f For Mycobacterium tuberculosis, phenotypic methods for drug susceptibility testing of second-line drugs are poorly standardized and technically challenging. The Sensititre MYCOTB MIC plate (MYCOTB) is a microtiter plate containing lyophilized antibiotics and configured for determination of MICs to first-and second-line antituberculosis drugs. To evaluate the performance of MYCOTB for M. tuberculosis drug susceptibility testing using the Middlebrook 7H10 agar proportion method (APM) as the comparator, we conducted a two-site study using archived M. tuberculosis isolates from Uganda and the Republic of Korea. Thawed isolates were subcultured, and dilutions were inoculated into MYCOTB wells and onto 7H10 agar. MYCOTB results were read at days 7, 10, 14, and 21; APM results were read at 21 days. A total of 222 isolates provided results on both platforms. By APM, 106/222 (47.7%) of isolates were resistant to at least isoniazid and rifampin. Agreement between MYCOTB and APM with respect to susceptibility or resistance was >92% for 7 of 12 drugs when a strict definition was used and >96% for 10 of 12 drugs when agreement was defined by allowing a ؎ one-well range of dilutions around the APM critical concentration. For ethambutol, agreement was 80% to 81%. For moxifloxacin, agreement was 83% to 85%; incorporating existing DNA sequencing information for discrepant analysis raised agreement to 91% to 96%. For MYCOTB, the median time to plate interpretation was 10 days and interreader agreement was >95% for all drugs. MYCOTB provided reliable results for M. tuberculosis susceptibility testing of first-and second-line drugs except ethambutol, and results were available sooner than those determined by APM.T he emergence and spread of drug-resistant strains of Mycobacterium tuberculosis comprise a serious threat to tuberculosis (TB) control (1, 2). Knowledge of M. tuberculosis drug susceptibility is important in optimizing individual patient management and TB control in populations. Genotypic methods have the potential for a very short time to results, but to date, the knowledge of the full spectrum of genetic loci and mutations associated with resistance to many antituberculosis drugs is incomplete (3,4,5,6,7). Phenotypic methods therefore remain important. The reference phenotypic method-the indirect agar proportion method (APM) using Middlebrook solid media-is qualitative and based on drug critical concentrations. Limitations of the APM and related methods include lack of standardization and in some cases the need for in-laboratory preparation of drug stocks and agar plates, which can be a source of variability over time and between laboratories. Critical concentrations are based on historical epidemiological data and for some drugs are not well-aligned with achievable drug serum concentrations or accurate in predicting clinical failure (8,9,10). Studies on molecular drug resistance mechanisms in M. tuberculosis have shown that, at least for some antibiotics, different mutations are associated with different MICs, furthe...

BackgroundPheochromocytoma and paraganglioma (PPGL) are tumours that arise from chromaffin cells. Some genetic mutations influence PPGL, among which, those in genes encoding subunits of succinate dehydrogenase (SDHA, SDHB, SDHC and SDHD) and assembly factor (SDHAF2) are the most relevant. However, the risk of metastasis posed by these mutations is not reported except for SDHB and SDHD mutations. This study aimed to update the metastatic risks, considering prevalence and incidence of each SDHx mutation, which were dealt formerly all together.MethodsWe searched EMBASE and MEDLINE and selected 27 articles. The patients included in the studies were divided into three groups depending on the presence of PPGL. We checked the heterogeneity between studies and performed a meta-analysis using Hartung-Knapp-Sidik-Jonkman method based on a random effect model.ResultsThe highest PPGL prevalence was for SDHB mutation, ranging from 23% to 31%, and for SDHC mutation (23%), followed by that for SDHA mutation (16%). The lowest prevalence was for SDHD mutation, ranging from 6% to 8%. SDHAF2 mutation showed no metastatic events. The PPGL incidence showed a tendency similar to that of its prevalence with the highest risk of metastasis posed by SDHB mutation (12%–41%) and the lowest risk by SDHD mutation (~4%).ConclusionThere was no integrated evidence of how SDHx mutations are related to metastatic PPGL. However, these findings suggest that SDHA, SDHB and SDHC mutations are highly associated and should be tested as indicators of metastasis in patients with PPGL.

Background: High cathepsin D has been associated with poor prognosis in breast cancer; however, the results of many studies are controversial. Here, we assessed the association between high cathepsin D levels and worse breast cancer prognosis by conducting a meta-analysis. Methods: A comprehensive search strategy was used to search relevant literature in PUBMED and EMBASE by September 2018. The meta-analysis was performed in Review Manager 5.3 using hazard ratios (HRs) with 95% confidence intervals (CIs). Results: A total of 15,355 breast cancer patients from 26 eligible studies were included in this meta-analysis. Significant associations between elevated high cathepsin D and poor overall survival (OS) (HR = 1.61, 95% CI: 1.35–1.92, p < 0.0001) and disease-free survival (DFS) (HR = 1.52, 95% CI: 1.31–2.18, p < 0.001) were observed. In the subgroup analysis for DFS, high cathepsin D was significantly associated with poor prognosis in node-positive patients (HR = 1.38, 95% CI: 1.25–1.71, p < 0.00001), node-negative patients (HR = 1.78, 95% CI: 1.39–2.27, p < 0.0001), early stage patients (HR = 1.73, 95% CI: 1.34–2.23, p < 0.0001), and treated with chemotherapy patients (HR = 1.60, 95% CI: 1.21–2.12, p < 0.001). Interestingly, patients treated with tamoxifen had a low risk of relapse when their cathepsin D levels were high (HR = 0.71, 95% CI: 0.52–0.98, p = 0.04) and a high risk of relapse when their cathepsin D levels were low (HR = 1.50, 95% CI: 1.22–1.85, p = 0.0001). Conclusions: Our meta-analysis suggests that high expression levels of cathepsin D are associated with a poor prognosis in breast cancer. Based on our subgroup analysis, we believe that cathepsin D can act as a marker for poor breast cancer prognosis and also as a therapeutic target for breast cancer.

Background Microbiome has been shown to substantially contribute to some cancers. However, the diagnostic implications of microbiome in head and neck squamous cell carcinoma (HNSCC) remain unknown. Methods To identify the molecular difference in the microbiome of oral and non-oral HNSCC, primary data was downloaded from the Kraken-TCGA dataset. The molecular differences in the microbiome of oral and non-oral HNSCC were identified using the linear discriminant analysis effect size method. Results In the study, the common microbiomes in oral and non-oral cancers were Fusobacterium, Leptotrichia, Selenomonas and Treponema and Clostridium and Pseudoalteromonas, respectively. We found unique microbial signatures that positively correlated with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways in oral cancer and positively and negatively correlated KEGG pathways in non-oral cancer. In oral cancer, positively correlated genes were mostly found in prion diseases, Alzheimer disease, Parkinson disease, Salmonella infection, and Pathogenic Escherichia coli infection. In non-oral cancer, positively correlated genes showed Herpes simplex virus 1 infection and Spliceosome and negatively correlated genes showed results from PI3K-Akt signaling pathway, Focal adhesion, Regulation of actin cytoskeleton, ECM-receptor interaction and Dilated cardiomyopathy. Conclusions These results could help in understanding the underlying biological mechanisms of the microbiome of oral and non-oral HNSCC. Microbiome-based oncology diagnostic tool warrants further exploration.

Background: Periodontitis (PT) is a multifactorial, chronic inflammatory disease that can have heterogeneous clinical presentations. The oral microbiome and its metabolites have been implicated as the causes and regulators of PT pathogenesis. In this study, we assessed the oral microbiome and its metabolome in PT patients to clarify the interactions between the microbiome and its metabolites. Methods: A total of 112 subjects were recruited. Buccal and supragingival samples were collected for microbiome analysis. Saliva samples were collected for metabolomic analyses. Microbiome and metabolome data were analyzed and further integrated for combined analysis using various bioinformatics approaches. Results: Oral metabolomic analysis identified 28 metabolites distinguishing the healthy (H) and PT groups. PT group were further clustered into two subgroups (PT_G1 and PT_G2) depending on metabolite profiles. Oral microbiome analysis revealed discriminatory bacterial species in the H, PT_G1, and PT_G2 microbiota. Interestingly, PT_G2 had significantly higher concentration of short chain fatty acids and higher abundance of pathogenic bacteria. Integrated analysis of the microbiome and metabolome showed close association. Conclusion:Our results provide evidence of a close interplay between the oral microbiome and metabolome. Multi-omics approach including microbiome and microbe-associated metabolites may serve as diagnostic biomarkers and enhance treatment prediction in periodontal disease.