Agricultural wastes, often considered non-productive byproducts, may contain valuable substances. With growing environmental concerns over fossil fuel use and resource depletion, there is a shift toward renewable energy sources. This study investigated the proximate composition and bioethanol production potential of sweet potato peels, watermelon rinds, and doum palm shells obtained from a farm in Aliero, Kebbi State, Nigeria. Proximate analysis followed AOAC standard methods, and bioethanol production involved dilute H2SO4 hydrolysis (2 M) at 121°C for 15 minutes. Significant differences (P<0.05) were observed in proximate composition: carbohydrate content was highest in doum palm shells (67.18%), followed by sweet potato peels (51.32%) and watermelon rinds (32.57%). FTIR analysis confirmed key ethanol functional groups, with O-H, C-H, and C=O peaks. Fuel property analysis showed significant variations (P<0.05), except for specific gravity. Flash points were 55.20°C (SPP), 47.60°C (WMR), and 45.90°C (DPS), while boiling points were 73.60°C, 78.00°C, and 62.80°C, respectively. These results suggest that the studied agricultural wastes have potential as bioethanol sources, which could be blended with conventional fuels. Further research is recommended to evaluate their suitability for combustion engines.

The Nigeria rapid expanding population, security concerns, high cost of fuel, oil theft, pollution caused by fossil fuels and uneven distribution of petroleum among the states has contributed to Nigeria increasing demand for energy. Balanites aegyptiaca (desert date) seed shells was used as substrate for the production of acid-base biofunctional catalyst and characterized through energy dispersive x-ray fluorescence (EDXRF), scanning electron microscope (SEM) Brunauer-emmett-teller (BET) and fourier transform infrared spectroscopy (FT-IR) respectively. The EDXRF elemental composition of both biochar and the produced acid-base biofunctional catalyst revealed the presence of , Ca, Fe, Ni, Cu, Zn, Br, Pb and Sr at different concentration while metallic oxides or oxide of metals showed the presence of FeO3, CuO, Ni2O, Al2O3, ZnO, Na2O, MgO, SO3, P2O5, CaO, K2O, MnO, Rb2O, and SrO at different concentrations. SEM morphology was conducted at magnification range of 500X and 1000X. The desert date (Balanites aegyptiaca) biochar DDSS-B showed higher level of porosity, overlapping surface and pores with spongy like structure while the produced acid–base biofunctional catalyst (DDSSAC/KOH/H2SO4) showed pores created during calcination filled with the active sites during the impregnation process, resulting in the changed of shape and particle sizes. BET analysis showed an increased in specific surface area, pore volume and pore size from DDSSB to DDSSAC/KOH/H2SO4. The functional groups attached to the surface of the DDSS was examined by FTIR at transmission spectra of 4000 – 650Cm3 showing –OH, C–H, C=O, C=C, C–O, S-O and P=O at different wave number among the samples. Based on the results desert date seed shells (DDSS) will be a promising feed stock for catalyst synthesis.

Cancer has been a menace to public health. Among all cancers, breast cancer is the most common in women. This research investigated the preventive effect of tomato and carrot supplementation in wistar rat administered breast carcinogen, Dimethylbenz-[a]-anthracene (DMBA). Tomato and carrot were dried under shade and pulverized. Forty-eight female rats were randomly distributed into 8 groups. Groups 1 and 2 comprised of unexposed rats fed standard diet and DMBA-administered group fed standard diet respectively. Groups 3-5 were DMBA-administered groups fed diets containing 20% tomato, 20% carrot, and 20% of an equally mixed tomato and carrot respectively. For groups 6-8, unexposed rats fed diets containing 20% tomato, carrot, and 20% of an equally mixed tomato and carrot respectively. Feeding was for a period of 10 weeks. Phytochemical characterization of the powdered plants was analyzed. Liver function parameters and inflammatory markers were assed. Histopathology of the kidney and liver was performed. Tomato powder had higher concentrations of carotenoids (23.99 mg/100g) and phenolics (0.51 mg/100g) but lower levels of flavonoids and alkaloids than carrot powder. There were significant decreases (p<0.05) in the activities of ALT, (AST) and (ALP) but an increase in the concentrations of TP in the (DMBA)-administered groups fed supplemented diets. All the groups had significantly (p<0.05) lower concentrations of C-reactive protein and tumor necrosis factor-alfa in comparison with the DMBA-administered group fed the standard diet. The DMBA- administered groups fed supplemented diets had improved cellular architecture in comparison with the DMBA-administered group fed standard diet. Supplementation of diets with either carrot or tomato reduces the risk of breast cancer in rats exposed to DMBA by ameliorating liver dysfunction, improving anti-inflammatory defense systems and by protecting cellular integrity. coordinated awareness about the importance of including tomato and carrot in the diet as means of preventing cancer and related diseases is encouraged.

Infertility is the most prevalent reproductive problem affecting 10-15% of young couples. Evidences suggest injuries to the testes by reactive oxygen species as a result of drugs. This study investigated the ameliorative effects of selected polyphenols (gallic, betulinic and protocatechuic acids) on menadione-induced testicular dysfunction in rats. Thirty (30) male rats were randomly selected into six groups of five rats each. Group A were fed with normal feed and distilled water, while the remaining were given 10 mg/kg body weight of menadione intraperitoneally for 7 days to induce testicular dysfunction. Thereafter, Group B was given distilled water, while Group C was given 10 mg/kg of addyzoa (reference drug) and Groups D, E, and F were treated with 50 mg/kg of betulinic, gallic and protocatechuic acids respectively for 7 days. Menadione significantly (P=0.05) reduced serum concentrations of Follicle stimulating hormone (FSH), Leutenizing hormone (LH), testosterone, testicular cholesterol and sialic acid. The specific activities of superoxide dismutase, catalase, glutathione peroxidase, glutathione transferase, acid phosphatase, alkaline phosphatase and lactate dehydrogenase were all significantly reduced in the untreated group. In contrast, there was a significant rise in malondialdehyde and protein carbonyl concentrations. Whereas, the polyphenolic compounds, significantly (P=0.05) increased the serum concentrations of FSH, LH, testosterone, testicular cholesterol and sialic acid levels, with a significant decline in malondialdehyde and protein carbonyl concentrations and a significant rise in the specific activities of antioxidant enzymes. Conclusively, gallic and betulinic acids were more effective than the protocatechuic acid in the reversal of testicular dysfunction induced by menadione.

Food fortification is a crucial strategy to combat micronutrient deficiencies, ensuring that populations receive essential vitamins and minerals. This study assessed the accuracy of micronutrient content in fortified foods sold in Kebbi State by comparing labeled claims with laboratory analysis results. A total of 20 fortified food samples, including rice, pasta, flour, vegetable oil, sugar, and salt, were analyzed for key micronutrients using Atomic Absorption Spectrophotometry (AAS) and standard AOAC methods for vitamin determination. The results revealed significant discrepancies between labeled claims and actual micronutrient content. For instance, Vitamin A in fortified rice ranged from 798.30±19.12 IU/kg to undetectable levels, far below the Standard requirement of 25,000 IU/kg. Similarly, iron concentrations varied from 6.55±0.43 mg/kg to 152.77±0.38 mg/kg, with inconsistencies across different products. Statistical analysis showed differences between labeled claims and laboratory results for several micronutrients. These findings highlight the need for stricter regulatory enforcement and improved quality control measures in fortified food production. It is recommended that routine monitoring and compliance checks be strengthened to ensure the effectiveness of fortification programs in addressing micronutrient deficiencies.

Weeds cause a significant reduction in overall yield of many major agricultural crops. However, understanding crop-weeds interactions in salt induced salt stress is still debatable. This study examined the growth dynamics and canopy structures of Solanum lycopersicum (tomato) and Capsicum annuum (pepper) in mono and mixed culture with two invasive alien weeds (Euphorbia heterophylla and Acanthospermum hispidum) under different sodium chloride (NaCl) concentrations. Data for the study was obtained from field measurement and the parameters measured includes: plant height, number of branches, number of leaves, stem girth, and canopy spread. The results showed a significant variation in growth responses across different treatments. Solanum lycopersicum had the highest plant height at 5% NaCl with the value 71.20±2.20 cm in monoculture, while in Capsicum annuum plant height was highest in a mixture with E. heterophylla at 25% NaCl concentration. The number of branches was highest in the mixed S. lycopersicum and E. heterophylla at 20% NaCl with the value of 15.63±0.03 whereas the lowest value of 14.09±0.07 was recorded in the mixture of S. lycopersicum and A. hispidum at 15% NaCl. But in C. annuum, the highest number of branches was recorded in monoculture with the value of 16.02±0.04 whereas the lowest value of 15.01±0.03 was recorded in mixture of C. annuum and A. hispidum at 25% NaCl. The study concludes that mixed cultures acquire maximum light throughout their growth and developmental stage, indicating a suppressive hurdle faced by crops grown with weeds. Plant breeders are advised to develop resistance crop varieties to minimize the menaces caused by weeds.

Taenia solium taeniosis/cysticercosis complex is a significant global public health and economic burden, contributing to neurocysticercosis in humans and financial losses in the pig farming industry due to pork downgrading. The disease remains endemic in many developing regions, including Nigeria, where poor sanitation and inadequate meat inspection facilitate its transmission. This study aimed to determine the prevalence of T. solium cysticercosis in pigs from the Zuru area of Kebbi State, Nigeria. A total of 184 pig sera samples were analyzed using antigen-ELISA to detect T. solium secretory/excretory antigens. The association between infection status and host factors (sex and age) was evaluated statistically. Of the 184 pig sera tested, 60 (32.6%) were positive for T. solium antigens, indicating active cysticercosis. There was no statistically significant association between sex and seroprevalence (p = 0.416); however, a significant association was observed between age and infection (p = 0.002), with older pigs exhibiting higher prevalence rates. The high seroprevalence of T. solium cysticercosis in pigs from the study area suggests an ongoing transmission cycle, posing a serious risk to public health and the local pig farming industry. Inadequate meat inspection and poor sanitation may contribute to the continued spread of the parasite, increasing the likelihood of human taeniosis and neurocysticercosis. Urgent interventions, including strengthened meat inspection, improved public health education, and targeted control measures, are necessary to mitigate the zoonotic and economic risks associated with T. solium transmission.

This research detailed the use of artificial intelligence (AI) in bioinformatics, current techniques for future trends, and an update on the most appropriate AI techniques for genomic data analysis, protein structure prediction, and modeling complex networks in biology. This review was conducted using multiple-step approach, including systematic surveys of existing literature on AI methodologies, and their applications, algorithmics comparison and case study investigations. Algorithmic comparison is made to evaluate the performance, accuracy, and computational cost of various AI models applied to bioinformatics problems. Similarly, domain-specific testing was performed using real bioinformatics case studies such as drug-target interaction predictions and the identification of cancer biomarkers. The findings from this review shows that in genomic data analysis, deep neural networks, including the use of convolutional neural networks (CNN) and recurrent neural networks (RNN), are found to be superior to conventional machine learning algorithms such as support vector machines (SVM), in variant calling and gene prediction. Comparative studies demonstrated that deep learning-based models, such as DeepVariant are more precise in the prediction of the single nucleotide polymorphism (SNP) than the standard statistical models, as sensitivity and specificity are enhanced. Wilcoxon signed-rank tests and cross-validation techniques were also used to provide stable performance comparison across different datasets and problem domains. The results of this research emphasize the transformative impact of AI on bioinformatics, demonstrating how various methodologies reinforce each other to improve the accuracy, speed, and interpretability of results in bioinformatics for future applications. It is recommended that future research should focus on integrating hybrid AI models uniting statistical, deep learning approaches with explainability methodologies for biomedical research and medical applications.

This research detailed the use of artificial intelligence (AI) in bioinformatics, current techniques for future trends, and an update on the most appropriate AI techniques for genomic data analysis, protein structure prediction, and modeling complex networks in biology. This review was conducted using multiple-step approach, including systematic surveys of existing literature on AI methodologies, and their applications, algorithmics comparison and case study investigations. Algorithmic comparison is made to evaluate the performance, accuracy, and computational cost of various AI models applied to bioinformatics problems. Similarly, domain-specific testing was performed using real bioinformatics case studies such as drug-target interaction predictions and the identification of cancer biomarkers. The findings from this review shows that in genomic data analysis, deep neural networks, including the use of convolutional neural networks (CNN) and recurrent neural networks (RNN), are found to be superior to conventional machine learning algorithms such as support vector machines (SVM), in variant calling and gene prediction. Comparative studies demonstrated that deep learning-based models, such as DeepVariant are more precise in the prediction of the single nucleotide polymorphism (SNP) than the standard statistical models, as sensitivity and specificity are enhanced. Wilcoxon signed-rank tests and cross-validation techniques were also used to provide stable performance comparison across different datasets and problem domains. The results of this research emphasize the transformative impact of AI on bioinformatics, demonstrating how various methodologies reinforce each other to improve the accuracy, speed, and interpretability of results in bioinformatics for future applications. It is recommended that future research should focus on integrating hybrid AI models uniting statistical, deep learning approaches with explainability methodologies for biomedical research and medical applications.

Various processing methods are used widely in preparation of complementary foods. These processing methods are known to have effect on the overall nutritional profiles of formulations. This study evaluated the effects of these methods, specifically, soaking and roasting on a local complementary food that is prepared by mixing cereals and legumes at 3:2 ratios respectively. The proximate, functional, antinutritional, mineral and Amino acid content, storage stability and microbial analysis were conducted on two samples F1 (Soaked) and F2 (Roasted) of the complementary food formulation using standard analytical techniques. The results showed a significant difference (P<0.05). in the proximate properties. F1 showed increased crude fat (0.33±23.43%), protein (0.22±10.93%), fiber (0.46±15.73%) and energy density (457.99k/cal) while F2 showed increased carbohydrate (0.41±63.05%), moisture (0.07±2.45%) and ash content (0.19±2.92%). A significant difference (P<0.05) was observed in the antinutritional content, with F2 having lower values compared to F1 for both oxalate (0.02±0.46meq/L) and phytate (0.03±0.36meq/L). However, no significant difference (P>0.05) was observed in storage stability and sensory attributes although F2 had the highest overall acceptability score (29.5) for the sensory attributes. A significant difference (P<0.05) was observed in bulk density, swelling index and mineral content. F1 sample recorded the highest level of sodium (0.81±15.86mg\dl), iron (0.00±1.51mg\dl), cupper (0.02±0.20 mg\dl), and potassium (0.00±24.57mg\dl) while F2 had higher amounts of phosphorus (0.52±18.81mg\dl), calcium (0.23±69.54mg\dl) and magnesium (0.08±19.64 mg\dl). Higher Amino acid content was observed in F1 except in theorine, whose value was higher in F2. Low bacterial count was recorded for both samples and no toxic microbes were identified. Overall, the result showed that each processing method (soaking and roasting) had a varying effect on the complementary food formulations; this can be used to target and improve specific desirable properties when preparing complementary foods.