Relationship between metabolic syndrome and angiographic severity of coronary artery disease

BACKGROUND: There are a few literature data on the correlation between metabolic syndrome (MetS) and coronary disease among Iranian population. This study aimed to find relationship between MetS and severity of coronary artery disease (CAD) in presence of diabetes. METHODS: Total of 192 patients were consecutively enrolled in the study who were admitted to coronary care unit because of acute coronary syndrome (ACS) and then underwent coronary angiography. MetS was defined by Iranian criteria. A coronary atherosclerosis score was used to quantify the extent of atherosclerotic involvement. The relationship between MetS and angiographic coronary artery disease (CAD) severity or clinical presentation was compared between them after adjusting for diabetes. RESULTS: Individuals with MetS (n = 125) had a higher prevalence of ST-elevation myocardial infarction (71 vs 30, P < 0.001), multi-vessel disease (50 vs. 34, P = 0.003), decreased ejection fraction (P = 0.001) and more severe angiographic stenosis based on both modified Gensini (P = 0.081) and syntax (P = 0.008) scores, compared to those without MetS. Syntax score showed statistically significant difference between two groups before (P = 0.021) and after adjustment for diabetes (P = 0.005). CONCLUSION: MetS was related to the severity of CAD both clinically and by angiographic scores but diabetes was a challenging factor and may independently increase the severity of CAD. © 2016,.Isfahan University of Medical Sciences(IUMS). All rights reserved

Relationship between metabolic syndrome and angiographic severity of coronary artery disease

BACKGROUND: There are a few literature data on the correlation between metabolic syndrome (MetS) and coronary disease among Iranian population. This study aimed to find relationship between MetS and severity of coronary artery disease (CAD) in presence of diabetes. METHODS: Total of 192 patients were consecutively enrolled in the study who were admitted to coronary care unit because of acute coronary syndrome (ACS) and then underwent coronary angiography. MetS was defined by Iranian criteria. A coronary atherosclerosis score was used to quantify the extent of atherosclerotic involvement. The relationship between MetS and angiographic coronary artery disease (CAD) severity or clinical presentation was compared between them after adjusting for diabetes. RESULTS: Individuals with MetS (n = 125) had a higher prevalence of ST-elevation myocardial infarction (71 vs 30, P < 0.001), multi-vessel disease (50 vs. 34, P = 0.003), decreased ejection fraction (P = 0.001) and more severe angiographic stenosis based on both modified Gensini (P = 0.081) and syntax (P = 0.008) scores, compared to those without MetS. Syntax score showed statistically significant difference between two groups before (P = 0.021) and after adjustment for diabetes (P = 0.005). CONCLUSION: MetS was related to the severity of CAD both clinically and by angiographic scores but diabetes was a challenging factor and may independently increase the severity of CAD. © 2016,.Isfahan University of Medical Sciences(IUMS). All rights reserved

Biofeedback efficacy to improve clinical symptoms and endoscopic signs of solitary rectal ulcer syndrome

Solitary rectal ulcer syndrome (SRUS) is often resistant to medical and surgical treatment. This study assessed the effect of biofeedback in decreasing the symptoms and the healing of endoscopic signs in SRUS patients. Before starting the treatment, endoscopy and colorectal manometry was performed to evaluate dyssynergic defecation. Patients were followed every four weeks, and during each visit their response to treatment was evaluated regarding to manometry pattern. After at least 50 improvement in manometry parameters, recipients underwent rectosigmoidoscopy. Endoscopic response to biofeedback treatment and clinical symptoms were investigated. Duration of symptoms was 43.11±36.42 months in responder and 63.9 ± 45.74 months in non-responder group (P=0.22). There were more ulcers in non-responder group than responder group (1.50 ±0.71 versus 1.33±- 0.71 before and 1.30 ± 0.95 versus 0.67 ±0.50 after biofeedback), although the difference was not significant (P=0.604, 0.10 respectively). The most prevalent symptoms were constipation (79), rectal bleeding (68) and anorectal pain (53). The most notable improvement in symptoms after biofeedback occured in abdominal pain and incomplete evacuation, and the least was seen in mucosal discharge and toilet waiting as shown in the bar chart. Endoscopic cure was observed in 4 of 10 patients of the non-responder group while 8 patients in responder group experienced endoscopic improvement. It seems that biofeedback has significant effect for pathophysiologic symptoms such as incomplete evacuation and obstructive defecation. Improvement of clinical symptoms does not mean endoscopic cure; so to demonstrate remission the patients have to go under rectosigmoidoscopy. © PAGEPress 2008-2018

Machine Learning Made Easy (MLme): A Comprehensive Toolkit for Machine Learning-Driven Data Analysis.

BACKGROUND Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance. RESULTS To address these challenges, we have developed a novel tool called Machine Learning Made Easy (MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating four essential functionalities, namely Data Exploration, AutoML, CustomML, and Visualization, MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on six distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme's feature selection functionality, we successfully identified significant markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations. CONCLUSION MLme serves as a valuable resource for leveraging machine learning (ML) to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at https://github.com/FunctionalUrology/MLme

SpheroScan: a user-friendly deep learning tool for spheroid image analysis.

BACKGROUND In recent years, 3-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional 2-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays. RESULTS To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results. CONCLUSION SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at https://github.com/FunctionalUrology/SpheroScan

SpheroScan: A User-Friendly Deep Learning Tool for Spheroid Image Analysis.

BACKGROUND In recent years, three-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional two-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays. RESULTS To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results. CONCLUSION SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at https://github.com/FunctionalUrology/SpheroScan

Approaches in biotechnological applications of natural polymers

Natural polymers, such as gums and mucilage, are biocompatible, cheap, easily available and non-toxic materials of native origin. These polymers are increasingly preferred over synthetic materials for industrial applications due to their intrinsic properties, as well as they are considered alternative sources of raw materials since they present characteristics of sustainability, biodegradability and biosafety. As definition, gums and mucilages are polysaccharides or complex carbohydrates consisting of one or more monosaccharides or their derivatives linked in bewildering variety of linkages and structures. Natural gums are considered polysaccharides naturally occurring in varieties of plant seeds and exudates, tree or shrub exudates, seaweed extracts, fungi, bacteria, and animal sources. Water-soluble gums, also known as hydrocolloids, are considered exudates and are pathological products; therefore, they do not form a part of cell wall. On the other hand, mucilages are part of cell and physiological products. It is important to highlight that gums represent the largest amounts of polymer materials derived from plants. Gums have enormously large and broad applications in both food and non-food industries, being commonly used as thickening, binding, emulsifying, suspending, stabilizing agents and matrices for drug release in pharmaceutical and cosmetic industries. In the food industry, their gelling properties and the ability to mold edible films and coatings are extensively studied. The use of gums depends on the intrinsic properties that they provide, often at costs below those of synthetic polymers. For upgrading the value of gums, they are being processed into various forms, including the most recent nanomaterials, for various biotechnological applications. Thus, the main natural polymers including galactomannans, cellulose, chitin, agar, carrageenan, alginate, cashew gum, pectin and starch, in addition to the current researches about them are reviewed in this article.. }To the Conselho Nacional de Desenvolvimento Cientfíico e Tecnológico (CNPq) for fellowships (LCBBC and MGCC) and the Coordenação de Aperfeiçoamento de Pessoal de Nvíel Superior (CAPES) (PBSA). This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit, the Project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462) and COMPETE 2020 (POCI-01-0145-FEDER-006684) (JAT)

Parameter Identification and Investigating the Effect of Higher-order Inertia in a Gradient Elasticity Model of Metamaterials in Dynamic Loading

In metamaterials with a complex microstructure, the role of higher-gradient terms in the mechanical response is not negligible. Here, our goal is to identify the parameters of a homogenized model for a type of metamaterials known as pantographic structures. For the description of the pantographic structure, we employ a 2D non-linear second-gradient model which considers the complex structure as a homogenized plate [1]. The parameters of the model are identified for the corresponding structure through an automatized optimization algorithm [2]. We validate the identified parameters for the dynamic regime by comparing displacement plots with experiments [3]. Experimental results are obtained by applying forced oscillations to pantographic specimens made by 3D-printing technology. Qualitative and quantitative analyses for different frequency ranges show a good agreement far away from the eigenfrequencies while discrepancies are present close to the eigenfrequencies. To investigate the effect of microinertia, we include higher-order inertia in the model. As a result, the computations moved favorably toward predicting the mechanical behavior close to the eigenfrequencies. However, the experimental characterization of higher-order inertial terms that exist in theories is not yet understood, therefore there is no clear methodology for determining their values up to now. Further studies in this direction are encouraged

Developing an automatized optimization problem in FEniCS for parameter determination of metamaterials

In this work, a novel automatized optimization process is developed for the inverse analysis and pa- rameter determination of metamaterials. Metamaterials are the family of materials designed to have tai- lored material properties, such as high strength-to-weight ratio or extreme elasticity, by using an opti- mized topology. Due to metamaterials’ inner substructure, it is of interest to simulate their mechanical behaviour using reduced-order modelling utilizing the generalized mechanics. We determine the con- stitutive parameters of such models by developing an automatized optimization process in FEniCS. This process utilizes the Trust Region Reflective optimization method, from Scipy, for minimizing the deviation of the continuum model from a detailed micro-scale model. The parameter identification procedure proves to be robust and reliable by testing it for the pantographic structures as an example of metamaterials