Journal: PLoS ONE

Article Title: Performance of GenoType MTBDR sl assay for detection of second-line drugs and ethambutol resistance directly from sputum specimens of MDR-TB patients in Bangladesh

doi: 10.1371/journal.pone.0261329

Figure Lengend Snippet: Demographic and clinical characteristics of 218 MDR-TB patients.

Article Snippet: The majority (85.8%) of the MDR-TB patients were from the three divisional regions of Chittagong, Rajshahi and Mymensingh (Individual data points are provided in the ).

Techniques:

Journal: BMC Infectious Diseases

Article Title: Time trend prediction and spatial–temporal analysis of multidrug-resistant tuberculosis in Guizhou Province, China, during 2014–2020

doi: 10.1186/s12879-022-07499-9

Figure Lengend Snippet: The SI of MDR-TB cases distribution in each month from 2014 to 2020 in Guizhou Province

Article Snippet: One most likely cluster and two secondary clusters were shown in Fig. . Fig. 7 The detected spatial–temporal cluster map of MDR-TB cases in Guizhou (2014–2020) The most likely cluster was detected from January 2015 to December 2016 with 154 reported cases (LLR = 272.06), covering 1 county (Nanming) in Guiyang prefecture.

Techniques:

Journal: BMC Infectious Diseases

Article Title: Time trend prediction and spatial–temporal analysis of multidrug-resistant tuberculosis in Guizhou Province, China, during 2014–2020

doi: 10.1186/s12879-022-07499-9

Figure Lengend Snippet: Comparison of actual and predicted cases of MDR-TB in Guizhou China. the black and green lines represent the observed values and predicted values, respectively; blue line represents 95% confidence intervals; after grey vertical line, the orange and yellow part represent 80% and 95% confidence intervals, respectively

Article Snippet: One most likely cluster and two secondary clusters were shown in Fig. . Fig. 7 The detected spatial–temporal cluster map of MDR-TB cases in Guizhou (2014–2020) The most likely cluster was detected from January 2015 to December 2016 with 154 reported cases (LLR = 272.06), covering 1 county (Nanming) in Guiyang prefecture.

Techniques: Comparison

Journal: BMC Infectious Diseases

Article Title: Time trend prediction and spatial–temporal analysis of multidrug-resistant tuberculosis in Guizhou Province, China, during 2014–2020

doi: 10.1186/s12879-022-07499-9

Figure Lengend Snippet: The geographical distribution of total MDR-TB cases in Guizhou from 2014 to 2020. a The geographical location of Guizhou in China; b The geographical location of 9 prefectures in Guizhou; c The geographical location of 88 counties in 9 prefectures, and distribution of total MDR-TB cases over seven years in each county

Article Snippet: One most likely cluster and two secondary clusters were shown in Fig. . Fig. 7 The detected spatial–temporal cluster map of MDR-TB cases in Guizhou (2014–2020) The most likely cluster was detected from January 2015 to December 2016 with 154 reported cases (LLR = 272.06), covering 1 county (Nanming) in Guiyang prefecture.

Techniques:

Journal: BMC Infectious Diseases

Article Title: Time trend prediction and spatial–temporal analysis of multidrug-resistant tuberculosis in Guizhou Province, China, during 2014–2020

doi: 10.1186/s12879-022-07499-9

Figure Lengend Snippet: Maps of local spatial autocorrelation cluster of MDR-TB for each county in Guizhou province from 2014 to 2020 by ArcMap software. Only those counties whose local Moran’s I have reached the significance level of 0.05 will be present on the map: a MDR-TB clusters in 2014; b MDR-TB clusters in 2015; c MDR-TB clusters in 2016; d MDR-TB clusters in 2017; e MDR-TB clusters in 2018; f MDR-TB clusters in 2019; g MDR-TB clusters in 2020

Article Snippet: One most likely cluster and two secondary clusters were shown in Fig. . Fig. 7 The detected spatial–temporal cluster map of MDR-TB cases in Guizhou (2014–2020) The most likely cluster was detected from January 2015 to December 2016 with 154 reported cases (LLR = 272.06), covering 1 county (Nanming) in Guiyang prefecture.

Techniques: Software

Journal: BMC Infectious Diseases

Article Title: Time trend prediction and spatial–temporal analysis of multidrug-resistant tuberculosis in Guizhou Province, China, during 2014–2020

doi: 10.1186/s12879-022-07499-9

Figure Lengend Snippet: The detected spatial–temporal cluster map of MDR-TB cases in Guizhou (2014–2020)

Article Snippet: One most likely cluster and two secondary clusters were shown in Fig. . Fig. 7 The detected spatial–temporal cluster map of MDR-TB cases in Guizhou (2014–2020) The most likely cluster was detected from January 2015 to December 2016 with 154 reported cases (LLR = 272.06), covering 1 county (Nanming) in Guiyang prefecture.

Techniques:

Journal: BMC Infectious Diseases

Article Title: Time trend prediction and spatial–temporal analysis of multidrug-resistant tuberculosis in Guizhou Province, China, during 2014–2020

doi: 10.1186/s12879-022-07499-9

Figure Lengend Snippet: Demographic characteristics of MDR-TB cases in Guizhou Province, 2014–2020 (n, %)

Article Snippet: One most likely cluster and two secondary clusters were shown in Fig. . Fig. 7 The detected spatial–temporal cluster map of MDR-TB cases in Guizhou (2014–2020) The most likely cluster was detected from January 2015 to December 2016 with 154 reported cases (LLR = 272.06), covering 1 county (Nanming) in Guiyang prefecture.

Techniques:

Journal: BMC Infectious Diseases

Article Title: Time trend prediction and spatial–temporal analysis of multidrug-resistant tuberculosis in Guizhou Province, China, during 2014–2020

doi: 10.1186/s12879-022-07499-9

Figure Lengend Snippet: The geographical distribution of MDR-TB cases in Guizhou Province, 2014–2020

Article Snippet: One most likely cluster and two secondary clusters were shown in Fig. . Fig. 7 The detected spatial–temporal cluster map of MDR-TB cases in Guizhou (2014–2020) The most likely cluster was detected from January 2015 to December 2016 with 154 reported cases (LLR = 272.06), covering 1 county (Nanming) in Guiyang prefecture.

Techniques:

Journal: BMC Infectious Diseases

Article Title: Time trend prediction and spatial–temporal analysis of multidrug-resistant tuberculosis in Guizhou Province, China, during 2014–2020

doi: 10.1186/s12879-022-07499-9

Figure Lengend Snippet: Spatial − temporal scan of MDR-TB in Guizhou from 2014 to 2020

Article Snippet: One most likely cluster and two secondary clusters were shown in Fig. . Fig. 7 The detected spatial–temporal cluster map of MDR-TB cases in Guizhou (2014–2020) The most likely cluster was detected from January 2015 to December 2016 with 154 reported cases (LLR = 272.06), covering 1 county (Nanming) in Guiyang prefecture.

Techniques:

Journal: BMC Infectious Diseases

Article Title: Time trend prediction and spatial–temporal analysis of multidrug-resistant tuberculosis in Guizhou Province, China, during 2014–2020

doi: 10.1186/s12879-022-07499-9

Figure Lengend Snippet: The geographical distribution of MDR-TB cases in Guizhou Province, 2014–2020

Article Snippet: The descriptive analysis gave a rough impression that 4 prefectures (Bijie, Guiyang, Liupanshui, Zunyi) had higher MDR-TB burden than other prefectures.

Techniques:

Journal: Journal of Clinical Microbiology

Article Title: Comparative Analytical Evaluation of Four Centralized Platforms for the Detection of Mycobacterium tuberculosis Complex and Resistance to Rifampicin and Isoniazid

doi: 10.1128/JCM.02168-20

Figure Lengend Snippet: Workflows for each platform for the detection of the M. tuberculosis complex. Each instrument runs the number of samples per run as indicated. Additionally, one positive and one negative control were included in the run, except for the BD Max MDR-TB assay, which does not include external controls. A sample processing control is included in each BD Max MDR-TB run. Run times indicated are for a run with the maximum number of samples. Sonication for the Roche product is done on a separate, dedicated instrument for one sample at a time; centrifugation is done at 3,000 × g . Note that the Roche and BD instruments are fully integrated, whereas for the Hain Lifescience and Abbott assays, two separate instruments are used (one for DNA extraction and one for amplification and detection), with a manual transfer step in between. MIS, microbial inactivation solution; IR, inactivation reagent.

Article Snippet: For RIF resistance detection, the S p scores ranged between 80% (Abbott RealTi m e MTB RIF/INH) and 100% (BD Max MDR-TB).

Techniques: Negative Control, Sonication, Centrifugation, DNA Extraction, Amplification

Journal: Journal of Clinical Microbiology

Article Title: Comparative Analytical Evaluation of Four Centralized Platforms for the Detection of Mycobacterium tuberculosis Complex and Resistance to Rifampicin and Isoniazid

doi: 10.1128/JCM.02168-20

Figure Lengend Snippet: Simple proportion and weighted proportion scores per assay calculated for rifampicin and isoniazid ^a

Article Snippet: For RIF resistance detection, the S p scores ranged between 80% (Abbott RealTi m e MTB RIF/INH) and 100% (BD Max MDR-TB).

Techniques:

Journal: Journal of Clinical Microbiology

Article Title: Comparative Analytical Evaluation of Four Centralized Platforms for the Detection of Mycobacterium tuberculosis Complex and Resistance to Rifampicin and Isoniazid

doi: 10.1128/JCM.02168-20

Figure Lengend Snippet: Accuracy for the detection of specific mutations conferring resistance to rifampicin and isoniazid ^a

Article Snippet: For RIF resistance detection, the S p scores ranged between 80% (Abbott RealTi m e MTB RIF/INH) and 100% (BD Max MDR-TB).

Techniques: Mutagenesis

Journal: BMC Public Health

Article Title: A qualitative meta-synthesis of facilitators and barriers to tuberculosis diagnosis and treatment in Nigeria

doi: 10.1186/s12889-021-10173-5

Figure Lengend Snippet: Overview of selected studies

Article Snippet: 3 , Bieh, 2017 [ ] , Hospitalized care for MDR-TB in Port Harcourt, Nigeria: a qualitative study , 2 gender based FGDs and 11 in-depth interviews with patients , Transcription of data, coding and thematic assembly and analysis , Patient-centered care improves access and removes stigma.

Techniques: Control, Infection