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Safflower (Carthamus Tinctorius L.) a Potential Source of Drugs against Cryptococcal Infections, Malaria and Leishmaniasis 被引量:1
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作者 Aknur Turgumbayeva Gulbaram Ustenova +9 位作者 Ubaidilla Datkhayev Khairolla Rahimov Silvijus Abramavicius Agile Tunaityte Kairat Zhakipbekov Kaldanay Kozhanova Saken Tulemissov Ozikhan Ustenova Gulmira Datkayeva Edgaras Stankevicius 《Phyton-International Journal of Experimental Botany》 SCIE 2020年第1期137-146,共10页
In this research we present that Carthamus Tinctorius L.(gen.Asteraceae,otherwise known as Safflower)(Fig.1)may contain agents active in Cryptococcal infections,malaria and Leishmaniasis,as treatment options are becom... In this research we present that Carthamus Tinctorius L.(gen.Asteraceae,otherwise known as Safflower)(Fig.1)may contain agents active in Cryptococcal infections,malaria and Leishmaniasis,as treatment options are becoming scarce due to drug resistance development.Phytochemistry and pharmacological activities(antimicrobial,antimalarial,antileishmanial)of C.tinctorius L.were analyzed.The composition of volatile oil of safflower dried flowers was analyzed by gas chromatography-mass spectrophotometry with flame ionization detector(GC-FID)and in vitro sensitivity assays were performed to assess biological activity.8 known and 3 unknown compounds were detected in the extract(Fig.1).Then the Safflower ointment was manufactured and its acute toxicity study on rats was tested.The volatile oil of C.tinctorius L exhibited activity against Cryptococcus neoformans,Plasmodium falciparum and Leishmania donovani.Safflower volatile oil has anticryptococcal,antimalarial and antileishmanial effects.The prepared ointment had an excellent acute toxicity safety profile. 展开更多
关键词 Carthamus tinctorius L. SAFFLOWER volatile oil GC-FID biological activity
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Artificial Intelligence in Medicine:Real Time Electronic Stethoscope for Heart Diseases Detection
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作者 Batyrkhan Omarov Nurbek Saparkhojayev +6 位作者 Shyrynkyz Shekerbekova Oxana Akhmetova Meruert Sakypbekova Guldina Kamalova Zhanna Alimzhanova Lyailya Tukenova Zhadyra Akanova 《Computers, Materials & Continua》 SCIE EI 2022年第2期2815-2833,共19页
Diseases of the cardiovascular system are one of the major causes of death worldwide.These diseases could be quickly detected by changes in the sound created by the action of the heart.This dynamic auscultations need ... Diseases of the cardiovascular system are one of the major causes of death worldwide.These diseases could be quickly detected by changes in the sound created by the action of the heart.This dynamic auscultations need extensive professional knowledge and emphasis on listening skills.There is also an unmet requirement for a compact cardiac condition early warning device.In this paper,we propose a prototype of a digital stethoscopic system for the diagnosis of cardiac abnormalities in real time using machine learning methods.This system consists of three subsystems that interact with each other(1)a portable digital subsystem of an electronic stethoscope,(2)a decision-making subsystem,and(3)a subsystemfor displaying and visualizing the results in an understandable form.The electronic stethoscope captures the patient’s phonocardiographic sounds,filters and digitizes them,and then sends the resulting phonocardiographic sounds to the decision-making system.The decision-making systemclassifies sounds into normal and abnormal using machine learning techniques,and as a result identifies abnormal heart sounds.The display and visualization subsystem demonstrates the results obtained in an understandable way not only for medical staff,but also for patients and recommends further actions to patients.As a result of the study,we obtained an electronic stethoscope that can diagnose cardiac abnormalities with an accuracy of more than 90%.More accurately,the proposed stethoscope can identify normal heart sounds with 93.5%accuracy,abnormal heart sounds with 93.25%accuracy.Moreover,speed is the key benefit of the proposed stethoscope as 15 s is adequate for examination. 展开更多
关键词 STETHOSCOPE PHONOCARDIOGRAM machine learning classification heart diseases PCG
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