Biological Tools to Deal with Pollution : Selected Advances and Novel Perspectives

Pollution  represents  a  problem  common  to economy and  public  health. Indeed, the public health, because of the  divers’  type of pollutions, is facing divers challenges for which urgent solutions are required.The biology provides approaches not only to deal with the pollution, but also to  obtain  economic  benefits. Some living  organisms  have  particular metabolisms  that allow  them  to  assimilate  and  metabolite  the polluting agents  and thus reduce the  impact  they have on both environment  and public health.  On  the other  hand,  the  metabolic  properties  of  specific organisms make  the  polluting  elements raw materials to  synthesize  other elements that are benefits  for  economy  and  non-toxic  for  the  ecology and  the  biohealth. Yet, other options such as the regulations and laws are  required  to improve the efficiency of these approaches

Unsupervised Frequency Tracking beyond the Nyquist Limit using Markov Chains

This paper deals with the estimation of a sequence of frequencies from a corresponding sequence of signals. This problem arises in fields such as Doppler imaging where its specificity is twofold. First, only short noisy data records are available (typically four sample long) and experimental constraints may cause spectral aliasing so that measurements provide unreliable, ambiguous information. Second, the frequency sequence is smooth. Here, this information is accounted for by a Markov model and application of the Bayes rule yields the a posteriori density. The maximum a postariori is computed by a combination of Viterbi and descent procedures. One of the major features of the method is that it is entirely unsupervised. Adjusting the hyperparameters that balance data-based and prior-based information is done automatically by ML using an EM-based gradient algorithm. We compared the proposed estimate to a reference one and found that it performed better: variance was greatly reduced and tracking was correct, even beyond the Nyquist frequency

Chronic Obstructive Pulmonary Disease as a Predictor of Cardiovascular Risk: A Case-Control Study.

This is an Accepted Manuscript of an article published by Taylor & Francis in on 13 December 2019, available online: https://doi.org/10.1080/15412555.2019.1694501Chronic obstructive pulmonary disease (COPD) is a complex multi-morbid disorder with significant cardiac mortality. Current cardiovascular risk prediction models do not include COPD. We investigated whether COPD modifies future cardiovascular risk to determine if it should be considered in risk prediction models.Case-control study using baseline data from two randomized controlled trials performed between 2012 and 2015. Of the 90 eligible subjects, 26 COPD patients with lung hyperinflation were propensity matched for 10-year global cardiovascular risk score (QRISK2) with 26 controls having normal lung function. Patients underwent cardiac magnetic resonance imaging, arterial stiffness and lung function measurements. Differences in pulse wave velocity (PWV), total arterial compliance (TAC) and aortic distensibility were main outcome measures.PWV (mean difference 1.0 m/s, 95% CI 0.02-1.92; p = 0.033) and TAC (mean difference -0.27 mL/m2/mmHg, 95% CI 0.39-0.15; p < 0.001) were adversely affected in COPD compared to the control group. The PWV difference equates to an age, sex and risk-factor adjusted increase in relative risk of cardiovascular events and mortality of 14% and 15%, respectively.There were no differences in aortic distensibility. In the whole cohort (n = 90) QRISK2 (β = 0.045, p = 0.005) was associated with PWV in multivariate analysis. The relationship between QRISK2 and PWV were modified by COPD, where the interaction term reached significance (p = 0.014). FEV1 (β = 0.055 (0.027), p = 0.041) and pulse (B = -0.006 (0.002), p = 0.003) were associated with TAC in multivariate analysis.Markers of cardiovascular outcomes are adversely affected in COPD patients with lung hyperinflation compared to controls matched for global cardiovascular risk. Cardiovascular risk algorithms may benefit from the addition of a COPD variable to improve risk prediction and guide management.HAPPY London ClinicalTrials.gov: NCT01911910 and HZC116601; ClinicalTrials.gov: NCT01691885.The COPD trial was funded by GlaxoSmithKline (GSK), London, United Kingdom (HZC116601); SmithKline Beecham Pharma; The HAPPY London Study was funded by The Barts Charity (437/1412), London, United Kingdom

Myocardial deformation assessment using cardiovascular magnetic resonance-feature tracking technique.

BACKGROUND: Cardiovascular magnetic resonance (CMR) imaging is an important modality that allows the assessment of regional myocardial function by measuring myocardial deformation parameters, such as strain and strain rate throughout the cardiac cycle. Feature tracking is a promising quantitative post-processing technique that is increasingly used. It is commonly applied to cine images, in particular steady-state free precession, acquired during routine CMR examinations. OBJECTIVE: To review the studies that have used feature tracking techniques in healthy subjects or patients with cardiovascular diseases. The article emphasizes the advantages and limitations of feature tracking when applied to regional deformation parameters. The challenges of applying the techniques in clinics and potential solutions are also reviewed. RESULTS: Research studies in healthy volunteers and/or patients either applied CMR-feature tracking alone to assess myocardial motion or compared it with either established CMR-tagging techniques or to speckle tracking echocardiography. These studies assessed the feasibility and reliability of calculating or determining global and regional myocardial deformation strain parameters. Regional deformation parameters are reviewed and compared. Better reproducibility for global deformation was observed compared with segmental parameters. Overall, studies demonstrated that circumferential was the most reproducible deformation parameter, usually followed by longitudinal strain; in contrast, radial strain showed high variability. CONCLUSION: Although feature tracking is a promising tool, there are still discrepancies in the results obtained using different software packages. This highlights a clear need for standardization of MRI acquisition parameters and feature tracking analysis methodologies. Validation, including physical and numerical phantoms, is still required to facilitate the use of feature tracking in routine clinical practice

Robust registration between cardiac MRI images and atlas for segmentation propagation

We propose a new framework to propagate the labels in a heart atlas to the cardiac MRI images for ventricle segmentations based on image registrations. The method employs the anatomical information from the atlas as priors to constrain the initialisation between the atlas and the MRI images using region based registrations. After the initialisation which minimises the possibility of local misalignments, a fluid registration is applied to fine-tune the labelling in the atlas to the detail in the MRI images. The heart shape from the atlas does not have to be representative of that of the segmented MRI images in terms of morphological variations of the heart in this framework. In the experiments, a cadaver heart atlas and a normal heart atlas were used to register to in-vivo data for ventricle segmentation propagations. The results have shown that the segmentations based on the proposed method are visually acceptable, accurate (surface distance against manual segmentations is 1.0 ± 1.0 mm in healthy volunteer data, and 1.6 ± 1.8 mm in patient data), and reproducible (0.7 ± 1.0 mm) for in-vivo cardiac MRI images. The experiments also show that the new initialisation method can correct the local misalignments and help to avoid producing unrealistic deformations in the nonrigid registrations with 21% quantitative improvement of the segmentation accuracy