RESUMEN
Objective: Unsafe medication practices and medication errors are a major cause of harm in healthcare systems around the world. This study aimed to explore the factors that influence the risk of medication and provide medication risk evaluation model for adults in Shanxi province, China. Methods: The data was obtained from the provincial questionnaire from May to December 2022, relying on the random distribution of questionnaires and online questionnaires by four hospitals in Shanxi Province. Multiple linear regression analysis was used to explore the factors affecting the KAP score of residents. Univariate and multivariate logistic regression was used to determine the independent risk factors, and the nomogram was verified by receiver operating characteristic curve, calibration and decision curve analysis. Results: A total of 3,388 questionnaires were collected, including 3,272 valid questionnaires. The average scores of drugs KAP were 63.2 ± 23.04, 33.05 ± 9.60, 23.67 ± 6.75 and 33.16 ± 10.87, respectively. On the evaluation criteria of the questionnaire, knowledge was scored "fair", attitude and practice were scored "good". Sex, monthly income, place of residence, insurance status, education level, and employment were regarded as independent risk factors for medication and a nomogram was established by them. Conclusion: Males, low-income, and low-educated people are important factors affecting the risk of medication. The application of the model can help residents understand the risk of their own medication behavior and reduce the harm of medication.
RESUMEN
Hawthorn (CFS) has commonly been applied as an important traditional Chinese medicine and food for thousands of years. The raw material of CFS is commonly processed by stir-frying to obtain yellow (CFY), dark brown (CFD), and carbon dark (CFC) colored products, which are used for different clinical uses. In this study, an intelligent sensory system (ISS) was used to obtain the color, gas, and flavor samples data, which were further employed to develop a novel and accurate method for the identification of CFS and its processed products using principal component analysis. Moreover, this research developed a model of an artificial neural network, which could be used to predict the total organic acid, total flavonoids, citric acid, hyperin, and 5-hydroxymethyl furfural via determination of the color, odor, and taste of a sample. In conclusion, the ISS and the artificial neural network are useful tools for rapid, accurate, and effective discrimination of CFS and its processed products.
RESUMEN
Areca nut, commonly known locally as Semen Arecae (SA) in China, has been used as an important Chinese herbal medicine for thousands of years. The raw SA (RAW) is commonly processed by stir-baking to yellow (SBY), stir-baking to dark brown (SBD), and stir-baking to carbon dark (SBC) for different clinical uses. In our present investigation, intelligent sensory technologies consisting of computer vision (CV), electronic nose (E-nose), and electronic tongue (E-tongue) were employed in order to develop a novel and accurate method for discrimination of SA and its processed products. Firstly, the color parameters and electronic sensory responses of E-nose and E-tongue of the samples were determined, respectively. Then, indicative components including 5-hydroxymethyl furfural (5-HMF) and arecoline (ARE) were determined by HPLC. Finally, principal component analysis (PCA) and discriminant factor analysis (DFA) were performed. The results demonstrated that these three instruments can effectively discriminate SA and its processed products. 5-HMF and ARE can reflect the stir-baking degree of SA. Interestingly, the two components showed close correlations to the color parameters and sensory responses of E-nose and E-tongue. In conclusion, this novel method based on CV, E-nose, and E-tongue can be successfully used to discriminate SA and its processed products.