Decoding children dental health risks:a machine learning approach to identifying key influencing factors

Introduction and objectives: This study investigates key factors influencing dental caries risk in children aged 7 and under using machine learning techniques. By addressing dental caries’ prevalence, it aims to enhance early identification and preventative strategies for high-risk individuals. Methods: Data from clinical examinations of 356 children were analyzed using Logistic Regression, Decision Trees, and Random Forests models. These models assessed the influence of dietary habits, fluoride exposure, and socio-economic status on caries risk, emphasizing accuracy, precision, recall, F1 score, and AUC metrics. Results: Poor oral hygiene, high sugary diet, and low fluoride exposure were identified as significant caries risk factors. The Random Forest model demonstrated superior performance, illustrating the potential of machine learning in complex health data analysis. Our SHAP analysis identified poor oral hygiene, high sugary diet, and low fluoride exposure as significant caries risk factors. Conclusion: Machine learning effectively identifies and quantifies dental caries risk factors in children. This approach supports targeted interventions and preventive measures, improving pediatric dental health outcomes. Clinical significance: By leveraging machine learning to pinpoint crucial caries risk factors, this research lays the groundwork for data-driven preventive strategies, potentially reducing caries prevalence and promoting better dental health in children

Decoding children dental health risks:a machine learning approach to identifying key influencing factors

Introduction and objectives: This study investigates key factors influencing dental caries risk in children aged 7 and under using machine learning techniques. By addressing dental caries’ prevalence, it aims to enhance early identification and preventative strategies for high-risk individuals. Methods: Data from clinical examinations of 356 children were analyzed using Logistic Regression, Decision Trees, and Random Forests models. These models assessed the influence of dietary habits, fluoride exposure, and socio-economic status on caries risk, emphasizing accuracy, precision, recall, F1 score, and AUC metrics. Results: Poor oral hygiene, high sugary diet, and low fluoride exposure were identified as significant caries risk factors. The Random Forest model demonstrated superior performance, illustrating the potential of machine learning in complex health data analysis. Our SHAP analysis identified poor oral hygiene, high sugary diet, and low fluoride exposure as significant caries risk factors. Conclusion: Machine learning effectively identifies and quantifies dental caries risk factors in children. This approach supports targeted interventions and preventive measures, improving pediatric dental health outcomes. Clinical significance: By leveraging machine learning to pinpoint crucial caries risk factors, this research lays the groundwork for data-driven preventive strategies, potentially reducing caries prevalence and promoting better dental health in children

Internationalization orientation in SMEs: the mediating role of technological innovation

This study examines the relationship between internationalization orientation and international performance of small and medium-sized enterprises (SMEs), and the mediating effect of technological innovation. Prior research suggests that internationalization is a prominent strategic choice for SMEs growth and profitability. However, there is still no explicit agreement on how internationalization affects international performance. Similarly, the role of innovation on performance has long been emphasized, but the implications of technological innovation on international performance are still eluding us. Our investigation of 116 SMEs in the United Kingdom reveals that internationalization orientation has a significant effect on their international performance, with SMEs adopting simultaneously an inward and outward international orientation achieving superior results. We further demonstrate that there is an inverted U-shaped relationship between technological innovation and international firm performance among SMEs. Meanwhile, we find that technological innovation positively mediates the effect of internationalization orientation on international firm performance, particularly for the SMEs exhibiting moderate levels of technological innovation activities. The findings of this study suggest that managers can improve international performance by combining inward and outward internationalization orientation with technological innovation activities in their strategic decisions

Decoding children dental health risks: a machine learning approach to identifying key influencing factors

Introduction and objectives: This study investigates key factors influencing dental caries risk in children aged 7 and under using machine learning techniques. By addressing dental caries’ prevalence, it aims to enhance early identification and preventative strategies for high-risk individuals. Methods: Data from clinical examinations of 356 children were analyzed using Logistic Regression, Decision Trees, and Random Forests models. These models assessed the influence of dietary habits, fluoride exposure, and socio-economic status on caries risk, emphasizing accuracy, precision, recall, F1 score, and AUC metrics. Results: Poor oral hygiene, high sugary diet, and low fluoride exposure were identified as significant caries risk factors. The Random Forest model demonstrated superior performance, illustrating the potential of machine learning in complex health data analysis. Our SHAP analysis identified poor oral hygiene, high sugary diet, and low fluoride exposure as significant caries risk factors. Conclusion: Machine learning effectively identifies and quantifies dental caries risk factors in children. This approach supports targeted interventions and preventive measures, improving pediatric dental health outcomes. Clinical significance: By leveraging machine learning to pinpoint crucial caries risk factors, this research lays the groundwork for data-driven preventive strategies, potentially reducing caries prevalence and promoting better dental health in children

Dental Caries Risk Assessment in Children 5 Years Old and under via Machine Learning

Background: Dental caries is a prevalent, complex, chronic illness that is avoidable. Better dental health outcomes are achieved as a result of accurate and early caries risk prediction in children, which also helps to avoid additional expenses and repercussions. In recent years, artificial intelligence (AI) has been employed in the medical field to aid in the diagnosis and treatment of medical diseases. This technology is a critical tool for the early prediction of the risk of developing caries. Aim: Through the development of computational models and the use of machine learning classification techniques, we investigated the potential for dental caries factors and lifestyle among children under the age of five. Design: A total of 780 parents and their children under the age of five made up the sample. To build a classification model with high accuracy to predict caries risk in 0–5-year-old children, ten different machine learning modelling techniques (DT, XGBoost, KNN, LR, MLP, RF, SVM (linear, rbf, poly, sigmoid)) and two assessment methods (Leave-One-Out and K-fold) were utilised. The best classification model for caries risk prediction was chosen by analysing each classification model’s accuracy, specificity, and sensitivity. Results: Machine learning helped with the creation of computer algorithms that could take a variety of parameters into account, as well as the identification of risk factors for childhood caries. The performance of the classifier is almost unbiased, making it generalizable. Among all applied machine learning algorithms, Multilayer Perceptron and Random Forest had the best accuracy, with 97.4%. Support Vector Machine with RBF Kernel (with an accuracy of 97.4%) was better than Extreme Gradient Boosting (with 94.9% accuracy). Conclusion: The outcomes of this study show the potential of regular screening of children for caries risk by experts and finding the risk scores of dental caries for any individual. Therefore, in order to avoid dental caries, it is possible to concentrate on each individual by utilizing machine learning modelling

Circular RNAs: New Players in Cardiomyopathy

Cardiomyopathies comprise a heterogeneous group of cardiac diseases identified by myocardium disorders and diminished cardiac function. They often lead to heart failure or heart transplantation and constitute one of the principal causes of morbidity and mortality worldwide. Circular RNAs (circRNAs) are a novel type of noncoding RNAs. They are covalently closed and single-stranded and derived from the exons and introns of genes by alternative splicing. This specific structure renders them resistant to exonuclease digestion. Many recent studies have demonstrated that circRNAs are highly abundant and conserved and can play central roles in biological functions such as microRNA (miRNA) sponging, splicing, and transcription regulation. Emerging evidence indicates that circRNAs can play significant roles in cardiovascular diseases, including cardiomyopathies. In this review, we briefly describe the current understanding regarding the classification, nomenclature, characteristics, and function of circRNAs and report recent significant findings concerning the roles of circRNAs in cardiomyopathies. Furthermore, we discuss the clinical application potential of circRNAs as the therapeutic targets and diagnostic biomarkers of cardiomyopathies

Women’s sexual health in old age: identification of influencing factors using focus group interviews based on socioecological approach

Despite its importance and numerous benefits, sexual health in the elderly is often a neglected topic due to gender- and age-specific taboos and stereotypes, especially those addressing elderly women. A qualitative study was conducted using focus group interviews to explore the factors influencing the sexual health of elderly women. Twenty-four married elderly women from Tehran with different socioeconomic statuses and a mean age of 65.33 ± 5.32 participated in 7 focus group interviews. Content analysis was done following the deductive method of Elo and Kyngäs (2008) and based on the different levels of socioecological approach. Overall, the findings indicated the importance of influential factors at 5 different levels as the main categories which included 14 sub-categories. In addition to individual factors such as health and age-related changes, the participants regarded the role of sexual adaptation and the level of satisfaction with it in different periods of life as important factors contributing to the elderly women’s sexual health. The role of the husband’s health and the quality of the relationship with him along with the children influences were also found to have a decisive role at the interpersonal level. At higher levels, factors such as having privacy, the attitude of health care workers, and socio-cultural beliefs also contributed to sexual health. Results of this study are expected to provide the basis for future research and strengthen sexual health programs for the elderly based on social structures

Serum Concentrations of Thyroid-Stimulating Hormone, Triiodothyronine, and Thyroxine in Outpatients Infected with SARS-CoV2 in Khuzestan Province, Iran: A Disease Clinical Course Approach

Background and Objectives: The virus SARS-CoV2, which causes COVID-19, affects the endocrine system. This study investigated serum concentrations of the thyroid-stimulating hormone (TSH), triiodothyronine (T3), and thyroxine (T4) in 53 outpatients infected with SARS-CoV2 and 53 non-infected matched participants in Khuzestan Province, Iran. We also examined the possible association of clinical symptoms progression and disease severity with serum concentrations of TSH, T3, and T4. Materials and Methods: A checklist was applied to collect demographic and clinical data. Blood samples were taken for biochemical analysis of serum concentrations of TSH, T3, and T4. Clinical symptoms of the infected outpatients were monitored weekly for 28 days. Results: Our results indicated that, as the severity of the disease increased, the respiratory and pulse rates raised significantly. Additionally, disease severity was significantly different between genders. Specifically, 79.5% of the asymptomatic/mild, and 38.5% of moderate outpatients were men. We also found significantly lower serum T3 but higher T4 in infected outpatients, compared with controls. However, serum TSH did not significantly differ between the two groups. The generalized estimating equation (GEE) analysis revealed no relationship between clinical symptoms progression and disease severity with serum concentrations of TSH, T3, and T4 in our study population. Additionally, GEE analysis showed that the odds ratio of neurological symptoms among women was 2.5 times that of men, the odds ratio of neurological symptoms in illiterates was 10 times higher than that of those without a high-school diploma, and the chance of developing pulmonary symptoms in those without high-school diploma was about 21 times higher than illiterates. Conclusion: In conclusion, this study showed that infected outpatients had significantly lower serum T3 but higher T4 than non-infected participants. There was no relation between symptom progression and disease severity with serum concentrations of TSH, T3, and T4, but educational status and sex significantly affected the chance of neurological and pulmonary symptoms occurring over 28 days. Our results may be used to develop potential therapies to treat COVID-19 disease