Pathogenicity locus, core genome, and accessory gene contributions to Clostridium difficile virulence

Clostridium difficile is a spore-forming anaerobic bacterium that causes colitis in patients with disrupted colonic microbiota. While some individuals are asymptomatic C. difficile carriers, symptomatic disease ranges from mild diarrhea to potentially lethal toxic megacolon. The wide disease spectrum has been attributed to the infected host’s age, underlying diseases, immune status, and microbiome composition. However, strain-specific differences in C. difficile virulence have also been implicated in determining colitis severity. Because patients infected with C. difficile are unique in terms of medical history, microbiome composition, and immune competence, determining the relative contribution of C. difficile virulence to disease severity has been challenging, and conclusions regarding the virulence of specific strains have been inconsistent. To address this, we used a mouse model to test 33 clinical C. difficile strains isolated from patients with disease severities ranging from asymptomatic carriage to severe colitis, and we determined their relative in vivo virulence in genetically identical, antibiotic-pretreated mice. We found that murine infections with C. difficile clade 2 strains (including multilocus sequence type 1/ribotype 027) were associated with higher lethality and that C. difficile strains associated with greater human disease severity caused more severe disease in mice. While toxin production was not strongly correlated with in vivo colonic pathology, the ability of C. difficile strains to grow in the presence of secondary bile acids was associated with greater disease severity. Whole-genome sequencing and identification of core and accessory genes identified a subset of accessory genes that distinguish high-virulence from lower-virulence C. difficile strains

Gastrointestinal microbiota composition predicts peripheral inflammatory state during treatment of human tuberculosis

The composition of the gastrointestinal microbiota influences systemic immune responses, but how this affects infectious disease pathogenesis and antibiotic therapy outcome is poorly understood. This question is rarely examined in humans due to the difficulty in dissociating the immunologic effects of antibiotic-induced pathogen clearance and microbiome alteration. Here, we analyze data from two longitudinal studies of tuberculosis (TB) therapy (35 and 20 individuals) and a cross sectional study from 55 healthy controls, in which we collected fecal samples (for microbiome analysis), sputum (for determination of Mycobacterium tuberculosis (Mtb) bacterial load), and peripheral blood (for transcriptomic analysis). We decouple microbiome effects from pathogen sterilization by comparing standard TB therapy with an experimental TB treatment that did not reduce Mtb bacterial load. Random forest regression to the microbiome-transcriptome-sputum data from the two longitudinal datasets reveals that renormalization of the TB inflammatory state is associated with Mtb pathogen clearance, increased abundance of Clusters IV and XIVa Clostridia, and decreased abundance of Bacilli and Proteobacteria. We find similar associations when applying machine learning to peripheral gene expression and microbiota profiling in the independent cohort of healthy individuals. Our findings indicate that antibiotic-induced reduction in pathogen burden and changes in the microbiome are independently associated with treatment-induced changes of the inflammatory response of active TB, and the response to antibiotic therapy may be a combined effect of pathogen killing and microbiome driven immunomodulation

Variable structure control with chattering reduction of a generalized T-S model

In this paper, a fuzzy logic controller (FLC) based variable structure control (VSC) is presented. The main objective is to obtain an improved performance of highly non-linear unstable systems. New functions for chattering reduction and error convergence without sacrificing invariant properties are proposed. The main feature of the proposed method is that the switching function is added as an additional fuzzy variable and will be introduced in the premise part of the fuzzy rules; together with the state variables. In this work, a tuning of the well known weighting parameters approach is proposed to optimize local and global approximation and modelling capability of the Takagi-Sugeno (T-S) fuzzy model to improve the choice of the performance index and minimize it. The main problem encountered is that the T-S identification method can not be applied when the membership functions are overlapped by pairs. This in turn restricts the application of the T-S method because this type of membership function has been widely used in control applications. The approach developed here can be considered as a generalized version of the T-S method. An inverted pendulum mounted on a cart is chosen to evaluate the robustness, effectiveness, accuracy and remarkable performance of the proposed estimation approach in comparison with the original T-S model. Simulation results indicate the potential, simplicity and generality of the estimation method and the robustness of the chattering reduction algorithm. In this paper, we prove that the proposed estimation algorithm converge the very fast, thereby making it very practical to use. The application of the proposed FLC-VSC shows that both alleviation of chattering and robust performance are achieved

Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning

Background: Accurately predicting outcomes for cancer patients with COVID-19 has been clinically challenging. Numerous clinical variables have been retrospectively associated with disease severity, but the predictive value of these variables, and how multiple variables interact to increase risk, remains unclear. Methods: We used machine learning algorithms to predict COVID-19 severity in 348 cancer patients at Memorial Sloan Kettering Cancer Center in New York City. Using only clinical variables collected on or before a patient’s COVID-19 positive date (time zero), we sought to classify patients into one of three possible future outcomes: Severe-early (the patient required high levels of oxygen support within 3 days of being tested positive for COVID-19), Severe-late (the patient required high levels of oxygen after 3 days), and Non-severe (the patient never required oxygen support). Results: Our algorithm classified patients into these classes with an area under the receiver operating characteristic curve (AUROC) ranging from 70 to 85%, significantly outperforming prior methods and univariate analyses. Critically, classification accuracy is highest when using a potpourri of clinical variables — including basic patient information, pre-existing diagnoses, laboratory and radiological work, and underlying cancer type — suggesting that COVID-19 in cancer patients comes with numerous, combinatorial risk factors. Conclusions: Overall, we provide a computational tool that can identify high-risk patients early in their disease progression, which could aid in clinical decision-making and selecting treatment options.Medicine, Faculty ofNon UBCReviewedFacult

Effect of Antifungal Therapy Timing on Mortality in Cancer Patients with Candidemia▿

Prior studies have shown that delays in treatment are associated with increased mortality in patients with candidemia. The purpose of this study was to measure three separate time periods comprising the diagnosis and treatment of candidemia and to determine which one(s) is associated with hospital mortality. Patients with blood cultures positive for Candida spp. were identified. Subjects were excluded if no antifungal therapy was given or if there was preexisting antifungal therapy. Collected data included the time from blood culture collection to positivity (incubation period), the time from blood culture positivity to provider notification (provider notification period), and the time from provider notification to the first dose of antifungal given (antifungal initiation period). These times were assessed as predictors of inpatient mortality. A repeat analysis was done with adjustments for age, sex, race, underlying cancer, catheter removal, APACHE III score, acute renal failure, neutropenia, and non-Candida albicans species. A total of 106 episodes of candidemia were analyzed. The median incubation time was 32.1 h and was associated with mortality (univariate hazard ratio per hour, 1.025; P = 0.001). The median provider notification and antifungal initiation periods were 0.3 and 7.5 h, respectively, and were not associated with mortality. Adjusted analysis yielded similar results. For cancer patients with candidemia, the incubation period accounts for a significant amount of time, compared with the provider notification and antifungal initiation times, and is associated with in-hospital mortality. Strategies to shorten the incubation time, such as utilizing rapid molecularly based diagnostic methods, may help reduce in-hospital mortality

Targeting the microbiome: from probiotics to fecal microbiota transplantation

Editorial summary The modern techniques of microbiome science can be applied to the development and evaluation of all microbiota-directed products, including probiotics and fecal microbiota transplantation