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Background: Oral vesiculobullous diseases (VBD) consist of disorders which result in blister formation. VBDs are considered to be a multifactorial disease process. Corticosteroids and immunomodulating drugs are used for disease management. Methods: The research team assessed peer-reviewed journal articles related to the dietary details of oral lichen planus, mucous membrane pemphigoid, and pemphigus vulgaris. The articles included in this study comprised literature reviews, randomized controlled trials, case studies, and systematic reviews published in the last 26 years (1997-2023). Results: The literature review revealed the promising role that nutrition plays in the management of VBD. Avoiding triggering food and maintaining a well-nourished diet can decrease the symptoms noted during active episodes of VBDs. Oral hygiene maintenance decreases the presence of infection which is beneficial to the recovery process. Conclusion: Studies have shown that the addition of essential vitamins, minerals, and supplements to the diet can aid in the management of VBDs, however, further research is needed. Practical Implications: Dental providers can use this guide as a way to educate patients on the dietary adjustments needed after the diagnosis of one of the vesiculobullous disorders. [ABSTRACT FROM AUTHOR]

Background Oral vesiculobullous diseases (VBD) consist of disorders which result in blister formation. VBDs are considered to be a multifactorial disease process. Corticosteroids and immunomodulating drugs are used for disease management.Methods The research team assessed peer-reviewed journal articles related to the dietary details of oral lichen planus, mucous membrane pemphigoid, and pemphigus vulgaris. The articles included in this study comprised literature reviews, randomized controlled trials, case studies, and systematic reviews published in the last 26 years (1997-2023).Results The literature review revealed the promising role that nutrition plays in the management of VBD. Avoiding triggering food and maintaining a well-nourished diet can decrease the symptoms noted during active episodes of VBDs. Oral hygiene maintenance decreases the presence of infection which is beneficial to the recovery process.Conclusion Studies have shown that the addition of essential vitamins, minerals, and supplements to the diet can aid in the management of VBDs, however, further research is needed.Practical Implications Dental providers can use this guide as a way to educate patients on the dietary adjustments needed after the diagnosis of one of the vesiculobullous disorders.

The subject matter of this research revolves around addressing the escalating global health threat posed by cardiovascular diseases, which have become a leading cause of mortality in recent times. The goal of this study was to develop a comprehensive diet recommendation system tailored explicitly for cardiac patients. The primary task of this study is to assist both medical practitioners and patients in developing effective dietary strategies to counter heart-related ailments. To achieve this goal, this study leverages the capabilities of machine learning (ML) to extract valuable insights from extensive datasets. This approach involves creating a sophisticated diet recommendation framework using diverse ML techniques. These techniques are meticulously applied to analyze data and identify optimal dietary choices for individuals with cardiac concerns. In pursuit of actionable dietary recommendations, classification algorithms are employed instead of clustering. These algorithms categorize foods as "heart-healthy" or "not heart-healthy," aligned with cardiac patients’ specific needs. In addition, this study delves into the intricate dynamics between different food items, exploring interactions such as the effects of combining protein- and carbohydrate-rich diets. This exploration serves as a focal point for in-depth data mining, offering nuanced perspectives on dietary patterns and their impact on heart health. The method used central to the diet recommendation system is the implementation of the Neural Random Forest algorithm, which serves as the cornerstone for generating tailored dietary suggestions. To ensure the system’s robustness and accuracy, a comparative assessment involving other prominent ML algorithms—namely Random Forest, Naïve Bayes, Support Vector Machine, and Decision Tree, was conducted. The results of this analysis underscore the superiority of the proposed -based system, demonstrating higher overall accuracy in delivering precise dietary recommendations compared with its counterparts. In conclusion, this study introduces an advanced diet recommendation system using ML, with the potential to notably reduce cardiac disease risk. By providing evidence-based dietary guidance, the system benefits both healthcare professionals and patients, showcasing the transformative capacity of ML in healthcare. This study underscores the significance of meticulous data analysis in refining dietary decisions for individuals with cardiac conditions.

The subject matter of this research revolves around addressing the escalating global health threat posed by cardiovascular diseases, which have become a leading cause of mortality in recent times. The goal of this study was to develop a comprehensive diet recommendation system tailored explicitly for cardiac patients. The primary task of this study is to assist both medical practitioners and patients in developing effective dietary strategies to counter heart-related ailments. To achieve this goal, this study leverages the capabilities of machine learning (ML) to extract valuable insights from extensive datasets. This approach involves creating a sophisticated diet recommendation framework using diverse ML techniques. These techniques are meticulously applied to analyze data and identify optimal dietary choices for individuals with cardiac concerns. In pursuit of actionable dietary recommendations, classification algorithms are employed instead of clustering. These algorithms categorize foods as "heart-healthy" or "not heart-healthy," aligned with cardiac patients' specific needs. In addition, this study delves into the intricate dynamics between different food items, exploring interactions such as the effects of combining protein- and carbohydrate-rich diets. This exploration serves as a focal point for indepth data mining, offering nuanced perspectives on dietary patterns and their impact on heart health. The method used central to the diet recommendation system is the implementation of the Neural Random Forest algorithm, which serves as the cornerstone for generating tailored dietary suggestions. To ensure the system's robustness and accuracy, a comparative assessment involving other prominent ML algorithms--namely Random Forest, Naïve Bayes, Support Vector Machine, and Decision Tree, was conducted. The results of this analysis underscore the superiority of the proposed -based system, demonstrating higher overall accuracy in delivering precise dietary recommendations compared with its counterparts. In conclusion, this study introduces an advanced diet recommendation system using ML, with the potential to notably reduce cardiac disease risk. By providing evidence-based dietary guidance, the system benefits both healthcare professionals and patients, showcasing the transformative capacity of ML in healthcare. This study underscores the significance of meticulous data analysis in refining dietary decisions for individuals with cardiac conditions. [ABSTRACT FROM AUTHOR]

We mean to use the new headway in innovation to foster dietary as evaluation arrangements which is a more advantageous way to deal with screen every day food admission and control dietary patterns. Food, calorie and sustenance estimation framework is extremely useful for people groups to gauge and deal with their every day food admission. AI will make proposals dependent on the things that are of client's advantage or loved by them before. Additionally these calculations are utilized to suggest the food that the client might like. The suggested food and food in standard plans set by client's own circumstance are in a similar group, which meets the client's healthful equilibrium. We feature the issue of determination of legitimate eating regimen that should satisfy everybody's nourishing prerequisites. [ABSTRACT FROM AUTHOR]

Obesity is considered an epidemic that is continuously growing around the world. Heart diseases, diabetes, and bone and joint diseases are some of the diseases that people who are overweight or obese can develop. One of the vital causes of those disorders is poor nutrition education; there is no raising awareness about eating healthy food and practicing physical activities to burn off the excess energy. Therefore, it is necessary to use new technologies to build methods/tools that help people to overcome these avoidable nutrition disorders. For this reason, we implemented a recommendation engine capable of identifying the different levels of overweight and obesity in users and providing dietary strategies to mitigate them. To do so, we defined the Ontology of Dietary Recommendations (ODR) with axioms to model recipes, ingredients, and a set of diets to assist people who suffer from obesity. We validated the defined model by using a real set of individuals who were anonymized. A panel of advisors evaluated each individual record and suggested the most appropriate diets from those included in the ontology. Then, the proposed system was asked to also provide diet recommendations for each individual, which were compared with those proposed by the advisors (ground truth), reaching a mean accuracy of 87%. [ABSTRACT FROM AUTHOR]

Introduction: The incremental swift pervasiveness and the associated health consequences, obesity is perceived as one of the most genuine health problems of the early 21st century. The predominance of obese and flabbiness expanded considerably over current decades. The reasons for stoutness are dramatic and multifactorial. Research on childhood obesity has shown the role of race, ethnicity, and social factors, for example, family income, family structure, and community protection. Aim: To devise an expert system that will provide timely educational resources to the children, parents and schools to educate about the impact of junk food, low physical activity and poor lifestyle, as well as effect of excessive weight on overall health of a person in the form of short videos. Materials and Methods: To create an expert system data will be collected, preprocessed and analysed statistically. Sample schools from urban and semi-urban regions would be identified for the study. Data from various sources will be integrated for better data analysis and a questionnaire will be structured to assess subject readiness. After assessing web-based and mobile-based expert system will be developed for counseling and bringing awareness on behaviour and lifestyle of the participant. Conclusion: The diet of kids is a serious concern now-a-days. The obesity leads to health concerns especially among even in the younger generations aging 5-12 years. There is an urgent need of system that can automatically track obese stages and recommend appropriate diet.

Introduction: The incremental swift pervasiveness and the associated health consequences, obesity is perceived as one of the most genuine health problems of the early 21st century. The predominance of obese and flabbiness expanded considerably over current decades. The reasons for stoutness are dramatic and multifactorial. Research on childhood obesity has shown the role of race, ethnicity, and social factors, for example, family income, family structure, and community protection. Aim: Proposed expert system will provide timely educational resources to the children, parents and schools to educate about the impact of junk food, low physical activity and poor lifestyle, as well as effect of excessive weight on overall health of a person in the form of short videos. Materials and Methods: To create an expert system data will be collected, preprocessed and analysed statistically. Sample schools from urban and semi-urban regions would be identified for the study. Data from various sources will be integrated for better data analysis and a questionnaire will be structured to assess subject readiness. After assessing webbased and mobile-based expert system will be developed for counseling and bringing awareness on behaviour and lifestyle of the participant. Conclusion: The diet of kids is a serious concern now-a-days. The obesity leads to health concerns especially among even in the younger generations aging 5-12 years. There is an urgent need of system that can automatically track obese stages and recommend appropriate diet. [ABSTRACT FROM AUTHOR]

Obesity is considered an epidemic that is continuously growing around the world. Heart diseases, diabetes, and bone and joint diseases are some of the diseases that people who are overweight or obese can develop. One of the vital causes of those disorders is poor nutrition education; there is no raising awareness about eating healthy food and practicing physical activities to burn off the excess energy. Therefore, it is necessary to use new technologies to build methods/tools that help people to overcome these avoidable nutrition disorders. For this reason, we implemented a recommendation engine capable of identifying the different levels of overweight and obesity in users and providing dietary strategies to mitigate them. To do so, we defined the Ontology of Dietary Recommendations (ODR) with axioms to model recipes, ingredients, and a set of diets to assist people who suffer from obesity. We validated the defined model by using a real set of individuals who were anonymized. A panel of advisors evaluated each individual record and suggested the most appropriate diets from those included in the ontology. Then, the proposed system was asked to also provide diet recommendations for each individual, which were compared with those proposed by the advisors (ground truth), reaching a mean accuracy of 87%.

We systematically reviewed interventions substituting sucrose for other macronutrients in apparently healthy adults to assess impact on cardiometabolic risk indicators. Multiple databases were searched to January 2012 and abstracts assessed by 2 reviewers. Twenty-five studies (29 papers) met inclusion criteria but varied in quality and duration. Weaknesses included small subject numbers, unclear reporting of allocation, unusual dietary regimes, differences in energy intake, fat composition or fibre between conditions, unhealthy subjects, heterogeneity of results, and selective reporting. Insufficient data were available to draw reliable conclusions except with regard to the substitution of sucrose for starch, where effects on plasma lipids were inconsistent, mostly explicable by other factors, or nonsignificant. Based on fewer studies, there was little evidence for significant effects on plasma glucose or insulin. Sucrose substitution for starch up to 25% energy does not appear to have adverse effects on cardiometabolic risk indicators in apparently healthy adults. Furthermore, there is no consistent evidence that restricting sucrose in an isoenergetic diet would affect risk indicators, except perhaps in people with certain preexisting metabolic abnormalities. Larger, high-quality studies, lasting several months and studying a wider range of outcomes, are needed in order to provide evidence on which to base public health initiatives. [ABSTRACT FROM AUTHOR]

It has been widely pointed out that classical ontology is not sufficient to deal with imprecise and vague knowledge for some real-world applications like personal diabetic-diet recommendation. On the other hand, fuzzy ontology can effectively help to handle and process uncertain data and knowledge. This paper proposes a novel ontology model, which is based on interval type-2 fuzzy sets (T2FSs), called type-2 fuzzy ontology (T2FO), with applications to knowledge representation in the field of personal diabetic-diet recommendation. The T2FO is composed of 1) a type-2 fuzzy personal profile ontology (type-2 FPPO); 2) a type-2 fuzzy food ontology (type-2 FFO); and 3) a type-2 fuzzy-personal food ontology (type-2 FPFO). In addition, the paper also presents a T2FS-based intelligent diet-recommendation agent (IDRA), including 1) T2FS construction; 2) a T2FS-based personal ontology filter; 3) a T2FS-based fuzzy inference mechanism; 4) a T2FS-based diet-planning mechanism; 5) a T2FS-based menu-recommendation mechanism; and 6) a T2FS-based semantic-description mechanism. In the proposed approach, first, the domain experts plan the diet goal for the involved diabetes and create the nutrition facts of common Taiwanese food. Second, the involved diabetics are requested to routinely input eaten items. Third, the ontology-creating mechanism constructs a T2FO, including a type-2 FPPO, a type-2 FFO, and a set of type-2 FPFOs. Finally, the T2FS-based IDRA retrieves the built T2FO to recommend a personal diabetic meal plan. The experimental results show that the proposed approach can work effectively and that the menu can be provided as a reference for the involved diabetes after diet validation by domain experts. [ABSTRACT FROM AUTHOR]