Ilorin Journal of Science

Valproic acid ameliorates liver and kidney dysfunctions in type 2 diabetic rats

A. Igunnu — 2023-06-01

Type 2 diabetes has been reported to impair both liver and kidney functions but the effect of valproic acid (VPA), an anti-diabetic agent, on liver and kidney dysfunctions in type 2 diabetic models is still elusive. Therefore, this study investigated the effect of VPA on selected functional parameters of liver and kidney in type 2 diabetic rats. Type 2 diabetes was induced in female Wistar rats fed on high-fat diet for 2 weeks and then by intraperitoneal injection of 35 mg/kg body weight (bw) of streptozotocin. The nondiabetic Wistar rats were given water (normal control) while the diabetic groups were administered water (diabetic control), two doses of VPA (50 and 100 mg/kg bw) and 100 mg/kg bw metformin for 14 days. Then, the rats were sacrificed; liver, kidney and serum tissues were collected and analyzed. High-fat diet and streptozotocin significantly (p < 0.05) altered the liver to body weight ratio, kidney to body weight ratio, liver function indices, kidney function indices and enzymes’ activities in the rat liver, kidney and serum but treatment with VPA and metformin ameliorated all the effects. In conclusion, results of this study indicate that VPA ameliorates liver and kidney dysfunctions in type 2 diabetic rats.

Comparative growth and yield responses of improved and local varieties of soybean (Glycine max (L.) Merrill) in Lafia, Nasarawa State

B. P. Mshelmbula — 2023-06-01

Soybean is native to Eastern Asia, mainly China, Korea and Japan, from where it spread to Europe and America and other parts of the world in the 18^th century. Soybean was first introduced to Africa in the early 19^th century, through Southern Africa and is now widespread across the continent. Soybean was first introduced to Nigeria in 1908. Soy bean has many nutritional benefits for man and livestock, as well as other industrial and commercial uses. The present study was aimed at comparing the growth and yield responses of both local and improved varieties of soya beans (Glycine max) in Lafia, Nasarawa State. Three improved varieties, T.G.S1448, T.G.S1449, and C.D.S 1448 and two local varieties of soybean, Mai Farin (MF) and Bakin Hanci (BH) were obtained from the Nasarawa Agricultural Development program (NADP). At 11 WAP, results revealed that TGS1449 recorded the highest plant height (65.00 cm), which differed significantly from TGS1448 (48.00 cm), CDS1448 (50.00 cm), and MF (50.33 cm) (P≤0.05). The MF variety recorded the highest number of leaves (52.67), which differed significantly from TGS1448 (43.67), and CDS1448 (46.33). Number of flowers was highest in TGS1449 (7.33), which differed significantly from the four other investigated varieties.

Beyond Linearity: Harnessing Spline Regression Models to Capture Non-linear Relationships

I. O. Ajao — 2023-06-01

This article explores the effectiveness of spline regression model in capturing non-linear relationships in data. A comparison of spline regression with other techniques, such as linear regression, polynomial regression, generalized additive, and log-transformed models, is conducted using simulated data. The performance metrics, including AIC, BIC, RMSE, MSE, MAE, and R-squared, are used to assess the goodness of fit for each model. The results indicate that the spline regression model outperforms other methods in accurately capturing non-linear relationships. The flexibility and smoothness provided by spline regression, through the incorporation of knots, result in better-fitted lines that closely match the data. This study recommends the use of spline regression for handling non-linear data and highlights its robustness and accuracy.

Flood Prediction in Nigeria Using Ensemble Machine Learning Techniques

K. R. Oloruntoba — 2023-06-01

Flooding is the most frequent and destructive natural catastrophe that may happen anywhere in the globe. The frequency and severity of flooding events have increased worldwide in recent years due to climate change and human activity. Flooding has caused widespread death and devastation of property, farms, and vegetation in several emerging African nations, including Nigeria, and has forced the relocation of many more. Flooding has been Nigeria's most common natural disaster during the last decade. Modern machine learning methods have shown great promise for improving flood prediction. The optimum machine learning algorithm for flood prediction is a matter of debate. To reduce the harm caused by floods, finding better ways to anticipate their occurrence is crucial. In this paper, 7 machine learning algorithms (SVM, CART, KNN, GLMNET, LG, LDA and NB) were initially applied on the default dataset. The results reveal fair accuracy (over 60%) and kappa values (< 0.4). The same set of ML algorithms were again applied on the transformed dataset using boxcox transformation technique; the accuracy and kappa values improved but not significantly. Finally, Models for predicting floods were implemented using five different ensemble algorithms: Bagged CART (BAG), Random Forest (RF), Stochastic Gradient Boosting (GBM), Extreme Gradient Boost (XG Boost), and C5.0 (C50). Compared to the other three models, the performance of RF (AUC = 0.93) and BAG (AUC = 0.92) indicated superior accuracy and Kappa.