Liquid Chromatography Mass Spectrometer (Lc/Ms) profile revealed flavonoids and terpenoids with antioxidant potential in aqueous fraction of Combretum micranthum leaf extract

Combretum micranthum (CM) is commonly used for its ethno-medicinal potentials without much or no scientific basis. Thus, the aim of this research is to evaluate aqueous fraction of C. micranthum leaf extract for possible antioxidant compounds using liquid chromatography mass spectrometer technique. In vitro antioxidant was carried out using 2,2-diphenyl-1-picrylhydrazyl (DPPH) and Ferric Reducing Power (FRAP) assay methods and the results indicated the free radical scavenging activity of 80.351±0.732 and 0.800± 0.00 at 2.50mg/m1 concentrations which is significantly different when compared to reference standard of 84.718±0.605 and 0.426±0.000 (ascorbic acid) respectively, with concentration or dose-dependent. LC-MS profile revealed the presence of medicinally important compounds like Myricetin (Flavone), Epioxylubimin (Sesquiterpenoids), Furocoumaric acid (Phenolic glycoside) among others. The findings of this study revealed that C. micranthum is rich in potential metabolites and was reflected as its antioxidant potential and could be used for drug and other oxidative stress related management

Comparative effects of organic manure sources and rates on performance of groundnut varieties

An experiment was conducted at the Teaching and Research farm of the Institute for Agricultural Research, Ahmadu Bello University Zaria. The aim was to study effects of different organic manure sources on performance of groundnut varieties. Treatment consisted of three organic manure source, (Poultry manure, (PM) cow dung (CD) and household waste (HW) each at two levels (1 ton and 2 tons), two varieties of groundnut SAMNUT 21 (V1) and SAMNUT 23 (V2) and a control. The treatments were factorially combined and assigned in a randomized complete block design and replicated three times. Growth data such as plant height, canopy spread and biomass weight and; yield data including, pod yield per plant, seed yield per plant, 100 seed weight were collected

Leveraging on the Transfer Learning with ResNet-50 for Efficient Classification of Waste Categories

The rapid urbanization and population expansion in Nigeria have resulted in a substantial rise in solid waste production, posing considerable issues for efficient waste management and environmental sustainability. This study addresses these difficulties by creating an automated trash classification system utilizing deep learning techniques to improve the efficiency and precision of waste classification. The ResNet-50 convolutional neural network was utilized via transfer learning to train a model using a meticulously assembled dataset of 2,527 waste images categorized into six types: cardboard, glass, metal, paper, plastic and trash. Data augmentation techniques, including random rotation, flipping, and zooming, were employed during preprocessing to enhance model generalization and resilience. The training procedure utilized a two-stage approach: first, the base ResNet-50 layers were frozen to leverage pre-acquired general image data, and subsequently, the top layers were fine-tuned to accommodate waste-specific attributes. Evaluation criteria such asccuracy, precision, recall, and F1-score showed consistently strong performance, with validation accuracy maintained at 97.8%. The confusion matrix demonstrated robust classification performance. The findings highlight the model's capability to efficiently automate waste classification, hence diminishing reliance on labor-intensive manual sorting commonly found in Nigeria. This study advances the overarching objective of sustainable urban trash management by offering a scalable, precise, and economical classification methodology

Yield and Yield Attributes of Extra-early Maize (Zea Mays L.) as Affected by Rates of Npk Fertilizer Succeeding Chilli Pepper (Capsicum Frutescens) Supplied with Different Rates Sheep Manure

Field experiment was conducted in 2005 and 2006 to study response of extra-early maize variety (95TZEE-Y1) to rates of NPK (0, 40:20:20, 80:40:40 and 120:60:60 kg N:P2O5:K2O ha-1) and residual FYM (0, 5, 10 and 15 t ha-1 applied to chilli pepper the previous season) in the semi-arid zone of Nigeria. Randomized complete block design with three replicates was used. Higher values for soil physical and chemical properties were obtained in plots supplied with manure the previous season with soil from 2006 experiment more fertile than for the first year, hence produced 21% more grain yield. All the applied NPK rates in 2005 and except 40:20:20 ha1 in 2006 had resulted in early maize crop as compared to control. Husked and de-husked cob and 100-grain weights and grain yield/ha were higher at 120:60:60 kg NPK ha-1. Maize grown in plot supplied with 15 t FYM ha1 the previous year matured earlier. Cobs and 100-grain weights and grain yield were highest in plot supplied with 10 t FYM ha1. The 10t FYM ha-1 had 69% and 68% more grain yield than the control in 2005 and 2006, respectively. Highest maize yield was obtained at 120:60:60 kg NPK ha-1 or 10t FYM ha-1. All the parameters measured significantly and positively related to each other when the two years data were combined

Phytochemical Constituents, and Antioxidant Activities of Sokoto-Grown Hibiscus Sabdariffa (Zobo)

Oxidative stress is a harmful physiological condition caused by an imbalance between free radical production and the body’s antioxidant defense system. It contributes to the development of many chronic diseases, including diabetes, cardiovascular disorders, neurodegenerative diseases, and cancer. Hibiscus sabdariffa Linn., locally known as “zobo” in Nigeria, is a medicinal plant traditionally consumed as a beverage and used for various therapeutic purposes. This study evaluated the phytochemical composition and antioxidant potential of Hibiscus sabdariffa leaf extracts. Dried red and black zobo leaves were collected from Tsohuwar Kasuwa market in Sokoto and authenticated at the Laboratory, Biochemistry Department, Usmanu Danfodiyo University, Sokoto. The samples were soaked in distilled water for 24 hours, filtered, and stored. Phytochemical screening was conducted using standard qualitative and quantitative methods, while antioxidant activity was assessed using the 2,2-Diphenyl-1-picrylhydrazyl (DPPH) radical scavenging assay. The phytochemical results revealed the presence of flavonoids, tannins, saponins, glycosides, cardiac glycosides, steroids, balsams, anthraquinones, and volatile oils. Quantitatively, black zobo exhibited higher flavonoid (2.26 ± 0.01%) and glycoside (3.82 ± 0.02%) contents, while red zobo contained more amount of saponins (2.78 ± 0.01%). Antioxidant activity showed that black zobo had a higher radical scavenging activity (73.86 ± 0.37%) compared to red zobo (66.42 ± 0.85%) at 0.2 mg/mL. A decline in activity was observed at higher concentrations. The results suggest that Hibiscus sabdariffa leaves, particularly the black variant, are rich in phytochemicals with significant antioxidant activity and could serve as a potential natural remedy for oxidative stress-related conditions

Detection and Classification of Dress Code Violations in Educational Environments Using Deep Learning

This paper explores the utilization of deep learning techniques for the detection and classification of dress code violations in educational environments, identifying the challenges of manual enforcement and the potential for systems that are automated. This paper exhibits a model that integrates Faster R-CNN for detection and EfficientNet for classification, which provides an accurate and very efficient system to monitor students’ compliance with the dress code policies. The model was trained on a dataset of images that were collected from Federal University Dutsin-Ma and were classified into “decent” and “indecent” dressing for both male and female students. The result achieved demonstrates that the model works efficiently, reaching a training accuracy of 98% and a validation accuracy of 96%, and with overall scores for precision, recall, and F1-score exceeding 97%, thereby proving its effectiveness in different dress code categories. The Uniformity across the techniques substantiates the feature extraction performance of the model and demonstrates its generalization ability. This paper outlines the benefits of automation in alleviating bias and human error by improving transparency and fairness and enforcing the dress code. The results showed how it is effective by combining powerful deep learning models with strong frameworks to solve problems of classification

Knowledge, attitude, and perception of low back pain and activities that may prevent it among adolescents in Nigeria

Background: Awareness of activities that may result in low back pain (LBP) among adolescents is fundamental in preventing adulthood LBP. Objective: The aim of this study was to investigate adolescents' knowledge, attitude, and perception of LBP and activities that may prevent LBP in Kano, North-western, Nigeria.Methods: This was a cross-sectional survey involving 400 school-going adolescents recruited using a multistage random sampling technique. Data was analysed using descriptive statistics and Chi-square test with 0.05 set as level of significance. Results: The mean age of the respondents was 16.0±1.50 years. LBP annual prevalence was 34.2%, with more girls (31.1%) reporting having LBP compared to boys (28.4%). More than half (59.3%) of the respondents had poor knowledge of LBP and activities that may prevent it. However, they had a good attitude (63%) and perception (74%) of LBP and activities that may prevent it. There was no significant association of levels of knowledge, attitude, and perception of LBP and activities that may prevent LBP with gender, age, and class of study (p > 0.05). Conclusion: Adolescents in Kano, North-western Nigeria had poor knowledge of LBP and activities that may prevent it. Therefore, there is a need to embark on an LBP prevention program among adolescents in Kano, North-western Nigeria. Keywords: Low back pain; prevention; adolescents; knowledge; attitude; perception

Genetic relationships among okra (Abelmoschus esculentus (L.) Moench) cultivars in Nigeria

This study was conducted on okra (Abelmoschus esculentus (L.) Moench) cultivars at the Teaching and Research Farm, University of Maiduguri, Nigeria. The objective was to evaluate the level of genetic divergence and heritability of eight characters in 2015 and 2016 dry seasons using irrigation. The results showed highly significant (p<0.01) differences in the ten okra cultivars for days to anthesis, plant height, fresh capsule length, fresh mass per capsule and fresh capsule diameter across the two years. A high genotypic coefficient of variation, heritability, and genetic advance were detected in all the characters except for days to anthesis and fresh capsule diameter. This implied that different genetic constitution and preponderance of additive effects governed these characters, thus presenting a significant opportunity for selection. The Mahanalobis D2 analysis allotted the ten cultivars into four clusters. The highest was cluster I comprising four cultivars, followed by cluster II containing three cultivars, cluster III consisting two cultivars, and cluster IV with mono genotypic. The three most superior okra cultivars (Salkade, Y’ar gagure and Kwadag) for yield and related characters could be exploited directly or introgressed with other promising ones in future breeding programs

Comparative effectiveness of low-level laser therapy versus muscle energy technique among diabetic patients with frozen shoulder: a study protocol for a parallel group randomised controlled trial

Background: Diabetes mellitus is one of the fastest-growing health challenges of the twenty-first century with multifactorial impact including high rates of morbidity and mortality as well as increased healthcare costs. It is associated with musculoskeletal complications, with frozen shoulder being commonly reported. While low-level laser therapy (LLLT) and muscle energy technique (MET) are commonly used to manage this condition, there remains a lack of agreement on the most effective approach, with limited research available on their comparative efficacy. Objectives: To evaluate the comparative effectiveness of LLLT versus MET among diabetic patients with frozen shoulder. Methods: This is a single-centre, prospective, single-blind, randomised controlled trial with three parallel groups to be conducted at Ahmadu Bello University Teaching Hospital, Zaria, Kaduna State, Nigeria. Sixty diabetic patients with frozen shoulder will be randomly assigned into LLLT group, MET group, or control group in a 1:1:1 ratio. All the groups will receive treatment three times weekly for 8 weeks. The primary outcome will be shoulder function and the secondary outcomes will include pain intensity, shoulder ROM, interleukin-6 (IL-6), depression, anxiety, and quality of life (QoL). All outcomes will be assessed at baseline, at post 8-week intervention, and at 3 months follow-up. Discussion: This will be the first randomised controlled trial to evaluate the comparative effectiveness of LLLT versus MET on both clinical and psychological parameters among diabetic patients with frozen shoulder. The findings of the study may provide evidence on the efficacy of these interventions and most likely, the optimal treatment approach for frozen shoulder related to diabetes, which may guide clinical practice. Trial Registration: Pan African Clinical Trials Registry (PACTR202208562111554). Registered on August 10, 2022

Detection and Classification of Dress Code Violations in Educational Environments Using Deep Learning

This paper explores the utilization of deep learning techniques for the detection and classification of dress code violations in educational environments, identifying the challenges of manual enforcement and the potential for systems that are automated. This paper exhibits a model that integrates Faster R-CNN for detection and EfficientNet for classification, which provides an accurate and very efficient system to monitor students’ compliance with the dress code policies. The model was trained on a dataset of images that were collected from Federal University Dutsin-Ma and were classified into “decent” and “indecent” dressing for both male and female students. The result achieved demonstrates that the model works efficiently, reaching a training accuracy of 98% and a validation accuracy of 96%, and with overall scores for precision, recall, and F1-score exceeding 97%, thereby proving its effectiveness in different dress code categories. The Uniformity across the techniques substantiates the feature extraction performance of the model and demonstrates its generalization ability. This paper outlines the benefits of automation in alleviating bias and human error by improving transparency and fairness and enforcing the dress code. The results showed how it is effective by combining powerful deep learning models with strong frameworks to solve problems of classification