Objective:To analyze scrum leptin levels in patients with malaria falciparum and compare them with healthy controls and correlate with development and outcome of malaria infection.Methods:Sixty cases of malaria falcip...Objective:To analyze scrum leptin levels in patients with malaria falciparum and compare them with healthy controls and correlate with development and outcome of malaria infection.Methods:Sixty cases of malaria falciparum were included in this study as patients.Thirty healthy individuals of comparable age,racial and body mass index were taken as controls.All patients were diagnosed by clinical picture and the presence of malaria parasites in blood film.Estimation of liver function test,kidney function test,complete blood count,fasting blood sugar,fasting serum insulin,pro-inflammatory cytokine tumor necrosis factor alpha(TNFα)and interleukin 1(IL1),estimation of morning serum leptin and calculation of body mass index(kg/m^2)were done in both groups on the day of admission,on discharge and 7 d after discharge.Results:At admission,leptin levels were significantly higher in patients group than in control while lasting serum insulin levels were not significantly different between the two groups.There were significant increases as regard to TNFαand IL1 in malaria patients.Significant differences were observed between the control and the patient group for leptin,TNFαand IL1 at the time of admission and discharge.After discharge for 7 d.a significant decline in scrum leptin levels,TNFαand IL1 in the patients group was observed as compared with time of admission and time of discharge,a positive correlation between serum leptin levels and TNFαand IL1.Conclusions:Leptin hormone level might play an important role in development and outcome of malaria infection.展开更多
Objective:To determine the effects ofCalotropis proceralatex on pubertal traits of immature male Wistar rats.Methods: A total of 30 immature male Wistar rats aged 3 weeks old were grouped randomly into 5 groups: group...Objective:To determine the effects ofCalotropis proceralatex on pubertal traits of immature male Wistar rats.Methods: A total of 30 immature male Wistar rats aged 3 weeks old were grouped randomly into 5 groups: group A (control group) was offered distilled water as a placebo;group B was daily oral dosed with suspension ofCalotropis procera latex at a dose rate of 5 mg/kg body weight(BW), group C 10 mg/kg BW;group D 15 mg/kg BW and group E 20 mg/kg BW. The rats were weighed daily to adjust the dose and record the BW changes, and the treatment continued for 4 weeks;thereafter, rats were sacrificed. Serum samples (n=30) were collected from all rats and kept frozen until assayed for reproductive hormones. Furthermore, the testes and epididymae were weighed;epididymal sperms were counted;sperm motility & abnormality were estimated;and histopathological sections of the testes were prepared. Results: The results of this study showed that oral dosing of immature male rats with Calotropis proceralatex at doses rate 10 mg/kg BW significantly (P<0.05) reduced the growth rate, BW, testicular & epididymal weights, the level of most of reproductive hormones as well as the sperm traits examined;however, it significantly (P<0.05) augmented the abnormalities of spermatozoa and the seminiferous epithelium.Conclusions: Latex ofCalotropis procera contains substances that have anti-androgenic activities and/or endocrine disrupting effects. If these substances are purified and identified, they can be used as male contraceptives.展开更多
Heart disease prognosis(HDP)is a difficult undertaking that requires knowledge and expertise to predict early on.Heart failure is on the rise as a result of today’s lifestyle.The healthcare business generates a vast ...Heart disease prognosis(HDP)is a difficult undertaking that requires knowledge and expertise to predict early on.Heart failure is on the rise as a result of today’s lifestyle.The healthcare business generates a vast volume of patient records,which are challenging to manage manually.When it comes to data mining and machine learning,having a huge volume of data is crucial for getting meaningful information.Several methods for predictingHDhave been used by researchers over the last few decades,but the fundamental concern remains the uncertainty factor in the output data,aswell as the need to decrease the error rate and enhance the accuracy of HDP assessment measures.However,in order to discover the optimal HDP solution,this study compares multiple classification algorithms utilizing two separate heart disease datasets from the Kaggle repository and the University of California,Irvine(UCI)machine learning repository.In a comparative analysis,Mean Absolute Error(MAE),Relative Absolute Error(RAE),precision,recall,fmeasure,and accuracy are used to evaluate Linear Regression(LR),Decision Tree(J48),Naive Bayes(NB),Artificial Neural Network(ANN),Simple Cart(SC),Bagging,Decision Stump(DS),AdaBoost,Rep Tree(REPT),and Support Vector Machine(SVM).Overall,the SVM classifier surpasses other classifiers in terms of increasing accuracy and decreasing error rate,with RAE of 33.2631 andMAEof 0.165,the precision of 0.841,recall of 0.835,f-measure of 0.833,and accuracy of 83.49 percent for the dataset gathered from UCI.The SC improves accuracy and reduces the error rate for the Kaggle dataset,which is 3.30%for RAE,0.016 percent for MAE,0.984%for precision,0.984 percent for recall,0.984 percent for f-measure,and 98.44%for accuracy.展开更多
Prognosis of HD is a complex task that requires experience andexpertise to predict in the early stage. Nowadays, heart failure is rising dueto the inherent lifestyle. The healthcare industry generates dense records of...Prognosis of HD is a complex task that requires experience andexpertise to predict in the early stage. Nowadays, heart failure is rising dueto the inherent lifestyle. The healthcare industry generates dense records ofpatients, which cannot be managed manually. Such an amount of data is verysignificant in the field of data mining and machine learning when gatheringvaluable knowledge. During the last few decades, researchers have used differentapproaches for the prediction of HD, but still, the major problem is theuncertainty factor in the output data and also there is a need to reduce theerror rate and increase the accuracy of evaluation metrics for HDP. However,this study largess the comparative analysis of diverse classification algorithmsgoing on two different heart disease datasets taken from the Kaggle repositoryand University of California, Irvine (UCI) machine learning repository tofind the best solution for HDP. Going through comparative analysis, tenclassifiers;LR, J48, NB, ANN, SC, Bagging, DS, AdaBoost, REPT, and SVMare evaluated using MAE, RAE, precision, recall, f-measure, and accuracy.The overall finding indicates that for the dataset taken from UCI, the SVMclassifier performs well as compared to other classifiers in terms of increasingaccuracy and reducing error rate that is 33.2631 for RAE, and 0.165 forMAE, 0.841 for precision, 0.835 for recall, 0.833 for f-measure and 83.49%for accuracy. Whereas for dataset taken from Kaggle, the SC performs well interms of increasing accuracy and reducing error rate that is 3.30% for RAE,0.016 for MAE, 0.984 for precision, 0.984 for recall, 0.984 for f-measure, and98.44% for accuracy.展开更多
基金supported by College of Health Sciences(Grant No,HS-10-01)
文摘Objective:To analyze scrum leptin levels in patients with malaria falciparum and compare them with healthy controls and correlate with development and outcome of malaria infection.Methods:Sixty cases of malaria falciparum were included in this study as patients.Thirty healthy individuals of comparable age,racial and body mass index were taken as controls.All patients were diagnosed by clinical picture and the presence of malaria parasites in blood film.Estimation of liver function test,kidney function test,complete blood count,fasting blood sugar,fasting serum insulin,pro-inflammatory cytokine tumor necrosis factor alpha(TNFα)and interleukin 1(IL1),estimation of morning serum leptin and calculation of body mass index(kg/m^2)were done in both groups on the day of admission,on discharge and 7 d after discharge.Results:At admission,leptin levels were significantly higher in patients group than in control while lasting serum insulin levels were not significantly different between the two groups.There were significant increases as regard to TNFαand IL1 in malaria patients.Significant differences were observed between the control and the patient group for leptin,TNFαand IL1 at the time of admission and discharge.After discharge for 7 d.a significant decline in scrum leptin levels,TNFαand IL1 in the patients group was observed as compared with time of admission and time of discharge,a positive correlation between serum leptin levels and TNFαand IL1.Conclusions:Leptin hormone level might play an important role in development and outcome of malaria infection.
文摘Objective:To determine the effects ofCalotropis proceralatex on pubertal traits of immature male Wistar rats.Methods: A total of 30 immature male Wistar rats aged 3 weeks old were grouped randomly into 5 groups: group A (control group) was offered distilled water as a placebo;group B was daily oral dosed with suspension ofCalotropis procera latex at a dose rate of 5 mg/kg body weight(BW), group C 10 mg/kg BW;group D 15 mg/kg BW and group E 20 mg/kg BW. The rats were weighed daily to adjust the dose and record the BW changes, and the treatment continued for 4 weeks;thereafter, rats were sacrificed. Serum samples (n=30) were collected from all rats and kept frozen until assayed for reproductive hormones. Furthermore, the testes and epididymae were weighed;epididymal sperms were counted;sperm motility & abnormality were estimated;and histopathological sections of the testes were prepared. Results: The results of this study showed that oral dosing of immature male rats with Calotropis proceralatex at doses rate 10 mg/kg BW significantly (P<0.05) reduced the growth rate, BW, testicular & epididymal weights, the level of most of reproductive hormones as well as the sperm traits examined;however, it significantly (P<0.05) augmented the abnormalities of spermatozoa and the seminiferous epithelium.Conclusions: Latex ofCalotropis procera contains substances that have anti-androgenic activities and/or endocrine disrupting effects. If these substances are purified and identified, they can be used as male contraceptives.
基金Authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for this research at Najran University,Kingdom of Saudi Arabia.
文摘Heart disease prognosis(HDP)is a difficult undertaking that requires knowledge and expertise to predict early on.Heart failure is on the rise as a result of today’s lifestyle.The healthcare business generates a vast volume of patient records,which are challenging to manage manually.When it comes to data mining and machine learning,having a huge volume of data is crucial for getting meaningful information.Several methods for predictingHDhave been used by researchers over the last few decades,but the fundamental concern remains the uncertainty factor in the output data,aswell as the need to decrease the error rate and enhance the accuracy of HDP assessment measures.However,in order to discover the optimal HDP solution,this study compares multiple classification algorithms utilizing two separate heart disease datasets from the Kaggle repository and the University of California,Irvine(UCI)machine learning repository.In a comparative analysis,Mean Absolute Error(MAE),Relative Absolute Error(RAE),precision,recall,fmeasure,and accuracy are used to evaluate Linear Regression(LR),Decision Tree(J48),Naive Bayes(NB),Artificial Neural Network(ANN),Simple Cart(SC),Bagging,Decision Stump(DS),AdaBoost,Rep Tree(REPT),and Support Vector Machine(SVM).Overall,the SVM classifier surpasses other classifiers in terms of increasing accuracy and decreasing error rate,with RAE of 33.2631 andMAEof 0.165,the precision of 0.841,recall of 0.835,f-measure of 0.833,and accuracy of 83.49 percent for the dataset gathered from UCI.The SC improves accuracy and reduces the error rate for the Kaggle dataset,which is 3.30%for RAE,0.016 percent for MAE,0.984%for precision,0.984 percent for recall,0.984 percent for f-measure,and 98.44%for accuracy.
基金the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia for this research through a grant(NU/IFC/ENT/01/014)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia.
文摘Prognosis of HD is a complex task that requires experience andexpertise to predict in the early stage. Nowadays, heart failure is rising dueto the inherent lifestyle. The healthcare industry generates dense records ofpatients, which cannot be managed manually. Such an amount of data is verysignificant in the field of data mining and machine learning when gatheringvaluable knowledge. During the last few decades, researchers have used differentapproaches for the prediction of HD, but still, the major problem is theuncertainty factor in the output data and also there is a need to reduce theerror rate and increase the accuracy of evaluation metrics for HDP. However,this study largess the comparative analysis of diverse classification algorithmsgoing on two different heart disease datasets taken from the Kaggle repositoryand University of California, Irvine (UCI) machine learning repository tofind the best solution for HDP. Going through comparative analysis, tenclassifiers;LR, J48, NB, ANN, SC, Bagging, DS, AdaBoost, REPT, and SVMare evaluated using MAE, RAE, precision, recall, f-measure, and accuracy.The overall finding indicates that for the dataset taken from UCI, the SVMclassifier performs well as compared to other classifiers in terms of increasingaccuracy and reducing error rate that is 33.2631 for RAE, and 0.165 forMAE, 0.841 for precision, 0.835 for recall, 0.833 for f-measure and 83.49%for accuracy. Whereas for dataset taken from Kaggle, the SC performs well interms of increasing accuracy and reducing error rate that is 3.30% for RAE,0.016 for MAE, 0.984 for precision, 0.984 for recall, 0.984 for f-measure, and98.44% for accuracy.
