Caries Incidence, by DMFT Index of Libyan School Children Concerning Socio-demographic Variables and Oral Health Behavior

Background and aims: dental caries are the most prevalent chronic disease and are caused due to complex interplay of behavioral, cultural, social, and dietary factors. The purpose of this study was to determine the prevalence of dental caries and its relation to socioeconomic variables among Libyan children in Benghazi. Material and method: A cross-sectional study was conducted with 207 of children aged 6-12 years in Benghazi, Libya. Patient’s data were recorded in a special form such as parent’s educations, employment, home density, family income, tooth brushing, mouth rinse, dental floss, and dental service. The diagnostic criteria for caries incidence were based on the oral Health Organization (WHO) Criteria. The child’s caries were measured by dmft and DMFT indices. Data were analyzed using SPSS version 16. A Chi-square test was used, whereas a p-value less than 0.05 were considered significant. Results: caries-free teeth in the permanent dentition were 63.8%, while caries teeth were 36.2%. The DMFT in boys and girls were (1.14±0.19, and 1.28±0.19 respectively). The (dmft) in primary dentition was higher in boys and girls (5.45±0.39 and 4.77±0.38 respectively). No significant differences were found for the DMFT index to gender, mother‘s employment, and family income (P >0.05). However, significant differences were observed regarding the mother’s education, father’s education, Father’s employment, and home density (P <0.0001). Conclusion: the socioeconomic levels an important predictor of caries presence in the children. The possibility of being caries free is increased with the increscent in the parent’s education; therefore the public health planners should consider these findings when planning interventions to promote dental health education and services

Studies of the Effect of Enterovirus Infection on Pancreatic Islet Cells

Enterovirus (EV) infections have been associated with the pathogenesis of Type 1 Diabetes (T1D). However, the pathway(s) by which EV may induce or accelerate diabetes is not well understood. The purpose of this thesis was to obtain new information on the mechanism by which EV infections, with different strains of EV, could cause damage to the insulin-producing β-cells in isolated human islets and in a rat insulin-producing cell line (RINm5F). Infection with EV strains isolated from T1D patients revealed replication/cell destruction in human islets and EV-like particles in the cytoplasm of the β-cell and infection with the isolates affected the release of insulin in response to glucose stimulation as early as three days post infection, before any decrease in cell viability was observed. A decrease in the induction/secretion of the chemokine RANTES in human islets during EV infection was also detected. When islets were cultured with nicotinamide (NA) the secretion of RANTES was increased irrespectively if the islets were infected or not. In addition, the degree of virus-induced cytolysis of human islets was reduced by NA, suggesting an antiviral effect of NA. Infection with EV strains revealed permissiveness to islet-derived cells. All EV strains used for infection were able to replicate in the RIN cell clusters (RCC) but not in the RIN cells that were cultured as a monolayer. This might be due to the differences in expression of the Coxsackie-adenovirus receptor (CAR), which only could be detected on the RCC. Infection of RCC with a CBV-4 strain did not affect cell viability and did not induce nitric oxide (NO) production alone or with the addition of IFN-γ. This was in contrast to the results obtained with synthetic dsRNA, poly(IC), which induced NO, suggesting that synthetic dsRNA does not mimic enteroviral intermediate dsRNA. During analyses performed with the samples from a family where the mother and one son where diagnosed with T1D on the same day, the results showed that the whole family had a proven EV infection at the time diagnosis. To conclude, the ability of EV strains to replicate in RIN cells is dependent on the growth pattern of the cells and this may be due to the upregulation and/or changed expression pattern of CAR in these cells. In the RIN cells, contrary to artificial dsRNA, viral dsRNA does not induce NO. The isolated EV virus strains used were able to infect and affect human pancreatic islets in vitro. The chemokine RANTES is reduced during an EV infection of human pancreatic islets and NA causes upregulation of RANTES in infected and uninfected islets

Methods for tobacco cessation

Tandvården har goda möjligheter att få kännedom om patienters tobaksvanor eftersom flertalet människor besöker tandvården regelbundet. Det finns därför goda möjligheter att utföra tobaksavvänjning med de som röker eller snusar. Syftet med litteraturstudien var att belysa metoder som finns för tobaksavvänjning och vilka resultat dessa metoder ger. Sökningen gjordes i databasen Pubmed och begränsades till artiklar som är publicerade under de senaste tio åren och till studier som har utförts inom tandvården. Litteraturstudien inkluderade åtta studier. Resultatet visade att det finns flera olika kombinationsmetoder som används för tobaksavvänjning. I tre artiklar har 5A metoden använts i kombination med nikotinersättningsmedel. I övriga artiklar användes fem olika kombinationsmetoder med olika uppföljningstider. I en av kombinationsmetoderna beskrevs två metoder. Utöver dessa metoder fanns en metod för snusavvänjning. Resultatet visade skillnader i lyckandefrekvens med upphörande av tobaksvanor. Tolv månaders avvänjning och uppföljning i två av kombinationsmetoderna samt metoden för snusavvänjning gav högst lyckandefrekvens (36 %, 25 % och 30 %). Lägst lyckandefrekvens var 7 % efter tolv månaders uppföljning i en av 5A metoderna. Studiens slutsats är att det finns få publicerade studier om tobaksavvänjning inom tandvården som är baserade på utvärdering av tobaksavvänjningsmetoder som är utförda på patienter. Uppföljning och rådgivning samt stöttning har betydelse för resultatet.Patients with tobacco habits visit the dental care regularly, therefore it is well placed to carry out tobacco cessation for those who smoke or use snuff. The purpose of this study was to highlight methods available for tobacco cessation and results of these methods. The authors searched in the database PubMed and was limited to articles published during the last ten years and performed in the dental care. The framework was limited to eight studies which were performed in the dental care. The results showed that there are several different combination methods for tobacco cessation. In three articles, the 5A method was used in combination with nicotine replacement therapy. In other articles five different combination methods with different follow-up times were used. In one of those with combined approach two methods are described. In addition a method was used for snuff cessation. The result showed differences in frequency of success to tobacco stop. The best result was shown after twelve months tobacco cessation and a follow up in two of the combination methods and the method for snuff cessation (36%, 25% and 30%). The lowest success rate was 7% after twelve months follow up with one of the 5A methods. The conclusion of the study is that there are few published studies regarding tobacco cessation in the dental care, which are based on evaluation of methods performed among patients. Follow up, counselling and support have essential effects on the result

Convolutional neural networks with feature fusion method for automatic modulation classification

The analogy and application of Automatic modulation classification (AMC) detects the modulation type of received signals. Henceforth, the received signals can be correctly demodulated and, consequently, the transmitted message can be recovered. In Deep Learning (DL) based modulation classification, one major category, challenge is to pre-processing a received signal and representing it in a proper format-manner, before passing the desired-signal into the neural network. However, most existing modulation classification algorithms are neglecting the fact of mixing features between different representations, and the importance of features fusion method. This paper, however, attempted a Feature fusion scheme, for AMC, using convolutional neural networks (CNN). The approach was taken, in order to attempt fuse features extracted from In-phase & Quadrature (IQ) sequences, as well as, the Amplitude & Phase (AP) Sequences and Constellation Diagram images. Finally, simulation results show that fusing features from different representations can incorporate and leads to the best accuracy figures, achieved from each representation separately. Furthermore, our model achieved a classification accuracy of 84.68% at 0dB and 75.29% at -2dB and over 90% accuracy for high SNRs with a maximum accuracy of 94.65%, were available