Abstract Antioxidant supplements from plants are vital to count the oxidative damage in cells. We assessed the antioxidants and antibacterial activity of green hull of Juglans regia in this study. According to our res...Abstract Antioxidant supplements from plants are vital to count the oxidative damage in cells. We assessed the antioxidants and antibacterial activity of green hull of Juglans regia in this study. According to our results the maximum antibacterial activity was observed in ethanolic extract when compared to other extract. So, the ethanolic extract was studied for antioxidant activity which exhibited high antiradical activity against DPPH, hydroxyl, and nitric oxide radicals. In conclusion, green hull of J. regia showed strong reducing power activity and total antioxidant capacity. The results justify the therapeutic application of plant in the indigenous system of medicine.展开更多
Sleep apnea syndrome(SAS)is a breathing disorder while a person is asleep.The traditional method for examining SAS is Polysomnography(PSG).The standard procedure of PSG requires complete overnight observation in a lab...Sleep apnea syndrome(SAS)is a breathing disorder while a person is asleep.The traditional method for examining SAS is Polysomnography(PSG).The standard procedure of PSG requires complete overnight observation in a laboratory.PSG typically provides accurate results,but it is expensive and time consuming.However,for people with Sleep apnea(SA),available beds and laboratories are limited.Resultantly,it may produce inaccurate diagnosis.Thus,this paper proposes the Internet of Medical Things(IoMT)framework with a machine learning concept of fully connected neural network(FCNN)with k-near-est neighbor(k-NN)classifier.This paper describes smart monitoring of a patient’s sleeping habit and diagnosis of SA using FCNN-KNN+average square error(ASE).For diagnosing SA,the Oxygen saturation(SpO2)sensor device is popularly used for monitoring the heart rate and blood oxygen level.This diagnosis information is securely stored in the IoMT fog computing network.Doctors can care-fully monitor the SA patient remotely on the basis of sensor values,which are efficiently stored in the fog computing network.The proposed technique takes less than 0.2 s with an accuracy of 95%,which is higher than existing models.展开更多
文摘Abstract Antioxidant supplements from plants are vital to count the oxidative damage in cells. We assessed the antioxidants and antibacterial activity of green hull of Juglans regia in this study. According to our results the maximum antibacterial activity was observed in ethanolic extract when compared to other extract. So, the ethanolic extract was studied for antioxidant activity which exhibited high antiradical activity against DPPH, hydroxyl, and nitric oxide radicals. In conclusion, green hull of J. regia showed strong reducing power activity and total antioxidant capacity. The results justify the therapeutic application of plant in the indigenous system of medicine.
基金Taif University Researchers Supporting Project Number(TURSP-2020/98),Taif University,Taif,Saudi Arabia.
文摘Sleep apnea syndrome(SAS)is a breathing disorder while a person is asleep.The traditional method for examining SAS is Polysomnography(PSG).The standard procedure of PSG requires complete overnight observation in a laboratory.PSG typically provides accurate results,but it is expensive and time consuming.However,for people with Sleep apnea(SA),available beds and laboratories are limited.Resultantly,it may produce inaccurate diagnosis.Thus,this paper proposes the Internet of Medical Things(IoMT)framework with a machine learning concept of fully connected neural network(FCNN)with k-near-est neighbor(k-NN)classifier.This paper describes smart monitoring of a patient’s sleeping habit and diagnosis of SA using FCNN-KNN+average square error(ASE).For diagnosing SA,the Oxygen saturation(SpO2)sensor device is popularly used for monitoring the heart rate and blood oxygen level.This diagnosis information is securely stored in the IoMT fog computing network.Doctors can care-fully monitor the SA patient remotely on the basis of sensor values,which are efficiently stored in the fog computing network.The proposed technique takes less than 0.2 s with an accuracy of 95%,which is higher than existing models.
