Finite volume analysis of temperature effects induced by active MRI implants: 2. Defects on active MRI implants causing hot spots

Background

Active magnetic resonance imaging implants, for example stents, stent grafts or vena cava filters, are constructed as wireless inductively coupled transmit and receive coils. They are built as a resonator tuned to the Larmor frequency of a magnetic resonance system. The resonator can be added to or incorporated within the implant. This technology can counteract the shielding caused by eddy currents inside the metallic implant structure. This may allow getting diagnostic information of the implant lumen (in stent stenosis or thrombosis for example). The electro magnetic rf-pulses during magnetic resonance imaging induce a current in the circuit path of the resonator. A by material fatigue provoked partial rupture of the circuit path or a broken wire with touching surfaces can set up a relatively high resistance on a very short distance, which may behave as a point-like power source, a hot spot, inside the body part the resonator is implanted to. This local power loss inside a small volume can reach ¼ of the total power loss of the intact resonating circuit, which itself is proportional to the product of the resonator volume and the quality factor and depends as well from the orientation of the resonator with respect to the main magnetic field and the imaging sequence the resonator is exposed to.

Methods

First an analytical solution of a hot spot for thermal equilibrium is described. This analytical solution with a definite hot spot power loss represents the worst case scenario for thermal equilibrium inside a homogeneous medium without cooling effects. Starting with this worst case assumptions additional conditions are considered in a numerical simulation, which are more realistic and may make the results less critical. The analytical solution as well as the numerical simulations use the experimental experience of the maximum hot spot power loss of implanted resonators with a definite volume during magnetic resonance imaging investigations. The finite volume analysis calculates the time developing temperature maps for the model of a broken linear metallic wire embedded in tissue. Half of the total hot spot power loss is assumed to diffuse into both wire parts at the location of a defect. The energy is distributed from there by heat conduction. Additionally the effect of blood perfusion and blood flow is respected in some simulations because the simultaneous appearance of all worst case conditions, especially the absence of blood perfusion and blood flow near the hot spot, is very unlikely for vessel implants.

Results

The analytical solution as worst case scenario as well as the finite volume analysis for near worst case situations show not negligible volumes with critical temperature increases for part of the modeled hot spot situations. MR investigations with a high rf-pulse density lasting below a minute can establish volumes of several cubic millimeters with temperature increases high enough to start cell destruction. Longer exposure times can involve volumes larger than 100 mm³. Even temperature increases in the range of thermal ablation are reached for substantial volumes. MR sequence exposure time and hot spot power loss are the primary factors influencing the volume with critical temperature increases. Wire radius, wire material as well as the physiological parameters blood perfusion and blood flow inside larger vessels reduce the volume with critical temperature increases, but do not exclude a volume with critical tissue heating for resonators with a large product of resonator volume and quality factor.

Conclusion

The worst case scenario assumes thermal equilibrium for a hot spot embedded in homogeneous tissue without any cooling due to blood perfusion or flow. The finite volume analysis can calculate the results for near and not close to worst case conditions. For both cases a substantial volume can reach a critical temperature increase in a short time. The analytical solution, as absolute worst case, points out that resonators with a small product of inductance volume and quality factor (Q V_ind < 2 cm³) are definitely save. Stents for coronary vessels or resonators used as tracking devices for interventional procedures therefore have no risk of high temperature increases. The finite volume analysis shows for sure that also conditions not close to the worst case reach physiologically critical temperature increases for implants with a large product of inductance volume and quality factor (Q V_ind > 10 cm³). Such resonators exclude patients from exactly the MRI investigation these devices are made for.     

Associations between a Polymorphism in the Pleiotropic GCKR and Age-Related Phenotypes: The HALCyon Programme

Background

The glucokinase regulatory protein encoded by GCKR plays an important role in glucose metabolism and a single nucleotide polymorphism (SNP) rs1260326 (P446L) in the gene has been associated with several age-related biomarkers, including triglycerides, glucose, insulin and apolipoproteins. However, associations between SNPs in the gene and other ageing phenotypes such as cognitive and physical capability have not been reported.

Methods

As part of the Healthy Ageing across the Life Course (HALCyon) collaborative research programme, men and women from five UK cohorts aged between 44 and 90+ years were genotyped for rs1260326. Meta-analysis was used to pool within-study genotypic associations between the SNP and several age-related phenotypes, including body mass index (BMI), blood lipid levels, lung function, and cognitive and physical capability.

Results

We confirm the associations between the minor allele of the SNP and higher triglycerides and lower glucose levels. We also observed a triglyceride-independent association between the minor allele and lower BMI (pooled beta on z-score = −0.04, p-value = 0.0001, n = 16,251). Furthermore, there was some evidence for gene-environment interactions, including physical activity attenuating the effects on triglycerides. However, no associations were observed with measures of cognitive and physical capability.

Conclusion

Findings from middle-aged to older adults confirm associations between rs1260326 GCKR and triglycerides and glucose, suggest possible gene-environment interactions, but do not provide evidence that its relevance extends to cognitive and physical capability.     

CLL cells are resistant to smac mimetics because of an inability to form a ripoptosome complex

In the lymph node (LN) environment, chronic lymphocytic leukemia (CLL) cells display increased NF-κB activity compared with peripheral blood CLL cells, which contributes to chemoresistance. Antagonists of cellular inhibitor of apoptosis proteins (cIAPs) can induce apoptosis in various cancer cells in a tumor necrosis factor-α (TNFα)-dependent manner and are in preclinical development. Smac-mimetics promote degradation of cIAP1 and cIAP2, which results in TNFR-mediated apoptosis via formation of a ripoptosome complex, comprising RIPK1, Fas-associated protein with death domain, FLICE-like inhibitory protein and caspase-8. CD40 stimulation of CLL cells in vitro is used as a model to mimic the LN microenvironment and results in NF-κB activation and TNFα production. In this study, we investigated the response of CLL cells to smac-mimetics in the context of CD40 stimulation. We found that treatment with smac-mimetics results in cIAP1 and cIAP2 degradation, yet although TNFα is produced, this did not induce apoptosis. Despite the presence of all components, the ripoptosome complex did not form upon smac-mimetic treatment in CLL cells. Thus, CLL cells seem to possess aberrant upstream NF-κB regulation that prevents ripoptosome formation upon IAP degradation. Unraveling the exact molecular mechanisms of disturbed ripoptosome formation may offer novel targets for treatment in CLL.     

Anti-α-fodrin antibodies do not add much to the diagnosis of Sjögren's syndrome

The presence of anti-α-fodrin autoantibodies has been reported to be a highly specific and sensitive test for the diagnosis of Sjögren's syndrome (SjS). We looked (in Nijmegen) for anti-α-fodrin, anti-Ro60, and anti-La autoantibodies in a cohort of 51 patients with rheumatic diseases (primary SjS [21], secondary SjS [6], rheumatoid arthritis [RA] [12], systemic lupus erythematosus [SLE] [6], and scleroderma [6]) and in 28 healthy subjects, using ELISA, immunoblotting, and immunoprecipitation. The same samples were analyzed with an alternative anti-α-fodrin ELISA in Hanover. The Nijmegen ELISA of the sera from primary SjS showed sensitivities of 43% and 48% for IgA- and IgG-type anti-α-fodrin antibodies, respectively. The Hanover ELISA showed sensitivities of 38% and 10% for IgA- and IgG-type anti-α-fodrin antibodies, respectively. The ELISAs for α-fodrin showed six (Nijmegen) and four (Hanover) anti-α-fodrin-positive RA sera. IgA and IgG anti-fodrin antibodies were also present in four patients with secondary SjS. The sensitivities of Ro60 and La-antibodies in the Nijmegen ELISA were 67% and 62%, respectively. Unlike anti-α-fodrin antibodies, all anti-Ro60 and anti-La positive sera could be confirmed by immunoblotting or RNA immunoprecipitation. Thus, anti-Ro and anti-La autoantibodies were more sensitive than anti-α-fodrin autoantibodies in ELISA and were more frequently confirmed by other techniques. Anti-La antibodies appear to be more disease-specific than anti-α-fodrin antibodies, which are also found in RA sera. Therefore, the measurement of anti-α-fodrin autoantibodies does not add much to the diagnosis of Sjögren's syndrome.     

The role of longitudinal cohort studies in epigenetic epidemiology: challenges and opportunities

ABSTRACT: Longitudinal cohort studies are ideal for investigating how epigenetic patterns change over time and relate to changing exposure patterns and the development of disease. We highlight the challenges and opportunities in this approach.     

Analyses of short-chain fatty acids and exhaled breath volatiles in dietary intervention trials for metabolic diseases

As an alternative to pharmacological treatment to diseases, lifestyle interventions, such as dietary changes and physical activities, can help maintain healthy metabolic conditions. Recently, the emerging analyses of volatile organic compounds (VOCs) from breath and short-chain fatty acids (SCFAs) from plasma/feces have been considered as useful tools for the diagnosis and mechanistic understanding of metabolic diseases. Furthermore, diet-induced changes of SCFAs in individuals with diagnosed metabolic abnormalities have been correlated with the composition changes of the gut microbiome. More interestingly, the analysis of exhaled breath (breathomics) has gained attention as a useful technique to measure the human VOC profile altered as a result of dietary interventions. In this mini-review, we examined recent clinical trials that performed promising dietary interventions, SCFAs analysis in plasma/feces, and VOC profile analysis in exhaling breath to understand the relationship between dietary intervention and metabolic health.     

Single-molecule anisotropy imaging

A novel method, single-molecule anisotropy imaging, has been employed to simultaneously study lateral and rotational diffusion of fluorescence-labeled lipids on supported phospholipid membranes. In a fluid membrane composed of 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine, in which the rotational diffusion time is on the order of the excited-state lifetime of the fluorophore rhodamine, a rotational diffusion constant, D(rot) = 7 x 10(7) rad(2)/s, was determined. The lateral diffusion constant, measured by direct analysis of single-molecule trajectories, was D(lat) = 3.5 x 10(-8) cm(2)/s. As predicted from the free-volume model for diffusion, the results exhibit a significantly enhanced mobility on the nanosecond time scale. For membranes of DPPC lipids in the L(beta) gel phase, the slow rotational mobility permitted the direct observation of the rotation of individual molecules characterized by D(rot) = 1.2 rad(2)/s. The latter data were evaluated by a mean square angular displacement analysis. The technique developed here should prove itself profitable for imaging of conformational motions of individual proteins on the time scale of milliseconds to seconds.     

Different models of genetic variation and their effect on genomic evaluation

Background

The theory of genomic selection is based on the prediction of the effects of quantitative trait loci (QTL) in linkage disequilibrium (LD) with markers. However, there is increasing evidence that genomic selection also relies on "relationships" between individuals to accurately predict genetic values. Therefore, a better understanding of what genomic selection actually predicts is relevant so that appropriate methods of analysis are used in genomic evaluations.

Methods

Simulation was used to compare the performance of estimates of breeding values based on pedigree relationships (Best Linear Unbiased Prediction, BLUP), genomic relationships (gBLUP), and based on a Bayesian variable selection model (Bayes B) to estimate breeding values under a range of different underlying models of genetic variation. The effects of different marker densities and varying animal relationships were also examined.

Results

This study shows that genomic selection methods can predict a proportion of the additive genetic value when genetic variation is controlled by common quantitative trait loci (QTL model), rare loci (rare variant model), all loci (infinitesimal model) and a random association (a polygenic model). The Bayes B method was able to estimate breeding values more accurately than gBLUP under the QTL and rare variant models, for the alternative marker densities and reference populations. The Bayes B and gBLUP methods had similar accuracies under the infinitesimal model.

Conclusions

Our results suggest that Bayes B is superior to gBLUP to estimate breeding values from genomic data. The underlying model of genetic variation greatly affects the predictive ability of genomic selection methods, and the superiority of Bayes B over gBLUP is highly dependent on the presence of large QTL effects. The use of SNP sequence data will outperform the less dense marker panels. However, the size and distribution of QTL effects and the size of reference populations still greatly influence the effectiveness of using sequence data for genomic prediction.