Hasil untuk "Microbiology"

L. H. Lee, Tinchun Chu

S. Lindow, M. Brandl

J. Clarridge

P. Bowler, B. Duerden, David G. Armstrong

M. Crobach, T. Planche, C. Eckert et al.

In 2009 the first European Society of Clinical Microbiology and Infectious Diseases (ESCMID) guideline for diagnosing Clostridium difficile infection (CDI) was launched. Since then newer tests for diagnosing CDI have become available, especially nucleic acid amplification tests. The main objectives of this update of the guidance document are to summarize the currently available evidence concerning laboratory diagnosis of CDI and to formulate and revise recommendations to optimize CDI testing. This update is essential to improve the diagnosis of CDI and to improve uniformity in CDI diagnosis for surveillance purposes among Europe. An electronic search for literature concerning the laboratory diagnosis of CDI was performed. Studies evaluating a commercial laboratory test compared to a reference test were also included in a meta-analysis. The commercial tests that were evaluated included enzyme immunoassays (EIAs) detecting glutamate dehydrogenase, EIAs detecting toxins A and B and nucleic acid amplification tests. Recommendations were formulated by an executive committee, and the strength of recommendations and quality of evidence were graded using the Grades of Recommendation Assessment, Development and Evaluation (GRADE) system. No single commercial test can be used as a stand-alone test for diagnosing CDI as a result of inadequate positive predictive values at low CDI prevalence. Therefore, the use of a two-step algorithm is recommended. Samples without free toxin detected by toxins A and B EIA but with positive glutamate dehydrogenase EIA, nucleic acid amplification test or toxigenic culture results need clinical evaluation to discern CDI from asymptomatic carriage.

R. H. Deurenberg, E. Bathoorn, M. Chlebowicz et al.

Current molecular diagnostics of human pathogens provide limited information that is often not sufficient for outbreak and transmission investigation. Next generation sequencing (NGS) determines the DNA sequence of a complete bacterial genome in a single sequence run, and from these data, information on resistance and virulence, as well as information for typing is obtained, useful for outbreak investigation. The obtained genome data can be further used for the development of an outbreak-specific screening test. In this review, a general introduction to NGS is presented, including the library preparation and the major characteristics of the most common NGS platforms, such as the MiSeq (Illumina) and the Ion PGM™ (ThermoFisher). An overview of the software used for NGS data analyses used at the medical microbiology diagnostic laboratory in the University Medical Center Groningen in The Netherlands is given. Furthermore, applications of NGS in the clinical setting are described, such as outbreak management, molecular case finding, characterization and surveillance of pathogens, rapid identification of bacteria using the 16S-23S rRNA region, taxonomy, metagenomics approaches on clinical samples, and the determination of the transmission of zoonotic micro-organisms from animals to humans. Finally, we share our vision on the use of NGS in personalised microbiology in the near future, pointing out specific requirements.

D. Granato, P. Putnik, D. Kovačević et al.

G. Pezzotti

Dongsheng Han, Ziyang Li, Rui Li et al.

Abstract Metagenomic next-generation sequencing (mNGS) is increasingly being applied in clinical laboratories for unbiased culture-independent diagnosis. Whether it can be a next routine pathogen identification tool has become a topic of concern. We review the current implementation of this new technology for infectious disease diagnostics and discuss the feasibility of transforming mNGS into a routine diagnostic test. Since 2008, numerous studies from over 20 countries have revealed the practicality of mNGS in the work-up of undiagnosed infectious diseases. mNGS performs well in identifying rare, novel, difficult-to-detect and coinfected pathogens directly from clinical samples and presents great potential in resistance prediction by sequencing the antibiotic resistance genes, providing new diagnostic evidence that can be used to guide treatment options and improve antibiotic stewardship. Many physicians recognized mNGS as a last resort method to address clinical infection problems. Although several hurdles, such as workflow validation, quality control, method standardisation, and data interpretation, remain before mNGS can be implemented routinely in clinical laboratories, they are temporary and can be overcome by rapidly evolving technologies. With more validated workflows, lower cost and turnaround time, and simplified interpretation criteria, mNGS will be widely accepted in clinical practice. Overall, mNGS is transforming the landscape of clinical microbiology laboratories, and to ensure that it is properly utilised in clinical diagnosis, both physicians and microbiologists should have a thorough understanding of the power and limitations of this method.

D. Church, L. Cerutti, Antoine Gürtler et al.

This review provides a state-of-the-art description of the performance of Sanger cycle sequencing of the 16S rRNA gene for routine identification of bacteria in the clinical microbiology laboratory. A detailed description of the technology and current methodology is outlined with a major focus on proper data analyses and interpretation of sequences. The remainder of the article is focused on a comprehensive evaluation of the application of this method for identification of bacterial pathogens based on analyses of 16S multialignment sequences. SUMMARY This review provides a state-of-the-art description of the performance of Sanger cycle sequencing of the 16S rRNA gene for routine identification of bacteria in the clinical microbiology laboratory. A detailed description of the technology and current methodology is outlined with a major focus on proper data analyses and interpretation of sequences. The remainder of the article is focused on a comprehensive evaluation of the application of this method for identification of bacterial pathogens based on analyses of 16S multialignment sequences. In particular, the existing limitations of similarity within 16S for genus- and species-level differentiation of clinically relevant pathogens and the lack of sequence data currently available in public databases is highlighted. A multiyear experience is described of a large regional clinical microbiology service with direct 16S broad-range PCR followed by cycle sequencing for direct detection of pathogens in appropriate clinical samples. The ability of proteomics (matrix-assisted desorption ionization-time of flight) versus 16S sequencing for bacterial identification and genotyping is compared. Finally, the potential for whole-genome analysis by next-generation sequencing (NGS) to replace 16S sequencing for routine diagnostic use is presented for several applications, including the barriers that must be overcome to fully implement newer genomic methods in clinical microbiology. A future challenge for large clinical, reference, and research laboratories, as well as for industry, will be the translation of vast amounts of accrued NGS microbial data into convenient algorithm testing schemes for various applications (i.e., microbial identification, genotyping, and metagenomics and microbiome analyses) so that clinically relevant information can be reported to physicians in a format that is understood and actionable. These challenges will not be faced by clinical microbiologists alone but by every scientist involved in a domain where natural diversity of genes and gene sequences plays a critical role in disease, health, pathogenicity, epidemiology, and other aspects of life-forms. Overcoming these challenges will require global multidisciplinary efforts across fields that do not normally interact with the clinical arena to make vast amounts of sequencing data clinically interpretable and actionable at the bedside.

M. Cintrón, J.R. Hauser, C. Otto et al.

S. Taylor, L. Leong, J. Choo et al.

&NA; Figure. No caption available. Background: Asthma pathophysiology and treatment responsiveness are predicted by inflammatory phenotype. However, the relationship between airway microbiology and asthma phenotype is poorly understood. Objective: We aimed to characterize the airway microbiota in patients with symptomatic stable asthma and relate composition to airway inflammatory phenotype and other phenotypic characteristics. Methods: The microbial composition of induced sputum specimens collected from adult patients screened for a multicenter randomized controlled trial was determined by using 16S rRNA gene sequencing. Inflammatory phenotypes were defined by sputum neutrophil and eosinophil cell proportions. Microbiota were defined by using &agr;‐ and &bgr;‐diversity measures, and interphenotype differences were identified by using similarity of percentages, network analysis, and taxon fold change. Phenotypic predictors of airway microbiology were identified by using multivariate linear regression. Results: Microbiota composition was determined in 167 participants and classified as eosinophilic (n = 84), neutrophilic (n = 14), paucigranulocytic (n = 60), or mixed neutrophilic‐eosinophilic (n = 9) asthma phenotypes. Airway microbiology was significantly less diverse (P = .022) and more dissimilar (P = .005) in neutrophilic compared with eosinophilic participants. Sputum neutrophil proportions, but not eosinophil proportions, correlated significantly with these diversity measures (&agr;‐diversity: Spearman r = −0.374, P < .001; &bgr;‐diversity: r = 0.238, P = .002). Interphenotype differences were characterized by a greater frequency of pathogenic taxa at high relative abundance and reduced Streptococcus, Gemella, and Porphyromonas taxa relative abundance in patients with neutrophilic asthma. Multivariate regression confirmed that sputum neutrophil proportion was the strongest predictor of microbiota composition. Conclusions: Neutrophilic asthma is associated with airway microbiology that is significantly different from that seen in patients with other inflammatory phenotypes, particularly eosinophilic asthma. Differences in microbiota composition might influence the response to antimicrobial and steroid therapies and the risk of lung infection.

J. Brooke

Stenotrophomonas maltophilia is an opportunistic pathogen of significant concern to susceptible patient populations. This pathogen can cause nosocomial and community-acquired respiratory and bloodstream infections and various other infections in humans. SUMMARY Stenotrophomonas maltophilia is an opportunistic pathogen of significant concern to susceptible patient populations. This pathogen can cause nosocomial and community-acquired respiratory and bloodstream infections and various other infections in humans. Sources include water, plant rhizospheres, animals, and foods. Studies of the genetic heterogeneity of S. maltophilia strains have identified several new genogroups and suggested adaptation of this pathogen to its habitats. The mechanisms used by S. maltophilia during pathogenesis continue to be uncovered and explored. S. maltophilia virulence factors include use of motility, biofilm formation, iron acquisition mechanisms, outer membrane components, protein secretion systems, extracellular enzymes, and antimicrobial resistance mechanisms. S. maltophilia is intrinsically drug resistant to an array of different antibiotics and uses a broad arsenal to protect itself against antimicrobials. Surveillance studies have recorded increases in drug resistance for S. maltophilia, prompting new strategies to be developed against this opportunist. The interactions of this environmental bacterium with other microorganisms are being elucidated. S. maltophilia and its products have applications in biotechnology, including agriculture, biocontrol, and bioremediation.

G. Doern, K. Carroll, D. Diekema et al.

In this review, we present a comprehensive discussion of matters related to the problem of blood culture contamination. Issues addressed include the scope and magnitude of the problem, the bacteria most often recognized as contaminants, the impact of blood culture contamination on clinical microbiology laboratory function, the economic and clinical ramifications of contamination, and, perhaps most importantly, a systematic discussion of solutions to the problem. SUMMARY In this review, we present a comprehensive discussion of matters related to the problem of blood culture contamination. Issues addressed include the scope and magnitude of the problem, the bacteria most often recognized as contaminants, the impact of blood culture contamination on clinical microbiology laboratory function, the economic and clinical ramifications of contamination, and, perhaps most importantly, a systematic discussion of solutions to the problem. We conclude by providing a series of unanswered questions that pertain to this important issue.

Stephen J. Goodswen, Joel L N Barratt, Paul J. Kennedy et al.

ABSTRACT To understand the intricacies of microorganisms at the molecular level requires making sense of copious volumes of data such that it may now be humanly impossible to detect insightful data patterns without an artificial intelligence application called machine learning. Applying machine learning to address biological problems is expected to grow at an unprecedented rate, yet it is perceived by the uninitiated as a mysterious and daunting entity entrusted to the domain of mathematicians and computer scientists. The aim of this review is to identify key points required to start the journey of becoming an effective machine learning practitioner. These key points are further reinforced with an evaluation of how machine learning has been applied so far in a broad scope of real-life microbiology examples. This includes predicting drug targets or vaccine candidates, diagnosing microorganisms causing infectious diseases, classifying drug resistance against antimicrobial medicines, predicting disease outbreaks and exploring microbial interactions. Our hope is to inspire microbiologists and other related researchers to join the emerging machine learning revolution.

Kieran M Lynch, S. Wilkinson, L. Daenen et al.

Water kefir is a sparkling, slightly acidic fermented beverage produced by fermenting a solution of sucrose, to which dried fruits have been added, with water kefir grains. These gelatinous grains are a symbiotic culture of bacteria and yeast embedded in a polysaccharide matrix. Lactic acid bacteria, yeast and acetic acid bacteria are the primary microbial members of the sugary kefir grain. Amongst other contributions, species of lactic acid bacteria produce the exopolysaccharide matrix from which the kefir grain is formed, while yeast assists the bacteria by a nitrogen source that can be assimilated. Exactly which species predominate within the grain microbiota, however, appears to be dependent on the geographical origin of the grains and the fermentation substrate and conditions. These factors ultimately affect the characteristics of the beverage produced in terms of aroma, flavour, and acidity, for example, but can also be controlled and exploited in the production of a beverage of desired characteristics. The production of water kefir has traditionally occurred on a small scale and the use of defined starter cultures is not commonly practiced. However, as water kefir increases in popularity as a beverage - in part because of consumer lifestyle trends and in part due to water kefir being viewed as a health drink with its purported health benefits - the need for a thorough understanding of the biology and dynamics of water kefir, and for defined and controlled production processes, will ultimately increase. The aim of this review is to provide an update into the current knowledge of water kefir.

Lucía Schmidt-Santiago, David Rodríguez-Temporal, Carlos Sevilla-Salcedo et al.

Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) has become a cornerstone technology in clinical microbiology, enabling rapid and accurate microbial identification. However, the development of data-driven diagnostic models remains limited by the lack of sufficiently large, balanced, and standardized spectral datasets. This study investigates the use of deep generative models to synthesize realistic MALDI-TOF MS spectra, aiming to overcome data scarcity and support the development of robust machine learning tools in microbiology. We adapt and evaluate three generative models, Variational Autoencoders (MALDIVAEs), Generative Adversarial Networks (MALDIGANs), and Denoising Diffusion Probabilistic Model (MALDIffusion), for the conditional generation of microbial spectra guided by species labels. Generation is conditioned on species labels, and spectral fidelity and diversity are assessed using diverse metrics. Our experiments show that synthetic data generated by MALDIVAE, MALDIGAN, and MALDIffusion are statistically and diagnostically comparable to real measurements, enabling classifiers trained exclusively on synthetic samples to reach performance levels similar to those trained on real data. While all models faithfully reproduce the peak structure and variability of MALDI-TOF spectra, MALDIffusion obtains this fidelity at a substantially higher computational cost, and MALDIGAN shows competitive but slightly less stable behaviour. In contrast, MALDIVAE offers the most favorable balance between realism, stability, and efficiency. Furthermore, augmenting minority species with synthetic spectra markedly improves classification accuracy, effectively mitigating class imbalance and domain mismatch without compromising the authenticity of the generated data.

Henrik Pichler, Janis Keuper, Matthew Copping

The process of quantifying mold colonies on Petri dish samples is of critical importance for the assessment of indoor air quality, as high colony counts can indicate potential health risks and deficiencies in ventilation systems. Conventionally the automation of such a labor-intensive process, as well as other tasks in microbiology, relies on the manual annotation of large datasets and the subsequent extensive training of models like YoloV9. To demonstrate that exhaustive annotation is not a prerequisite anymore when tackling a new vision task, we compile a representative dataset of 5000 Petri dish images annotated with bounding boxes, simulating both a traditional data collection approach as well as few-shot and low-shot scenarios with well curated subsets with instance level masks. We benchmark three vision foundation models against traditional baselines on task specific metrics, reflecting realistic real-world requirements. Notably, MaskDINO attains near-parity with an extensively trained YoloV9 model while finetuned only on 150 images, retaining competitive performance with as few as 25 images, still being reliable on $\approx$ 70% of the samples. Our results show that data-efficient foundation models can match traditional approaches with only a fraction of the required data, enabling earlier development and faster iterative improvement of automated microbiological systems with a superior upper-bound performance than traditional models would achieve.

Nuiyen Aussanee, Khumin Vinta, Wichai Siriwan

Cadmium contamination found in paddy fields in the Maesot District of Tak Province, Thailand. This area was remediated using 50mg/L of ZVI. The study aimed to isolate and identify soil bacteria in the soil and rice roots and to investigate ZVI’s effect on the isolated bacterial cells. The results indicated no significant difference in soil bacteria content before and after remediation at the 95% confidence level. Twelve isolates of nitrogen-fixing bacteria were obtained. Those isolates could grow at high concentrations of 300 mg/L of ZVI. RH17 had a high tolerance for TSA with 300 mg/L of ZVI at only 10 CFU/ml. The effects of ZVI at 150 mg/L on RH17 cells, a small amount of ZVI was observed adhering to the cells’ surface and forming giant cells, while at 300 mg/L of ZVI, caused a reduction in growth by 81.0%. The nifH gene of RH17 was related to Rhizobium sp. strain 5-1-2. The results demonstrated the cadmium remediation process with 50mg/L of ZVI did not affect the cell count of soil bacteria in the paddy field. However, at 150 mg/L or higher, ZVI damaged the isolated Rhizobium sp. cell membrane. So, the remediation using ZVI must consider the appropriate concentration.