The Influence of Perinatal Education on Breastfeeding Decision and Duration

Objectives: to evaluate factors influencing breastfeeding duration in an integrative model, considering both the organization of medical care and the perinatal education.Methods: We conducted a cross sectional study with data collected in a face to face interview of 1,008 mothers with children aged between 9 and 14 months The questionnaire focused on the main characteristics of a Mother-Baby Friendly Hospital initiative and the type of perinatal education received by pregnant women. Correlation and partial correlation tests, non-parametric tests and classification tests were applied. Data were processed in SPSS 12.0 software.Results: The positive effects of Mother Baby Friendly Hospitals Organization initiative organization were confirmed. However, the main differentiator for breastfeeding duration was the level of formal education of pregnant mothers and the active seeking of perinatal education (r = 0.22, p< 0.001). The perinatal counseling was correlated with breastfeeding duration only for the subgroup participating to structured, dedicated time slot apart from the regular medical consultation (r = 0.079; p = 0.014), independently of mother’s age, education, residence, time to first breastfeeding contact, type of birth delivery and rooming in. Our results support a broader approach to perinatal education than medical counseling during pregnancy to increase the voluntary participation of future mothers to the perinatal educational programs.Conclusion: As mothers’ motivation to maintain the optimum duration of breastfeeding is a determinant factor, an earlier and sustained educational process, before pregnancy and after birth delivery, is necessary in order to create a general favorability for exclusive breastfeeding

The assessment of bacterial film cariogenic potential changes following the action of remineralisation agents

Rezumat. Unul dintre factorii etiologici chee, care joaca un rol important în dezvoltarea cariei dentare şi a afecţiunilor parodontale este microflora biofilmului bacterian. Scopul acestui studiu este de a evalua acţiunea unor preparate de remineralizare asupra cariogenităţii biofilmului bacterian. Material si metodă: s-a utilizat in evaluarea cariogenitatii biofilmului bacterian testul Hardwick J.L., Manly E.B. înainte şi după aplicarea preparatelor de remineralizare pe bază de calciu, fosfat şi fluor. Rezultate. Se constată o îmbunătăţire a situaţiei cariogene din cavitatea orală sub acţiunea acestor preparate. Deşi nu este o diferenţă semnificativă d.p.d.v. statistic, un efect mai favorabil îl au preparatele ce conţin calciu, fosfat şi fluor, comparativ cu cele ce conţin doar calciu şi fosfat.Summary. The bacterial film is a key ethiological factor with a major role in dental caries and periodontal diseases development and evolution. Aim. The aim of this study is to assess the action of some remineralisation products over the cariogenic action of bacterial biofilm. Materials and method. The cariogenical potential bacterial biofilm test Hardwick J.L.&Manly E.B. was performed before and after the application of remineralisation products with calcium, phosphat and fluor. Results. The results show an improvement of cariogenic situation after the action of remineralisation agents. The products that contain calcium, phosphat and fluor have a more efficient action comparing with products based only on calcium and phosphat

Study regardind the early carious lesion treatment using ICON method

Rezumat: Una din metodele cele mai actuale şi de perspectivă ale orientării terapiei conservatoare în caria incipientă este metoda utilizării locale a unor preparate de sigilare şi respectiv de infiltrare a ţesuturilor dure dentare. Abordarea temei alese, a fost determinată de necesitatea unei înţelegeri cât mai corecte a importanţei unui diagnostic cât mai precoce, corect şi precis al leziunilor carioase incipiente asociate tratamentelor ortodontice, a particularităţilor acestora în ceea ce priveşte apariţia, evoluţia, diagnosticul, cât şi a posibilităţilor terapeutice ce pot fi instituite în acest caz. Studiul a fost efectuat pe un lot de pacienţi cu vârste cuprinse între 10 şi 35 ani, evaluându-se comparativ eficienţa terapiei lezunilor carioase asociate tratamentului ortodontic fix prin metoda de infiltrare ICON comparativ cu metoda de remineralizare profundă. În urma analizei rezultatelor obţinute în cadrul acestei cercetări putem concluziona că ambele metode alese în tratamentul leziunilor carioase incipiente asociate tratamentului odontic fix sunt eficiente în oprirea evoluţiei proceselor carioase incipiente. Metoda infiltrării ICON oferă în schimb rezultate estetice superioare.Summary: Sealing or infiltrating dental hard tissues are modern methods in conservative treatment of early carious lesions. An early and precise diagnostic of incipient carious lesion associated with orthodontical treatment is very important in order to establish therapeutical procedures. In this context, the theme of our study is one of great interest. The study group was represented by patients of 10-35 years old. We used ICON method and deep remineralisation method in the treatment of carious lesions associated with orthodontical treatment. The results showed the efficiency of both methods in arresting carious evolution. The ICON method has a real advantage from the point of vue of the aesthetic result

Texture spectrum coupled with entropy and homogeneity image features for myocardium muscle characterization

People in middle/later age often suffer from heart muscle damage due to coronary artery disease associated to myocardial infarction. In young people, the genetic forms of cardiomyopathies (heart muscle disease) are the utmost protuberant cause of myocardial disease. Accurate early detected information regarding the myocardial tissue structure is a key answer for tracking the progress of several myocardial diseases. The present work proposes a new method for myocardium muscle texture classification based on entropy, homogeneity and on the texture unit-based texture spectrum approaches. Entropy and homogeneity are generated in moving windows of size 3x3 and 5x5 to enhance the texture features and to create the premise of differentiation of the myocardium structures. Texture is then statistically analyzed using the texture spectrum approach. Texture classification is achieved based on a fuzzy c–means descriptive classifier. The noise sensitivity of the fuzzy c–means classifier is overcome by using the image features. The proposed method is tested on a dataset of 80 echocardiographic ultrasound images in both short-axis and long-axis in apical two chamber view representations, for normal and infarct pathologies. The results established that the entropy-based features provided superior clustering results compared to homogeneity

Clinical study to evaluate the factors involved in the evolution of the halitosis in a group of students

Halitoza este o problemă medico-socială universală, în toate comunităţile şi se referă la mirosul neplăcut care se emană din cavitatea orală. Obiectivele acestui studiu sunt: de a investiga prevalenţa halitozei evaluându-se prin mijloace clinice, paraclinice şi printr-un screening tip anchetă a prezenţei halitozei, a factorilor cauzali implicaţi: patologia cavităţii orale asociate, cum ar fi cariile dentare şi boala parodontală, practicile de igienă orală, şi bolile generale implicate, în rândul unui eşantion de studenţi de la Facultatea de Medicină Dentară UMF Gr.T. Popa. Analiza rezultatelor obţinute cu stabilitatea corespondenţei dintre percepţia propriei halitoze şi a măsurilor de igienă orală efectuate de participanţii la studiu. Caracteristicile şi etiologia respiraţiei urât mirositoare s-au analizat pe un grup de 176 de studenţi, anul III-IV de la facultatea de Medicină Dentară, care au fost supuşi unei evaluări: printr-un chestionar standard şi un examen clinic odonto-parodontal complet, inclusiv a unui examen paraclinic cu un dispozitiv portabil (Detector de halenă ), stabilindu-se punctajele organoleptice măsurate.Halitosis is a universal medical and social problem in all communities and refers to the bad odor that emanates from the oral cavity. The objectives of this study are: to investigate the prevalence of the Halitosis by clinical and laboratory methods, to determine the causal factors involved: oral cavity associated pathology such as dental caries and periodontal disease, oral hygiene practices, and general diseases involved among a sample of students from the Faculty of Dental Gr. T. Popa. Stability analysis results obtained with the correspondence between their perception of halitosis and oral hygiene measures by survey participants. Characteristics and etiology of bad breath were analyzed in a group of 176 students. They were subjected to an assessment: through a standard questionnaire and a clinical examination including a paraclinical examination with a portable device (Halitosis Detector)

Skin Lesion Classification Based on Surface Fractal Dimensions and Statistical Color Cluster Features Using an Ensemble of Machine Learning Techniques

(1) Background: An approach for skin cancer recognition and classification by implementation of a novel combination of features and two classifiers, as an auxiliary diagnostic method, is proposed. (2) Methods: The predictions are made by k-nearest neighbor with a 5-fold cross validation algorithm and a neural network model to assist dermatologists in the diagnosis of cancerous skin lesions. As a main contribution, this work proposes a descriptor that combines skin surface fractal dimension and relevant color area features for skin lesion classification purposes. The surface fractal dimension is computed using a 2D generalization of Higuchi’s method. A clustering method allows for the selection of the relevant color distribution in skin lesion images by determining the average percentage of color areas within the nevi and melanoma lesion areas. In a classification stage, the Higuchi fractal dimensions (HFDs) and the color features are classified, separately, using a kNN-CV algorithm. In addition, these features are prototypes for a Radial basis function neural network (RBFNN) classifier. The efficiency of our algorithms was verified by utilizing images belonging to the 7-Point, Med-Node, and PH2 databases; (3) Results: Experimental results show that the accuracy of the proposed RBFNN model in skin cancer classification is 95.42% for 7-Point, 94.71% for Med-Node, and 94.88% for PH2, which are all significantly better than that of the kNN algorithm. (4) Conclusions: 2D Higuchi’s surface fractal features have not been previously used for skin lesion classification purpose. We used fractal features further correlated to color features to create a RBFNN classifier that provides high accuracies of classification

Combining Sparse and Dense Features to Improve Multi-Modal Registration for Brain DTI Images

A new solution to overcome the constraints of multimodality medical intra-subject image registration is proposed, using the mutual information (MI) of image histogram-oriented gradients as a new matching criterion. We present a rigid, multi-modal image registration algorithm based on linear transformation and oriented gradients for the alignment of T2-weighted (T2w) images (as a fixed reference) and diffusion tensor imaging (DTI) (b-values of 500 and 1250 s/mm2) as floating images of three patients to compensate for the motion during the acquisition process. Diffusion MRI is very sensitive to motion, especially when the intensity and duration of the gradient pulses (characterized by the b-value) increases. The proposed method relies on the whole brain surface and addresses the variability of anatomical features into an image stack. The sparse features refer to corners detected using the Harris corner detector operator, while dense features use all image pixels through the image histogram of oriented gradients (HOG) as a measure of the degree of statistical dependence between a pair of registered images. HOG as a dense feature is focused on the structure and extracts the oriented gradient image in the x and y directions. MI is used as an objective function for the optimization process. The entropy functions and joint entropy function are determined using the HOGs data. To determine the best image transformation, the fiducial registration error (FRE) measure is used. We compare the results against the MI-based intensities results computed using a statistical intensity relationship between corresponding pixels in source and target images. Our approach, which is devoted to the whole brain, shows improved registration accuracy, robustness, and computational cost compared with the registration algorithms, which use anatomical features or regions of interest areas with specific neuroanatomy. Despite the supplementary HOG computation task, the computation time is comparable for MI-based intensities and MI-based HOG methods

Image Moment-Based Features for Mass Detection in Breast US Images via Machine Learning and Neural Network Classification Models

Differentiating between malignant and benign masses using machine learning in the recognition of breast ultrasound (BUS) images is a technique with good accuracy and precision, which helps doctors make a correct diagnosis. The method proposed in this paper integrates Hu’s moments in the analysis of the breast tumor. The extracted features feed a k-nearest neighbor (k-NN) classifier and a radial basis function neural network (RBFNN) to classify breast tumors into benign and malignant. The raw images and the tumor masks provided as ground-truth images belong to the public digital BUS images database. Certain metrics such as accuracy, sensitivity, precision, and F1-score were used to evaluate the segmentation results and to select Hu’s moments showing the best capacity to discriminate between malignant and benign breast tissues in BUS images. Regarding the selection of Hu’s moments, the k-NN classifier reached 85% accuracy for moment M1 and 80% for moment M5 whilst RBFNN reached an accuracy of 76% for M1. The proposed method might be used to assist the clinical diagnosis of breast cancer identification by providing a good combination between segmentation and Hu’s moments

Dispersive Optical Solitons with Schrödinger–Hirota Equation by Laplace-Adomian Decomposition Approach

This paper studies dispersive bright and dark optical solitons, modeled by the Schrödinger–Hirota equation, numerically by the aid of the Adomian decomposition. The surface plots of the algorithm yielded an impressively small measure. The effects of soliton radiation are ignored

Highly Dispersive Optical Solitons in Absence of Self-Phase Modulation by Laplace-Adomian Decomposition

This article studies highly dispersive optical solitons without of self-phase modulation effect. The numerical algorithm implemented in this work is Laplace-Adomian decomposition method. Both bright and dark solitons are addressed. The error measure for the adopted scheme is impressively low