Journal of medical microbiology最新文献

Introduction. Simethicone is an over-the-counter product that is frequently used by clinicians during endoscopic procedures to reduce foaming and improve visualization. Published studies have found simethicone residue on endoscopes after cleaning and disinfecting the devices as per the manufacturer's instructions. Some literature suggests that simethicone residue may reduce disinfection efficacy and increase the risk of patient infections.Gap Statement. However, there appears to be a lack of direct evidence in the literature to either disprove this or correlate simethicone presence with an increased microbial risk.Aim: Research was conducted to evaluate the in vitro impact of simethicone on disinfection efficacy.Methodology. Bacteria were grown in a microtitre plate assay in the presence of a range of simethicone concentrations and then treated with a disinfectant. Bacterial growth was assessed by spotting each microtitre well onto an agar plate.Results. The results demonstrated that, under the conditions tested, simethicone did not reduce the efficacy of Cidex ortho-phthalaldehyde disinfectant, which demonstrated at least a 6-log unit reduction in bacterial viability. Additional experiments showed that direct exposure to 66 mg ml^-1 of simethicone reduced bacterial viability.Conclusion. These results indicate that simethicone may not reduce the bactericidal efficacy of disinfectant during reprocessing, under certain conditions.

Introduction. Negative changes in the microbial composition have been extensively studied in individuals with periodontal disease.Gap Statement. The changes in the oral microbiota after treating this disease are still unknown.Aim. We sought to elucidate the distinctive traits of salivary microbiota in individuals displaying healthy gums and those with severe periodontitis (SP) and examine the influence of periodontal therapy.Methodology. Periodontal pocket depths were examined to determine disease severity. The presence and quantity of oral Helicobacter pylori (associated with periodontal disease) were determined. Sequencing of 16S ribosomal DNA and bioinformatic analyses were performed to assess oral bacterial compositions in patients.Results. Sequencing analysis of 16S ribosomal DNA revealed a significant reduction in the abundance of operational taxonomic units (OTUs) and Chao1 and Abundance coverage-based estimator(ACE) indices in the oral cavities of individuals with SP compared to those of the healthy controls. However, these parameters showed significant recovery after appropriate treatment severe periodontitis after treatment (TSP). Additionally, the levels of harmful Bacillales and Spirochetes significantly increased, whereas the presence of beneficial Euryarchaeota significantly decreased in the SP group. The TSP group exhibited considerably augmented abundances of Burkholderiaceae and Veillonella, while noteworthy reductions in the pathogenic microbiota (Clostridia, Fusobacteria and Spirochaetes) were noticed compared to those of the SP group. Functionally, these modified OTUs were extensively implicated in 41 metabolic pathways.Conclusion. Our study demonstrates that nonsurgical periodontal therapy can effectively reduce the diversity of the oral microbiota, thereby potentially enhancing the treatment efficacy in patients with periodontal disease.

Introduction. Enteropathogenic Escherichia coli (EPEC) strains pose a significant threat as a leading cause of severe childhood diarrhoea in developing nations. EPEC pathogenicity relies on the type III secretion system (T3SS) encoded by the locus of enterocyte effacement (LEE), facilitating the secretion and translocation of bacterial effector proteins.Gap Statement. While the regulatory roles of PerC (plasmid-encoded regulator) and GrlA (global regulator of LEE-activator) in ler expression and LEE gene activation are well-documented in the EPEC prototype strain E2348/69, understanding the variability in LEE gene expression control mechanisms among clinical EPEC isolates remains an area requiring further investigation.Aim. This study aims to explore the diversity in LEE gene expression control mechanisms among clinical EPEC isolates through a comparative analysis of secretion profiles under defined growth conditions favouring either PerC- or GrlA-mediated activation of LEE expression.Methodology. We compared T3SS-dependent secretion patterns and promoter expression in both typical EPEC (tEPEC) and atypical EPEC (aEPEC) clinical isolates under growth conditions favouring either PerC- or GrlA-mediated activation of LEE expression. Additionally, we conducted promoter reporter activity assays, quantitative real-time PCR and Western blot experiments to assess gene expression activity.Results. Significant differences in T3SS-dependent secretion were observed among tEPEC and aEPEC strains, independent of LEE sequence variations or T3SS gene functionality. Notably, a clinical tEPEC isolate exhibited increased secretion levels under repressive growth conditions and in the absence of both PerC and GrlA, implicating an alternative mechanism in the activation of Ler (LEE-encoded regulator) expression.Conclusion. Our findings indicate that uncharacterized LEE regulatory mechanisms contribute to phenotypic diversity among clinical EPEC isolates, though their impact on clinical outcomes remains unknown. This challenges the conventional understanding based on reference strains and highlights the need to investigate beyond established models to comprehensively elucidate EPEC pathogenesis.

Introduction. The Coronavirus Disease 2019 (COVID-19) pandemic has had a significant impact on global healthcare, with high mortality and severe complications remaining a major concern. Understanding the predictors of COVID-19 severity may improve patient management and outcomes. While considerable research has focused on the pathogenesis of the virus and vaccine development, the identification of reliable demographic, clinical and laboratory predictors of severe disease remains critical.Hypothesis. Specific demographic factors, clinical signs and laboratory markers can reliably predict the severity of COVID-19. A comprehensive analysis integrating these predictors could provide a more accurate prognosis and guide timely interventions.Aim. The aim of this study is to identify and evaluate the demographic, clinical and laboratory factors that can serve as reliable predictors of severe COVID-19, thereby aiding in the prediction and prevention of adverse outcomes.Methodology. The methods of analysis, synthesis, generalization and descriptive statistics were used to achieve this objective.Results. The analysis showed that demographic factors such as age over 60 and male sex are significant predictors of severe COVID-19. Clinical predictors include respiratory symptoms, especially dyspnoea, and comorbidities such as hypertension, coronary artery disease, chronic obstructive pulmonary disease, respiratory failure, asthma, diabetes mellitus and obesity. Laboratory markers with high prognostic value include elevated levels of C-reactive protein, interleukin-6, ferritin, neutrophil/lymphocyte ratio, d-dimer, aspartate aminotransferase enzyme and decreased lymphocyte count.Conclusion. The study concludes that a holistic approach incorporating demographic, clinical and laboratory data is essential to accurately predict the severity of COVID-19. This integrated model may significantly improve patient prognosis by facilitating early identification of high-risk individuals and allowing timely, targeted interventions. The results highlight the importance of comprehensive patient assessment in managing and mitigating the impact of COVID-19.

Introduction. The study addresses the challenge of utilizing human gut microbiome data for the early detection of colorectal cancer (CRC). The research emphasizes the potential of using machine learning techniques to analyze complex microbiome datasets, providing a non-invasive approach to identifying CRC-related microbial markers.Hypothesis/Gap Statement. The primary hypothesis is that a robust machine learning-based analysis of 16S rRNA microbiome data can identify specific microbial features that serve as effective biomarkers for CRC detection, overcoming the limitations of classical statistical models in high-dimensional settings.Aim. The primary objective of this study is to explore and validate the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for colorectal cancer (CRC) detection and progression. The focus is on developing a classifier that effectively predicts the presence of CRC and normal samples based on the analysis of three previously published faecal 16S rRNA sequencing datasets.Methodology. To achieve the aim, various machine learning techniques are employed, including random forest (RF), recursive feature elimination (RFE) and a robust correlation-based technique known as the fuzzy forest (FF). The study utilizes these methods to analyse the three datasets, comparing their performance in predicting CRC and normal samples. The emphasis is on identifying the most relevant microbial features (taxa) associated with CRC development via partial dependence plots, i.e. a machine learning tool focused on explainability, visualizing how a feature influences the predicted outcome.Results. The analysis of the three faecal 16S rRNA sequencing datasets reveals the consistent and superior predictive performance of the FF compared to the RF and RFE. Notably, FF proves effective in addressing the correlation problem when assessing the importance of microbial taxa in explaining the development of CRC. The results highlight the potential of the human microbiome as a non-invasive means to detect CRC and underscore the significance of employing FF for improved predictive accuracy.Conclusion. In conclusion, this study underscores the limitations of classical statistical techniques in handling high-dimensional information such as human microbiome data. The research demonstrates the potential of the human microbiome, specifically in the colon, as a valuable source of biomarkers for CRC detection. Applying machine learning techniques, particularly the FF, is a promising approach for building a classifier to predict CRC and normal samples. The findings advocate for integrating FF to overcome the challenges associated with correlation when identifying crucial microbial features linked to CRC development.

Introduction. The discordance between phenotypic and molecular methods of rifampicin (RIF) drug susceptibility testing (DST) in Mycobacterium tuberculosis poses a significant challenge, potentially resulting in misdiagnosis and inappropriate treatment.Hypothesis/gap statement. A comparison of RIF phenotypic and molecular methods for DST, including whole genome sequencing (WGS), may provide a better understanding of resistance mechanisms.Aim. This study aims to compare RIF DST in M. tuberculosis using two phenotypic and molecular methods including the GeneXpert RIF Assay (GX) and WGS for better understanding.Methodology. The study evaluated two phenotypic liquid medium methods [Lowenstein-Jensen (LJ) and Mycobacterium Growth Indicator Tube (MGIT)], one targeted molecular method (GX), and one WGS method. Moreover, mutational frequency in ponA1 and ponA2 was also screened in the current and previous RIF resistance M. tuberculosis genomic isolates to find their compensatory role.Results. A total of 25 RIF-resistant isolates, including nine from treatment failures and relapse cases with both discordant and concordant DST results on LJ, MGIT and GX, were subjected to WGS. The phenotypic DST results indicated that 11 isolates (44%) were susceptible on LJ and MGIT but resistant on GX. These isolates exhibited multiple mutations in rpoB, including Thr444>Ala, Leu430>Pro, Leu430>Arg, Asp435>Gly, His445>Asn and Asn438>Lys. Conversely, four isolates that were susceptible on GX and MGIT but resistant on LJ were wild type for rpoB in WGS. However, these isolates possessed several novel mutations in the PonA1 gene, including a 10 nt insertion and two nonsynonymous mutations (Ala394>Ser, Pro631>Ser), as well as one nonsynonymous mutation (Pro780>Arg) in PonA2. The discordance rate of RIF DST is higher on MGIT than on LJ and GX when compared to WGS. These discordances in the Delhi/CAS lineages were primarily associated with failure and relapse cases.Conclusion. The WGS of RIF resistance is relatively expensive, but it may be considered for isolates with discordant DST results on MGIT, LJ and GX to ensure accurate diagnosis and appropriate treatment options.

Introduction. Disruptions in gut microbiota, known as dysbiosis, have been increasingly linked to pathogenic infections, with Salmonella Typhimurium being a notable contributor to these disturbances.Hypothesis. We hypothesize that the S. Typhimurium 14028 WT strain induces significant dysbiosis in the rat gut microbiota and that the dam and seqA genes play crucial roles in this process.Aim. In this study, it was aimed at investigating the dysbiotic activity of the S. Typhimurium 14028 WT strain on the rat gut microbiota and the roles of dam and seqA genes on this activity.Method. Changes in the rat gut microbiota were determined by examining the anal swap samples taken from the experimental groups of these animals using 16S rRNA high-throughput sequencing technology.Results. In the experimental groups, the dominant phyla were determined to be Firmicutes and Bacteroidetes (P<0.05). However, while the rate of Bacteroidetes was significantly reduced in those treated with the WT and seqA mutants, no significant difference was observed in the dam mutant compared to the control group (P<0.05). In all experimental animals, the dominant species was determined to be Prevotella copri, regardless of the experiment time and application. The analysis results of the samples taken on the third day from the rat groups infected with the S. Typhimurium 14028 WT strain (W2) presented the most striking data of this study.Conclusion. Through distance analysis, we demonstrated that a successful Salmonella infection completely changes the composition of the microbiota, dramatically reduces species diversity and richness in the microbiota and encourages the growth of opportunistic pathogens.

Background. The COVID-19 pandemic demonstrated a need for robust SARS-CoV-2 test evaluation infrastructure to underpin biosecurity and protect the population during a pandemic health emergency.Gap statement. The first generation of rapid antigen tests was less accurate than molecular methods due to their inherent sensitivity and specificity shortfalls, compounded by the consequences of self-testing. This created a need for more accurate point-of-care SARS-CoV-2 detection methods.Aim. Here we present the lessons-learned during the COVID-19 emergency response in Western Australia including the detailed set-up, evaluation and operation of rapid antigen test in a state-run drive-through sample collection service during the COVID-19 pandemic after the strict border shutdown ended.Methods. We report a conformity assessment of a novel, second-generation rapid antigen test (Virulizer) comprising a technician-operated rapid lateral flow immunoassay with fluorescence-based detection.Results. The Virulizer rapid antigen test demonstrated up to 100% sensitivity (95% CI: 61.0-100%), 91.94% specificity (95% CI: 82.5-96.5%) and 92.65% accuracy when compared to a commercial PCR assay method. Wide confidence intervals in our series reflect the limits of small sample size. Nevertheless, the Virulizer assay performance was well-suited to point-of-care screening for SARS-CoV-2 in a drive-through clinic setting.Conclusion. The adaptive evaluation process necessary under changing pandemic conditions enabled assessment of a simple sample collection and point-of-care testing process, and showed how this system could be rapidly deployed for SARS-CoV-2 testing, including to regional and remote settings.

Introduction. Hepatocellular carcinoma (HCC) is one of the deadliest cancers worldwide.Gap statement. Monitoring of HCC and predicting its immunotherapy responses are challenging.Aim. This study explored the potential of the gut microbiome for HCC monitoring and predicting HCC immunotherapy responses.Methods. DNA samples were collected from the faeces of 22 patients with HCC treated with atezolizumab/bevacizumab (Atz/Bev) and 85 healthy controls. The gut microbiome was analysed using 16S rRNA next-generation sequencing and quantitative PCR (qPCR).Results. The microbiomes of patients with HCC demonstrated significant enrichment of Lactobacillus, particularly Lactobacillus fermentum, and Streptococcus, notably Streptococcus anginosus. Comparative analysis between Atz/Bev responders (R) and non-responders (NR) revealed a higher abundance of Bacteroides stercoris in the NR group and Bacteroides coprocola in the R group. Using qPCR analysis, we observed elevated levels of S. anginosus and reduced levels of 5α-reductase genes, essential for the synthesis of isoallolithocholic acid, in HCC patients compared to controls. Additionally, the analysis confirmed a significantly lower abundance of B. stercoris in the Atz/Bev R group relative to the NR group.Conclusions. The gut microbiome analysis and specific gene quantification via qPCR could provide a rapid, less invasive, and cost-effective approach for assessing the increased risk of HCC, monitoring patient status, and predicting immunotherapy responses.