Evaluation of leptin,interleukin-1 beta and tumor necrosis factor alpha in serum of malaria patients as prognostic markers of treatment outcome

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.

Antiandrogenic activity ofCalotropis procera latex in rats

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.

Heart Disease Risk Prediction Expending of Classification Algorithms

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.