Effects of Rosa canina L. fruit on glycemia and lipid profile in type 2 diabetic patients: A randomized, double-blind, placebo-controlled clinical trial

Background: Rosa canina L. (rose hip) has been traditionally used to treat diabetes mellitus in Iran. However, no scientific human study has determined its efficacy in diabetic patients. Objective: This study was conducted to evaluate the efficacy and safety of R. canina fruit aqueous extract in type 2 diabetic patients. Methods: Sixty patients with type 2 diabetes, aged 35-60 years with fasting blood glucose levels between 130 to 200 mg/dL and HbA1c between 7-9 despite using conventional oral hypoglycemic drugs were divided randomly to two groups. Two groups of 25 and 23 patients completing the trial received 750 mg R. canina fruit extract and 750 mg toast powder as placebo two times a day respectively for three months. Fasting blood glucose (FBG) and glycosylated hemoglobin (HbA1c) as primary outcomes and postprandial blood glucose (PBG), lipid profile and hepatic and renal function tests as secondary outcomes were determined at baseline and at endpoint of treatment. The patients were asked to note down any gastrointestinal or other side effects during the study. Results: The FBG level decreased significantly (P = 0.002) in R. canina group after 3 months compared to the baseline. In addition total cholesterol/HDL-C was significantly (P = 0.02) decreased in the R. canina group compared to the baseline. Other blood parameters were not significantly changed during the study compared with placebo and baseline. No serious side effects were reported in both groups during the study. Conclusion: Rosa canina 3-month administration to type 2 diabetic patients may reduce fasting blood glucose and total cholesterol/HDL-C without any side effect

A machine learning-based system for detecting leishmaniasis in microscopic images

Background Leishmaniasis, a disease caused by a protozoan, causes numerous deaths in humans each year. After malaria, leishmaniasis is known to be the deadliest parasitic disease globally. Direct visual detection of leishmania parasite through microscopy is the frequent method for diagnosis of this disease. However, this method is time-consuming and subject to errors. This study was aimed to develop an artificial intelligence-based algorithm for automatic diagnosis of leishmaniasis. Methods We used the Viola-Jones algorithm to develop a leishmania parasite detection system. The algorithm includes three procedures: feature extraction, integral image creation, and classification. Haar-like features are used as features. An integral image was used to represent an abstract of the image that significantly speeds up the algorithm. The adaBoost technique was used to select the discriminate features and to train the classifier. Results A 65 recall and 50 precision was concluded in the detection of macrophages infected with the leishmania parasite. Also, these numbers were 52 and 71, respectively, related to amastigotes outside of macrophages. Conclusion The developed system is accurate, fast, easy to use, and cost-effective. Therefore, artificial intelligence might be used as an alternative for the current leishmanial diagnosis methods