Photodynamic therapy enhances liposomal doxorubicin distribution in tumors during isolated perfusion of rodent lungs.

BACKGROUND: Photodynamic therapy (PDT) at low drug-light conditions can enhance the transport of intravenously injected macromolecular therapeutics through the tumor vasculature. Here we determined the impact of PDT on the distribution of liposomal doxorubicin (Liporubicin™) administered by isolated lung perfusion (ILP) in sarcomas grown on rodent lungs. METHODS: A syngeneic methylcholanthrene-induced sarcoma cell line was implanted subpleurally in the left lung of Fischer rats. Treatment schemes consisted in ILP alone (400 μg of Liporubicin), low-dose (0.0625 mg/kg Visudyne®, 10 J/cm(2) and 35 mW/cm(2)) and high-dose left lung PDT (0.125 mg/kg Visudyne, 10 J/cm(2) and 35 mW/cm(2)) followed by ILP (400 μg of Liporubicin). The uptake and distribution of Liporubicin in tumor and lung tissues were determined by high-performance liquid chromatography and fluorescence microscopy in each group. RESULTS: Low-dose PDT significantly improved the distribution of Liporubicin in tumors compared to high-dose PDT (p < 0.05) and ILP alone (p < 0.05). However, both PDT pretreatments did not result in a higher overall drug uptake in tumors or a higher tumor-to-lung drug ratio compared to ILP alone. CONCLUSIONS: Intraoperative low-dose Visudyne-mediated PDT enhances liposomal doxorubicin distribution administered by ILP in sarcomas grown on rodent lungs which is predicted to improve tumor control by ILP.

Which prior knowledge? Quantification of in vivo brain 13C MR spectra following 13C glucose infusion using AMARES.

The recent developments in high magnetic field 13C magnetic resonance spectroscopy with improved localization and shimming techniques have led to important gains in sensitivity and spectral resolution of 13C in vivo spectra in the rodent brain, enabling the separation of several 13C isotopomers of glutamate and glutamine. In this context, the assumptions used in spectral quantification might have a significant impact on the determination of the 13C concentrations and the related metabolic fluxes. In this study, the time domain spectral quantification algorithm AMARES (advanced method for accurate, robust and efficient spectral fitting) was applied to 13 C magnetic resonance spectroscopy spectra acquired in the rat brain at 9.4 T, following infusion of [1,6-(13)C2 ] glucose. Using both Monte Carlo simulations and in vivo data, the goal of this work was: (1) to validate the quantification of in vivo 13C isotopomers using AMARES; (2) to assess the impact of the prior knowledge on the quantification of in vivo 13C isotopomers using AMARES; (3) to compare AMARES and LCModel (linear combination of model spectra) for the quantification of in vivo 13C spectra. AMARES led to accurate and reliable 13C spectral quantification similar to those obtained using LCModel, when the frequency shifts, J-coupling constants and phase patterns of the different 13C isotopomers were included as prior knowledge in the analysis.

Consequences of smoking for body weight, body fat distribution, and insulin resistance

Our aim was to critically evaluate the relations among smoking, body weight, body fat distribution, and insulin resistance as reported in the literature. In the short term, nicotine increases energy expenditure and could reduce appetite, which may explain why smokers tend to have lower body weight than do nonsmokers and why smoking cessation is frequently followed by weight gain. In contrast, heavy smokers tend to have greater body weight than do light smokers or nonsmokers, which likely reflects a clustering of risky behaviors (eg, low degree of physical activity, poor diet, and smoking) that is conducive to weight gain. Other factors, such as weight cycling, could also be involved. In addition, smoking increases insulin resistance and is associated with central fat accumulation. As a result, smoking increases the risk of metabolic syndrome and diabetes, and these factors increase risk of cardiovascular disease. In the context of the worldwide obesity epidemic and a high prevalence of smoking, the greater risk of (central) obesity and insulin resistance among smokers is a matter of major concern

Fusion of Multi-Atlas Segmentations with Spatial Distribution Modeling

In recent years, multi-atlas fusion methods have gainedsignificant attention in medical image segmentation. Inthis paper, we propose a general Markov Random Field(MRF) based framework that can perform edge-preservingsmoothing of the labels at the time of fusing the labelsitself. More specifically, we formulate the label fusionproblem with MRF-based neighborhood priors, as an energyminimization problem containing a unary data term and apairwise smoothness term. We present how the existingfusion methods like majority voting, global weightedvoting and local weighted voting methods can be reframedto profit from the proposed framework, for generatingmore accurate segmentations as well as more contiguoussegmentations by getting rid of holes and islands. Theproposed framework is evaluated for segmenting lymphnodes in 3D head and neck CT images. A comparison ofvarious fusion algorithms is also presented.

Sparse regularization for fiber ODF reconstruction: from the suboptimality of ℓ2 and ℓ1 priors to ℓ0.

Diffusion MRI is a well established imaging modality providing a powerful way to probe the structure of the white matter non-invasively. Despite its potential, the intrinsic long scan times of these sequences have hampered their use in clinical practice. For this reason, a large variety of methods have been recently proposed to shorten the acquisition times. Among them, spherical deconvolution approaches have gained a lot of interest for their ability to reliably recover the intra-voxel fiber configuration with a relatively small number of data samples. To overcome the intrinsic instabilities of deconvolution, these methods use regularization schemes generally based on the assumption that the fiber orientation distribution (FOD) to be recovered in each voxel is sparse. The well known Constrained Spherical Deconvolution (CSD) approach resorts to Tikhonov regularization, based on an ℓ(2)-norm prior, which promotes a weak version of sparsity. Also, in the last few years compressed sensing has been advocated to further accelerate the acquisitions and ℓ(1)-norm minimization is generally employed as a means to promote sparsity in the recovered FODs. In this paper, we provide evidence that the use of an ℓ(1)-norm prior to regularize this class of problems is somewhat inconsistent with the fact that the fiber compartments all sum up to unity. To overcome this ℓ(1) inconsistency while simultaneously exploiting sparsity more optimally than through an ℓ(2) prior, we reformulate the reconstruction problem as a constrained formulation between a data term and a sparsity prior consisting in an explicit bound on the ℓ(0)norm of the FOD, i.e. on the number of fibers. The method has been tested both on synthetic and real data. Experimental results show that the proposed ℓ(0) formulation significantly reduces modeling errors compared to the state-of-the-art ℓ(2) and ℓ(1) regularization approaches.

Imaging liver and brain glycogen metabolism at the nanometer scale.

In mammals, glycogen synthesis and degradation are dynamic processes regulating blood and cerebral glucose-levels within a well-defined physiological range. Despite the essential role of glycogen in hepatic and cerebral metabolism, its spatiotemporal distribution at the molecular and cellular level is unclear. By correlating electron microscopy and ultra-high resolution ion microprobe (NanoSIMS) imaging of tissue from fasted mice injected with (13)C-labeled glucose, we demonstrate that liver glycogenesis initiates in the hepatocyte perinuclear region before spreading toward the cell membrane. In the mouse brain, we observe that (13)C is inhomogeneously incorporated into astrocytic glycogen at a rate ~25 times slower than in the liver, in agreement with prior bulk studies. This experiment, using temporally resolved, nanometer-scale imaging of glycogen synthesis and degradation, provides greater insight into glucose metabolism in mammalian organs and shows how this technique can be used to explore biochemical pathways in healthy and diseased states. FROM THE CLINICAL EDITOR: By correlating electron microscopy and ultra-high resolution ion microprobe imaging of tissue from fasting mice injected with (13)C-labeled glucose, the authors demonstrate a method to image glycogen metabolism at the nanometer scale.

Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution.

Waist-hip ratio (WHR) is a measure of body fat distribution and a predictor of metabolic consequences independent of overall adiposity. WHR is heritable, but few genetic variants influencing this trait have been identified. We conducted a meta-analysis of 32 genome-wide association studies for WHR adjusted for body mass index (comprising up to 77,167 participants), following up 16 loci in an additional 29 studies (comprising up to 113,636 subjects). We identified 13 new loci in or near RSPO3, VEGFA, TBX15-WARS2, NFE2L3, GRB14, DNM3-PIGC, ITPR2-SSPN, LY86, HOXC13, ADAMTS9, ZNRF3-KREMEN1, NISCH-STAB1 and CPEB4 (P = 1.9 × 10⁻⁹ to P = 1.8 × 10⁻⁴⁰) and the known signal at LYPLAL1. Seven of these loci exhibited marked sexual dimorphism, all with a stronger effect on WHR in women than men (P for sex difference = 1.9 × 10⁻³ to P = 1.2 × 10⁻&supl;³). These findings provide evidence for multiple loci that modulate body fat distribution independent of overall adiposity and reveal strong gene-by-sex interactions.

Knowing the past to predict the future: land-use change and the distribution of invasive bullfrogs

Biological invasions and land-use changes are two major causes of the global modifications of biodiversity. Habitat suitability models are the tools of choice to predict potential distributions of invasive species. Although land-use is a key driver of alien species invasions, it is often assumed that land-use is constant in time. Here we combine historical and present day information, to evaluate whether land-use changes could explain the dynamic of invasion of the American bullfrog Rana catesbeiana (=Lithobathes catesbeianus) in Northern Italy, from the 1950s to present-day. We used maxent to build habitat suitability models, on the basis of past (1960s, 1980s) and present-day data on land-uses and species distribution. For example, we used models built using the 1960s data to predict distribution in the 1980s, and so on. Furthermore, we used land-use scenarios to project suitability in the future. Habitat suitability models predicted well the spread of bullfrogs in the subsequent temporal step. Models considering land-use changes predicted invasion dynamics better than models assuming constant land-use over the last 50 years. Scenarios of future land-use suggest that suitability will remain similar in the next years. Habitat suitability models can help to understand and predict the dynamics of invasions; however, land-use is not constant in time: land-use modifications can strongly affect invasions; furthermore, both land management and the suitability of a given land-use class may vary in time. An integration of land-use changes in studies of biological invasions can help to improve management strategies.

Reduction of small infrarenal abdominal aortic aneurysm expansion rate by statins.

BACKGROUND: To evaluate the effect of statins on the annual expansion rate (ER) of small infrarenal abdominal aortic aneurysms (AAA). PATIENTS AND METHODS: All patients under regular surveillance for small AAA between January 2000 and September 2007, in the Department of Angiology, Lausanne University Hospital, were included. Inclusion criteria were baseline abdominal aortic diameter between 25 and 55 mm, at least two measurements of AAA diameter and a minimum follow up of 6 months. Patients with Marfan disease, infectious or inflammatory AAA, and patients with prior AAA repair were excluded. The influence of statin use and other factors on ER were examined by bivariate and multivariate analysis. RESULTS: Among 589 patients who underwent an abdominal aorta evaluation, 94 patients (89 % men, mean age 69.1 years) were finally included in the analysis. Baseline AAA size was 39.9 ± 7.7 mm (mean±SE) and 48.7 ± 8.4 mm at end of follow-up. Patients had a regular aneurysm size assessment during 38.5 ± 27.7 months. Mean ER was 3.59 mm/y (± 2.81). The 50 patients who were treated with statin during the study period had a lower ER compared to the 44 controls (2.91 vs 4.37 mm/year, p = 0.01). CONCLUSIONS: This study confirms the considerable individual variations in the AAA expansion rate, and emphasizes the need for regular aortic diameter assessments. In this study, patients treated with statin demonstrate a significant decrease in the ER compared to controls. This finding need to be evaluated in prospective interventional studies powered to demonstrate the potential benefit of statin treatment.

A Modulated Closed Form solution for Quantitative Susceptibility Mapping - A thorough evaluation and comparison to iterative methods based on edge prior knowledge.

The aim of this study is to perform a thorough comparison of quantitative susceptibility mapping (QSM) techniques and their dependence on the assumptions made. The compared methodologies were: two iterative single orientation methodologies minimizing the l2, l1TV norm of the prior knowledge of the edges of the object, one over-determined multiple orientation method (COSMOS) and anewly proposed modulated closed-form solution (MCF). The performance of these methods was compared using a numerical phantom and in-vivo high resolution (0.65mm isotropic) brain data acquired at 7T using a new coil combination method. For all QSM methods, the relevant regularization and prior-knowledge parameters were systematically changed in order to evaluate the optimal reconstruction in the presence and absence of a ground truth. Additionally, the QSM contrast was compared to conventional gradient recalled echo (GRE) magnitude and R2* maps obtained from the same dataset. The QSM reconstruction results of the single orientation methods show comparable performance. The MCF method has the highest correlation (corrMCF=0.95, r(2)MCF =0.97) with the state of the art method (COSMOS) with additional advantage of extreme fast computation time. The l-curve method gave the visually most satisfactory balance between reduction of streaking artifacts and over-regularization with the latter being overemphasized when the using the COSMOS susceptibility maps as ground-truth. R2* and susceptibility maps, when calculated from the same datasets, although based on distinct features of the data, have a comparable ability to distinguish deep gray matter structures.

New genetic loci link adipose and insulin biology to body fat distribution.

Body fat distribution is a heritable trait and a well-established predictor of adverse metabolic outcomes, independent of overall adiposity. To increase our understanding of the genetic basis of body fat distribution and its molecular links to cardiometabolic traits, here we conduct genome-wide association meta-analyses of traits related to waist and hip circumferences in up to 224,459 individuals. We identify 49 loci (33 new) associated with waist-to-hip ratio adjusted for body mass index (BMI), and an additional 19 loci newly associated with related waist and hip circumference measures (P < 5 × 10(-8)). In total, 20 of the 49 waist-to-hip ratio adjusted for BMI loci show significant sexual dimorphism, 19 of which display a stronger effect in women. The identified loci were enriched for genes expressed in adipose tissue and for putative regulatory elements in adipocytes. Pathway analyses implicated adipogenesis, angiogenesis, transcriptional regulation and insulin resistance as processes affecting fat distribution, providing insight into potential pathophysiological mechanisms.

Differential expression of peroxisome proliferator-activated receptors (PPARs): tissue distribution of PPAR-alpha, -beta, and -gamma in the adult rat.

Peroxisome proliferator-activated receptors (PPARs) are members of the nuclear hormone receptor superfamily that can be activated by various xenobiotics and natural fatty acids. These transcription factors primarily regulate genes involved in lipid metabolism and also play a role in adipocyte differentiation. We present the expression patterns of the PPAR subtypes in the adult rat, determined by in situ hybridization using specific probes for PPAR-alpha, -beta and -gamma, and by immunohistochemistry using a polyclonal antibody that recognizes the three rat PPAR subtypes. In numerous cell types from either ectodermal, mesodermal, or endodermal origin, PPARs are coexpressed, with relative levels varying between them from one cell type to the other. PPAR-alpha is highly expressed in hepatocytes, cardiomyocytes, enterocytes, and the proximal tubule cells of kidney. PPAR-beta is expressed ubiquitously and often at higher levels than PPAR-alpha and -gamma. PPAR-gamma is expressed predominantly in adipose tissue and the immune system. Our results suggest new potential directions to investigate the functions of the different PPAR subtypes.

Combining food web and species distribution models for improved community projections

The ability to model biodiversity patterns is of prime importance in this era of severe environmental crisis. Species assemblage along environmental gradient is subject to the interplay of biotic interactions in complement to abiotic environmental filtering. Accounting for complex biotic interactions for a wide array of species remains so far challenging. Here, we propose to use food web models that can infer the potential interaction links between species as a constraint in species distribution models. Using a plant-herbivore (butterfly) interaction dataset, we demonstrate that this combined approach is able to improve both species distribution and community forecasts. Most importantly, this combined approach is very useful in rendering models of more generalist species that have multiple potential interaction links, where gap in the literature may be recurrent. Our combined approach points a promising direction forward to model the spatial variation of entire species interaction networks. Our work has implications for studies of range shifting species and invasive species biology where it may be unknown how a given biota might interact with a potential invader or in future climate.