Maternal supplementation with LGG reduces vaccine-specific immune responses in infants at high-risk of developing allergic disease

Probiotics are defined as live micro-organisms that when administered in adequate amounts confer a health benefit on the host. Among their pleiotropic effects, inhibition of pathogen colonization at the mucosal surface as well as modulation of immune responses are widely recognized as the principal biological activities of probiotic bacteria. In recent times, the immune effects of probiotics have led to their application as vaccine adjuvants, offering a novel strategy for enhancing the efficacy of current vaccines. Such an approach is particularly relevant in regions where infectious disease burden is greatest and where access to complete vaccination programs is limited. In this study, we report the effects of the probiotic, Lactobacillus rhamnosus GG (LGG) on immune responses to tetanus, Haemophilus influenzae type b (Hib) and pneumococcal conjugate (PCV7) vaccines in infants. This study was conducted as part of a larger clinical trial assessing the impact of maternal LGG supplementation in preventing the development of atopic eczema in infants at high-risk for developing allergic disease. Maternal LGG supplementation was associated with reduced antibody responses against tetanus, Hib, and pneumococcal serotypes contained in PCV7 (N = 31) compared to placebo treatment (N = 30) but not total IgG levels. Maternal LGG supplementation was also associated with a trend to increased number of tetanus toxoid-specific T regulatory in the peripheral blood compared to placebo-treated infants. These findings suggest that maternal LGG supplementation may not be beneficial in terms of improving vaccine-specific immunity in infants. Further clinical studies are needed to confirm these findings. As probiotic immune effects can be species/strain specific, our findings do not exclude the potential use of other probiotic bacteria to modulate infant immune responses to vaccines

Defining signal thresholds in DNA microarrays: exemplary application for invasive cancer

BACKGROUND: Genome-wide or application-targeted microarrays containing a subset of genes of interest have become widely used as a research tool with the prospect of diagnostic application. Intrinsic variability of microarray measurements poses a major problem in defining signal thresholds for absent/present or differentially expressed genes. Most strategies have used fold-change threshold values, but variability at low signal intensities may invalidate this approach and it does not provide information about false-positives and false negatives. RESULTS: We introduce a method to filter false-positives and false-negatives from DNA microarray experiments. This is achieved by evaluating a set of positive and negative controls by receiver operating characteristic (ROC) analysis. As an advantage of this approach, users may define thresholds on the basis of sensitivity and specificity considerations. The area under the ROC curve allows quality control of microarray hybridizations. This method has been applied to custom made microarrays developed for the analysis of invasive melanoma derived tumor cells. It demonstrated that ROC analysis yields a threshold with reduced missclassified genes in microarray experiments. CONCLUSIONS: Provided that a set of appropriate positive and negative controls is included on the microarray, ROC analysis obviates the inherent problem of arbitrarily selecting threshold levels in microarray experiments. The proposed method is applicable to both custom made and commercially available DNA microarrays and will help to improve the reliability of predictions from DNA microarray experiments

Effect of location of opening incision on astigmatic correction after small-incision lenticule extraction

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Early outcomes after small incision lenticule extraction and photorefractive keratectomy for correction of high myopia

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Sorafenib dose escalation is not uniformly associated with blood pressure elevations in normotensive patients with advanced malignancies.

Hypertension after treatment with vascular endothelial growth factor (VEGF) receptor inhibitors is associated with superior treatment outcomes for advanced cancer patients. To determine whether increased sorafenib doses cause incremental increases in blood pressure (BP), we measured 12-h ambulatory BP in 41 normotensive advanced solid tumor patients in a randomized dose-escalation study. After 7 days' treatment (400 mg b.i.d.), mean diastolic BP (DBP) increased in both study groups. After dose escalation, group A (400 mg t.i.d.) had marginally significant further increase in 12-h mean DBP (P = 0.053), but group B (600 mg b.i.d.) did not achieve statistically significant increases (P = 0.25). Within groups, individuals varied in BP response to sorafenib dose escalation, but these differences did not correlate with changes in steady-state plasma sorafenib concentrations. These findings in normotensive patients suggest BP is a complex pharmacodynamic biomarker of VEGF inhibition. Patients have intrinsic differences in sensitivity to sorafenib's BP-elevating effects

Avoiding catastrophic failure in correlated networks of networks

Networks in nature do not act in isolation but instead exchange information, and depend on each other to function properly. An incipient theory of Networks of Networks have shown that connected random networks may very easily result in abrupt failures. This theoretical finding bares an intrinsic paradox: If natural systems organize in interconnected networks, how can they be so stable? Here we provide a solution to this conundrum, showing that the stability of a system of networks relies on the relation between the internal structure of a network and its pattern of connections to other networks. Specifically, we demonstrate that if network inter-connections are provided by hubs of the network and if there is a moderate degree of convergence of inter-network connection the systems of network are stable and robust to failure. We test this theoretical prediction in two independent experiments of functional brain networks (in task- and resting states) which show that brain networks are connected with a topology that maximizes stability according to the theory.Comment: 40 pages, 7 figure

Epigenome targeting by probiotic metabolites

BACKGROUND: The intestinal microbiota plays an important role in immune development and homeostasis. A disturbed microbiota during early infancy is associated with an increased risk of developing inflammatory and allergic diseases later in life. The mechanisms underlying these effects are poorly understood but are likely to involve alterations in microbial production of fermentation-derived metabolites, which have potent immune modulating properties and are required for maintenance of healthy mucosal immune responses. Probiotics are beneficial bacteria that have the capacity to alter the composition of bacterial species in the intestine that can in turn influence the production of fermentation-derived metabolites. Principal among these metabolites are the short-chain fatty acids butyrate and acetate that have potent anti-inflammatory activities important in regulating immune function at the intestinal mucosal surface. Therefore strategies aimed at restoring the microbiota profile may be effective in the prevention or treatment of allergic and inflammatory diseases. PRESENTATION OF THE HYPOTHESIS: Probiotic bacteria have diverse effects including altering microbiota composition, regulating epithelial cell barrier function and modulating of immune responses. The precise molecular mechanisms mediating these probiotic effects are not well understood. Short-chain fatty acids such as butyrate are a class of histone deacetylase inhibitors important in the epigenetic control of host cell responses. It is hypothesized that the biological function of probiotics may be a result of epigenetic modifications that may explain the wide range of effects observed. Studies delineating the effects of probiotics on short-chain fatty acid production and the epigenetic actions of short-chain fatty acids will assist in understanding the association between microbiota and allergic or autoimmune disorders. TESTING THE HYPOTHESIS: We propose that treatment with specific probiotic bacteria under in vivo conditions would offer the ideal conditions to examine the microbiological, immunological and epigenetic mechanisms of action. Advances in epigenetic technology now allow investigators to better understand the complex biological properties of probiotics and their metabolites. IMPLICATIONS OF THE HYPOTHESIS: Determining the precise mechanisms of probiotic action will lead to more specific and efficacious therapeutic strategies in the prevention or treatment of chronic inflammatory conditions

Comparison of the Early Clinical Outcomes between Combined Small-Incision Lenticule Extraction and Collagen Cross-Linking versus SMILE for Myopia

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Improved model identification for non-linear systems using a random subsampling and multifold modelling (RSMM) approach

In non-linear system identification, the available observed data are conventionally partitioned into two parts: the training data that are used for model identification and the test data that are used for model performance testing. This sort of 'hold-out' or 'split-sample' data partitioning method is convenient and the associated model identification procedure is in general easy to implement. The resultant model obtained from such a once-partitioned single training dataset, however, may occasionally lack robustness and generalisation to represent future unseen data, because the performance of the identified model may be highly dependent on how the data partition is made. To overcome the drawback of the hold-out data partitioning method, this study presents a new random subsampling and multifold modelling (RSMM) approach to produce less biased or preferably unbiased models. The basic idea and the associated procedure are as follows. First, generate K training datasets (and also K validation datasets), using a K-fold random subsampling method. Secondly, detect significant model terms and identify a common model structure that fits all the K datasets using a new proposed common model selection approach, called the multiple orthogonal search algorithm. Finally, estimate and refine the model parameters for the identified common-structured model using a multifold parameter estimation method. The proposed method can produce robust models with better generalisation performance

Decentralized learning with budgeted network load using Gaussian copulas and classifier ensembles

We examine a network of learners which address the same classification task but must learn from different data sets. The learners cannot share data but instead share their models. Models are shared only one time so as to preserve the network load. We introduce DELCO (standing for Decentralized Ensemble Learning with COpulas), a new approach allowing to aggregate the predictions of the classifiers trained by each learner. The proposed method aggregates the base classifiers using a probabilistic model relying on Gaussian copulas. Experiments on logistic regressor ensembles demonstrate competing accuracy and increased robustness in case of dependent classifiers. A companion python implementation can be downloaded at https://github.com/john-klein/DELC