Using random forest and decision tree models for a new vehicle prediction approach in computational toxicology

yesDrug vehicles are chemical carriers that provide beneficial aid to the drugs they bear. Taking advantage of their favourable properties can potentially allow the safer use of drugs that are considered highly toxic. A means for vehicle selection without experimental trial would therefore be of benefit in saving time and money for the industry. Although machine learning is increasingly used in predictive toxicology, to our knowledge there is no reported work in using machine learning techniques to model drug-vehicle relationships for vehicle selection to minimise toxicity. In this paper we demonstrate the use of data mining and machine learning techniques to process, extract and build models based on classifiers (decision trees and random forests) that allow us to predict which vehicle would be most suited to reduce a drug’s toxicity. Using data acquired from the National Institute of Health’s (NIH) Developmental Therapeutics Program (DTP) we propose a methodology using an area under a curve (AUC) approach that allows us to distinguish which vehicle provides the best toxicity profile for a drug and build classification models based on this knowledge. Our results show that we can achieve prediction accuracies of 80 % using random forest models whilst the decision tree models produce accuracies in the 70 % region. We consider our methodology widely applicable within the scientific domain and beyond for comprehensively building classification models for the comparison of functional relationships between two variables

Decellularised skeletal muscles allow functional muscle regeneration by promoting host cell migration

Pathological conditions affecting skeletal muscle function may lead to irreversible volumetric muscle loss (VML). Therapeutic approaches involving acellular matrices represent an emerging and promising strategy to promote regeneration of skeletal muscle following injury. Here we investigated the ability of three different decellularised skeletal muscle scaffolds to support muscle regeneration in a xenogeneic immune-competent model of VML, in which the EDL muscle was surgically resected. All implanted acellular matrices, used to replace the resected muscles, were able to generate functional artificial muscles by promoting host myogenic cell migration and differentiation, as well as nervous fibres, vascular networks, and satellite cell (SC) homing. However, acellular tissue mainly composed of extracellular matrix (ECM) allowed better myofibre three-dimensional (3D) organization and the restoration of SC pool, when compared to scaffolds which also preserved muscular cytoskeletal structures. Finally, we showed that fibroblasts are indispensable to promote efficient migration and myogenesis by muscle stem cells across the scaffolds in vitro. This data strongly support the use of xenogeneic acellular muscles as device to treat VML conditions in absence of donor cell implementation, as well as in vitro model for studying cell interplay during myogenesis

Biological response of an in vitro human 3D lung cell model exposed to brake wear debris varies based on brake pad formulation

Wear particles from automotive friction brake pads of various sizes, morphology, and chemical composition are significant contributors towards particulate matter. Knowledge concerning the potential adverse effects following inhalation exposure to brake wear debris is limited. Our aim was, therefore, to generate brake wear particles released from commercial low-metallic and non-asbestos organic automotive brake pads used in mid-size passenger cars by a full-scale brake dynamometer with an environmental chamber simulating urban driving and to deduce their potential hazard in vitro. The collected fractions were analysed using scanning electron microscopy via energy-dispersive X-ray spectroscopy (SEM-EDS) and Raman microspectroscopy. The biological impact of the samples was investigated using a human 3D multicellular model consisting of human epithelial cells (A549) and human primary immune cells (macrophages and dendritic cells) mimicking the human epithelial tissue barrier. The viability, morphology, oxidative stress, and (pro-)inflammatory response of the cells were assessed following 24 h exposure to similar to 12, similar to 24, and similar to 48 A mu g/cm(2) of non-airborne samples and to similar to 3.7 A mu g/cm(2) of different brake wear size fractions (2-4, 1-2, and 0.25-1 A mu m) applying a pseudo-air-liquid interface approach. Brake wear debris with low-metallic formula does not induce any adverse biological effects to the in vitro lung multicellular model. Brake wear particles from non-asbestos organic formulated pads, however, induced increased (pro-)inflammatory mediator release from the same in vitro system. The latter finding can be attributed to the different particle compositions, specifically the presence of anatase.Web of Science9272351233

Targeting of SUMO substrates to a Cdc48-Ufd1-Npl4 segregase and STUbL pathway in fission yeast

In eukaryotes, the conjugation of proteins to the small ubiquitin-like modifier (SUMO) regulates numerous cellular functions. A proportion of SUMO conjugates are targeted for degradation by SUMO-targeted ubiquitin ligases (STUbLs) and it has been proposed that the ubiquitin-selective chaperone Cdc48/p97-Ufd1-Npl4 facilitates this process. However, the extent to which the two pathways overlap, and how substrates are selected, remains unknown. Here we address these questions in fission yeast through proteome-wide analyses of SUMO modification sites. We identify over a thousand sumoylated lysines in a total of 468 proteins and quantify changes occurring in the SUMO modification status when the STUbL or Ufd1 pathways are compromised by mutations. The data suggest the coordinated processing of several classes of SUMO conjugates, many dynamically associated with centromeres or telomeres. They provide new insights into subnuclear organization and chromosome biology, and, altogether, constitute an extensive resource for the molecular characterization of SUMO function and dynamics

The unfolded protein response in immunity and inflammation.

The unfolded protein response (UPR) is a highly conserved pathway that allows the cell to manage endoplasmic reticulum (ER) stress that is imposed by the secretory demands associated with environmental forces. In this role, the UPR has increasingly been shown to have crucial functions in immunity and inflammation. In this Review, we discuss the importance of the UPR in the development, differentiation, function and survival of immune cells in meeting the needs of an immune response. In addition, we review current insights into how the UPR is involved in complex chronic inflammatory diseases and, through its role in immune regulation, antitumour responses.This work was supported by the Netherlands Organization for Scientific Research Rubicon grant 825.13.012 (J.G.); US National Institutes of Health (NIH) grants DK044319, DK051362, DK053056 and DK088199, and the Harvard Digestive Diseases Center (HDDC) grant DK034854 (R.S.B.); National Institutes of Health grants DK042394, DK088227, DK103183 and CA128814 (R.J.K.); and European Research Council (ERC) Starting Grant 260961, ERC Consolidator Grant 648889, and the Wellcome Trust Investigator award 106260/Z/14/Z (A.K.).This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/nri.2016.6

Nomogram based on virtual hyperemic pullback pressure gradients for predicting the suboptimal post-PCI QFR outcome after stent implantation

Background: Growing evidence shows an association between higher post-PCI quantitative flow ratios (QFR) and improved clinical prognosis, however, no models are available to predict suboptimal QFRs (&lt; 0.91) after angiographically successful PCI. This study aims to establish a prediction nomogram for this domain.Methods: This study included 450 vessels derived from 421 consecutive patients enrolled in the PIONEER IV trial, which were randomly assigned in a 1:1 ratio to a training (N = 225) and internal validation (N = 225) set, with external validation performed in 97 vessels from 95 consecutive patients enrolled in the ASET Japan trial. LASSO regression was used for optimal feature selection, and multivariate logistic regression was subsequently utilized to construct the nomogram. The performance of the nomograms was assessed and validated by area under the receiver operating characteristics curve (AUC), calibration curves, decision curve analysis, and clinical impact curves.Results: The nomogram was constructed incorporating a novel metric, quantitative flow ratio derived pullback pressure gradient (QFR-PPG), alongside four conventional parameters: left anterior descending artery disease, pre-procedural QFR, reference vessel diameter, and percent diameter stenosis. AUCs of the nomogram were 0.866 (95%CI:0.818–0.914), 0.784 (95% CI:0.722–0.847), and 0.781 (95% CI:0.682–0.879) in the training, internal validation and external validation sets, respectively. Bias-corrected curves revealed a strong consistency between actual observations and prediction.Conclusion: The risk of a suboptimal post-PCI QFR in patients after angiographically successful PCI can be effectively predicted using a nomogram incorporating five variables available pre-PCI, with its performance and clinical predictive value confirming its utility in helping clinicians with decision-making and planning revascularization. Trial registration: Registered on clinicaltrial.gov (NCT04923191 and NCT05117866).</p

Search for Dark Matter and Supersymmetry with a Compressed Mass Spectrum in the Vector Boson Fusion Topology in Proton-Proton Collisions at root s=8 TeV

Peer reviewe

Nanofluids Containing γ-Fe2O3 Nanoparticles and Their Heat Transfer Enhancements

Homogeneous and stable magnetic nanofluids containing γ-Fe2O3 nanoparticles were prepared using a two-step method, and their thermal transport properties were investigated. Thermal conductivities of the nanofluids were measured to be higher than that of base fluid, and the enhanced values increase with the volume fraction of the nanoparticles. Viscosity measurements showed that the nanofluids demonstrated Newtonian behavior and the viscosity of the nanofluids depended strongly on the tested temperatures and the nanoparticles loadings. Convective heat transfer coefficients tested in a laminar flow showed that the coefficients increased with the augment of Reynolds number and the volume fraction

Evaluating the impact of MEDLINE filters on evidence retrieval: study protocol

<p>Abstract</p> <p>Background</p> <p>Rather than searching the entire MEDLINE database, clinicians can perform searches on a filtered set of articles where relevant information is more likely to be found. Members of our team previously developed two types of MEDLINE filters. The 'methods' filters help identify clinical research of high methodological merit. The 'content' filters help identify articles in the discipline of renal medicine. We will now test the utility of these filters for physician MEDLINE searching.</p> <p>Hypothesis</p> <p>When a physician searches MEDLINE, we hypothesize the use of filters will increase the number of relevant articles retrieved (increase 'recall,' also called sensitivity) and decrease the number of non-relevant articles retrieved (increase 'precision,' also called positive predictive value), compared to the performance of a physician's search unaided by filters.</p> <p>Methods</p> <p>We will survey a random sample of 100 nephrologists in Canada to obtain the MEDLINE search that they would first perform themselves for a focused clinical question. Each question we provide to a nephrologist will be based on the topic of a recently published, well-conducted systematic review. We will examine the performance of a physician's unaided MEDLINE search. We will then apply a total of eight filter combinations to the search (filters used in isolation or in combination). We will calculate the recall and precision of each search. The filter combinations that most improve on unaided physician searches will be identified and characterized.</p> <p>Discussion</p> <p>If these filters improve search performance, physicians will be able to search MEDLINE for renal evidence more effectively, in less time, and with less frustration. Additionally, our methodology can be used as a proof of concept for the evaluation of search filters in other disciplines.</p