Enteric Infections Circulating during Hajj Seasons, 2011-2013.

Hajj, the annual Muslim pilgrimage to Mecca, Saudi Arabia, is a unique mass gathering event that raises public health concerns in the host country and globally. Although gastroenteritis and diarrhea are common among Hajj pilgrims, the microbial etiologies of these infections are unknown. We collected 544 fecal samples from pilgrims with medically attended diarrheal illness from 40 countries during the 2011-2013 Hajj seasons and screened the samples for 16 pathogens commonly associated with diarrheal infections. Bacteria were the main agents detected, in 82.9% of the 228 positive samples, followed by viral (6.1%) and parasitic (5.3%) agents. Salmonella spp., Shigella/enteroinvasive Escherichia coli, and enterotoxigenic E. coli were the main pathogens associated with severe symptoms. We identified genes associated with resistance to third-generation cephalosporins ≈40% of Salmonella- and E. coli-positive samples. Hajj-associated foodborne infections pose a major public health risk through the emergence and transmission of antimicrobial drug-resistant bacteria.

Development of a deep learning model for automatic localization of radiographic markers of proposed dental implant site locations.

Objectives: To develop a Deep Learning Artificial Intelligence (AI) model that automatically localizes the position of radiographic stent gutta percha (GP) markers in cone beam computed tomography (CBCT) images to identify proposed implant sites within the images, and to test the performance of the newly developed AI model. Materials and Methods: Thirty-four CBCT datasets were used for initial model training, validation and testing. The CBCT datasets were those of patients who had a CBCT examination performed wearing a radiographic stent for implant treatment planning. The datasets were exported in Digital Imaging and Communications in Medicine (DICOM), then imported into the software Horos ®. Each GP marker was manually labelled for object detection and recognition by the deep learning model by drawing rectangles around the GP markers in all axial images, then the labelled images were split into training, validation, and test sets. The axial sections of 30 CBCT datasets were randomly divided into training and validation sets. four CBCT datasets were used for testing the performance of the deep learning model. Descriptive statistics were calculated for the number of GP markers present, number of correct and incorrect identifications of GP markers. Result: The AI model had an 83% true positive rate for identification of the GP markers. Of the areas labelled by the AI model as GP markers, 28 % were not truly GP markers, but the overall false positive rate was 2.8 %. Conclusion: An AI model for localization of GP markers in CBCT images was able to identify most of the GP markers, but 2.8% of the results were false positive and 17% were missed GP markers. Using only axial images for training an AI program is not enough to give an accurate AI model performance.

Removal of Bacteria and Organic Carbon by an Integrated Ultrafiltration-Nanofiltration Desalination Pilot Plant.

Fouling caused by organic matter and bacteria remains a significant challenge for the membrane-based desalination industry. Fouling decreases the permeate quality and membrane performance and also increases energy demands. Here, we quantified the amount of organic matter and bacteria at several stages along the water-treatment train of an integrated ultrafiltration-nanofiltration seawater treatment pilot plant. We quantified the organic matter, in terms of Total Organic Carbon (TOC) and Assimilable Organic Carbon (AOC), and evaluated its composition using Liquid Chromatography for Organic Carbon Detection (LC-OCD). The bacterial cells were counted using Bactiquant. We found that ultrafiltration (UF) was effective at removing bacterial cells (99.7%) but not TOC. By contrast, nanofiltration (NF) successfully removed both TOC (95%) and bacterial cells. However, the NF permeate showed higher amounts of AOC than seawater. LC-OCD analysis suggested that the AOC was mostly composed of low molecular weight neutral substances. Furthermore, we found that the cleaning of the UF membrane using chemically enhanced backwash reduced the amount of AOC released into the UF permeate. By implementing the cleaning-in-place of the NF membrane, the pressure drop was restored to the normal level. Our results show that the UF and NF membrane cleaning regimes investigated in this study improved membrane performance. However, AOC remained the hardest-to-treat fraction of organic carbon. AOC should, therefore, be monitored closely and regularly to mitigate biofouling in downstream processes.

Genome-wide analysis of multi- and extensively drug-resistant Mycobacterium tuberculosis.

To characterize the genetic determinants of resistance to antituberculosis drugs, we performed a genome-wide association study (GWAS) of 6,465 Mycobacterium tuberculosis clinical isolates from more than 30 countries. A GWAS approach within a mixed-regression framework was followed by a phylogenetics-based test for independent mutations. In addition to mutations in established and recently described resistance-associated genes, novel mutations were discovered for resistance to cycloserine, ethionamide and para-aminosalicylic acid. The capacity to detect mutations associated with resistance to ethionamide, pyrazinamide, capreomycin, cycloserine and para-aminosalicylic acid was enhanced by inclusion of insertions and deletions. Odds ratios for mutations within candidate genes were found to reflect levels of resistance. New epistatic relationships between candidate drug-resistance-associated genes were identified. Findings also suggest the involvement of efflux pumps (drrA and Rv2688c) in the emergence of resistance. This study will inform the design of new diagnostic tests and expedite the investigation of resistance and compensatory epistatic mechanisms.

Author Correction: Genome-wide analysis of multi- and extensively drug-resistant Mycobacterium tuberculosis.

In the version of this article initially published, the URL listed for TubercuList was incorrect. The correct URL is https://mycobrowser.epfl.ch/. The error has been corrected in the HTML and PDF versions of the article.