Characterization of Phenolic Compounds in Extra Virgin Olive Oil from Granada (Spain) and Evaluation of Its Neuroprotective Action.

The olive oil sector is a fundamental food in the Mediterranean diet. It has been demonstrated that the consumption of extra virgin olive oil (EVOO) with a high content of phenolic compounds is beneficial in the prevention and/or treatment of many diseases. The main objective of this work was to study the relationship between the content of phenolic compounds and the in vitro neuroprotective and anti-inflammatory activity of EVOOs from two PDOs in the province of Granada. To this purpose, the amounts of phenolic compounds were determined by liquid chromatography coupled to mass spectrometry (HPLC-MS) and the inhibitory activity of acetylcholinesterase (AChE) and cyclooxygenase-2 (COX-2) enzymes by spectrophotometric and fluorimetric assays. The main families identified were phenolic alcohols, secoiridoids, lignans, flavonoids, and phenolic acids. The EVOO samples with the highest total concentration of compounds and the highest inhibitory activity belonged to the Picual and Manzanillo varieties. Statistical analysis showed a positive correlation between identified compounds and AChE and COX-2 inhibitory activity, except for lignans. These results confirm EVOO's compounds possess neuroprotective potential.

Recognizing Lipofuscinosis as a Guide in Antiepileptic Treatment: Clinical Description of the First Mexican Case With Neuronal Ceroid Lipofuscinosis Type 7 (NCL7).

Neuronal ceroid lipofuscinosis type 7 (NCL7) is a rare form of childhood dementia; it is part of a group of diseases characterized by rapid progressive cognitive decline, blindness associated with retinitis pigmentosa, and seizures. We report the clinical and molecular characteristics of the first Mexican patient with NCL7, highlighting a particularly atypical disease course. The typical presentation form is expected to have reduced life expectancy and an average age of ambulation loss at 12 years. Our 27-year-old patient retains the ability to walk. The patient's unique presentation could, in part, be attributed to her genetic profile: a hypomorphic allele carrying a missense variant (c.1390G>A) and an almost null allele with a frameshift variant (c.1086del), contributing to the preservation of some protein function. Throughout her childhood and early adulthood, our patient experienced a variable response to antiseizure drugs, attributed to a lack of recognition of the disease and the specific efficacy of certain antiseizure medications. Our findings underscore the significance of considering this genetic condition and acknowledging its clinical heterogeneity.

Dapagliflozin Improved Cardiac Function and Structure in Diabetic Patients with Preserved Ejection Fraction: Results of a Single Centre, Observational Prospective Study.

Sodium-glucose cotransporter inhibitors (SGLT2i) have demonstrated a reduction in cardiovascular events in diabetes and heart failure (HF). The mechanisms underlying this benefit are not well known and data are contradictory. The purpose of this study is to analyse the effect of dapagliflozin on cardiac structure and function in patients with normal ejection fraction. Between October 2020 and October 2021, we consecutively included 31 diabetic patients without prior history of SGLT2i use. In all of them, dapagliflozin treatment was started. At inclusion and during six months of follow-up, different clinical, ECG, analytical, and echocardiographic (standard, 3D, and speckle tracking) variables were recorded. After a follow-up period of 6.6 months, an average reduction of 18 g (p = 0.028) in 3D-estimated left ventricle mass was observed. An increase in absolute left ventricle global longitudinal strain (LV-GLS) of 0.3 (p = 0.036) was observed, as well as an increase in isovolumetric relaxation time (IVRT) of 10.5 ms (p = 0.05). Moreover, dapagliflozin decreased the levels of plasma creatin-kinase (CK-MB) and atrial natriuretic peptide (ANP). In conclusion, our data show that the use of SGLT2i is associated with both structural (myocardial mass) and functional (IVRT, LV-GLS) cardiac improvements in a population of diabetic patients with normal ejection fraction.

Networks Disrupted in Linguistic Variants of Frontotemporal Dementia.

The non-fluent/agrammatic variant of primary progressive aphasia (nfvPPA) and semantic variant (svPPA) of frontotemporal dementia (FTD) are neurodegenerative diseases. Previous works have shown alterations of fractional anisotropy (FA) and mean diffusivity (MD) from diffusion tensor images (DTIs). This manuscript is aimed at using DTI images to build a global tractography and to identify atrophy patterns of white matter in each variant. Twenty patients with svPPA, 20 patients with nfvPPA, 26 patients with behavioral variant of FTD (bvFTD) and, 33 controls were included. An analysis based on the connectivity of structural networks showed changes in FA and MD in svPPA and nfvPPA with respect to bvFTD. Much damage in the internal networks of the left temporal lobe was found in svPPA patients; in contrast, patients with nfvPPA showed atrophy in networks from the basal ganglia to motor and premotor areas. Those findings support the dual stream model of speech and language.

A sparse Bayesian representation for super-resolution of cardiac MR images.

High-quality cardiac magnetic resonance (CMR) images can be hardly obtained when intrinsic noise sources are present, namely heart and breathing movements. Yet heart images may be acquired in real time, the image quality is really limited and most sequences use ECG gating to capture images at each stage of the cardiac cycle during several heart beats. This paper presents a novel super-resolution algorithm that improves the cardiac image quality using a sparse Bayesian approach. The high-resolution version of the cardiac image is constructed by combining the information of the low-resolution series -observations from different non-orthogonal series composed of anisotropic voxels - with a prior distribution of the high-resolution local coefficients that enforces sparsity. In addition, a global prior, extracted from the observed data, regularizes the solution. Quantitative and qualitative validations were performed in synthetic and real images w.r.t to a baseline, showing an average increment between 2.8 and 3.2 dB in the Peak Signal-to-Noise Ratio (PSNR), between 1.8% and 2.6% in the Structural Similarity Index (SSIM) and 2.% to 4% in quality assessment (IL-NIQE). The obtained results demonstrated that the proposed method is able to accurately reconstruct a cardiac image, recovering the original shape with less artifacts and low noise.

Extracting salient brain patterns for imaging-based classification of neurodegenerative diseases.

Neurodegenerative diseases comprise a wide variety of mental symptoms whose evolution is not directly related to the visual analysis made by radiologists, who can hardly quantify systematic differences. Moreover, automatic brain morphometric analyses, that do perform this quantification, contribute very little to the comprehension of the disease, i.e., many of these methods classify but they do not produce useful anatomo-functional correlations. This paper presents a new fully automatic image analysis method that reveals discriminative brain patterns associated to the presence of neurodegenerative diseases, mining systematic differences and therefore grading objectively any neurological disorder. This is accomplished by a fusion strategy that mixes together bottom-up and top-down information flows. Bottom-up information comes from a multiscale analysis of different image features, while the top-down stage includes learning and fusion strategies formulated as a max-margin multiple-kernel optimization problem. The capacity of finding discriminative anatomic patterns was evaluated using the Alzheimer's disease (AD) as the use case. The classification performance was assessed under different configurations of the proposed approach in two public brain magnetic resonance datasets (OASIS-MIRIAD) with patients diagnosed with AD, showing an improvement varying from 6.2% to 13% in the equal error rate measure, with respect to what has been reported by the feature-based morphometry strategy. In terms of the anatomical analysis, discriminant regions found by the proposed approach highly correlates to what has been reported in clinical studies of AD.

Single-image super-resolution of brain MR images using overcomplete dictionaries.

Resolution in Magnetic Resonance (MR) is limited by diverse physical, technological and economical considerations. In conventional medical practice, resolution enhancement is usually performed with bicubic or B-spline interpolations, strongly affecting the accuracy of subsequent processing steps such as segmentation or registration. This paper presents a sparse-based super-resolution method, adapted for easily including prior knowledge, which couples up high and low frequency information so that a high-resolution version of a low-resolution brain MR image is generated. The proposed approach includes a whole-image multi-scale edge analysis and a dimensionality reduction scheme, which results in a remarkable improvement of the computational speed and accuracy, taking nearly 26 min to generate a complete 3D high-resolution reconstruction. The method was validated by comparing interpolated and reconstructed versions of 29 MR brain volumes with the original images, acquired in a 3T scanner, obtaining a reduction of 70% in the root mean squared error, an increment of 10.3 dB in the peak signal-to-noise ratio, and an agreement of 85% in the binary gray matter segmentations. The proposed method is shown to outperform a recent state-of-the-art algorithm, suggesting a substantial impact in voxel-based morphometry studies.

Cortical surface mapping using topology correction, partial flattening and 3D shape context-based non-rigid registration for use in quantifying atrophy in Alzheimer's disease.

Magnetic resonance (MR) provides a non-invasive way to investigate changes in the brain resulting from aging or neurodegenerative disorders such as Alzheimer's disease (AD). Performing accurate analysis for population studies is challenging because of the interindividual anatomical variability. A large set of tools is found to perform studies of brain anatomy and population analysis (FreeSurfer, SPM, FSL). In this paper we present a newly developed surface-based processing pipeline (MILXCTE) that allows accurate vertex-wise statistical comparisons of brain modifications, such as cortical thickness (CTE). The brain is first segmented into the three main tissues: white matter, gray matter and cerebrospinal fluid, after CTE is computed, a topology corrected mesh is generated. Partial inflation and non-rigid registration of cortical surfaces to a common space using shape context are then performed. Each of the steps was firstly validated using MR images from the OASIS database. We then applied the pipeline to a sample of individuals randomly selected from the AIBL study on AD and compared with FreeSurfer. For a population of 50 individuals we found correlation of cortical thickness in all the regions of the brain (average r=0.62 left and r=0.64 right hemispheres). We finally computed changes in atrophy in 32 AD patients and 81 healthy elderly individuals. Significant differences were found in regions known to be affected in AD. We demonstrated the validity of the method for use in clinical studies which provides an alternative to well established techniques to compare different imaging biomarkers for the study of neurodegenerative diseases.

Segmentación seiiautomática del volumen del hígado en imágenes de tomografía computriizada / Semi-Automatic Liver Volume Segmentation in Computed Tomography Images

El volumen del hígado es un parámetro determinante en cirugía para la extracción de tumores, trasplantes, y en tratamientos de regeneración. Generalmente, la estimación de este volumen se calcula a partir de segmentaciones manuales realizadas por especialistas, siendo éste un proceso tedioso y con poca reproducibilidad de sus resultados. En este trabajo se presenta un método semiautomático para la segmentación del volumen del hígado en imágenes de TAC. El método consiste en superponer manualmente una superficie de triángulos en las imágenes, y deformarla por medio de una ecuación de movimiento asociada a cada uno de sus vértices, para delimitar las fronteras del hígado. La dinámica de la superficie depende de información de intensidades y gradientes, y de relaciones de vecindad entre los vértices, hasta cumplir un número de iteraciones. Comparaciones entre las segmentaciones del método con las segmentaciones de referencia en 20 estudios de TAC, muestran la adaptabilidad de la superficie a la forma y fronteras difusas del hígado, dos de los principales problemas de la segmentación.

Liver volume is a significant parameter in surgery for tumor extraction, transplants, and regeneration treatments. Generally, the volume estimation is obtained from manual segmentations performed by specialists, resulting in a tedious process with low reproducibility. In this work a semi-automatic method for the liver volume segmentation in CT images is presented. The method consist in manually superimpose a triangular surface on the images, and use a movement equation associated to each vertex to deform the surface and delimit the liver boundaries. Surface dynamics depend on intensity and gradient information, and neighboring relationships between vertices, until a fixed number of iterations is reached. Comparison between the obtained results and reference segmentations in 20 CT scans, show the surface adaptability to the shape and the diffuse boundaries of the liver, two of the principal segmentation problems.

Topology-corrected segmentation and local intensity estimates for improved partial volume classification of brain cortex in MRI.

In magnetic resonance imaging (MRI), accuracy and precision with which brain structures may be quantified are frequently affected by the partial volume (PV) effect. PV is due to the limited spatial resolution of MRI compared to the size of anatomical structures. Accurate classification of mixed voxels and correct estimation of the proportion of each pure tissue (fractional content) may help to increase the precision of cortical thickness estimation in regions where this measure is particularly difficult, such as deep sulci. The contribution of this work is twofold: on the one hand, we propose a new method to label voxels and compute tissue fractional content, integrating a mechanism for detecting sulci with topology preserving operators. On the other hand, we improve the computation of the fractional content of mixed voxels using local estimation of pure tissue intensity means. Accuracy and precision were assessed using simulated and real MR data and comparison with other existing approaches demonstrated the benefits of our method. Significant improvements in gray matter (GM) classification and cortical thickness estimation were brought by the topology correction. The fractional content root mean squared error diminished by 6.3% (p<0.01) on simulated data. The reproducibility error decreased by 8.8% (p<0.001) and the Jaccard similarity measure increased by 3.5% on real data. Furthermore, compared with manually guided expert segmentations, the similarity measure was improved by 12.0% (p<0.001). Thickness estimation with the proposed method showed a higher reproducibility compared with the measure performed after partial volume classification using other methods.