Machine learning and materials modelling interpretation of in vivo toxicological response to TiO2 nanoparticles library (UV and non-UV exposure)

Assessing the risks of nanomaterials/nanoparticles (NMs/NPs) under various environmental conditions requires a more systematic approach, including the comparison of effects across many NMs with identified different but related characters/descriptors. Hence, there is an urgent need to provide coherent (eco)toxicological datasets containing comprehensive toxicity information relating to a diverse spectra of NPs characters. These datasets are test benches for developing holistic methodologies with broader applicability. In the present study we assessed the effects of a custom design Fe-doped TiO2 NPs library, using the soil invertebrate Enchytraeus crypticus (Oligochaeta), via a 5-day pulse via aqueous exposure followed by a 21-days recovery period in soil (survival, reproduction assessment). Obviously, when testing TiO2, realistic conditions should include UV exposure. The 11 Fe-TiO2 library contains NPs of size range between 5-27 nm with varying þ (enabling the photoactivation of TiO2 at energy wavelengths in the visible-light range). The NPs were each described by 122 descriptors, being a mixture of measured and atomistic model descriptors. The data were explored using single and univariate statistical methods, combined with machine learning and multiscale modelling techniques. An iterative pruning process was adopted for identifying automatically the most significant descriptors. TiO2 NPs toxicity decreased when combined with UV. Notably, the short-term water exposure induced lasting biological responses even after longer-term recovery in clean exposure. The correspondence with Fe-content correlated with the band-gap hence the reduction of UV oxidative stress. The inclusion of both measured and modelled materials data benefitted the explanation of the results, when combined with machine learning

Myelin water imaging from multi-echo T-2 MR relaxometry data using a joint sparsity constraint

Demyelination is the key pathological process in multiple sclerosis (MS). The extent of demyelination can be quantified with magnetic resonance imaging by assessing the myelin water fraction (MWF). However, long computation times and high noise sensitivity hinder the translation of MWF imaging to clinical practice. In this work, we introduce a more efficient and noise robust method to determine the MWF using a joint sparsity constraint and a pre-computed B-1(+)-T-2 dictionary.A single component analysis with this dictionary is used in an initial step to obtain a B-1(+) map. The T-2 distribution is then determined from a reduced dictionary corresponding to the estimated B-1(+) map using a combination of a non-negativity and a joint sparsity constraint.The non-negativity constraint ensures that a feasible solution with non-negative contribution of each T-2 component is obtained. The joint sparsity constraint restricts the T-2 distribution to a small set of T-2 relaxation times shared between all voxels and reduces the noise sensitivity.The applied Sparsity Promoting Iterative Joint NNLS (SPIJN) algorithm can be implemented efficiently, reducing the computation time by a factor of 50 compared to the commonly used regularized non-negative least squares algorithm. The proposed method was validated in simulations and in 8 healthy subjects with a 3D multiecho gradient- and spin echo scan at 3 T. In simulations, the absolute error in the MWF decreased from 0.031 to 0.013 compared to the regularized NNLS algorithm for SNR = 250. The in vivo results were consistent with values reported in literature and improved MWF-quantification was obtained especially in the frontal white matter. The maximum standard deviation in mean MWF in different regions of interest between subjects was smaller for the proposed method (0.0193) compared to the regularized NNLS algorithm (0.0266). In conclusion, the proposed method for MWF estimation is less computationally expensive and less susceptible to noise compared to state of the art methods. These improvements might be an important step towards clinical translation of MWF measurements.Neuro Imaging Researc

The ischemic preconditioning effect of adenosine in patients with ischemic heart disease

<p>Abstract</p> <p>Introduction</p> <p><it>In vivo </it>and <it>in vitro </it>evidence suggests that adenosine and its agonists play key roles in the process of ischemic preconditioning. The effects of low-dose adenosine infusion on ischemic preconditioning have not been thoroughly studied in humans.</p> <p>Aims</p> <p>We hypothesised that a low-dose adenosine infusion could reduce the ischemic burden evoked by physical exercise and improve the regional left ventricular (LV) systolic function.</p> <p>Materials and methods</p> <p>We studied nine severely symptomatic male patients with severe coronary artery disease. Myocardial ischemia was induced by exercise on two separate occasions and quantified by Tissue Doppler Echocardiography. Prior to the exercise test, intravenous low-dose adenosine or placebo was infused over ten minutes according to a randomized, double blind, cross-over protocol. The LV walls were defined as ischemic if a reduction, no increment, or an increment of < 15% in peak systolic velocity (PSV) was observed during maximal exercise compared to the baseline values observed prior to placebo-infusion. Otherwise, the LV walls were defined as non-ischemic.</p> <p>Results</p> <p>PSV increased from baseline to maximal exercise in non-ischemic walls both during placebo (<it>P </it>= 0.0001) and low-dose adenosine infusion (<it>P </it>= 0.0009). However, in the ischemic walls, PSV increased only during low-dose adenosine infusion <it>(P </it>= 0.001), while no changes in PSV occurred during placebo infusion (<it>P </it>= NS).</p> <p>Conclusion</p> <p>Low-dose adenosine infusion reduced the ischemic burden and improved LV regional systolic function in the ischemic walls of patients with exercise-induced myocardial ischemia, confirming that adenosine is a potential preconditioning agent in humans.</p

Tissue Doppler echocardiographic quantification. Comparison to coronary angiography results in Acute Coronary Syndrome patients

BACKGROUND: Multiples indices have been described using tissue Doppler imaging (DTI) capabilities. The aim of this study was to assess the capability of one or several regional DTI parameters in separating control from ischemic myocardium. METHODS: Twenty-eight patients with acute myocardial infarction were imaged within 24-hour following an emergent coronary angioplasty. Seventeen controls without any coronary artery or myocardial disease were also explored. Global and regional left ventricular functions were assessed. High frame rate color DTI cineloop recordings were made in apical 4 and 2-chamber for subsequent analysis. Peak velocity during isovolumic contraction time (IVC), ejection time, isovolumic relaxation (IVR) and filling time were measured at the mitral annulus and the basal, mid and apical segments of each of the walls studied as well as peak systolic displacement and peak of strain. RESULTS: DTI-analysis enabled us to discriminate between the 3 populations (controls, inferior and anterior AMI). Even in non-ischemic segments, velocities and displacements were reduced in the 2 AMI populations. Peak systolic displacement was the best parameter to discriminate controls from AMI groups (wall by wall, p was systematically < 0.01). The combination IVC + and IVR< 1 discriminated ischemic from non-ischemic segments with 82% sensitivity and 85% specificity. CONCLUSION: DTI-analysis appears to be valuable in ischemic heart disease assessment. Its clinical impact remains to be established. However this simple index might really help in intensive care unit routine practice

Longitudinal peak strain detects a smaller risk area than visual assessment of wall motion in acute myocardial infarction

<p>Abstract</p> <p>Background</p> <p>Opening of an occluded infarct related artery reduces infarct size and improves survival in acute ST-elevation myocardial infarction (STEMI). In this study we performed tissue Doppler analysis (peak strain, displacement, mitral annular movement (MAM)) and compared with visual assessment for the study of the correlation of measurements of global, regional and segmental function with final infarct size and transmurality. In addition, myocardial risk area was determined and a prediction sought for the development of infarct transmurality ≥50%.</p> <p>Methods</p> <p>Twenty six patients with STEMI submitted for primary percutaneous coronary intervention (PCI) were examined with echocardiography on the catheterization table. Four to eight weeks later repeat echocardiography was performed for reassessment of function and magnetic resonance imaging for the determination of final infarct size and transmurality.</p> <p>Results</p> <p>On a global level, wall motion score index (WMSI), ejection fraction (EF), strain, and displacement all showed significant differences (p ≤ 0.001, p ≤ 0.001, p ≤ 0.001 and p = 0.03) between the two study visits, but MAM did not (p = 0.17). On all levels (global, regional and segmental) and both pre- and post PCI, WMSI showed a higher correlation with scar transmurality compared to strain. We found that both strain and WMSI predicted the development of scar transmurality ≥50%, but strain added no significant information to that obtained with WMSI in a logistic regression analysis.</p> <p>Conclusions</p> <p>In patients with acute STEMI, WMSI, EF, strain, and displacement showed significant changes between the pre- and post PCI exam. In a ROC-analysis, strain had 64% sensitivity at 80% specificity and WMSI around 90% sensitivity at 80% specificity for the detection of scar with transmurality ≥50% at follow-up.</p

Relevance of tissue Doppler in the quantification of stress echocardiography for the detection of myocardial ischemia in clinical practice

In the present article we review the main published data on the application of Tissue Doppler Imaging (TDI) to stress echocardiography for the detection of myocardial ischemia. TDI has been applied to stress echocardiography in order to overcome the limitations of visual analysis for myocardial ischemia. The introduction of a new technology for clinical routine use should pass through the different phases of scientific assessment from feasibility studies to large multicenter studies, from efficacy to effectiveness studies. Nonetheless the pro-technology bias plays a major role in medicine and expensive and sophisticated techniques are accepted before their real usefulness and incremental value to the available ones is assessed. Apparently, TDI is not exempted by this approach : its applications are not substantiated by strong and sound results. Nonetheless, conventional stress echocardiography for myocardial ischemia detection is heavily criticized on the basis of its subjectivity. Stress echocardiography has a long lasting history and the evidence collected over 20 years positioned it as an established tool for the detection and prognostication of coronary artery disease. The quantitative assessment of myocardial ischemia remains a scientific challenge and a clinical goal but time has not come for these newer ultrasonographic techniques which should be restricted to research laboratories

Source apportionment of fine particulate matter in Houston, Texas: insights to secondary organic aerosols

Online and offline measurements of ambient particulate matter (PM) near the urban and industrial Houston Ship Channel in Houston, Texas, USA, during May 2015 were utilized to characterize its chemical composition and to evaluate the relative contributions of primary, secondary, biogenic, and anthropogenic sources. Aerosol mass spectrometry (AMS) on nonrefractory PM1 (PM  ≤  1&thinsp;µm) indicated major contributions from sulfate (averaging 50&thinsp;% by mass), organic aerosol (OA, 40&thinsp;%), and ammonium (14&thinsp;%). Positive matrix factorization (PMF) of AMS data categorized OA on average as 22&thinsp;% hydrocarbon-like organic aerosol (HOA), 29&thinsp;% cooking-influenced less-oxidized oxygenated organic aerosol (CI-LO-OOA), and 48&thinsp;% more-oxidized oxygenated organic aerosol (MO-OOA), with the latter two sources indicative of secondary organic aerosol (SOA). Chemical analysis of PM2.5 (PM  ≤  2.5&thinsp;µm) filter samples agreed that organic matter (35&thinsp;%) and sulfate (21&thinsp;%) were the most abundant components. Organic speciation of PM2.5 organic carbon (OC) focused on molecular markers of primary sources and SOA tracers derived from biogenic and anthropogenic volatile organic compounds (VOCs). The sources of PM2.5 OC were estimated using molecular marker-based positive matric factorization (MM-PMF) and chemical mass balance (CMB) models. MM-PMF resolved nine factors that were identified as diesel engines (11.5&thinsp;%), gasoline engines (24.3&thinsp;%), nontailpipe vehicle emissions (11.1&thinsp;%), ship emissions (2.2&thinsp;%), cooking (1.0&thinsp;%), biomass burning (BB, 10.6&thinsp;%), isoprene SOA (11.0&thinsp;%), high-NOx anthropogenic SOA (6.6&thinsp;%), and low-NOx anthropogenic SOA (21.7&thinsp;%). Using available source profiles, CMB apportioned 41&thinsp;% of OC to primary fossil sources (gasoline engines, diesel engines, and ship emissions), 5&thinsp;% to BB, 15&thinsp;% to SOA (including 7.4&thinsp;% biogenic and 7.6&thinsp;% anthropogenic), and 39&thinsp;% to other sources that were not included in the model and are expected to be secondary.This study presents the first application of in situ AMS-PMF, MM-PMF, and CMB for OC source apportionment and the integration of these methods to evaluate the relative roles of biogenic, anthropogenic, and BB-SOA. The three source apportionment models agreed that  ∼ &thinsp;50&thinsp;% of OC is associated with primary emissions from fossil fuel use, particularly motor vehicles. Differences among the models reflect their ability to resolve sources based upon the input chemical measurements, with molecular marker-based methods providing greater source specificity and resolution for minor sources. By combining results from MM-PMF and CMB, BB was estimated to contribute 11&thinsp;% of OC, with 5&thinsp;% primary emissions and 6&thinsp;% BB-SOA. SOA was dominantly anthropogenic (28&thinsp;%) rather than biogenic (11&thinsp;%) or BB-derived. The three-model approach demonstrates significant contributions of anthropogenic SOA to fine PM. More broadly, the findings and methodologies presented herein can be used to advance local and regional understanding of anthropogenic contributions to SOA.</p