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Recent Submissions
Exploiting plant parts of artocarpus heterophyllus (jack fruit tree) for treatment of benign prostatic hyperplasia in male winstar rats
(Federal University of Technology, Owerri, 2025-12) Nwako, Chinwe Angela
Benign prostatic hyperplasia (BPH) is a common urological disorder affecting millions of men worldwide. This study employed a multi-analytical approach to evaluate the phytochemical, biochemical, nutritional, hematological, and histological properties of Artocarpus heterophyllus (jackfruit) stem bark, pulp, and leaf extracts, as well as their therapeutic effects in a testosterone propionate (TP)-induced prostate model in animals. Fifty five male winstar rats were divided into 11 groups of 5 rats each: Group 1 (normal control; feed and water only), Group 2 (negative control; BPH-induced with 5.0 mg/kg testosterone propionate), Group 3 (standard control; BPH-induced and treated with 5 mg/kg finasteride), and Groups 4–11 treated with various doses (250 or 500 mg/kg b.w.) of leaf, stem bark, pulp, or combined extracts for 21 days. Analysis of serum prostatespecific antigen (PSA) levels showed that the negative control group (1.34 ± 0.05 to 1.37 ± 0.08 ng/mL) exhibited no significant change, while TP-treated groups receiving extracts demonstrated significant reductions. Group 3 showed a PSA decrease from 2.98 ± 0.03 to 1.50 ± 0.11 ng/mL (~49.7%), Group 11 exhibited a similar reduction from 2.94 ± 0.08 to 1.48 ± 0.04 ng/mL (~49.7%), and Group 7 showed a decrease from 2.91 ± 0.05 to 1.53 ± 0.07 ng/mL (~47.4%), while moderate reductions occurred in Group 4 (2.98 ± 0.08 to 2.19 ± 0.16 ng/mL) and Group 8 (3.01 ± 0.04 to 2.29 ± 0.07 ng/mL). These results indicate dose-dependent and extract-specific effects likely mediated by flavonoids, tannins, and artocarpin through attenuation of oxidative stress, suppression of proinflammatory cytokines (e.g.IL-6 ). Acute toxicity testing confirmed safety up to 5000 mg/kg with no adverse behavioral or physiological effects. GC–MS identified 64, 94, and 48 compounds in stem bark, pulp, and leaf extracts respectively, including n-hexadecanoic acid, oleic acid, cisvaccenic acid, tetradecanoic acid, and squalene, associated with antioxidant, anti-inflammatory, and anticancer activities. FTIR revealed hydroxyl, alkane, sulfur, nitro, nitrogenous, and aromatic functional groups across the extracts, indicating chemical diversity. Nutritional profiling showed proline (42.16 ppm) dominance in stem bark; threonine (16.39 ppm), isoleucine (10.06 ppm), and arginine (9.16 ppm) abundance in leaves; and high tyrosine (29.70 ppm), tryptophan (3.61 ppm), methionine (2.54 ppm), cystine (1.62 ppm), and glutamate (4.85 ppm) in pulp. Vitamins C, E, and A were highest in the leaf extract (22.14, 9.32, and 6.48 mg/100 g, respectively), alongside thiamine (0.45–0.61 mg/100 g), riboflavin (0.38–0.55 mg/100 g), and niacin (1.26– 1.85 mg/100 g). Mineral analysis showed high potassium (215.64 mg/100 g), calcium (128.54 mg/100 g), and magnesium (87.61 mg/100 g), with trace zinc (2.48–3.11 mg/100 g) and iron (6.84–7.96 mg/100 g). Histology revealed normal hepatic structure in controls, severe degeneration in untreated toxicant groups, and hepatoprotection in extract-treated rats, especially pulp and stem bark extracts, which restored hepatocyte architecture. In the BPH model, untreated rats had elevated absolute (0.85 ± 0.21 g) and relative (0.43 ± 0.10 g) prostate weights compared to controls (0.41 ± 0.05 g and 0.24 ± 0.05 g), while stem and combination extracts (500 mg/kg) significantly reduced prostate weights to near-control levels, comparable to finasteride. These reductions were accompanied by lowered serum testosterone, PSA, TNF-α, and IL-6 levels, supporting antiandrogenic and anti-inflammatory mechanisms. Hematological results showed stable WBC counts (99.90 ± 0.00 ×10⁹/L) with slight decreases in pulp (500 mg) and combination extract groups, elevated lymphocytes in the negative control (54.30 ± 3.52%) versus control (27.67 ± 0.81%), and increased hemoglobin (up to 18.30 ± 2.39 g/dL) and RBC counts (up to 14.80 ± 3.20 ×10¹²/dL) in treated groups. MCV, MCH, and MCHC values remained within normal ranges, and platelet counts (8806–9933 ×10⁹/L) were stable. Overall, the findings demonstrate that A. heterophyllus extracts possess significant antioxidant, anti-inflammatory, and anti-BPH properties, with strong safety profiles and diverse biochemical and nutritional potentials that support their therapeutic use against androgen-mediated prostate diso
Extraction and application of natural dyes from rothmannia whitfieldii (Uri) and baphia nitida (Uhie)
(Federal University of Technology, Owerri, 2016-12) Nnorom, Onyekachi Onyinyechi
The renewed interest in natural dyes across the globe is due to some discoveries of environmental hazard and toxic effect of synthetic dyes to humans. Unfortunately, most natural dye yielding plants and their properties are neither known nor documented. In this research work dyes were extracted from locally prepared Baphia nitida plant cakes (uhie) and dry Rothmannia whitfieldii fruit (uri). Extraction methods used are water, alkali, organic acid and organic solvent at room temperature and at 65OC with a material to liquor ratio of 1:50. It was found that only alkali solution extracted dye from both plant sources while organic solvent extracted only from uhie. More dye was extracted at 65OC and the spectrophotometer results of wavelength of maximum absorption of the dye extracted by alkali were 620nm for uhie dye and 670nm for uri dye. Analysis of dye extracts in the area of fabric dyeing using alum (kAl(SO4)2.12H2O) as mordant and sodium dithionite (Na2S2O4) as reducing agent, FTIR spectroscopy and colour fastness to washing and rubbing indicated that both uri and uhie dyes tend to be direct dyes. Both dyes do not necessarily require mordant and reducing agent to dye a cotton fabric at room temperature and at 60OC. Uri dyes better at temperatures above room temperature. From FTIR spectroscopy the possible presence of conjugated double bonds were observed in both dyes and tannins only in uri dye. Uri dye had a better wet and rub fastness than uhie dye but similar wash fastness. Also both dyes did not stain adjacent cotton fabric during washing. However, there is a need to further purify both dyes and study their interactions with other fibres.
Development of suitable failure criteria for designing sufficient mud weights for Niger Delta well
(Federal University of Technology, Owerri, 2022-02) Nwanro, Chidindu Peace
Drilling the well to the target depth comes with it a lot of challenges; part of the issues related to drilling both vertical and deviated wellbores is related to the type of formation failures and well problems observed during drilling. Wellbore direction and deviation changes during drilling while the formation is the same, the effect of transformed stresses around the wellbore wall will be different. This is obvious that neither rock properties nor in-situ stresses can be changed to mitigate the failures of the wellbore. However, the density of the drilling mud can significantly control the situation. Large wellbore pressure due to using high mud density could enforce the formation to open in tensile mode, which in due course may result in mud loss or ultimately fracturing the formation. Also, using a low density mud, corresponding to low wellbore pressure may result in rock failure in shear mode and consequently breakouts. Most drilling engineers do not incorporate thorough wellbore stability analysis as part of well planning due to the complexity of wellbore stability models and thus employs the traditional practice which is identifying the pore pressure and adding extra pressure typically 100-200psi in mud equivalent as safety margin. Predicted mud weights in this manner end up in failed states resulting in several types of problems such as stuck pipe, lost circulation, formation damage and well control problems simply because either the mud weights designed are not sufficiently screened or those operating them lack the basic understanding of such mud systems. Five case studies were considered in this work to analyze the well and mud data from previously drilled wells with typical wellbore stability issues in the Niger Delta Region. A problem diagnostic was conducted to determine the main cause of the instability issues in these wells. Based on our findings, an excel based spreadsheet was developed for all the data and to perform all calculations. A coupled Mogi-Coulomb criterion further developed predicts the safe mud weight window in an iterative manner and describes the rock failure more accurately than does the traditional Mohr-Coulomb criterion which is independent of the intermediate principal stress, and presents a very slim mud weight window. Results of this study can help well trajectory optimization, proper mud weight determination, hydraulic fracturing job, sanding analysis and reduce non-productive time (NPT) while drilling.
Sampling strategies for handling nonresponse and measurement errors in poststratified sampling scheme
(Federal University of Technology, Owerri, 2021-12-07) Izunobi, Chinyeaka Hostensia
Nonresponse and measurement errors, often referred to as nonsampling errors, pose serious challenges in surveys. The focus of the present study is on the development of appropriate sampling strategies for handling of these problems under the poststratified sampling scheme. Three (3) broad cases considered in the study were the occurrence of nonresponse alone, the occurrence of measurement errors alone and the joint occurrence of nonresponse and measure ment errors. These three (3) broad cases were considered when each occurred on both the study and auxiliary variables, on only the study variable, and on only the auxiliary variable, giving rise to nine (9) distinct cases. Three (3) separate-type estimators of the population mean of the study variable were then proposed for each of the nine (9) distinct cases, giving a total of twenty-seven (27) sampling strategies. These three (3) types of estimators are the customary type sample mean estimators, the difference-type estimators, and a class of ratio/product-type estimators. Properties of the proposed estimators were obtained both for an achieved sample configuration (conditional argument) and for repeated samples of fixed size n (unconditional argument). The proposed customary-type and difference-type estimators were unbiased, while the proposed classes of ratio/product-type estimators were generally biased for the study vari able population mean. The results showed that there exists an optimum class of estimators among the proposed classes of estimators. The theoretical results were subjected to numeri cal validation and verification, using two datasets, namely, Dataset-1, for positively correlated study and auxiliary variables, and Dataset-2, for negatively correlated study and auxiliary variables. Both the theoretical and empirical results showed that the optimum estimators in the proposed classes of estimators performed better than the usual post stratified sample mean estimator (when there are no nonresponse and measurement errors). The optimum estimators also generally performed better than the proposed customary-type estimators and other proposed ratio/product-type estimators. The study recommends, among others, the use of auxiliary in formation when developing improved sampling strategies for the handling of the problem of nonresponse and measurement errors in poststratified sampling scheme.
An inelligent hybrid model for tomato leaf didease detection and control recommendations
(Federal University of Technology, Owerri, 2023-09) Esomonu, Nkechi Faustina
Crop diseases pose a significant threat to global food security, with the negative impact on food production stemming from a lack of early detection and effective management. Despite technological advancements, especially in artificial intelligence (AI), smallholder farmers face challenges in accessing and utilizing these tools. Tomato, a crucial vegetable crop in the global food chain, is particularly susceptible to various debilitating diseases, leading to annual losses for farmers. This research aims to address the impact of disease related losses on tomato farmers, specifically in Nigeria, by developing an AI-powered system for early detection, precise diagnosis, and continuous monitoring of tomato diseases. The motivation for this research stems from the recognition of the limited access smallholder farmers have to efficient technology tools for managing tomato diseases. The goal is to minimize the impact of these diseases on farmers' livelihoods and, by extension, society. Through early detection and effective management, the research seeks to empower farmers with the necessary tools to mitigate losses and enhance food production. The primary objective of this research is to develop a hybrid AI-powered system that aids smallholder farmers in early detection, effective management, and continuous monitoring of tomato plantation health. The approach involves the integration of deep and shallow learning models, with a focus on transfer learning. The functional unit comprises a convolutional neural network model (MobileNetv2), a support vector machine (SVM) model, and a natural language processing component for image processing, feature extraction, classification, and management counseling. To achieve the research objective, a hybrid architecture combining deep and shallow learning models was developed. The model utilized 11,000 tomato leaf images for training, ensuring a balanced representation of healthy and diseased types. The problem was formulated as a ten-class (multiclass) classification task. Evaluation of the proposed solution employed standard metrics, and the results demonstrated state-of-the-art performance. The hybridized MobileNetv2 and SVM achieved impressive metrics, including 93.68% precision, 93.84% F-1 score, 93.81% accuracy, and 92% recall. The evaluation of the proposed solution showcased its efficacy in achieving high precision and accuracy. The hybridized model, incorporating MobileNetv2 and SVM, demonstrated state-of-the-art performance in recognizing and diagnosing tomato diseases. The results indicate the potential of the developed AI-powered system for practical implementation in agriculture. In conclusion, this research contributes significantly to addressing the challenges faced by smallholder tomato farmers in Nigeria. The developed AI-powered system, combining deep and shallow learning models, demonstrates remarkable performance in early detection, precise diagnosis, and continuous monitoring of tomato diseases. The successful implementation of the model in an Android application suggests its potential for acceptability and adoption among the farming population. However, to enhance usability, further development of the natural language component, including speech processing capabilities, is recommended, particularly considering the literacy constraints of rural farmers. This research aligns with Sustainable Development Goals 1, 2, and 3, and it is recommended for further development and support by relevant government agencies to fulfill the promise of technology utilization in improving societal well-being through increased food availability.