740 research outputs found

    Strengthening of steel-reinforced concrete structural elements by externally bonded FRP sheets and evaluation of their load carrying capacity to face changed load service conditions

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    The paper has proposed a limit analysis procedure for a preliminary design of RC elements strengthened by externally bonded FRP sheets. The procedure, based on a multi-yield-criteria limit analysis approach, has led to a reliable prediction of peak loads and failure modes of the analyzed elements (slabs) by simultaneously considering the limit state of the constituent materials, so resulting very useful in many applications of engineering interest. The attention has been focused on hospital applications in which increment of service loads or realization of openings can weaken some structural elements that have been strengthened by FRP sheets

    Stimulation of S1PR5 with A-971432, a selective agonist, preserves blood-brain barrier integrity and exerts therapeutic effect in an animal model of Huntington's disease

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    Huntington's disease (HD) is themost common neurodegenerative disorder for which no effective cure is yet available. Although several agents have been identified to provide benefits so far, the number of therapeutic options remains limited with only symptomatic treatment available. Over the past few years, we have demonstrated that sphingolipid-based approachesmay open the door to newandmore targeted treatments for the disease. In this study, we investigated the therapeutic potential of stimulating sphingosine-1-phosphate (S1P) receptor 5 by the new selective agonist A-971432 (provided by AbbVie) in R6/2mice, a widely used HD animalmodel. Chronic administration of low-dose (0.1mg/kg) A-971432 slowed down the progression of the disease and significantly prolonged lifespan in symptomatic R6/2mice. Such beneficial effects were associated with activation of pro-survival pathways (BDNF, AKT and ERK) and with reduction of mutant huntingtin aggregation. A-971432 also protected blood-brain barrier (BBB) homeostasis in the same mice. Interestingly, when administered early in the disease, before any overt symptoms, A-971432 completely protected HDmice fromthe classic progressivemotor deficit and preserved BBB integrity. Beside representing a promising strategy to take into consideration for the development of alternative therapeutic options for HD, selective stimulation of S1P receptor 5may be also seen as an effective approach to target brain vasculature defects in the disease

    Gadolinium-chelating nanogels as MR contrast agesnts specifically targeting tumor cells

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    Development of multifunctional nanogels coordinating paramagnetic ions and displacing targeting ligands for preferential accumulation into tumors. Low molecular-weight Gd-chelates are widely used in clinical MRI for various purposes. However, these contrast agents (CAs) have several shortcomings: they rapidly extravasate from blood vessels to the interstitial space, have a short circulation times and show poor contrast at high magnetic fields. Incorporating gadolinium into flexible nanogels has the potential of increasing intravascular half-life, accumulation and retention in specific body compartments of the CA as well as increasing the MR signal, since many metal ions can be coordinated to the same nanoparticl

    A comprehensive study on different modelling approaches to predict platelet deposition rates in a perfusion chamber

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    Thrombus formation is a multiscale phenomenon triggered by platelet deposition over a protrombotic surface (eg. a ruptured atherosclerotic plaque). Despite the medical urgency for computational tools that aid in the early diagnosis of thrombotic events, the integration of computational models of thrombus formation at different scales requires a comprehensive understanding of the role and limitation of each modelling approach. We propose three different modelling approaches to predict platelet deposition. Specifically, we consider measurements of platelet deposition under blood flow conditions in a perfusion chamber for different time periods (3, 5, 10, 20 and 30 minutes) at shear rates of 212 s(-1), 1390 s(-1) and 1690 s(-1). Our modelling approaches are: i) a model based on the mass-transfer boundary layer theory; ii) a machine-learning approach; and iii) a phenomenological model. The results indicate that the three approaches on average have median errors of 21%, 20.7% and 14.2%, respectively. Our study demonstrates the feasibility of using an empirical data set as a proxy for a real-patient scenario in which practitioners have accumulated data on a given number of patients and want to obtain a diagnosis for a new patient about whom they only have the current observation of a certain number of variables.Peer reviewe

    Metabolomic profile of neuroendocrine tumors (NETs) identifies methionine, porphyrin and tryptophan metabolism as key dysregulated pathways associated with patient survival

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    Objective: Metabolic profiling is a valuable tool to characterize tumor biology but remains largely unexplored in neuroendocrine tumors (NETs). Our aim was to comprehensively assess the metabolomic profile of NETs and identify novel prognostic biomarkers and dysregulated molecular pathways.Design and Methods: Multiplatform untargeted metabolomic profiling (GC-MS, CE-MS, and LC-MS) was performed in plasma from 77 patients with G1-2 extra-pancreatic NETs enrolled in the AXINET trial (NCT01744249) (study cohort) and from 68 non-cancer individuals (control). The prognostic value of each differential metabolite (n = 155) in NET patients (P < .05) was analyzed by univariate and multivariate analyses adjusted for multiple testing and other confounding factors. Related pathways were explored by Metabolite Set Enrichment Analysis (MSEA) and Metabolite Pathway Analysis (MPA).Results: Thirty-four metabolites were significantly associated with progression-free survival (PFS) (n = 16) and/or overall survival (OS) (n = 27). Thirteen metabolites remained significant independent prognostic factors in multivariate analysis, 3 of them with a significant impact on both PFS and OS. Unsupervised clustering of these 3 metabolites stratified patients in 3 distinct prognostic groups (1-year PFS of 71.1%, 47.7%, and 15.4% (P = .012); 5-year OS of 69.7%, 32.5%, and 27.7% (P = .003), respectively). The MSEA and MPA of the 13-metablolite signature identified methionine, porphyrin, and tryptophan metabolisms as the 3 most relevant dysregulated pathways associated with the prognosis of NETs.Conclusions: We identified a metabolomic signature that improves prognostic stratification of NET patients beyond classical prognostic factors for clinical decisions. The enriched metabolic pathways identified reveal novel tumor vulnerabilities that may foster the development of new therapeutic strategies for these patients

    Stimulation of Sphingosine Kinase 1 (SPHK1) Is Beneficial in a Huntington’s Disease Pre-clinical Model

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    Although several agents have been identified to provide therapeutic benefits in Huntington disease (HD), the number of conventionally used treatments remains limited and only symptomatic. Thus, it is plausible that the need to identify new therapeutic targets for the development of alternative and more effective treatments is becoming increasingly urgent. Recently, the sphingosine-1-phosphate (S1P) axis has been reported to be a valid potential novel molecular target for therapy development in HD. Modulation of aberrant metabolism of S1P in HD has been proved to exert neuroprotective action in vitro settings including human HD iPSC-derived neurons. In this study, we investigated whether promoting S1P production by stimulating Sphingosine Kinase 1 (SPHK1) by the selective activator, K6PC-5, may have therapeutic benefit in vivo in R6/2 HD mouse model. Our findings indicate that chronic administration of 0.05 mg/kg K6PC-5 exerted an overall beneficial effect in R6/2 mice. It significantly slowed down the progressive motor deficit associated with disease progression, modulated S1P metabolism, evoked the activation of pro-survival pathways and markedly reduced the toxic mutant huntingtin (mHtt) aggregation. These results suggest that K6PC-5 may represent a future therapeutic option in HD and may potentially counteract the perturbed brain function induced by deregulated S1P pathways

    Effect of natalizumab on disease progression in secondary progressive multiple sclerosis (ASCEND). a phase 3, randomised, double-blind, placebo-controlled trial with an open-label extension

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    Background: Although several disease-modifying treatments are available for relapsing multiple sclerosis, treatment effects have been more modest in progressive multiple sclerosis and have been observed particularly in actively relapsing subgroups or those with lesion activity on imaging. We sought to assess whether natalizumab slows disease progression in secondary progressive multiple sclerosis, independent of relapses. Methods: ASCEND was a phase 3, randomised, double-blind, placebo-controlled trial (part 1) with an optional 2 year open-label extension (part 2). Enrolled patients aged 18–58 years were natalizumab-naive and had secondary progressive multiple sclerosis for 2 years or more, disability progression unrelated to relapses in the previous year, and Expanded Disability Status Scale (EDSS) scores of 3·0–6·5. In part 1, patients from 163 sites in 17 countries were randomly assigned (1:1) to receive 300 mg intravenous natalizumab or placebo every 4 weeks for 2 years. Patients were stratified by site and by EDSS score (3·0–5·5 vs 6·0–6·5). Patients completing part 1 could enrol in part 2, in which all patients received natalizumab every 4 weeks until the end of the study. Throughout both parts, patients and staff were masked to the treatment received in part 1. The primary outcome in part 1 was the proportion of patients with sustained disability progression, assessed by one or more of three measures: the EDSS, Timed 25-Foot Walk (T25FW), and 9-Hole Peg Test (9HPT). The primary outcome in part 2 was the incidence of adverse events and serious adverse events. Efficacy and safety analyses were done in the intention-to-treat population. This trial is registered with ClinicalTrials.gov, number NCT01416181. Findings: Between Sept 13, 2011, and July 16, 2015, 889 patients were randomly assigned (n=440 to the natalizumab group, n=449 to the placebo group). In part 1, 195 (44%) of 439 natalizumab-treated patients and 214 (48%) of 448 placebo-treated patients had confirmed disability progression (odds ratio [OR] 0·86; 95% CI 0·66–1·13; p=0·287). No treatment effect was observed on the EDSS (OR 1·06, 95% CI 0·74–1·53; nominal p=0·753) or the T25FW (0·98, 0·74–1·30; nominal p=0·914) components of the primary outcome. However, natalizumab treatment reduced 9HPT progression (OR 0·56, 95% CI 0·40–0·80; nominal p=0·001). In part 1, 100 (22%) placebo-treated and 90 (20%) natalizumab-treated patients had serious adverse events. In part 2, 291 natalizumab-continuing patients and 274 natalizumab-naive patients received natalizumab (median follow-up 160 weeks [range 108–221]). Serious adverse events occurred in 39 (13%) patients continuing natalizumab and in 24 (9%) patients initiating natalizumab. Two deaths occurred in part 1, neither of which was considered related to study treatment. No progressive multifocal leukoencephalopathy occurred. Interpretation: Natalizumab treatment for secondary progressive multiple sclerosis did not reduce progression on the primary multicomponent disability endpoint in part 1, but it did reduce progression on its upper-limb component. Longer-term trials are needed to assess whether treatment of secondary progressive multiple sclerosis might produce benefits on additional disability components. Funding: Biogen

    Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictability

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    Background Predicting progression from Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) is an utmost open issue in AD-related research. Neuropsychological assessment has proven to be useful in identifying MCI patients who are likely to convert to dementia. However, the large battery of neuropsychological tests (NPTs) performed in clinical practice and the limited number of training examples are challenge to machine learning when learning prognostic models. In this context, it is paramount to pursue approaches that effectively seek for reduced sets of relevant features. Subsets of NPTs from which prognostic models can be learnt should not only be good predictors, but also stable, promoting generalizable and explainable models. Methods We propose a feature selection (FS) ensemble combining stability and predictability to choose the most relevant NPTs for prognostic prediction in AD. First, we combine the outcome of multiple (filter and embedded) FS methods. Then, we use a wrapper-based approach optimizing both stability and predictability to compute the number of selected features. We use two large prospective studies (ADNI and the Portuguese Cognitive Complaints Cohort, CCC) to evaluate the approach and assess the predictive value of a large number of NPTs. Results The best subsets of features include approximately 30 and 20 (from the original 79 and 40) features, for ADNI and CCC data, respectively, yielding stability above 0.89 and 0.95, and AUC above 0.87 and 0.82. Most NPTs learnt using the proposed feature selection ensemble have been identified in the literature as strong predictors of conversion from MCI to AD. Conclusions The FS ensemble approach was able to 1) identify subsets of stable and relevant predictors from a consensus of multiple FS methods using baseline NPTs and 2) learn reliable prognostic models of conversion from MCI to AD using these subsets of features. The machine learning models learnt from these features outperformed the models trained without FS and achieved competitive results when compared to commonly used FS algorithms. Furthermore, the selected features are derived from a consensus of methods thus being more robust, while releasing users from choosing the most appropriate FS method to be used in their classification task.PTDC/EEI-SII/1937/2014; SFRH/BD/95846/2013; SFRH/BD/118872/2016info:eu-repo/semantics/publishedVersio
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