Etude Des Facteurs De Risque De L’obésité Chez Le Personnel Du CHUD/Borgou à Parakou (Bénin) en 2013

The objective of this study was to determine the prevalence of obesity among employees CHUD in Parakou and risk factors in 2013. Methods: This was a cross sectional study, descriptive analytical referred to place from 05 August to 05 September 2013. The study population consists of employees of CHUD in Parakou. Data were collected using a questionnaire and by anthropometric measures. Results: The overall prevalence of overweight and obesity was 55.9%. The sex ratio was 0.8. The mean age of subjects was 37.2 ± 9.0 years. Factors associated with obesity were: female gender (p = 10-11), those aged 30-49 years (p = 0.04), subjects with a level of secondary education limited (p = 0.01), subjects with a daily consumption and accidental alcohol (p = 10-9), snacking (p = 0.00012). Conclusion: Obesity prevention should involve the establishment and maintenance during the lifetime of healthy eating habits and regular physical activity

Big Data Framework Using Spark Architecture for Dose Optimization Based on Deep Learning in Medical Imaging

Deep learning and machine learning provide more consistent tools and powerful functions for recognition, classification, reconstruction, noise reduction, quantification and segmentation in biomedical image analysis. Some breakthroughs. Recently, some applications of deep learning and machine learning for low-dose optimization in computed tomography have been developed. Due to reconstruction and processing technology, it has become crucial to develop architectures and/or methods based on deep learning algorithms to minimize radiation during computed tomography scan inspections. This chapter is an extension work done by Alla et al. in 2020 and explain that work very well. This chapter introduces the deep learning for computed tomography scan low-dose optimization, shows examples described in the literature, briefly discusses new methods for computed tomography scan image processing, and provides conclusions. We propose a pipeline for low-dose computed tomography scan image reconstruction based on the literature. Our proposed pipeline relies on deep learning and big data technology using Spark Framework. We will discuss with the pipeline proposed in the literature to finally derive the efficiency and importance of our pipeline. A big data architecture using computed tomography images for low-dose optimization is proposed. The proposed architecture relies on deep learning and allows us to develop effective and appropriate methods to process dose optimization with computed tomography scan images. The real realization of the image denoising pipeline shows us that we can reduce the radiation dose and use the pipeline we recommend to improve the quality of the captured image

Tuberculose vaginale révélée par une fièvre prolongée chez une femme immunodéprimée par le VIH à Cotonou, Bénin

Résumé La tuberculose vaginale est exceptionnelle et sous diagnostiquée sous nos cieux. Nous rapportons le cas d’une patiente de 53 ans, immunodéprimée par le VIH hospitalisée dans le service pour altération de l’état général dans un contexte de fièvre au long cours. L’interrogatoire, et l’examen physique avaient retrouvé les éléments suivants : tousseur chronique dans l’entourage, partenaire sexuel multiple, leucorrhées persistantes. Le Mycobactérium tuberculosis était retrouvé dans les leucorrhées à l’examen direct. La sérologie VIH était positive au VIH1, le taux des lymphocytes TCD4 était à 22 cells/μL. Le diagnostic de tuberculose vaginal sur terrain immunodéprimé sévère au VIH a été retenu. Un traitement antituberculeux fut institué. Le traitement Antirétroviral a démarré deux semaines plus tard. L’évolution a été rapidement favorable et après 6 mois de traitement la patiente était déclarée guérie de la tuberculose. Chez un patient immunodéprimé au VIH, tout écoulement purulent persistant même vaginal doit faire rechercher une tuberculos

Dynamical Properties and Chaos Synchronization in a Fractional-Order Two-Stage Colpitts Oscillator

In this paper, the dynamics and synchronization of a fractional-order four dimensional nonlinear system based on a twostage Colpitts oscillator is investigated, using the Grünwald-Letnikov method. The study of the fractional-order stability of the equilibrium states of the system is carried out. The bifurcation diagram confirms the occurrence of Hopf bifurcation in the proposed system when the fractional-order passes a sequence of critical values, and reveals in addition various bifurcation scenarios including period-doubling and interior crisis transitions to chaos. In order to promote chaos-based fractional-order synchronization of this type of oscillators, a synchronization strategy based upon the design of a nonlinear state observer is successfully adapted. Numerical simulations are performed to demonstrate the effectiveness and applicability of the proposed technique

Introducing a deep learning method for brain tumor classification using MRI data towards better performance

Magnetic resonance imaging (MRI) is widely used to detect brain tumors, but its accuracy depends on the physician's expertise and often requires biopsy confirmation. Deep learning, especially in the field of computer vision, has revolutionized the diagnosis and classification of brain tumors using MRI. This study aims to design a sequential brain tumor detection and classification model based on deep learning and using fully convolutional neural networks. The proposed model consists of two steps: distinguishing non-neoplastic brain from neoplastic brain and determining the tumor type of the latter. Two models were trained using the Brain Tumor MRI Dataset. Four optimizers are studied for three classification tasks (Adam, Nesterov momentum, root-mean-square propagation, and adaptive gradient) to achieve the best results. Adam performed best at distinguishing tumor from non-tumor brains, with 100 % training accuracy and 98 % validation and test accuracy. Nesterov Momentum performed best at differentiating the three tumor types, with 100 % training accuracy and 92 % validation and testing accuracy. Nesterov also performed best on the third classification task, with 100 % training accuracy and 95 % validation and test accuracy. Nesterov-based sequence models show significant results compared to literature works. The proposed Nesterov momentum-based sequence model achieves high accuracy in MRI brain tumor detection and classification

Helping Carers to Care: feasibility of the 10/66 Dementia Research Group caregiver intervention in rural Benin

International audienceAbstract Background In sub‐Saharan Africa, the number of people living with dementia is expected to double every 20 years, from 2.7 to 7.6 million. The shortage of trained health professionals leads family members of older people living with dementia to provide informal care. However, lack of knowledge and understanding about dementia leads to difficulties. There is therefore an urgent need to develop interventions to improve the lives of people with dementia and their families in this region. The objective of this study was to determine the feasibility of the Helping Carers to Care (HC2C) caregiver intervention in rural Benin. Method This was a before‐and‐after quasi‐experimental study conducted from January to December 2022 in Djidja‐Abomey‐Agbangnizoun, Benin. Two groups of 30 dyad (caregiver / person with dementia) were to receive the intervention in the beginning of the trial or six months later. The intervention consisted of three modules: 1) assessment; 2) basic education about dementia; and 3) training regarding specific problem behaviors. Main outcome measures for caregivers and patients with dementia were assessed at baseline, at 3‐month and at 6‐month. For caregivers, measures included strain (Zarit Burden Interview), psychological distress (SRQ‐20), and quality of life (WHOQOL‐BREF). Dementia participants completed scales assessing behavioral and psychological symptoms (NPI‐Q) and quality of life (DEMQOL). Result The study population consisted of 22 elderly people living with dementia and their primary caregiver divided into two groups ‐ control and intervention ‐ of 11 dyads. Both groups were similar with regards to sex, education, marital status, occupation, dementia severity but people in the control group were older. Participation rate was 100% and most of them found the intervention beneficial and advisable to others in the same situation. Full description of the outcomes and group differences at different times will be presented in details. Conclusion The need for such intervention among the families of people living with dementia in Benin is obvious. Preliminary results show that implementation is challenging in rural Benin, where dementia awareness is low and care structures are lacking. These results will add to the scarce evidence on feasibility and benefits of caregiver interventions in low‐and middle‐income countries

Le Miel : Facteurs Influençant sa Qualité

Le miel est un aliment sain, naturel et ayant des vertues pour la santé de l’Homme telles que des propriétés antibactériennes, cicatrisantes, anti-inflammatoires, antioxydantes, nutritionnelles, digestives mais aussi respiratoires. Il est produit par les abeilles qui sont des insectes qui jouent un grand rôle dans la pollinisation des plantes.Plusieurs facteurs affectent la qualité du miel partant de la production jusqu’au stockage. Le présent article fait la synthèse des travaux de recherche sur le processus de production du miel, sa composition, ses propriétés et son stockage. Ce qui est à retenir est que les espèces de plantes butinées, la manière de récolter, la manière de conserver impactent la qualité du miel. AbstractHoney is a healthy, natural food with virtues for human health such as antibacterial, healing, anti-inflammatory, antioxidant, nutritional, digestive and respiratory properties. It is produced by bees which are insects playing a great role in the pollination of plants. Several factors affect the quality of honey from production to storage. This article highlights research work on the honey production process, its composition, properties and storage. What is important to remember is that the species of nectar-bearing plants, the way of harvesting, the way of preserving affect the quality of honey.Keywords: honey, bees, virtu

An optimal big data workflow for biomedical image analysis

Background and objective: In the medical field, data volume is increasingly growing, and traditional methods cannot manage it efficiently. In biomedical computation, the continuous challenges are: management, analysis, and storage of the biomedical data. Nowadays, big data technology plays a significant role in the management, organization, and analysis of data, using machine learning and artificial intelligence techniques. It also allows a quick access to data using the NoSQL database. Thus, big data technologies include new frameworks to process medical data in a manner similar to biomedical images. It becomes very important to develop methods and/or architectures based on big data technologies, for a complete processing of biomedical image data. Method: This paper describes big data analytics for biomedical images, shows examples reported in the literature, briefly discusses new methods used in processing, and offers conclusions. We argue for adapting and extending related work methods in the field of big data software, using Hadoop and Spark frameworks. These provide an optimal and efficient architecture for biomedical image analysis. This paper thus gives a broad overview of big data analytics to automate biomedical image diagnosis. A workflow with optimal methods and algorithm for each step is proposed. Results: Two architectures for image classification are suggested. We use the Hadoop framework to design the first, and the Spark framework for the second. The proposed Spark architecture allows us to develop appropriate and efficient methods to leverage a large number of images for classification, which can be customized with respect to each other. Conclusions: The proposed architectures are more complete, easier, and are adaptable in all of the steps from conception. The obtained Spark architecture is the most complete, because it facilitates the implementation of algorithms with its embedded libraries. Keywords: Biomedical images, Big data, Artificial intelligence, Machine learning, Hadoop/spar

Helping Carers to Care: feasibility of the 10/66 Dementia Research Group caregiver intervention in rural Benin

International audienc

Biomedical Image Classification in a Big Data Architecture Using Machine Learning Algorithms

In modern-day medicine, medical imaging has undergone immense advancements and can capture several biomedical images from patients. In the wake of this, to assist medical specialists, these images can be used and trained in an intelligent system in order to aid the determination of the different diseases that can be identified from analyzing these images. Classification plays an important role in this regard; it enhances the grouping of these images into categories of diseases and optimizes the next step of a computer-aided diagnosis system. The concept of classification in machine learning deals with the problem of identifying to which set of categories a new population belongs. When category membership is known, the classification is done on the basis of a training set of data containing observations. The goal of this paper is to perform a survey of classification algorithms for biomedical images. The paper then describes how these algorithms can be applied to a big data architecture by using the Spark framework. This paper further proposes the classification workflow based on the observed optimal algorithms, Support Vector Machine and Deep Learning as drawn from the literature. The algorithm for the feature extraction step during the classification process is presented and can be customized in all other steps of the proposed classification workflow