Anti-inflammatory Components from Functional Foods for Obesity

Obesity, defined as excessive fat accumulation that may impair health, has been described throughout human history, but it has now reached epidemic proportions with the WHO estimating that 39% of the world’s adults over 18 years of age were overweight or obese in 2016. Obesity is a chronic low-grade inflammatory state leading to organ damage with an increased risk of common diseases including cardiovascular and metabolic disease, non-alcoholic fatty liver disease, osteo-arthritis and some cancers. This inflammatory state may be influenced by adipose tissue hypoxia and changes in the gut microbiota. There has been an increasing focus on functional foods and nutraceuticals as treatment options for obesity as drug treatments are limited in efficacy. This chapter summarises the importance of anthocyanin-containing fruits and vegetables, coffee and its components, tropical fruit and food waste as sources of phytochemicals for obesity treatment. We emphasise that preclinical studies can form the basis for clinical trials to determine the effectiveness of these treatments in humans

Estudios fitoquímicos y farmacológicos de swietenia humilis zucc. (meliaceae) :una fuente potencial de fitofármacos para el tratamiento de la diabetes mellitus

Ciencias Química

Transfer Learning for Stenosis Detection in X-ray Coronary Angiography

Coronary artery disease is the most frequent type of heart disease caused by an abnormal narrowing of coronary arteries, also called stenosis or atherosclerosis. It is also the leading cause of death globally. Currently, X-ray Coronary Angiography (XCA) remains the gold-standard imaging technique for medical diagnosis of stenosis and other related conditions. This paper presents a new method for the automatic detection of coronary artery stenosis in XCA images, employing a pre-trained (VGG16, ResNet50, and Inception-v3) Convolutional Neural Network (CNN) via Transfer Learning. The method is based on a network-cut and fine-tuning approach. The optimal cut and fine-tuned layers were selected following 20 different configurations for each network. The three networks were fine-tuned using three strategies: only real data, only artificial data, and artificial with real data. The synthetic dataset consists of 10,000 images (80% for training, 20% for validation) produced by a generative model. These different configurations were analyzed and compared using a real dataset of 250 real XCA images (125 for testing and 125 for fine-tuning), regarding their randomly initiated CNNs and a fourth custom CNN, trained as well with artificial and real data. The results showed that pre-trained VGG16, ResNet50, and Inception-v3 cut on an early layer and fine-tuned, overcame the referencing CNNs performance. Specifically, Inception-v3 provided the best stenosis detection with an accuracy of 0.95, a precision of 0.93, sensitivity, specificity, and F1 score of 0.98, 0.92, and 0.95, respectively. Moreover, a class activation map is applied to identify the high attention regions for stenosis detection

A Deep Learning-Based Visual Map Generation for Mobile Robot Navigation

Visual map-based robot navigation is a strategy that only uses the robot vision system, involving four fundamental stages: learning or mapping, localization, planning, and navigation. Therefore, it is paramount to model the environment optimally to perform the aforementioned stages. In this paper, we propose a novel framework to generate a visual map for environments both indoors and outdoors. The visual map comprises key images sharing visual information between consecutive key images. This learning stage employs a pre-trained local feature transformer (LoFTR) constrained with a 3D projective transformation (a fundamental matrix) between two consecutive key images. Outliers are efficiently detected using marginalizing sample consensus (MAGSAC) while estimating the fundamental matrix. We conducted extensive experiments to validate our approach in six different datasets and compare its performance against hand-crafted methods

LRSE-Net: Lightweight Residual Squeeze-and-Excitation Network for Stenosis Detection in X-ray Coronary Angiography

Coronary heart disease is the primary cause of death worldwide. Among these, ischemic heart disease and stroke are the most common diseases induced by coronary stenosis. This study presents a Lightweight Residual Squeeze-and-Excitation Network (LRSE-Net) for stenosis classification in X-ray Coronary Angiography images. The proposed model employs redundant kernel deletion and tensor decomposition by Depthwise Separable Convolutions to reduce the model parameters up to 48.6 x concerning a Vanilla Residual Squeeze-and-Excitation Network. Furthermore, the reduction ratios of each Squeeze-and-Excitation module are optimized individually to improve the feature recalibration. Experimental results for Stenosis Detection on the publicly available Deep Stenosis Detection Dataset and Angiographic Dataset demonstrate that the proposed LRSE-Net achieves the best Accuracy—0.9549/0.9543, Sensitivity—0.6320/0.8792, Precision—0.5991/0.8944, and F1-score—0.6103/0.8944, as well as competitive Specificity of 0.9620/0.9733

Attention Mechanisms Evaluated on Stenosis Detection using X-ray Angiography Images

Coronary stenosis results from unnatural narrowing of the heart arteries due to the accumulation of adipose depots, leading to different heart diseases and yielding top mortality worldwide. Thus far, deep learning-based methods for automatic stenosis over X-ray Coronary Angiography (XCA) have employed state-of-the-art architectures to solve the ImageNet challenge. With the advance of deep learning, contemporary architectures incorporated a variety of attention mechanisms to improve performance. Therefore, this paper presents a study of three attention mechanisms for stenosis detection in XCA images. Extensive experiments and comparisons over different Residual backbone networks are presented to verify the effectiveness of including such attention modules. An improvement of 4%, 10%, and 10% on the accuracy, recall, and F1-score was achieved using the approach, reaching mean values of 0.8787, 0.8610, and 0.8732, respectively

Chemical Analysis and Antidiabetic Potential of a Decoction from <i>Stevia serrata</i> Roots

A decoction of the roots (31.6–316 mg/kg) from Stevia serrata Cav. (Asteraceae) as well as the main component (5–150 mg/kg) showed hypoglycemic and antihyperglycemic effects in mice. The fractionation of the active extract led to the isolation of dammaradiene acetate (1), stevisalioside A (2), and three new chemical entities characterized by spectroscopic methods and named stevisaliosides B–D (3–5). Glycoside 2 (5 and 50 mg/kg) decreased blood glucose levels and the postprandial peak during oral glucose and insulin tolerance tests in STZ-hyperglycemic mice. Compounds 1–5 were tested also against PTP1B1–400 and showed IC50 values of 1180.9 ± 0.33, 526.8 ± 0.02, 532.1 ± 0.03, 928.2 ± 0.39, and 31.8 ± 1.09 μM, respectively. Compound 5 showed an IC50 value comparable to that of ursolic acid (IC50 = 30.7 ± 0.00 μM). Docking studies revealed that 2–5 and their aglycones bind to PTP1B1–400 in a pocket formed by the C-terminal region. The volatilome of S. serrata was characterized by a high content of (E)-longipinene, spathulenol, guaiadiene, seychellene, and aromandendrene. Finally, a UHPLC-UV method was developed and validated to quantify the content of 2 in the decoction of the plant

Chemical Analysis and Antidiabetic Potential of a Decoction from <i>Stevia serrata</i> Roots

A decoction of the roots (31.6–316 mg/kg) from Stevia serrata Cav. (Asteraceae) as well as the main component (5–150 mg/kg) showed hypoglycemic and antihyperglycemic effects in mice. The fractionation of the active extract led to the isolation of dammaradiene acetate (1), stevisalioside A (2), and three new chemical entities characterized by spectroscopic methods and named stevisaliosides B–D (3–5). Glycoside 2 (5 and 50 mg/kg) decreased blood glucose levels and the postprandial peak during oral glucose and insulin tolerance tests in STZ-hyperglycemic mice. Compounds 1–5 were tested also against PTP1B1–400 and showed IC50 values of 1180.9 ± 0.33, 526.8 ± 0.02, 532.1 ± 0.03, 928.2 ± 0.39, and 31.8 ± 1.09 μM, respectively. Compound 5 showed an IC50 value comparable to that of ursolic acid (IC50 = 30.7 ± 0.00 μM). Docking studies revealed that 2–5 and their aglycones bind to PTP1B1–400 in a pocket formed by the C-terminal region. The volatilome of S. serrata was characterized by a high content of (E)-longipinene, spathulenol, guaiadiene, seychellene, and aromandendrene. Finally, a UHPLC-UV method was developed and validated to quantify the content of 2 in the decoction of the plant