Mcclanahanvest5081
Both clinicians and clinical microbiology laboratories should remain vigilant regarding emerging fungal infections. These may be difficult both to diagnose and treat due to the lack of experience of clinicians and laboratory personnel with these organisms and the infections they may cause. Many of these fungal infections have been associated with poor clinical outcomes, either due to inappropriate therapy or the development of antifungal resistance.
Both clinicians and clinical microbiology laboratories should remain vigilant regarding emerging fungal infections. These may be difficult both to diagnose and treat due to the lack of experience of clinicians and laboratory personnel with these organisms and the infections they may cause. Many of these fungal infections have been associated with poor clinical outcomes, either due to inappropriate therapy or the development of antifungal resistance.
Pneumonia is a common illness, accounting for a staggering amount of worldwide morbidity and mortality. The diagnosis of pneumonia is challenging given the variety of responsible pathogens. Diagnostic testing for bacterial pneumonia has traditionally relied on time-consuming culture-based methods, though recently multiplexed molecular approaches have been described. Multiplexed molecular assays for pneumonia have the potential to provide broad diagnostic information in a rapid timeframe. Much has yet to be learned about these assays regarding analytical performance, potential impact, and optimal implementation strategy.
Herein we provide a summary of what is known and what has yet to be learned about multiplexed molecular pneumonia assays. We provide a comparison of the different commercially available assays and summarize the most current performance data for each. We further describe outcome data and lessons learned from those who have implemented these assays worldwide. Finally, based on the current stial to positively impact patient care. The extent to which this is realized varies from setting to setting, though is dependent on thoughtful implementation and a focus on delivering clear, rapid, and actionable results that can be interpreted in the appropriate context.
Hepatitis E virus (HEV) is a major cause of acute viral hepatitis. Better understanding of HEV subtypes involved in hepatitis E infections is essential. Investigation of sources and routes of transmission and the identification of potential clusters/outbreaks rely upon molecular typing of viral strains. A study was carried out to evaluate the ability of laboratories to undertake molecular typing with genotype and subtype determination.
A blinded panel of 11 different Orthohepevirus A strains was distributed to 28 laboratories performing HEV sequence analysis. Laboratories used their routine HEV sequencing and genotyping methods.
Results were returned by 25 laboratories. Overall, 93% samples were assigned to the correct genotype and 81% were assigned to the correct subtype. Fragments amplified for typing ranged in size and the sequencing assays targeted both the structural and non-structural protein-coding regions. There was good agreement between the reported sequences where methods targeted overlappingween these resources is essential.
Starkly highlighted by the current COVID-19 pandemic, infectious diseases continue to have an outsized impact on human health worldwide. Diagnostic testing for infection can be challenging due to resource limitations, time constraints, or shortcomings in the accuracy of existing diagnostics. Rapid, simple diagnostics are highly desirable. There is increasing interest in the development of diagnostics that use exhaled breath analysis as a convenient and safe diagnostic method, as breath sampling is noninvasive, secure, and easy to perform. Volatile organic compounds (VOCs) present in exhaled breath reflect the fingerprint of the underlying metabolic and biophysical processes during disease.
In this review, we overview the major biomarkers present in exhaled breath in infectious diseases. We outline the promising recent advances in breath-based diagnosis of respiratory infections, including those caused by influenza virus, SARS-CoV-2, Mycobacterium tuberculosis, Pseudomonas aeruginosa, and Aspergillus fumigatus. In addition, we review the current landscape of diagnosis of 2 other globally important infections Helicobacter pylori gastrointestinal infection and malaria.
Characteristic and reproducible breath VOCs are associated with several infectious diseases, suggesting breath analysis as a promising strategy for diagnostic development. Ongoing challenges include poor standardization of breath collection and analysis and lack of validation studies. Further research is required to expand the applicability of breath analysis to clinical settings.
Characteristic and reproducible breath VOCs are associated with several infectious diseases, suggesting breath analysis as a promising strategy for diagnostic development. Ongoing challenges include poor standardization of breath collection and analysis and lack of validation studies. Further research is required to expand the applicability of breath analysis to clinical settings.
Metagenomic next-generation sequencing (mNGS) for pathogen detection is becoming increasingly available as a method to identify pathogens in cases of suspected infection. mNGS analyzes the nucleic acid content of patient samples with high-throughput sequencing technologies to detect and characterize microorganism DNA and/or RNA. This unbiased approach to organism detection enables diagnosis of a broad spectrum of infection types and can identify more potential pathogens than any single conventional test. This can lead to improved ability to diagnose patients, although there remains concern regarding contamination and detection of nonclinically significant organisms.
We describe the laboratory approach to mNGS testing and highlight multiple considerations that affect diagnostic performance. We also summarize recent literature investigating the diagnostic performance of mNGS assays for a variety of infection types and recommend further studies to evaluate the improvement in clinical outcomes and cost-effectorganism detections correlate with the expected pathogen spectrum based on patient presentations, there are relatively few formal studies demonstrating whether these are true-positive infections and benefits to clinical outcomes. Reduced specificity due to contamination and clinically nonsignificant organism detections remains a major concern, emphasizing the importance of careful interpretation of the organism pathogenicity and potential association with the clinical syndrome. Further research is needed to determine the possible improvement in clinical outcomes and cost-effectiveness of mNGS testing.
Although it has been 30 years since the first automation systems were introduced in the microbiology laboratory, total laboratory automation (TLA) has only recently been recognized as a valuable component of the laboratory. iJMJD6 A growing number of publications illustrate the potential impact of automation. TLA can improve standardization, increase laboratory efficiency, increase workplace safety, and reduce long-term costs.
This review provides a preview of the current state of automation in clinical microbiology and covers the main developments during the last years. We describe the available hardware systems (that range from single function devices to multifunction workstations) and the challenging alterations on workflow and organization of the laboratory that have to be implemented to optimize automation.
Despite the many advantages in efficiency, productivity, and timeliness that automation offers, it is not without new and unique challenges. For every advantage that laboratory automation provides, thecessful implementation. TLA represents, moreover, a substantial initial investment. Nevertheless, if properly approached, there are a number of important benefits that can be achieved through implementation of automation in the clinical microbiology laboratory. Future developments in the field of automation will likely focus on image analysis and artificial intelligence improvements. Patient care, however, should remain the epicenter of all future directions and there will always be a need for clinical microbiology expertise to interpret the complex clinical and laboratory information.
The plasma separation card (PSC) is a new device for collecting finger-pricking-derived small amount of blood in a solid support that is stable at room temperature and can be archived, mailed, and processed at a later time. This tool can facilitate screening at risk populations located in rural areas without local health care infrastructures. We evaluated the performance of PSC in the collection and preparation of blood samples for the determination of hepatitis B and C serological markers.
Blood obtained from 334 consecutive patients referred for the detection of hepatitis B surface antigens (HBsAg), hepatitis B surface antibodies (anti-HBs) and hepatitis C antibodies (anti-HCV) was analyzed in parallel using standard (STD) and PSC-based sample collection and preparation procedures. Results obtained from STD or PSC processed samples were compared for their detection rate and correlation.
Using STD, we detected 5 samples positive for HBsAg, 150 for anti-HBs, and 23 for anti-HCV with a rate of concordance with PSC of 100%, 100%, and 91% respectively. The 100% concordance observed for anti-HBs was based on a cutoff of 2.6 IU/L for PSC-derived sample corresponding to the 10 IU/L threshold associated with immunity to hepatitis B. STD and PSC showed a good correlation (R2 = 0.85) in the detection of anti-HBs titers. The 2 anti-HCV PSC negative samples had no detectable viremia.
These data confirm the utility of PSC as a tool to support viral hepatitis screening programs in rural areas lacking local clinical infrastructures and testing facilities.
These data confirm the utility of PSC as a tool to support viral hepatitis screening programs in rural areas lacking local clinical infrastructures and testing facilities.
Artificial intelligence (AI) and machine learning (ML) are poised to transform infectious disease testing. Uniquely, infectious disease testing is technologically diverse spaces in laboratory medicine, where multiple platforms and approaches may be required to support clinical decision-making. Despite advances in laboratory informatics, the vast array of infectious disease data is constrained by human analytical limitations. Machine learning can exploit multiple data streams, including but not limited to laboratory information and overcome human limitations to provide physicians with predictive and actionable results. As a quickly evolving area of computer science, laboratory professionals should become aware of AI/ML applications for infectious disease testing as more platforms are become commercially available.
In this review we (a) define both AI/ML, (b) provide an overview of common ML approaches used in laboratory medicine, (c) describe the current AI/ML landscape as it relates infectious disease tesof "data fusion" describing the future of laboratory testing where multiple data streams are integrated by AI/ML to provide actionable clinical knowledge.
