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Background Support vector machines (SVM) are a powerful tool to analyze data with a number of predictors approximately equal or larger than the number of observations. However, originally, application of SVM to analyze biomedical data was limited because SVM was not designed to evaluate importance of predictor variables. Creating predictor models based on only the most relevant variables is essential in biomedical research. Currently, substantial work has been done to allow assessment of variable importance in SVM models but this work has focused on SVM implemented with linear kernels. The power of SVM as a prediction model is associated with the flexibility generated by use of non-linear kernels. Moreover, SVM has been extended to model survival outcomes. This paper extends the Recursive Feature Elimination (RFE) algorithm by proposing three approaches to rank variables based on non-linear SVM and SVM for survival analysis. Results The proposed algorithms allows visualization of each one the RFE iterations, and hence, identification of the most relevant predictors of the response variable. Using simulation studies based on time-to-event outcomes and three real datasets, we evaluate the three methods, based on pseudo-samples and kernel principal component analysis, and compare them with the original SVM-RFE algorithm for non-linear kernels. The three algorithms we proposed performed generally better than the gold standard RFE for non-linear kernels, when comparing the truly most relevant variables with the variable ranks produced by each algorithm in simulation studies. Generally, the RFE-pseudo-samples outperformed the other three methods, even when variables were assumed to be correlated in all tested scenarios. Conclusions The proposed approaches can be implemented with accuracy to select variables and assess direction and strength of associations in analysis of biomedical data using SVM for categorical or time-to-event responses. Conducting variable selection and interpreting direction and strength of associations between predictors and outcomes with the proposed approaches, particularly with the RFE-pseudo-samples approach can be implemented with accuracy when analyzing biomedical data. These approaches, perform better than the classical RFE of Guyon for realistic scenarios about the structure of biomedical data.

Background The necessity to analyze medium-throughput data in epidemiological studies with small sample size, particularly when studying biomedical data may hinder the use of classical statistical methods. Support vector machines (SVM) models can be successfully applied in this setting because they are a powerful tool to analyze data with large number of predictors and limited sample size, especially when handling binary outcomes. However, biomedical research often involves analysis of time-to-event outcomes and has to account for censoring. Methods to handle censored data in the SVM framework can be divided into two classes: those based on support vector regression (SVR) and those based on binary classification. Methods based on SVR seem to be suboptimal to handle sparse data and yield results comparable to Cox proportional hazards model and kernel Cox regression. The limited work dedicated to assess methods based on of SVM for binary classification has been based on SVM learning using privileged information and SVM with uncertain classes. Results This paper proposes alternative methods and extensions within the binary classification framework, specifically, a conditional survival approach for weighting censored observations and a semi-supervised SVM with local invariances. Using simulation studies and some real datasets, we evaluate those two methods and compare them with a weighted SVM model, SVM extensions found in the literature, kernel Cox regression and Cox model. Conclusions Our proposed methods perform generally better under a wide variety of realistic scenarios about the structure of biomedical data. Specifically, the local invariances method using the conditional survival approach is the most robust method under different scenarios and is a good approach to consider as an alternative to other time-to-event methods. When analysing real data is a method to be considered and recommended since outperforms other methods in proportional and non-proportional scenarios and sparse data, which is something usual in biomedical data and biomarkers analysis.

The prevention and treatment of obesity is primary based on the follow-up of a healthy lifestyle, which includes a healthy diet with an important presence of bioactive compounds such as polyphenols. For many years, the health benefits of polyphenols have been attributed to their anti-oxidant capacity as free radical scavengers. More recently it has been described that polyphenols activate other cell-signaling pathways that are not related to ROS production but rather involved in metabolic regulation. In this review, we have summarized the current knowledge in this field by focusing on the metabolic e ects of flavonoids. Flavonoids are widely distributed in the plant kingdom where they are used for growing and defensing. They are structurally characterized by two benzene rings and a heterocyclic pyrone ring and based on the oxidation and saturation status of the heterocyclic ring flavonoids are grouped in seven di erent subclasses. The present work is focused on describing the molecular mechanisms underlying the metabolic impact of flavonoids in obesity and obesity-related diseases. We described the e ects of each group of flavonoids in liver, white and brown adipose tissue and central nervous system and the metabolic and signaling pathways involved on them.

Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Enhancing SVM for survival data using local invariances and weighting [RAW_REF_TEXT] Hector Sanz 1 , [/RAW_REF_TEXT] [RAW_REF_TEXT] Ferran Reverter1,2 & [/RAW_REF_TEXT] [RAW_REF_TEXT] Clarissa Valim3,4 [/RAW_REF_TEXT] BMC Bioinformatics volume 21, Article number: 371 (2020) Cite this article [RAW_REF_TEXT] 108 Accesses [/RAW_REF_TEXT] [RAW_REF_TEXT] 1 Altmetric [/RAW_REF_TEXT] [RAW_REF_TEXT] Metrics details [/RAW_REF_TEXT] [RAW_REF_TEXT] The original article was published in BMC Bioinformatics 2020 21:193 [/RAW_REF_TEXT] Correction to: BMC Bioinformatics 21, 193 (2020) https://doi.org/10.1186/s12859-020-3481-2 Following publication of the original article [1], the authors identified errors in the equations. Enhancing SVM for survival data using local invariances and weighting [RAW_REF_TEXT] Hector Sanz 1 , Ferran Reverter1,2 & Clarissa Valim3,4 [/RAW_REF_TEXT] BMC Bioinformatics volume 21, Article number: 371 (2020) Cite this article [RAW_REF_TEXT] 108 Accesses 1 Altmetric Metrics details The original article was published in BMC Bioinformatics 2020 21:193

Reducing variability of quantitative suspension array assays is key for multi-center and large sero-epidemiological studies. To maximize precision and robustness of an in-house IgG multiplex assay, we analyzed the effect of several conditions on variability to find the best combination. The following assay conditions were studied through a fractional factorial design: antigen-bead coupling (stock vs. several), sample predilution (stock vs. daily), temperature of incubation of sample with antigen-bead (22°C vs. 37°C), plate washing (manual vs. automatic) and operator expertise (expert vs. apprentice). IgG levels against seven P. falciparum antigens with heterogeneous immunogenicities were measured in test samples, in a positive control and in blanks. We assessed the variability and MFI quantification range associated to each combination of conditions, and their interactions, and evaluated the minimum number of samples and blank replicates to achieve good replicability. Results showed that antigen immunogenicity and sample seroreactivity defined the optimal dilution to assess the effect of assay conditions on variability. We found that a unique antigen-bead coupling, samples prediluted daily, incubation at 22°C, and automatic washing, had lower variability. However, variability increased when performing several couplings and incubating at 22°C vs. 37°C. In addition, no effect of temperature was seen with a unique coupling. The expertise of the operator had no effect on assay variability but reduced the MFI quantification range. Finally, differences between sample replicates were minimal, and two blanks were sufficient to capture assay variability, as suggested by the constant Intraclass Correlation Coefficient of three and two blanks. To conclude, a single coupling was the variable that most consistently reduced assay variability, being clearly advisable. In addition, we suggest having more sample dilutions instead of replicates to increase the likelihood of sample MFIs falling in the linear part of the antigen-specific curve, thus increasing precision.

Introduction: Aging induces functional and structural changes in the lung, characterized by a decline in elasticity and diminished pulmonary remodeling and regenerative capacity. Emerging evidence suggests that most biomechanical alterations in the lung result from changes in the composition of the lung extracellular matrix (ECM), potentially modulating the behavior of pulmonary cells and increasing the susceptibility to chronic lung diseases. Therefore, it is crucial to investigate the mechanical properties of the aged lung. This study aims to assess the mechanical alterations in the lung ECM due to aging at both residual (RV) and functional (FV) lung volumes and to evaluate their effects on the survival and proliferation of mesenchymal stromal cells (MSCs). Methods: The lungs from young (4-6-month-old) and aged (20-24-month-old) mice were inflated with optimal cutting temperature compound to reach FV or non-inflated (RV). ECM proteins laminin, collagen I and fibronectin were quantified by immunofluorescence and the mechanical properties of the decellularized lung sections were assessed using atomic force microscopy. To investigate whether changes in ECM composition by aging and/or mechanical properties at RV and FV volumes affects MSCs, their viability and proliferation were evaluated after 72 h. Results: Laminin presence was significantly reduced in aged mice compared to young mice, while fibronectin and collagen I were significantly increased in aged mice. In RV conditions, the acellular lungs from aged mice were significantly softer than from young mice. By contrast, in FV conditions, the aged lung ECM becomes stiffer than that of in young mice, revealing that strain hardening significantly depends on aging. Results after MSCs recellularization showed similar viability and proliferation rate in all conditions. Discussion: This data strongly suggests that biomechanical measurements, especially in aging models, should be carried out in physiomimetic conditions rather than following the conventional non-inflated lung (RV) approach. The use of decellularized lung scaffolds from aged and/or other lung disease murine/human models at physiomimetic conditions will help to better understand the potential role of mechanotransduction on the susceptibility and progression of chronic lung diseases, lung regeneration and cancer.

Multiplex bead-based immunoassays are used to measure concentrations of several analytes simultaneously. These assays include control standard curves (SC) to reduce between-plate variability and normalize quantitation of analytes of biological samples. Suboptimal calibration might result in large random error and decreased number of samples with analyte concentrations within the limits of quantification. Suboptimal calibration may be a consequence of poor fitness of the functions used for the SC, the treatment of the background noise and the method used to estimate the limits of quantification. Currently assessment of fitness of curves is largely dependent on operator and that may add additional error. Moreover, there is no software to automate data managing and quality control. In this article we present a R package, drLumi, with functions for managing data, calibrating assays and performing quality control. To optimize the assay the package implements: i) three dose-response functions, ii) four approaches for treating background noise and iii) three methods for estimating limits of quantifications. Other implemented functions are focused on the quality control of the fitted standard curve: detection of outliers, estimation of the confidence or prediction interval, and estimation of summary statistics. With demonstration purpose, we apply the software to 30 cytokines, chemokines and growth factors measured in a multiplex bead-based immunoassay in a study aiming to measure correlates of risk or protection from malaria of the RTS,S malaria vaccine nested in the Phase 3 randomized controlled trial of this vaccine.

Background Factors driving inter-individual differences in immune responses upon different types of prenatal malaria exposure (PME) and subsequent risk of malaria in infancy remain poorly understood. In this study, we examined the impact of four types of PME (i.e., maternal peripheral infection and placental acute, chronic, and past infections) on both spontaneous and toll-like receptors (TLRs)-mediated cytokine production in cord blood and how these innate immune responses modulate the risk of malaria during the first year of life. Methods We conducted a birth cohort study of 313 mother-child pairs nested within the COSMIC clinical trial (NCT01941264), which was assessing malaria preventive interventions during pregnancy in Burkina Faso. Malaria infections during pregnancy and infants’ clinical malaria episodes detected during the first year of life were recorded. Supernatant concentrations of 30 cytokines, chemokines, and growth factors induced by stimulation of cord blood with agonists of TLRs 3, 7/8, and 9 were measured by quantitative suspension array technology. Crude concentrations and ratios of TLR-mediated cytokine responses relative to background control were analyzed. Results Spontaneous production of innate immune biomarkers was significantly reduced in cord blood of infants exposed to malaria, with variation among PME groups, as compared to those from the non-exposed control group. However, following TLR7/8 stimulation, which showed higher induction of cytokines/chemokines/growth factors than TLRs 3 and 9, cord blood cells of infants with evidence of past placental malaria were hyper-responsive in comparison to those of infants not-exposed. In addition, certain biomarkers, which levels were significantly modified depending on the PME category, were independent predictors of either malaria risk (GM-CSF TLR7/8 crude) or protection (IL-12 TLR7/8 ratio and IP-10 TLR3 crude, IL-1RA TLR7/8 ratio) during the first year of life. Conclusions These findings indicate that past placental malaria has a profound effect on fetal immune system and that the differential alterations of innate immune responses by PME categories might drive heterogeneity between individuals to clinical malaria susceptibility during the first year of life.

RTS,S/AS01E has been tested in a phase 3 malaria vaccine study with partial efficacy in African children and infants. In a cohort of 1028 subjects from one low (Bagomoyo) and two high (Nanoro, Kintampo) malaria transmission sites, we analysed IgG plasma/serum concentration and avidity to CSP (NANP-repeat and C-terminal domains) after a 3-dose vaccination against time to clinical malaria events during 12-months. Here we report that RTS,S/AS01E induces substantial increases in IgG levels from pre- to post-vaccination ( p  < 0.001), higher in NANP than C-terminus (2855 vs 1297 proportional change between means), and higher concentrations and avidities in children than infants ( p  < 0.001). Baseline CSP IgG levels are elevated in malaria cases than controls ( p  < 0.001). Both, IgG magnitude to NANP (hazard ratio [95% confidence interval] 0.61 [0.48–0.76]) and avidity to C-terminus (0.07 [0.05–0.90]) post-vaccination are significantly associated with vaccine efficacy. IgG avidity to the C-terminus emerges as a significant contributor to RTS,S/AS01E-mediated protection. RTS,S/AS01E has been tested in a phase 3 malaria vaccine trial and has shown partial efficacy in children and infants. Here, the authors analyze IgG concentration and avidity to CSP in ~1000 participants and show that IgG avidity to the C-terminus of CSP is significantly associated with vaccine-mediated protection.

Background. The RTS,S/AS01E malaria vaccine has moderate efficacy, lower in infants than children. Current efforts to enhance RTS,S/AS01E efficacy would benefit from learning about the vaccine-induced immunity and identifying correlates of malaria protection, which could, for instance, inform the choice of adjuvants. Here, we sought cellular immunity-based correlates of malaria protection and risk associated with RTS,S/AS01E vaccination. Methods. We performed a matched case-control study nested within the multicenter African RTS,S/AS01E phase 3 trial. Children and infant samples from 57 clinical malaria cases (32 RTS,S/25 comparator vaccinees) and 152 controls without malaria (106 RTS,S/46 comparator vaccinees) were analyzed. We measured 30 markers by Luminex following RTS,S/AS01E antigen stimulation of cells 1 month postimmunization. Crude concentrations and ratios of antigen to background control were analyzed. Results. Interleukin (IL) 2 and IL-5 ratios were associated with RTS,S/AS01E vaccination (adjusted P ≤ .01). IL-5 circumsporozoite protein (CSP) ratios, a helper T cell type 2 cytokine, correlated with higher odds of malaria in RTS,S/AS01E vaccinees (odds ratio, 1.17 per 10% increases of CSP ratios; P value adjusted for multiple testing = .03). In multimarker analysis, the helper T cell type 1 (TH1)–related markers interferon-γ, IL-15, and granulocyte-macrophage colony-stimulating factor protected from subsequent malaria, in contrast to IL-5 and RANTES, which increased the odds of malaria. Conclusions. RTS,S/AS01E-induced IL-5 may be a surrogate of lack of protection, whereas TH1-related responses may be involved in protective mechanisms. Efforts to develop second-generation vaccine candidates may concentrate on adjuvants that modulate the immune system to support enhanced TH1 responses and decreased IL-5 responses.