Validation of a Gas Chromatography-Mass Spectrometry Method for the Measurement of the Redox State Metabolic Ratios Lactate/Pyruvate and β-Hydroxybutyrate/Acetoacetate in Biological Samples

The metabolic ratios lactate/pyruvate and β-hydroxybutyrate/acetoacetate are considered valuable tools to evaluate the in vivo redox cellular state by estimating the free NAD+/NADH in cytoplasm and mitochondria, respectively. The aim of the current study was to validate a gas-chromatography mass spectrometry method for simultaneous determination of the four metabolites in plasma and liver tissue. The procedure included an o-phenylenediamine microwave-assisted derivatization, followed by liquid-liquid extraction with ethyl acetate and silylation with bis(trimethylsilyl)trifluoroacetamide:trimethylchlorosilane 99:1. The calibration curves presented acceptable linearity, with a limit of quantification of 0.001 mM for pyruvate, β-hydroxybutyrate and acetoacetate and of 0.01 mM for lactate. The intra-day and inter-day accuracy and precision were within the European Medicines Agency’s Guideline specifications. No significant differences were observed in the slope coefficient of three-point standard metabolite-spiked curves in plasma or liver and water, and acceptable recoveries were obtained in the metabolite-spiked samples. Applicability of the method was tested in precision-cut liver rat slices and also in HepG2 cells incubated under different experimental conditions challenging the redox state. In conclusion, the validated method presented good sensitivity, specificity and reproducibility in the quantification of lactate/pyruvate and β-hydroxybutyrate/acetate metabolites and may be useful in the evaluation of in vivo redox states.     

Stage-Related Defense Response Induction in Tomato Plants by Nesidiocoris tenuis

The beneficial effects of direct predation by zoophytophagous biological control agents (BCAs), such as the mirid bug Nesidiocoris tenuis, are well-known. However, the benefits of zoophytophagous BCAs’ relation with host plants, via induction of plant defensive responses, have not been investigated until recently. To date, only the females of certain zoophytophagous BCAs have been demonstrated to induce defensive plant responses in tomato plants. The aim of this work was to determine whether nymphs, adult females, and adult males of N. tenuis are able to induce defense responses in tomato plants. Compared to undamaged tomato plants (i.e., not exposed to the mirid), plants on which young or mature nymphs, or adult males or females of N. tenuis fed and developed were less attractive to the whitefly Bemisia tabaci, but were more attractive to the parasitoid Encarsia formosa. Female-exposed plants were more repellent to B. tabaci and more attractive to E. formosa than were male-exposed plants. When comparing young- and mature-nymph-exposed plants, the same level of repellence was obtained for B. tabaci, but mature-nymph-exposed plants were more attractive to E. formosa. The repellent effect is attributed to the signaling pathway of abscisic acid, which is upregulated in N. tenuis-exposed plants, whereas the parasitoid attraction was attributed to the activation of the jasmonic acid signaling pathway. Our results demonstrate that all motile stages of N. tenuis can trigger defensive responses in tomato plants, although these responses may be slightly different depending on the stage considered.     

A multidimensional segmentation evaluation for medical image data

Evaluation of segmentation methods is a crucial aspect in image processing, especially in the medical imaging field, where small differences between segmented regions in the anatomy can be of paramount importance. Usually, segmentation evaluation is based on a measure that depends on the number of segmented voxels inside and outside of some reference regions that are called gold standards. Although some other measures have been also used, in this work we propose a set of new similarity measures, based on different features, such as the location and intensity values of the misclassified voxels, and the connectivity and the boundaries of the segmented data. Using the multidimensional information provided by these measures, we propose a new evaluation method whose results are visualized applying a Principal Component Analysis of the data, obtaining a simplified graphical method to compare different segmentation results. We have carried out an intensive study using several classic segmentation methods applied to a set of MRI simulated data of the brain with several noise and RF inhomogeneity levels, and also to real data, showing that the new measures proposed here and the results that we have obtained from the multidimensional evaluation, improve the robustness of the evaluation and provides better understanding about the difference between segmentation methods.     

Atlas-based segmentation of pathological MR brain images using a model of lesion growth

We propose a method for brain atlas deformation in the presence of large space-occupying tumors, based on an a priori model of lesion growth that assumes radial expansion of the lesion from its starting point. Our approach involves three steps. First, an affine registration brings the atlas and the patient into global correspondence. Then, the seeding of a synthetic tumor into the brain atlas provides a template for the lesion. The last step is the deformation of the seeded atlas, combining a method derived from optical flow principles and a model of lesion growth. Results show that a good registration is performed and that the method can be applied to automatic segmentation of structures and substructures in brains with gross deformation, with important medical applications in neurosurgery, radiosurgery, and radiotherapy.     

Constructing a unifying theory of dynamic programming DCOP algorithms via the generalized distributive law

In this paper we propose a novel message-passing algorithm, the so-called Action-GDL, as an extension to the generalized distributive law (GDL) to efficiently solve DCOPs. Action-GDL provides a unifying perspective of several dynamic programming DCOP algorithms that are based on GDL, such as DPOP and DCPOP algorithms. We empirically show how Action-GDL using a novel distributed post-processing heuristic can outperform DCPOP, and by extension DPOP, even when the latter uses the best arrangement provided by multiple state-of-the-art heuristics.     

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.     

A cross validation study of deep brain stimulation targeting: from experts to atlas-based, segmentation-based and automatic registration algorithms

Validation of image registration algorithms is a difficult task and open-ended problem, usually application-dependent. In this paper, we focus on deep brain stimulation (DBS) targeting for the treatment of movement disorders like Parkinson's disease and essential tremor. DBS involves implantation of an electrode deep inside the brain to electrically stimulate specific areas shutting down the disease's symptoms. The subthalamic nucleus (STN) has turned out to be the optimal target for this kind of surgery. Unfortunately, the STN is in general not clearly distinguishable in common medical imaging modalities. Usual techniques to infer its location are the use of anatomical atlases and visible surrounding landmarks. Surgeons have to adjust the electrode intraoperatively using electrophysiological recordings and macrostimulation tests. We constructed a ground truth derived from specific patients whose STNs are clearly visible on magnetic resonance (MR) T2-weighted images. A patient is chosen as atlas both for the right and left sides. Then, by registering each patient with the atlas using different methods, several estimations of the STN location are obtained. Two studies are driven using our proposed validation scheme. First, a comparison between different atlas-based and nonrigid registration algorithms with a evaluation of their performance and usability to locate the STN automatically. Second, a study of which visible surrounding structures influence the STN location. The two studies are cross validated between them and against expert's variability. Using this scheme, we evaluated the expert's ability against the estimation error provided by the tested algorithms and we demonstrated that automatic STN targeting is possible and as accurate as the expert-driven techniques currently used. We also show which structures have to be taken into account to accurately estimate the STN location.     

Indoloazepinone‐Constrained Oligomers as Cell‐Penetrating and Blood–Brain‐Barrier‐Permeating Compounds

Non‐cationic and amphipathic indoloazepinone‐constrained (Aia) oligomers have been synthesized as new vectors for intracellular delivery. The conformational preferences of the [l ‐Aia‐Xxx]_n oligomers were investigated by circular dichroism (CD) and NMR spectroscopy. Whereas Boc‐[l ‐Aia‐Gly]_2,4‐OBn oligomers 12 and 13 and Boc‐[l ‐Aia‐β³‐h‐l ‐Ala]_2,4‐OBn oligomers 16 and 17 were totally or partially disordered, Boc‐[l ‐Aia‐l ‐Ala]₂‐OBn ( 14 ) induced a typical turn stabilized by C₅‐ and C₇‐membered H‐bond pseudo‐cycles and aromatic interactions. Boc‐[l ‐Aia‐l ‐Ala]₄‐OBn ( 15 ) exhibited a unique structure with remarkable T‐shaped π‐stacking interactions involving the indole rings of the four l ‐Aia residues forming a dense hydrophobic cluster. All of the proposed FITC‐6‐Ahx‐[l ‐Aia‐Xxx]₄‐NH₂ oligomers 19 – 23 , with the exception of FITC‐6‐Ahx‐[l ‐Aia‐Gly]₄‐NH₂ ( 18 ), were internalized by MDA‐MB‐231 cells with higher efficiency than the positive references penetratin and Arg₈. In parallel, the compounds of this series were successfully explored in an in vitro blood–brain barrier (BBB) permeation assay. Although no passive diffusion permeability was observed for any of the tested Ac‐[l ‐Aia‐Xxx]₄‐NH₂ oligomers in the PAMPA model, Ac‐[l ‐Aia‐l ‐Arg]₄‐NH₂ ( 26 ) showed significant permeation in the in vitro cell‐based human model of the BBB, suggesting an active mechanism of cell penetration.