Focus on the ethnobotany of north Moroccan sage, false yellowhead, and carrot: insights into their pharmacological potential

Introduction: Medicinal plants, including spontaneous or cultured herbaceous and forest products, represent an inexhaustible source of traditional and effective remedies thanks to their active major compounds. The present work consists of an ethnobotanical study of three species namely, Daucus carota, Dittrichia viscosa, and Salvia officinalis, commonly used in Taounate region (Northern Morocco) to treat various diseases. Methods: An ethnopharmacological survey was conducted in Taounate region during a period of three months from January to March 2022, using semi-structured individual interviews. Then, the collected data were analyzed statistically using Microsoft Office software "Excel 2013" and System Package for Social Sciences (SPSS). Results: Leaves, flowers, and stems were the most common parts used to prepare traditional remedies. Decoction, infusion, and cataplasm were the most used preparation methods, and the oral route was the most common method of administration for the studied plants. Moreover, the plants intervened in the treatment of digestive, genito-urinary, dermatological, neurological, and metabolic diseases. Conclusion: Information collected during this study shed light on the interesting know-how in traditional herbal medicine in the study area, and on the frequent use of medicinal plants as an alternative to synthetic drugs by the population of Taounate, to treat different diseases. Thereafter, the study’s results can constitute an important database for pharmacologists, phytochemists, toxicologists, and clinical researchers for the development of new drugs based on natural substances

Machine Learning for Predicting the Risk for Childhood Asthma Using Prenatal, Perinatal, Postnatal and Environmental Factors

The prevalence rate for childhood asthma and its associated risk factors vary significantly across countries and regions. In the case of Morocco, the scarcity of available medical data makes scientific research on diseases such as asthma very challenging. In this paper, we build machine learning models to predict the occurrence of childhood asthma using data from a prospective study of 202 children with and without asthma. The association between different factors and asthma diagnosis is first assessed using a Chi-squared test. Then, predictive models such as logistic regression analysis, decision trees, random forest and support vector machine are used to explore the relationship between childhood asthma and the various risk factors. First, data were pre-processed using a Chi-squared feature selection, 19 out of the 36 factors were found to be significantly associated (p-value < 0.05) with childhood asthma; these include: history of atopic diseases in the family, presence of mites, cold air, strong odors and mold in the child’s environment, mode of birth, breastfeeding and early life habits and exposures. For asthma prediction, random forest yielded the best predictive performance (accuracy = 84.9%), followed by logistic regression (accuracy = 82.57%), support vector machine (accuracy = 82.5%) and decision trees (accuracy = 75.19%). The decision tree model has the advantage of being easily interpreted. This study identified important maternal and prenatal risk factors for childhood asthma, the majority of which are avoidable. Appropriate steps are needed to raise awareness about the prenatal risk factors