Resistance testing for Ethambutol and Pyrazinamide in multidrug-resistant tuberculosis clinical isolates
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PROVIDER: PRJEB27354 | ENA |
REPOSITORIES: ENA
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Project description:Pyrazinamide Susceptibility Testing in M. tuberculosis
| PRJEB42011 | ENA
Project description:The antimicrobials isoniazid and pyrazinamide, used for the treatment of tuberculosis are known to cause drug-induced liver injury in humans. This limits the effectiveness of tuberculosis treatment, resulting in incomplete cure, relapse and the development of antimicrobial resistance. MicroRNAs are known to be good biomarkers of disease, with the microRNA miR-122 being diagnostic for liver injury. In this study zebrafish larvae were exposed to the anti-tuberculosis drugs isoniazid and pyrazinamide at concentrations which demonstrated liver injury by microscopy and histology. The aim of this study is to understand small RNA changes occurring in anti-tuberculosis drug-induced liver injury and to attempt to identify novel microRNA biomarkers of liver injury.
2023-12-25 | GSE146260 | GEO
Project description:Accurate diagnostic discrimination of benign renal oncocytoma (OC) and malignant renal cell carcinomas (RCC) is not only useful for planning appropriate treatment strategies of patients with renal masses but also for estimating prognosis. Classification of renal neoplasms solely by histopathology can often be challenging for a variety of reasons. The aim of this study was to develop and validate a genomic algorithm for molecular classification of renal cortical neoplasms that could be implemented in a routine clinical diagnostic setting. Using TCGA (The Cancer Genome Atlas) copy number profiles of over 600 RCC specimens, prior FISH studies and published literature, a classification algorithm was developed consisting of 15 genomic markers: loss of VHL, 3p21, 8p, and chromosomes 1, 2, 6, 10 and 17, and gain of 5qter, 16p, 17q, 20q, and chromosomes 3, 7, and 12. Criteria for scoring specimens for the presence of each genomic marker were established. As validation, 191 surgically resected formalin-fixed paraffin-embedded renal neoplasms were blindly submitted to targeted array-CGH and were classified according to the algorithm. Upon histologic re-review leading to exclusion of three specimens and using histology as the gold standard, the algorithm correctly classified 58 of 62 (93%) clear cell renal cell carcinoma, 51 of 56 (91%) papillary RCC, and 33 of 34 (97%) chromophobe RCC. Of the 36 OC specimens, 17 were classified as OC, two as a malignant subtype, 14 as benign, and three exhibited alterations not associated with a specific subtype. In ten of the latter two groups, CCND1-rearrangement was detected by fluorescence in situ hybridization, affording a classification as OC. Together, 33 of 36 (92%) OC were classified as OC or benign. For the entire validation cohort, an overall diagnostic sensitivity of 93% and above 97% specificity was achieved, suggesting that the implementation of genome-based molecular classification in a clinical diagnostic setting could impact the overall management and outcome of patients with renal tumors.
2015-04-29 | GSE57162 | GEO
Project description:Accurate diagnostic discrimination of benign renal oncocytoma (OC) and malignant renal cell carcinomas (RCC) is not only useful for planning appropriate treatment strategies of patients with renal masses but also for estimating prognosis. Classification of renal neoplasms solely by histopathology can often be challenging for a variety of reasons. The aim of this study was to develop and validate a genomic algorithm for molecular classification of renal cortical neoplasms that could be implemented in a routine clinical diagnostic setting. Using TCGA (The Cancer Genome Atlas) copy number profiles of over 600 RCC specimens, prior FISH studies and published literature, a classification algorithm was developed consisting of 15 genomic markers: loss of VHL, 3p21, 8p, and chromosomes 1, 2, 6, 10 and 17, and gain of 5qter, 16p, 17q, 20q, and chromosomes 3, 7, and 12. Criteria for scoring specimens for the presence of each genomic marker were established. As validation, 191 surgically resected formalin-fixed paraffin-embedded renal neoplasms were blindly submitted to targeted array-CGH and were classified according to the algorithm. Upon histologic re-review leading to exclusion of three specimens and using histology as the gold standard, the algorithm correctly classified 58 of 62 (93%) clear cell renal cell carcinoma, 51 of 56 (91%) papillary RCC, and 33 of 34 (97%) chromophobe RCC. Of the 36 OC specimens, 17 were classified as OC, two as a malignant subtype, 14 as benign, and three exhibited alterations not associated with a specific subtype. In ten of the latter two groups, CCND1-rearrangement was detected by fluorescence in situ hybridization, affording a classification as OC. Together, 33 of 36 (92%) OC were classified as OC or benign. For the entire validation cohort, an overall diagnostic sensitivity of 93% and above 97% specificity was achieved, suggesting that the implementation of genome-based molecular classification in a clinical diagnostic setting could impact the overall management and outcome of patients with renal tumors. A total of 191 RCC FFPE samples are analyzed including 63 clear cell RCC (ccRCC), 57 papillary RCC (pRCC), 35 chromophobe RCC (chrRCC) and 36 oncocytoma (OC). Two-color array-comparative genomic hybdrization on custom designed using RCC DNA as test and normal sex-matched DNA as reference.
2015-04-29 | E-GEOD-57162 | biostudies-arrayexpress
Project description:Pyrazinamide (PZA) is one of the first line antibiotics used for the treatment of tuberculosis (TB). we have used human monocyte and a mouse model of pulmonary TB to investigate whether treatment with PZA, in addition to its known anti-mycobacterial properties, modulate the host immune response during Mycobacterium tuberculosis (Mtb) infection.
2013-06-28 | GSE48027 | GEO
Project description:To explore the mechanism of drug resistance, most works focused on resistance associated genetic mutations, whereas concerns for resistance related gene expression are relatively low. Here a global expression analysis was performed between a reference strain H37Rv and two clinical extensively drug-resistant (XDR) strains with three anti-TB drug exposures {isoniazid (INH), capreomycin (CAP), rifampicin (RIF)}
2014-01-07 | GSE53843 | GEO
Project description:Cluster of graft-to-host transmission of HSV1 in cornea transplant setting
| PRJEB42849 | ENA
Project description:In previous work, we developed a novel algorithm, VIPR, for analyzing diagnostic microarray data using a training set of empirical hybridizations of infected and uninfected samples. We have expanded up our previous implementation by incorporating a hidden Markov model (HMM) to detect recombination. We trained our HMM on a set of nonrecombinant parental viruses and applied our method to 11 recombinant alphaviruses and 4 recombinant flaviviruses hybridized to a diagnostic microarray in order to evaluate performance of the HMM. VIPR HMM identified 95% of the 62 inter-species recombinant breakpoints in the validation set and only two false positive breakpoints were predicted. This study represents the first description and validation of an algorithm capable of identifying recombination in viruses based on diagnostic microarray hybridization patterns.
2012-10-18 | GSE34490 | GEO
Project description:we assessed characteristic molecular and proteomic signatures in rat liver treated with drugs (pyrazinamide, ranitidine, enalapril, carbamazepine, and chlorpromazine) that are known to cause DILI in humans.
2013-08-16 | GSE49631 | GEO
Project description:Diagnostic algorithm for detection of carbapenemases and ESBLs in carbapenem-resistant Pseudomonas aeruginosa
| PRJEB82838 | ENA