Bacteremia due to Acinetobacter ursingii in infants: Reports of two cases

Acinetobacter ursingii is an aerobic, gram-negative, opportunistic microorganism which is rarely isolated among Acinetobacter species. We present two immunocompetent infants who developed bacteremia due to A.ursingii. The first patient is a two -month- old boy who had been hospitalized in pediatric surgery unit for suspected tracheo-esophageal fistula because of recurrent aspiration pneumonia unresponsive to antibiotic therapy. The second patient is a fourteen -month- old boy with prolonged vomiting and diarrhea. A. ursingii was isolated from their blood cultures. They were successfully treated with ampicillin-sulbactam. Although A.ursingii has recently been isolated from a clinical specimen; reports of infection with A.ursingii in children are rare. A.ursingii should be kept in mind as an opportunistic microorganism in children.Pan African Medical Journal 2016; 2

microBiomeGSM: the identification of taxonomic biomarkers from metagenomic data using grouping, scoring and modeling (G-S-M) approach

Numerous biological environments have been characterized with the advent of metagenomic sequencing using next generation sequencing which lays out the relative abundance values of microbial taxa. Modeling the human microbiome using machine learning models has the potential to identify microbial biomarkers and aid in the diagnosis of a variety of diseases such as inflammatory bowel disease, diabetes, colorectal cancer, and many others. The goal of this study is to develop an effective classification model for the analysis of metagenomic datasets associated with different diseases. In this way, we aim to identify taxonomic biomarkers associated with these diseases and facilitate disease diagnosis. The microBiomeGSM tool presented in this work incorporates the pre-existing taxonomy information into a machine learning approach and challenges to solve the classification problem in metagenomics disease-associated datasets. Based on the G-S-M (Grouping-Scoring-Modeling) approach, species level information is used as features and classified by relating their taxonomic features at different levels, including genus, family, and order. Using four different disease associated metagenomics datasets, the performance of microBiomeGSM is comparatively evaluated with other feature selection methods such as Fast Correlation Based Filter (FCBF), Select K Best (SKB), Extreme Gradient Boosting (XGB), Conditional Mutual Information Maximization (CMIM), Maximum Likelihood and Minimum Redundancy (MRMR) and Information Gain (IG), also with other classifiers such as AdaBoost, Decision Tree, LogitBoost and Random Forest. microBiomeGSM achieved the highest results with an Area under the curve (AUC) value of 0.98% at the order taxonomic level for IBDMD dataset. Another significant output of microBiomeGSM is the list of taxonomic groups that are identified as important for the disease under study and the names of the species within these groups. The association between the detected species and the disease under investigation is confirmed by previous studies in the literature. The microBiomeGSM tool and other supplementary files are publicly available at: https://github.com/malikyousef/microBiomeGSM

microBiomeGSM: the identification of taxonomic biomarkers from metagenomic data using grouping, scoring and modeling (G-S-M) approach

Numerous biological environments have been characterized with the advent of metagenomic sequencing using next generation sequencing which lays out the relative abundance values of microbial taxa. Modeling the human microbiome using machine learning models has the potential to identify microbial biomarkers and aid in the diagnosis of a variety of diseases such as inflammatory bowel disease, diabetes, colorectal cancer, and many others. The goal of this study is to develop an effective classification model for the analysis of metagenomic datasets associated with different diseases. In this way, we aim to identify taxonomic biomarkers associated with these diseases and facilitate disease diagnosis. The microBiomeGSM tool presented in this work incorporates the pre-existing taxonomy information into a machine learning approach and challenges to solve the classification problem in metagenomics disease-associated datasets. Based on the G-S-M (Grouping-Scoring-Modeling) approach, species level information is used as features and classified by relating their taxonomic features at different levels, including genus, family, and order. Using four different disease associated metagenomics datasets, the performance of microBiomeGSM is comparatively evaluated with other feature selection methods such as Fast Correlation Based Filter (FCBF), Select K Best (SKB), Extreme Gradient Boosting (XGB), Conditional Mutual Information Maximization (CMIM), Maximum Likelihood and Minimum Redundancy (MRMR) and Information Gain (IG), also with other classifiers such as AdaBoost, Decision Tree, LogitBoost and Random Forest. microBiomeGSM achieved the highest results with an Area under the curve (AUC) value of 0.98% at the order taxonomic level for IBDMD dataset. Another significant output of microBiomeGSM is the list of taxonomic groups that are identified as important for the disease under study and the names of the species within these groups. The association between the detected species and the disease under investigation is confirmed by previous studies in the literature. The microBiomeGSM tool and other supplementary files are publicly available at: https://github.com/malikyousef/microBiomeGSM

Novel M tuberculosis Antigen-Specific T-Cells Are Early Markers of Infection and Disease Progression

Mycobacterium tuberculosis Region-of-Difference-1 gene products present opportunities for specific diagnosis of M. tuberculosis infection, yet immune responses to only two gene-products, Early Secretory Antigenic Target-6 (ESAT-6) and Culture Filtrate Protein-10 (CFP-10), have been comprehensively investigated.T-cell responses to Rv3873, Rv3878 and Rv3879c were quantified by IFN-γ-enzyme-linked-immunospot (ELISpot) in 846 children with recent household tuberculosis exposure and correlated with kinetics of tuberculin skin test (TST) and ESAT-6/CFP-10-ELISpot conversion over six months and clinical outcome over two years.Responses to Rv3873, Rv3878, and Rv3879c were present in 20-25% of contacts at enrolment. Rv3873 and Rv3879c responses were associated with and preceded TST conversion (P=0.02 and P=0.04 respectively), identifying these antigens as early targets of cell-mediated immunity following M. tuberculosis exposure. Responses to Rv3873 were additionally associated with subsequent ESAT-6/CFP-10-ELISpot conversion (P=0.04). Responses to Rv3873 and Rv3878 predicted progression to active disease (adjusted incidence rate ratio [95% CI] 3.06 [1.05,8.95; P=0.04], and 3.32 [1.14,9.71; P=0.03], respectively). Presence of a BCG-vaccination scar was associated with a 67% (P=0.03) relative risk reduction for progression to active tuberculosis.These RD1-derived antigens are early targets of cellular immunity following tuberculosis exposure and T-cells specific for these antigens predict progression to active tuberculosis suggesting diagnostic and prognostic utility

A Prospective Study of Etiology of Childhood Acute Bacterial Meningitis, Turkey

Vaccines to prevent bacterial meningitis in this region must provide reliable protection against serogroup W-135

sodC-Based Real-Time PCR for Detection of Neisseria meningitidis

Real-time PCR (rt-PCR) is a widely used molecular method for detection of Neisseria meningitidis (Nm). Several rt-PCR assays for Nm target the capsule transport gene, ctrA. However, over 16% of meningococcal carriage isolates lack ctrA, rendering this target gene ineffective at identification of this sub-population of meningococcal isolates. The Cu-Zn superoxide dismutase gene, sodC, is found in Nm but not in other Neisseria species. To better identify Nm, regardless of capsule genotype or expression status, a sodC-based TaqMan rt-PCR assay was developed and validated. Standard curves revealed an average lower limit of detection of 73 genomes per reaction at cycle threshold (Ct) value of 35, with 100% average reaction efficiency and an average R2 of 0.9925. 99.7% (624/626) of Nm isolates tested were sodC-positive, with a range of average Ct values from 13.0 to 29.5. The mean sodC Ct value of these Nm isolates was 17.6±2.2 (±SD). Of the 626 Nm tested, 178 were nongroupable (NG) ctrA-negative Nm isolates, and 98.9% (176/178) of these were detected by sodC rt-PCR. The assay was 100% specific, with all 244 non-Nm isolates testing negative. Of 157 clinical specimens tested, sodC detected 25/157 Nm or 4 additional specimens compared to ctrA and 24 more than culture. Among 582 carriage specimens, sodC detected Nm in 1 more than ctrA and in 4 more than culture. This sodC rt-PCR assay is a highly sensitive and specific method for detection of Nm, especially in carriage studies where many meningococcal isolates lack capsule genes