Ethnopharmacological study of medicinal plants from khoy city of west Azerbaijan-Iran

2-s2.0-85084817073The main objective of this study was to gather information on the use of plants by native people along with therapies suggested by the conventional healers of Khoy. It was analyzed and some important indices including, frequency of citation (FC), cultural importance index (IC), use report (UR) and informants consensus factor (ICF) were calculated. A total of 123 plant taxa belonging to 46 families used for cure of various ailments are reported in this investigation. Among the plants evaluated Apiaceae, Lamiaceae and Asteraceae were the dominant families. The most repeatedly utilized parts are aerial parts (23.2%), followed by leaves (18%). Most frequently used method for consumption has been raw (19.7%), followed by infusion (16.5%). Maximum value of ICF was obtained in digestive system category (with 0.81), followed by respiratory and blood use categories (each with 0.80). Malva neglecta Wallr. was the most cited plant, followed by Mentha longifolia (L.) L. and Plantago major L., Cichorium intybus L. and Salix aegyptiaca L. seem to be the most culturally important plants. The indices like IC and FC could be helpful in selecting important medicinal plant species for further pharmacological investigations in order to find new biologically active compounds. © 2020, National Institute of Science Communication and Information Resources (NISCAIR). All rights reserved.Ege ÜniversitesiAuthors would like to acknowledge the help extended to us by Ms Naeimeh Refahi and Mohadeseh Attari in data collection. Our sincere thanks go to the Ahar Faculty of Agriculture and Natural Resources, University of Tabriz, Ahar from Iran and Ege University as well as Hatay Mustafa Kemal University in Turkey for their full support in this Project and ongoing project collaborations

Sleep paralysis in medieval Persia – the Hidayat of Akhawayni (? –983 AD)

Samad EJ Golzari,1 Kazem Khodadoust,5 Farid Alakbarli,6 Kamyar Ghabili,2 Ziba Islambulchilar,3 Mohammadali M Shoja,1 Majid Khalili,1 Feridoon Abbasnejad,1 Niloufar Sheikholeslamzadeh,7 Nasrollah Moghaddam Shahabi,4 Seyed Fazel Hosseini,2 Khalil Ansarin11Tuberculosis and Lung Disease Research Center, Tabriz University of Medical Sciences; 2Medical Philosophy and History Research Center, Tabriz University of Medical Sciences; 3Department of Pharmaceutics, Tabriz University of Medical Sciences; 4Students' Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran; 5Azerbaijan National Academy of Sciences; 6Institute of Manuscripts of the Azerbaijan National Academy of Sciences, Baku, Azerbaijan; 7Faculty of Law, Central Tehran Branch, Islamic Azad University, Tehran, IranAbstract: Among the first three manuscripts written in Persian, Akhawayni's Hidayat al-muta`allemin fi al-tibb was the most significant work compiled in the 10th century. Along with the hundreds of chapters on hygiene, anatomy, physiology, symptoms and treatments of the diseases of various organs, there is a chapter on sleep paralysis (night-mare) prior to description and treatment of epilepsy. The present article is a review of the Akhawayni's teachings on sleep paralysis and of descriptions and treatments of sleep paralysis by the Greek, medieval, and Renaissance scholars. Akhawayni's descriptions along with other early writings provide insight into sleep paralysis during the Middle Ages in general and in Persia in particular.Keywords: sleep paralysis, night-mare, Akhawayni, Persi

Machine learning derived segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification

Abstract Background Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming manual segmentation which can require human correction. Advances in machine learning have markedly improved automated processing, but have yet to be applied to PC-CMR. This study tested a novel machine learning model for fully automated analysis of PC-CMR aortic flow. Methods A machine learning model was designed to track aortic valve borders based on neural network approaches. The model was trained in a derivation cohort encompassing 150 patients who underwent clinical PC-CMR then compared to manual and commercially-available automated segmentation in a prospective validation cohort. Further validation testing was performed in an external cohort acquired from a different site/CMR vendor. Results Among 190 coronary artery disease patients prospectively undergoing CMR on commercial scanners (84% 1.5T, 16% 3T), machine learning segmentation was uniformly successful, requiring no human intervention: Segmentation time was < 0.01 min/case (1.2 min for entire dataset); manual segmentation required 3.96 ± 0.36 min/case (12.5 h for entire dataset). Correlations between machine learning and manual segmentation-derived flow approached unity (r = 0.99, p < 0.001). Machine learning yielded smaller absolute differences with manual segmentation than did commercial automation (1.85 ± 1.80 vs. 3.33 ± 3.18 mL, p < 0.01): Nearly all (98%) of cases differed by ≤5 mL between machine learning and manual methods. Among patients without advanced mitral regurgitation, machine learning correlated well (r = 0.63, p < 0.001) and yielded small differences with cine-CMR stroke volume (∆ 1.3 ± 17.7 mL, p = 0.36). Among advanced mitral regurgitation patients, machine learning yielded lower stroke volume than did volumetric cine-CMR (∆ 12.6 ± 20.9 mL, p = 0.005), further supporting validity of this method. Among the external validation cohort (n = 80) acquired using a different CMR vendor, the algorithm yielded equivalently small differences (∆ 1.39 ± 1.77 mL, p = 0.4) and high correlations (r = 0.99, p < 0.001) with manual segmentation, including similar results in 20 patients with bicuspid or stenotic aortic valve pathology (∆ 1.71 ± 2.25 mL, p = 0.25). Conclusion Fully automated machine learning PC-CMR segmentation performs robustly for aortic flow quantification - yielding rapid segmentation, small differences with manual segmentation, and identification of differential forward/left ventricular volumetric stroke volume in context of concomitant mitral regurgitation. Findings support use of machine learning for analysis of large scale CMR datasets