Knowledge, attitudes, practices of teenagers on sexual health in the district of Ambohidratrimo

Background: Talking about sex, sexuality, sexual health in many countries, including Madagascar, is very difficult because of the sacred and taboos that surround these questions but especially because of certain puritanism. The objective of this study was to assess the knowledge, attitudes and practices of adolescents in matters of sexual health.Methods: A retrospective, descriptive cross-sectional study was carried out in adolescents aged 10 to 19 seen in Ambohidratrimo district. The data were collected during the month of June and July 2019 and relate to data for the twelve months before the survey.Results: A total of 210 adolescents were recruited whose average age was 15.82±2.75 years and the sex ratio was 1:04. Eighty-six percent of the adolescents surveyed had heard of sexual health. Nine out of 10 adolescents would go to a health worker if they contract an STI. Almost a quarter or 23.8% of respondents declared having already had sexual intercourse.Conclusions: At the end of this study on the knowledge, attitudes and practices of adolescents on sexual health, they certainly have knowledge but considered average. Therefore, there is a need for sexuality education, for improving knowledge and understanding of sexual development, human procreation, healthy and adapted sexual behavior and different means of contraception, on the part of adolescents, but also with the aim of improving communications between adolescents and their parent

Patterns of regional diastolic function in the normal human left ventricle: An ultrafast computed tomographic study

AbstractThe detailed evaluation of regional diastolic filling at multiple ventricular levels in the normal human left ventricle has not previously been reported. Ultrafast computed tomography was used to characterize global and regional early diastolic filling in the left ventricle of 11 normal male volunteers. Regional early diastolic filling data from six distinct ventricular levels (apex to base) were fit to a third-order polynomial curve, and the peak rate of diastolic filling and time of peak filling were determined. Peak filling rate was 259 ± 17 ml/s (±SEM) as a global average, where peak filling rate referenced to end-diastolic volume and stroke volume across the levels examined was 3.78 ± 0.17 s−and 4.83 ± 0.20 s−respectively. Average filling fraction was 39 ± I%, and time to peak filling from end-systole was 145 ± 5 ms.Regional (tomographic) peak filling rates, except for the most apical level examined, were not statistically different across the ventricle. Filling fraction and time to peak filling were remarkably constant from one level to another. However, reference of regional peak filling rate to regional end-diastolic volume demonstrated significant nonuniformity from apex (120% of average for all levels) to base (87% of average for all levels). Peak filling rate referenced to tomographic stroke volume was less variable and not statistically different across the ventricle as a whole.In conclusion, values of regional absolute early peak diastolic ventricular filling rate or values normalized for regional end-diastolic volume are characteristically nonuniform across the left ventricle, whereas other variables such as filling fraction, time to peak filling and regional peak filling rate referenced to regional stroke volume are highly uniform. This confirms an intimate relation between rates of regional diastolic filling and regional ventricular size and stroke volume in the normal human heart

Operational framework based on modeling languages to support product repository implementation.

Part 3: Tools and MethodologiesInternational audienceEmbracing Product Lifecycle Management approach involves integrating a product repository in the company information system. From customer's needs to disposal stage, several product representations exist. The product repository purpose is to secure consistency of one product representation with the others. This paper presents an operational modeling framework that supports product repository implementation. In order to ensure consistency, this framework identifies correspondences between entities of languages (“trade” languages and standard languages). The presented concepts are illustrated with correspondences between language entities of product designed and productplanned to be built Bills of Materials

Knowledge graph prediction of unknown adverse drug reactions and validation in electronic health records

Abstract Unknown adverse reactions to drugs available on the market present a significant health risk and limit accurate judgement of the cost/benefit trade-off for medications. Machine learning has the potential to predict unknown adverse reactions from current knowledge. We constructed a knowledge graph containing four types of node: drugs, protein targets, indications and adverse reactions. Using this graph, we developed a machine learning algorithm based on a simple enrichment test and first demonstrated this method performs extremely well at classifying known causes of adverse reactions (AUC 0.92). A cross validation scheme in which 10% of drug-adverse reaction edges were systematically deleted per fold showed that the method correctly predicts 68% of the deleted edges on average. Next, a subset of adverse reactions that could be reliably detected in anonymised electronic health records from South London and Maudsley NHS Foundation Trust were used to validate predictions from the model that are not currently known in public databases. High-confidence predictions were validated in electronic records significantly more frequently than random models, and outperformed standard methods (logistic regression, decision trees and support vector machines). This approach has the potential to improve patient safety by predicting adverse reactions that were not observed during randomised trials

A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records Data

The ability to perform accurate prognosis of patients is crucial for proactive clinical decision making, informed resource management and personalised care. Existing outcome prediction models suffer from a low recall of infrequent positive outcomes. We present a highly-scalable and robust machine learning framework to automatically predict adversity represented by mortality and ICU admission from time-series vital signs and laboratory results obtained within the first 24 hours of hospital admission. The stacked platform comprises two components: a) an unsupervised LSTM Autoencoder that learns an optimal representation of the time-series, using it to differentiate the less frequent patterns which conclude with an adverse event from the majority patterns that do not, and b) a gradient boosting model, which relies on the constructed representation to refine prediction, incorporating static features of demographics, admission details and clinical summaries. The model is used to assess a patient's risk of adversity over time and provides visual justifications of its prediction based on the patient's static features and dynamic signals. Results of three case studies for predicting mortality and ICU admission show that the model outperforms all existing outcome prediction models, achieving PR-AUC of 0.891 (95$$ CI: 0.878 - 0.969) in predicting mortality in ICU and general ward settings and 0.908 (95$$ CI: 0.870-0.935) in predicting ICU admission.Comment: 14 page

The double infection diagnosis, fungal and insect on date palm offshoot Phoenix dactylifera with treatment pesticides and practical technique

This study was carried out several times in the field during September 2019 in Baghdad Governorate and these data are the results of the last observation in the field. Cultivation of palm offshoots of the yellow Barhi variety, the first strain in quality and production were the number offshoots was 75, 25-40 kg weighed, which were divided into three groups, each one was 25 offshoots. The first group was subjected to a comparison treatment (control), while in the second group the offshoots were treated by immersion before planting with a solution containing a systemic fungicide (Biltanol) at a concentration of 1 mL L-1, and then immersed in a solution containing a type of insecticide (Cinfidor) at a concentration of 1 mL L-1 and then planted and controlled after 2 months of planting by the same pesticides and with the same concentration. In the case of third group, it was controlled after 2 months of planting with the same pesticides above, and after a year of planting and services. A success rate was obtained as 60%, 100% and 96% in the first, second and third treatment consecutively. The insect was diagnosed as a red scale parlatotoria Blanchardi, while the fungus was diagnosed in the laboratory by a direct examination and using a light microscope as Fusarium oxysporium. The aim of the study was to reduce the number of dead offshoots after planting in the field