The Experts below are selected from a list of 165 Experts worldwide ranked by ideXlab platform
Gilbert Greub - One of the best experts on this subject based on the ideXlab platform.
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towards automated detection semi quantification and identification of microbial growth in Clinical Bacteriology a proof of concept
Biomedical journal, 2017Co-Authors: Antony Croxatto, Guy Prodhom, Raphael Marcelpoil, Cedrick Rene Orny, Didier Morel, Gilbert GreubAbstract:Abstract Background Automation in microbiology laboratories impacts management, workflow, productivity and quality. Further improvements will be driven by the development of intelligent image analysis allowing automated detection of microbial growth, release of sterile samples, identification and quantification of bacterial colonies and reading of AST disk diffusion assays. We investigated the potential benefit of intelligent imaging analysis by developing algorithms allowing automated detection, semi-quantification and identification of bacterial colonies. Methods Defined monomicrobial and Clinical urine samples were inoculated by the BD Kiestra™ InoqulA™ BT module. Image acquisition of plates was performed with the BD Kiestra™ ImagA BT digital imaging module using the BD Kiestra™ Optis™ imaging software. The algorithms were developed and trained using defined data sets and their performance evaluated on both defined and Clinical samples. Results The detection algorithms exhibited 97.1% sensitivity and 93.6% specificity for microbial growth detection. Moreover, quantification accuracy of 80.2% and of 98.6% when accepting a 1 log tolerance was obtained with both defined monomicrobial and Clinical urine samples, despite the presence of multiple species in the Clinical samples. Automated identification accuracy of microbial colonies growing on chromogenic agar from defined isolates or Clinical urine samples ranged from 98.3% to 99.7%, depending on the bacterial species tested. Conclusion The development of intelligent algorithm represents a major innovation that has the potential to significantly increase laboratory quality and productivity while reducing turn-around-times. Further development and validation with larger numbers of defined and Clinical samples should be performed before transferring intelligent imaging analysis into diagnostic laboratories.
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laboratory automation in Clinical Bacteriology what system to choose
Clinical Microbiology and Infection, 2016Co-Authors: Antony Croxatto, Guy Prodhom, F Faverjon, Y Rochais, Gilbert GreubAbstract:Automation was introduced many years ago in several diagnostic disciplines such as chemistry, haematology and molecular biology. The first laboratory automation system for Clinical Bacteriology was released in 2006, and it rapidly proved its value by increasing productivity, allowing a continuous increase in sample volumes despite limited budgets and personnel shortages. Today, two major manufacturers, BD Kiestra and Copan, are commercializing partial or complete laboratory automation systems for Bacteriology. The laboratory automation systems are rapidly evolving to provide improved hardware and software solutions to optimize laboratory efficiency. However, the complex parameters of the laboratory and automation systems must be considered to determine the best system for each given laboratory. We address several topics on laboratory automation that may help Clinical bacteriologists to understand the particularities and operative modalities of the different systems. We present (a) a comparison of the engineering and technical features of the various elements composing the two different automated systems currently available, (b) the system workflows of partial and complete laboratory automation, which define the basis for laboratory reorganization required to optimize system efficiency, (c) the concept of digital imaging and teleBacteriology, (d) the connectivity of laboratory automation to the laboratory information system, (e) the general advantages and disadvantages as well as the expected impacts provided by laboratory automation and (f) the laboratory data required to conduct a workflow assessment to determine the best configuration of an automated system for the laboratory activities and specificities.
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automation in Bacteriology a changing way to perform Clinical diagnosis in infectious diseases
Clinical Microbiology and Infection, 2016Co-Authors: Gerard Lina, Gilbert GreubAbstract:Automated systems were first developed for Clinical chemistry, haematology and coagulation, and immunoassays, and these systems have shown success over the past decade. The concept of automating the Bacteriology laboratory processes first became a topic of general interest during the end of the twentieth century [1]. Under increasing pressure to reduce costs while maintaining or improving quality, companies involved in Clinical diagnostic microbiology looked to manufacturing models of production encompassing semiautomation and total laboratory automation. The delay to obtain automated systems for Bacteriology may be explained by the greater diversity in Clinical specimen size and sampling material, types of growth media, as well as the large variety of inoculation, identification and production processes [2]. Nevertheless, during the 20th ECCMID in 2010, different companies presented, in their showrooms, microbiology plating instruments, pointing out that the time for automation of microbial plating had arrived. In alphabetical order, the companies were Becton Dickinson/Dynacon with the Innova, bioMerieux with PREVI Isola, Copan Diagnostics with the Walk Away Specimen Processor, and Kiestra with the InoquIA Full Automatic [3]. Six years later, only two companies Becton Dickinson (BD)/Kiestra and Copan Diagnostics have implemented automated systems in Clinical microbiological laboratories that combine microbial streaking, smart incubators with plate readers and conveyors. The first Clinical Microbiology and Infectious Diseases special issue on microbiological automation was mainly written by companies in 2011 [4,5]. These companies predicted that automation in Clinical Bacteriology will improve time to results,
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automation in Clinical Bacteriology what system to choose
Clinical Microbiology and Infection, 2011Co-Authors: Gilbert Greub, Guy ProdhomAbstract:With increased activity and reduced financial and human resources, there is a need for automation in Clinical Bacteriology. Initial processing of Clinical samples includes repetitive and fastidious steps. These tasks are suitable for automation, and several instruments are now available on the market, including the WASP (Copan), Previ-Isola (BioMerieux), Innova (Becton-Dickinson) and Inoqula (KIESTRA) systems. These new instruments allow efficient and accurate inoculation of samples, including four main steps: (i) selecting the appropriate Petri dish; (ii) inoculating the sample; (iii) spreading the inoculum on agar plates to obtain, upon incubation, well-separated bacterial colonies; and (iv) accurate labelling and sorting of each inoculated media. The challenge for Clinical bacteriologists is to determine what is the ideal automated system for their own laboratory. Indeed, different solutions will be preferred, according to the number and variety of samples, and to the types of sample that will be processed with the automated system. The final choice is troublesome, because audits proposed by industrials risk being biased towards the solution proposed by their company, and because these automated systems may not be easily tested on site prior to the final decision, owing to the complexity of computer connections between the laboratory information system and the instrument. This article thus summarizes the main parameters that need to be taken into account for choosing the optimal system, and provides some clues to help Clinical bacteriologists to make their choice.
Makeda Semret - One of the best experts on this subject based on the ideXlab platform.
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Diagnostic Bacteriology in District Hospitals in Sub-Saharan Africa: At the Forefront of the Containment of Antimicrobial Resistance.
Frontiers in Medicine, 2019Co-Authors: Jan Jacobs, Cedric P Yansouni, Dissou Affolabi, Makeda Semret, Liselotte Hardy, Octavie Lunguya, Olivier VandenbergAbstract:This review provides an update on the factors fuelling antimicrobial resistance and shows the impact of these factors in low-resource settings. We detail the challenges and barriers to integrating Clinical Bacteriology in hospitals in low-resource settings, as well as the opportunities provided by the recent capacity building efforts of national laboratory networks focused on vertical single-disease programmes. The programmes for HIV, tuberculosis and malaria have considerably improved laboratory medicine in sub-Saharan Africa, paving the way for Clinical Bacteriology. Furthermore, special attention is paid to topics that are less familiar to the general medical community, such as the crucial role of regulatory frameworks for diagnostics and the educational profile required for a productive laboratory workforce in low-resource settings. Traditionally, Clinical Bacteriology laboratories have been a part of higher levels of care, and, as a result, they were poorly linked to Clinical practices and thus underused. By establishing and consolidating Clinical Bacteriology laboratories at the hospital referral level in low-resource settings, routine patient care data can be collected for surveillance, antibiotic stewardship and infection prevention and control. Together, these activities form a synergistic tripartite effort at the frontline of the emergence and spread of multi-drug resistant bacteria. If challenges related to staff, funding, scale and the specific nature of Clinical Bacteriology are prioritized, a major leap forward in the containment of antimicrobial resistance can be achieved. The mobilization of resources coordinated by national laboratory plans and interventions tailored by a good understanding of the hospital microcosm will be crucial to success, and further contributions will be made by market interventions and business models for diagnostic laboratories. The future Clinical Bacteriology laboratory in a low-resource setting will not be an “entry-level version” of its counterparts in high-resource settings, but a purpose-built, well-conceived, cost-effective and efficient diagnostic facility at the forefront of antimicrobial resistance containment.
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Clinical Bacteriology in low resource settings today s solutions
Lancet Infectious Diseases, 2018Co-Authors: Cedric P Yansouni, Sien Ombelet, Jeanbaptiste Ronat, Timothy R Walsh, Erika Vlieghe, Delphine Martiny, Makeda SemretAbstract:Summary Low-resource settings are disproportionately burdened by infectious diseases and antimicrobial resistance. Good quality Clinical Bacteriology through a well functioning reference laboratory network is necessary for effective resistance control, but low-resource settings face infrastructural, technical, and behavioural challenges in the implementation of Clinical Bacteriology. In this Personal View, we explore what constitutes successful implementation of Clinical Bacteriology in low-resource settings and describe a framework for implementation that is suitable for general referral hospitals in low-income and middle-income countries with a moderate infrastructure. Most microbiological techniques and equipment are not developed for the specific needs of such settings. Pending the arrival of a new generation diagnostics for these settings, we suggest focus on improving, adapting, and implementing conventional, culture-based techniques. Priorities in low-resource settings include harmonised, quality assured, and tropicalised equipment, consumables, and techniques, and rationalised bacterial identification and testing for antimicrobial resistance. Diagnostics should be integrated into Clinical care and patient management; Clinically relevant specimens must be appropriately selected and prioritised. Open-access t raining materials and information management tools should be developed. Also important is the need for onsite validation and field adoption of diagnostics in low-resource settings, with considerable shortening of the time between development and implementation of diagnostics. We argue that the implementation of Clinical Bacteriology in low-resource settings improves patient management, provides valuable surveillance for local antibiotic treatment guidelines and national policies, and supports containment of antimicrobial resistance and the prevention and control of hospital-acquired infections.
Jan Jacobs - One of the best experts on this subject based on the ideXlab platform.
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Diagnostic Bacteriology in District Hospitals in Sub-Saharan Africa: At the Forefront of the Containment of Antimicrobial Resistance.
Frontiers in Medicine, 2019Co-Authors: Jan Jacobs, Cedric P Yansouni, Dissou Affolabi, Makeda Semret, Liselotte Hardy, Octavie Lunguya, Olivier VandenbergAbstract:This review provides an update on the factors fuelling antimicrobial resistance and shows the impact of these factors in low-resource settings. We detail the challenges and barriers to integrating Clinical Bacteriology in hospitals in low-resource settings, as well as the opportunities provided by the recent capacity building efforts of national laboratory networks focused on vertical single-disease programmes. The programmes for HIV, tuberculosis and malaria have considerably improved laboratory medicine in sub-Saharan Africa, paving the way for Clinical Bacteriology. Furthermore, special attention is paid to topics that are less familiar to the general medical community, such as the crucial role of regulatory frameworks for diagnostics and the educational profile required for a productive laboratory workforce in low-resource settings. Traditionally, Clinical Bacteriology laboratories have been a part of higher levels of care, and, as a result, they were poorly linked to Clinical practices and thus underused. By establishing and consolidating Clinical Bacteriology laboratories at the hospital referral level in low-resource settings, routine patient care data can be collected for surveillance, antibiotic stewardship and infection prevention and control. Together, these activities form a synergistic tripartite effort at the frontline of the emergence and spread of multi-drug resistant bacteria. If challenges related to staff, funding, scale and the specific nature of Clinical Bacteriology are prioritized, a major leap forward in the containment of antimicrobial resistance can be achieved. The mobilization of resources coordinated by national laboratory plans and interventions tailored by a good understanding of the hospital microcosm will be crucial to success, and further contributions will be made by market interventions and business models for diagnostic laboratories. The future Clinical Bacteriology laboratory in a low-resource setting will not be an “entry-level version” of its counterparts in high-resource settings, but a purpose-built, well-conceived, cost-effective and efficient diagnostic facility at the forefront of antimicrobial resistance containment.
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implementation of quality management for Clinical Bacteriology in low resource settings
Clinical Microbiology and Infection, 2017Co-Authors: Barbara Barbe, Cedric P Yansouni, Dissou Affolabi, Jan JacobsAbstract:Abstract Background The declining trend of malaria and the recent prioritization of containment of antimicrobial resistance have created a momentum to implement Clinical Bacteriology in low-resource settings. Successful implementation relies on guidance by a quality management system (QMS). Over the past decade international initiatives were launched towards implementation of QMS in HIV/AIDS, tuberculosis and malaria. Aims To describe the progress towards accreditation of medical laboratories and to identify the challenges and best practices for implementation of QMS in Clinical Bacteriology in low-resource settings. Sources Published literature, online reports and websites related to the implementation of laboratory QMS, accreditation of medical laboratories and initiatives for containment of antimicrobial resistance. Content Apart from the limitations of infrastructure, equipment, consumables and staff, QMS are challenged with the complexity of Clinical Bacteriology and the healthcare context in low-resource settings (small-scale laboratories, attitudes and perception of staff, absence of laboratory information systems). Likewise, most international initiatives addressing laboratory health strengthening have focused on public health and outbreak management rather than on hospital based patient care. Best practices to implement quality-assured Clinical Bacteriology in low-resource settings include alignment with national regulations and public health reference laboratories, participating in external quality assurance programmes, support from the hospital's management, starting with attainable projects, conducting error review and daily bench-side supervision, looking for locally adapted solutions, stimulating ownership and extending existing training programmes to Clinical Bacteriology. Implications The implementation of QMS in Clinical Bacteriology in hospital settings will ultimately boost a culture of quality to all sectors of healthcare in low-resource settings.
Sien Ombelet - One of the best experts on this subject based on the ideXlab platform.
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Clinical Bacteriology in low resource settings today s solutions
Lancet Infectious Diseases, 2018Co-Authors: Cedric P Yansouni, Sien Ombelet, Jeanbaptiste Ronat, Timothy R Walsh, Erika Vlieghe, Delphine Martiny, Makeda SemretAbstract:Summary Low-resource settings are disproportionately burdened by infectious diseases and antimicrobial resistance. Good quality Clinical Bacteriology through a well functioning reference laboratory network is necessary for effective resistance control, but low-resource settings face infrastructural, technical, and behavioural challenges in the implementation of Clinical Bacteriology. In this Personal View, we explore what constitutes successful implementation of Clinical Bacteriology in low-resource settings and describe a framework for implementation that is suitable for general referral hospitals in low-income and middle-income countries with a moderate infrastructure. Most microbiological techniques and equipment are not developed for the specific needs of such settings. Pending the arrival of a new generation diagnostics for these settings, we suggest focus on improving, adapting, and implementing conventional, culture-based techniques. Priorities in low-resource settings include harmonised, quality assured, and tropicalised equipment, consumables, and techniques, and rationalised bacterial identification and testing for antimicrobial resistance. Diagnostics should be integrated into Clinical care and patient management; Clinically relevant specimens must be appropriately selected and prioritised. Open-access t raining materials and information management tools should be developed. Also important is the need for onsite validation and field adoption of diagnostics in low-resource settings, with considerable shortening of the time between development and implementation of diagnostics. We argue that the implementation of Clinical Bacteriology in low-resource settings improves patient management, provides valuable surveillance for local antibiotic treatment guidelines and national policies, and supports containment of antimicrobial resistance and the prevention and control of hospital-acquired infections.
Antony Croxatto - One of the best experts on this subject based on the ideXlab platform.
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laboratory automation in Clinical Bacteriology
2018Co-Authors: Antony CroxattoAbstract:Automation in Clinical Bacteriology has been neglected for many years until the emergence of innovative technologies and laboratories consolidation which have triggered the development and implementation of different automated solution for Bacteriology. The commercialized automated systems can be categorized in different level of automation covering partially or totally multiple laboratory activities from sample inoculation to agar plate incubation, digital imaging, and reading. Moreover, the major manufacturers of automated systems are working on the development or various hardware and software solutions that will further improve the level of automation including digital imaging and expert system applications for auto-release and/or support for human validation of laboratory results.
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towards automated detection semi quantification and identification of microbial growth in Clinical Bacteriology a proof of concept
Biomedical journal, 2017Co-Authors: Antony Croxatto, Guy Prodhom, Raphael Marcelpoil, Cedrick Rene Orny, Didier Morel, Gilbert GreubAbstract:Abstract Background Automation in microbiology laboratories impacts management, workflow, productivity and quality. Further improvements will be driven by the development of intelligent image analysis allowing automated detection of microbial growth, release of sterile samples, identification and quantification of bacterial colonies and reading of AST disk diffusion assays. We investigated the potential benefit of intelligent imaging analysis by developing algorithms allowing automated detection, semi-quantification and identification of bacterial colonies. Methods Defined monomicrobial and Clinical urine samples were inoculated by the BD Kiestra™ InoqulA™ BT module. Image acquisition of plates was performed with the BD Kiestra™ ImagA BT digital imaging module using the BD Kiestra™ Optis™ imaging software. The algorithms were developed and trained using defined data sets and their performance evaluated on both defined and Clinical samples. Results The detection algorithms exhibited 97.1% sensitivity and 93.6% specificity for microbial growth detection. Moreover, quantification accuracy of 80.2% and of 98.6% when accepting a 1 log tolerance was obtained with both defined monomicrobial and Clinical urine samples, despite the presence of multiple species in the Clinical samples. Automated identification accuracy of microbial colonies growing on chromogenic agar from defined isolates or Clinical urine samples ranged from 98.3% to 99.7%, depending on the bacterial species tested. Conclusion The development of intelligent algorithm represents a major innovation that has the potential to significantly increase laboratory quality and productivity while reducing turn-around-times. Further development and validation with larger numbers of defined and Clinical samples should be performed before transferring intelligent imaging analysis into diagnostic laboratories.
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laboratory automation in Clinical Bacteriology what system to choose
Clinical Microbiology and Infection, 2016Co-Authors: Antony Croxatto, Guy Prodhom, F Faverjon, Y Rochais, Gilbert GreubAbstract:Automation was introduced many years ago in several diagnostic disciplines such as chemistry, haematology and molecular biology. The first laboratory automation system for Clinical Bacteriology was released in 2006, and it rapidly proved its value by increasing productivity, allowing a continuous increase in sample volumes despite limited budgets and personnel shortages. Today, two major manufacturers, BD Kiestra and Copan, are commercializing partial or complete laboratory automation systems for Bacteriology. The laboratory automation systems are rapidly evolving to provide improved hardware and software solutions to optimize laboratory efficiency. However, the complex parameters of the laboratory and automation systems must be considered to determine the best system for each given laboratory. We address several topics on laboratory automation that may help Clinical bacteriologists to understand the particularities and operative modalities of the different systems. We present (a) a comparison of the engineering and technical features of the various elements composing the two different automated systems currently available, (b) the system workflows of partial and complete laboratory automation, which define the basis for laboratory reorganization required to optimize system efficiency, (c) the concept of digital imaging and teleBacteriology, (d) the connectivity of laboratory automation to the laboratory information system, (e) the general advantages and disadvantages as well as the expected impacts provided by laboratory automation and (f) the laboratory data required to conduct a workflow assessment to determine the best configuration of an automated system for the laboratory activities and specificities.