APPLYING NANOPARTICLES FOR TREATING GIARDIA INFECTION: A SYSTEMATIC REVIEW

At present, chemotherapy with some drugs such as nitroimidazoes derivatives is the preferred treatment for giardiasis. However, these agents are associated with adverse side effects ranging from nausea to possible genotoxicity. The present investigation was designed to systematically review the in vitro, in vivo, and clinical studies about the efficacy of nanoparticles against giardiasis. The study was carried out based on the 06-PRISMA guideline and registered in the CAMARADES-NC3Rs Preclinical Systematic Review and Meta-analysis Facility (SyRF) database. The search was performed in five English databases, including Scopus, PubMed, Web of Science, EMBASE, and Google Scholar, without time limitation for publications around the world about anti-Giardia effects of all organic and inorganic nanoparticles without date limitation in order to identify all the published articles. The searched words and terms were “Giardiasis”, “Giardia lamblia”, “Giardia intestinalis”, “Giardia duodenalis”, “nanoparticles”, “nanomedicine”, “in vitro”, in vivo”, and “clinical trial”. Out of 312 papers, 10 papers, including 4 in vitro (40.0%), 5 in vivo (50.0%), and 1 in vitro/in vivo (10.0%) up to 2021 met the inclusion criteria for discussion in this systematic review. The most common type of nanoparticles was metal nanoparticles (5 studies, 50.0%) such as silver, gold, etc., followed by organic nanoparticles such as chitosan nanoparticles (4 studies, 40.0%). The results of this review study showed the high efficacy of a wide range of organic and non-organic NPs against giardiasis, indicating that nanoparticles could be considered as an alternative and complementary resource for treating giardiasis, since they have no significant toxicity. However, more studies are required to elucidate this conclusion, especially in clinical systems

Developing a national formulary based on a unified payment system in Iran

Introduction: The national formulary plays an important role in increasing access to medicine and correct drug information based on national considerations. However, this study aimed to provide a model for development of national formulary based on a unified payment system in Iran. Methods: This study used a combination of descriptive, comparative, and qualitative methods. It was an applied developmental study in 2016. The data were collected using a form based on the World Health Organization’s (WHO) standard model for national formulary. Using census method, all national formulary of countries available on the WHO website (n=14) were selected for study. The similarities and differences of national formulary of these countries and Iran were compared with the WHO’s standard model. Then, Iran’s national formulary content was determined using comparative study results and opinions of an expert panel consisting of 12 faculty members and assistants of the medicine economy. Results: Results showed that the content of national formulary in studied countries is consistent with the WHO model. They consisted of four parts: introductory information, medicine information and monograph, appendices, and alphabetical index. In the introductory, which was out of elements of the WHO, the drug selection and advice to patients’ criteria in the preliminary information part of used dose and its side effects in drug monograph and information had the highest frequency. The lowest frequency was for medicine pharmacology and pharmacokinetics in the medicine monograph section. The most common data element in the appendix was related to drug interactions, and the lowest frequency was related to hepatic impairment and renal impairment. All data elements were confirmed by an expert panel. They stated that, after the component of common brand name, the drug cost effectiveness and drug code are necessary for each drug in the drug monograph section. Conclusion: This study provided an updated model and structure for developing national formulary of Iran based on a unified reimbursement system, WHO model, comparative study of national formulary of selected countries, and the opinion of an expert panel in the field of medicine economy. This model may provide reliable information for health employees and managers and improve the effective and safe use of medicines. Also, the creation of drug formulary based on this model and using it may facilitate the selection of standard and high- quality medicines from among different companies and brands, comparing them with each other, prescribing high-quality medicine with lower price, and avoiding the impact from advertisement

Development of an intelligent clinical decision support system for the early prediction of diabetic nephropathy

Background: Diabetic nephropathy (DN) is the most common microvascular complication of diabetes mellitus (DM) and is identified as a leading cause of the end-stage renal disease (ESRD). Considering the importance of early prediction of individuals at risk of this complication, the use of intelligent models through machine learning (ML) algorithms can be helpful. Therefore, this study aimed to identify the influential variables in predicting DN and fed them as inputs to develop an ML-based decision support system (DSS) for DN diagnosis. Methods: The data of 327 patients with diabetes (types 1 and 2) were retrospectively analyzed. After data preparation, the genetic algorithm (GA) feature selection method was used to identify the predictor variables affecting DN. Then, several ML algorithms, including the support vector machine (SVM), decision tree (DT), K-nearest neighbors (KNN), and artificial neural networks (ANN) were used to train predictive models based on the selected features. Afterward, the performance of the developed models was evaluated using sensitivity, specificity, and accuracy criteria in 10 independent runs. Finally, the DSS was developed based on the best fit model in the C# programming language. Results: Our findings illustrated that age, hemoglobin A1c (HbA1c) test, diastolic arterial pressure (DAP), systolic arterial pressure (SAP), fasting glycemia rate (FGR), and DM involvement time were the most important factors in predicting DN. Moreover, to predict the DN, GA combined with the DT algorithm obtained the highest performance in terms of accuracy, sensitivity, specificity, and area under the curve (AUC), equal to 98.9, 98.6, 99.2, and 98.9%, respectively. Conclusions: The results revealed that GA combined with the DT classifier predicted DN with significant accuracy. Thus, the DSS developed based on DT can be considered a reliable tool to help physicians make decisions. Future studies are warranted to further validate the applicability of our model in clinical settings