/10.1038/s41598-018-33731-1 ; Drug-resistant tuberculosis poses a persistent public health threat. The ReSeqTB platform is a collaborative, curated knowledgebase, designed to standardize and aggregate global Mycobacterium tuberculosis complex (MTBC) variant data from whole genome sequencing (WGS) with phenotypic drug susceptibility testing (DST) and clinical data. We developed a unified analysis variant pipeline (UVP) ( https://github.com/CPTR-ReSeqTB/UVP ) to identify variants and assign lineage from MTBC sequence data. Stringent thresholds and quality control measures were incorporated in this open source tool. The pipeline was validated using a well-characterized dataset of 90 diverse MTBC isolates with conventional DST and DNA Sanger sequencing data. The UVP exhibited 98.9% agreement with the variants identified using Sanger sequencing and was 100% concordant with conventional methods of assigning lineage. We analyzed 4636 publicly available MTBC isolates in the ReSeqTB platform representing all seven major MTBC lineages. The variants detected have an above 94% accuracy of predicting drug based on the accompanying DST results in the platform. The aggregation of variants over time in the platform will establish confidence-graded mutations statistically associated with phenotypic drug resistance. These tools serve as critical reference standards for future molecular diagnostic assay developers, researchers, public health agencies and clinicians working towards the control of drug-resistant tuberculosis. ; This study was supported by the Bill & Melinda Gates Foundation under grant agreement OPP1115887 to C-Path for developing the ReSeqTB drug resistance data sharing platform and under grant agreement FIND OPP1115209 to address how to score mutations in the ReSeqTB data sharing platform initiative. The South African MRC and the EDCTP support K. Dheda. I. Comas is supported by the Ministerio de Economía y Competitividad (Spanish Government) research grant SAF2016-77346-R and the European Research Council (ERC) (638553-TB-ACCELERATE). L. Chindelevitch acknowledges support by NSERC, Genome Canada, and the Sloan Foundation. Use of trade names is for identification only and does not constitute endorsement by the US Department of Health and Human Services, the US Public Health Service, or the Centers for Disease Control and Prevention. The findings and conclusions in this report are those of the authors and do not necessarily represent the views of the funding agency. ; Peer reviewed