University of Bamenda

This study evaluated the proximate, vitamin, and mineral composition of alternative feeds formulated from fermented maize cob and black soldier fly (BSF) larvae composite for fish and poultry. Four feed formulations were developed using varying proportions of fermented maize cob powder and BSF larvae flour: 60/40, 70/30, 80/20, and 90/10, with commercial feed as a control. The raw materials and composite feeds were analysed using standard AOAC methods. Results showed that BSF larvae contained significantly higher protein (29.30%), fat (8.97%), ash (12.18%), and fiber (8.50%) compared to maize cob. The composite feeds exhibited protein content ranging from 13.48-27.00%, with the 60/40 formulation achieving the highest protein level. Moisture content ranged from 21.42-27.53%, indicating the need for additional drying to enhance storage stability. Vitamin analysis revealed that BSF larvae were rich in vitamin B2 (11.93 mg/100g) and B6 (0.91 mg/100g), while vitamins A and E were absent in all samples. The vitamin C content was extremely low across all samples, ranging from (0.01-0.34)mg/100g. Mineral composition showed BSF as an excellent source of iron (7.75 mg/100g), zinc (1.87 mg/100g), Sodium (128.62 mg/100g), and Potassium (464.92 mg/100g), lead content ranged from 0.05mg/100g in sample 80/20 to 0.2mg/100g in maize cob powder (MCP). The 60/40 formulation demonstrated an optimal nutritional profile for high-protein-requiring species, suggesting that fermented maize cob-BSF composites represent viable, sustainable alternatives to conventional feeds. This analysis was subjected to a one-way analysis of variance and Turkish test was used to compare the means.

Purpose: This paper critically examines how the British colonial administration, operating through the indirect rule system, structured the agro-pastoral economy of British Southern Cameroons between 1922 and 1945 within the framework of the League of Nations Class "B" Mandate, and assesses whether these efforts genuinely promoted indigenous welfare or served the structural interests of colonial extraction. Methodology: The study employs a qualitative historical methodology, drawing on primary sources, colonial administrative reports, veterinary records, Native Authority correspondence, and League of Nations visiting mission reports, alongside secondary literature in African colonial history and agrarian economics. Analysis is conducted within a dual theoretical framework combining Dependency Theory and Historical Institutionalism. Findings: The study reveals that colonial agro-pastoral institutions, including experimental farms at Barombi-Kang and Bambui, and veterinary centres at Jakiri and Nkambe, were not designed to ensure indigenous food sovereignty but to protect cattle as taxable, exportable assets. The njangali tax, constituting over fifty percent of Native Authority revenue, functioned primarily as a fiscal extraction mechanism. The institutionalization of Fulani grazing rights alongside fixed farming allocations created path-dependent conditions for farmer-grazier conflicts, land use disputes, and inter-ethnic tensions that persist into the twenty-first century. Unique Contribution to Theory, Practice and Policy: The study deploys Dependency Theory and Historical Institutionalism as mutually reinforcing analytical lenses, the former explaining the structural orientation of the economy toward metropolitan extraction, the latter illuminating the precise institutional mechanisms of layering, path dependence, and critical junctures through which that dependency was constructed and made durable. This dual-theoretical approach offers a replicable model for analyzing colonial economic legacies across sub-Saharan African mandate and trust territories.

Introduction Sub-Saharan Africa faces increasing environmental pressure amid rising energy demand, economic growth, and rapid population expansion. While renewable energy is often promoted as a pathway to sustainable development, fossil fuel dependence remains widespread in the region. This study examines the dynamic effects of renewable and non-renewable energy consumption on environmental sustainability in 27 Sub-Saharan African countries over the period 2009–2021. Methods The study employs a dynamic panel framework using the System Generalized Method of Moments (GMM) estimator to address potential endogeneity, unobserved heterogeneity, and dynamic persistence in environmental indicators. The empirical model incorporates renewable energy consumption, fossil fuel consumption, economic growth, population growth, industrialization, and electricity consumption as key determinants of environmental sustainability. Results and discussion The findings reveal that renewable energy consumption significantly reduces environmental degradation, confirming its ecological benefits in the region. In contrast, fossil fuel consumption emerges as a major driver of environmental deterioration. Economic growth and population expansion significantly intensify environmental pressure, underscoring the challenge of reconciling development objectives with sustainability goals. Industrialization and electricity consumption exhibit only limited effects on environmental degradation. Discussion and policy implications The results highlight the urgent need to accelerate the transition from carbon-intensive energy sources to cleaner alternatives in Sub-Saharan Africa. Policymakers should prioritize investment in renewable energy infrastructure, implement targeted strategies to reduce fossil fuel dependence, and strengthen institutional frameworks to enhance energy governance. Such measures are essential not only for achieving environmental sustainability but also for promoting long-term economic resilience in the region.

Accurate and timely monitoring of soil nutrient concentrations, particularly nitrogen (N), phosphorus (P), and potassium (K), is crucial for optimizing agricultural productivity and minimizing environmental impact. In this study, we present the characterization of an optical sensor designed for the rapid and non-destructive assessment of N, P, and K levels in soil samples. The sensor employs Visible-Near Infrared (VIS–NIR) spectroscopy to analyse the spectral signatures of soil samples and extract quantitative information regarding nutrient concentrations. A total of 30 experimental samples classified into six distinct categories, comprising various soil-fertilizer mixtures, were prepared to calibrate the sensor's performance. These samples were made from four primary constituents: black soil (sourced from a farm in the Auvergne-Rhone-Alpes region of France), NPK (6:3:12) fertilizer, ammonium nitrate, and urea. The six categories were grouped into two sets: sample type 1 consisted of pure samples, while sample types 2 through 6 were mixtures of soil and fertilizer. The mixed samples were prepared with nutrient concentrations higher than those typically found in agricultural soils to enable controlled calibration of sensor performance. The pure sample, specifically Sample10, consisting of humus soil without fertilizer amendment, was included as a reference to approximate real-soil conditions. Laboratory experiments involved illuminating the samples with specific wavelengths of light and measuring the intensity of reflected light within a controlled environment. Calibration curves and equations specific to each macronutrient were developed through regression analysis using data collected from the experiments. Performance evaluation metrics, including Root Mean Square Error (RMSE), Coefficient of Determination (R2), Predicted Residual Error Sum of Squares (PRESS), and Standard Error (s), were employed to assess model accuracy. Results demonstrated satisfactory performance across all sample types, with average R2, RMSE, PRESS, and s values of 0.7835 V, 0.0097 V, 0.0003 V, and 0.0003 V, respectively. Statistical analyses further validated the sensor, indicating average standard errors of 0.003 V for repeatability and 0.001 V for reproducibility, along with a sensitivity of 7% and a linearity of R2 = 0.8. These findings validate the effectiveness of the optical sensor in accurately quantifying soil macronutrient concentrations.

This article explores the bond between agricultural production and money supply within the Central African Economic and Monetary Community between 1991 and 2022. Agricultural production is proxied by the agricultural portion added to the gross domestic product. The exogenous variables include: broad money supply ratio, credit given to the private sector, inflation, capital, and labour are. This study uses the Granger causality and autoregressive distributed lag techniques. The results indicate a negative link between money supply ratio and agricultural value added. A non-significantly negative relationship exists between credit to the private sector and agricultural production. This implies that the credit granted to the private sector is not effectively used for agricultural production; some of it is stifled by inflation while other portions are used in other economic sectors. Inflation, physical capital, and labour positively relate to agricultural production. Unidirectional causality flows from agricultural production to inflation, money supply ratio, and labour. Bidirectional causality oscillates between agricultural production and credit granted to the private sector. The governments should apply monetary policies sweeping market processes to regulate the money supply. The Bank of Central African Economic and Monetary Community (BEAC) should use productive policy to ensure satisfactory credit drift to the productive sector. The agricultural and financial sectors should mutually benefit from credit provided by financial institutions. Literature is conflicting and debating on the nexus between money supply and agricultural production. There are studies that show significantly positive effects of money supply on agricultural production. Others indicate that the money supply has negative effects on agricultural production. Various researches disclose bidirectional causality linking money supply and agricultural production. Others show unit-directional causality among money supply and agricultural production. Also, advanced statistical methods like Granger causality and autoregressive distributed lag techniques are employed which have been rarely used by many past researches. This study reveals a negative correlation between money supply and agricultural value added. The main findings indicate that inflation, physical capital, and labour positively influence agricultural output. Furthermore, the analysis uncovers unidirectional causality from agricultural output to money supply and inflation, alongside bidirectional causality between domestic credit to the private sector and agricultural production. These insights underscore the necessity for effective monetary policies tailored to support agricultural growth in CEMAC zone. The research contributes to understand how monetary policy dynamics affect agricultural productivity, providing a foundation for future investigations into policy frameworks which enhance the agricultural sector's performance in the CEMAC region.

Background: Chemotherapy remains central to cancer management in sub-Saharan Africa but is frequently complicated by treatment resistance and cumulative hepato-renal toxicity. Longitudinal biomarker monitoring may improve early detection of subclinical organ dysfunction and therapeutic response. This study evaluated longitudinal changes in hepatic, renal, inflammatory, and tumor-associated biomarkers to elucidate chemotherapy response and resistance patterns among cancer patients receiving systemic therapy in Cameroon. Materials and Methods: A longitudinal observational study was conducted among 120 cancer patients treated at the Cameroon Oncology Centre (February-July 2025). Serum liver enzymes (aspartate aminotransferase (AST), alanine aminotransferase (ALT)), albumin, urea, creatinine-derived estimated glomerular filtration rate (eGFR), C-reactive protein (CRP) measurement was done once at the end of the chemotherapeutic period were measured at baseline and over three follow-up time points at two-month intervals, while cancer biomarkers, namely carcinoembryonic antigen (CEA), and cancer antigen 15-3 (CA15-3) were screened within two interval periods. Non-parametric analyses (Kruskal–Wallis, Friedman tests) assessed group differences and monotonic trends, while Spearman’s correlation evaluated treatment–biomarker associations. Results: Participants were predominantly female (77.5%), with advanced-stage disease (Stage III–IV: 59.2%). Liver enzymes remained largely stable throughout follow-up, indicating preserved hepatocellular integrity. In contrast, albumin exhibited a significant monotonic decline (−1.20%, <i>p</i> = 0.004), reflecting cumulative metabolic and inflammatory stress. Renal function showed a modest but significant decline in eGFR (−3.77%, <i>p</i> = 0.044), particularly among platinum-based regimens, despite stable urea levels. Tumour marker analysis revealed a pronounced and consistent reduction in CA15-3 (−12.98%, <i>p</i> = 0.006), whereas CEA showed no significant longitudinal trend. Drug-specific correlations supported time-dependent renal and hepatic effects, particularly with cisplatin and combination therapies. Conclusion: Longitudinal biomarker profiling reveals subclinical renal stress, systemic metabolic burden, and differential tumour marker responsiveness during chemotherapy. CA15-3 and eGFR emerged as sensitive indicators of treatment response and toxicity, underscoring the value of integrated biomarker monitoring in resource-limited oncology settings.

Access to specialty surgical care is growing in many low-income countries, but it remains unclear how hospital workforces can leverage technology to manage large numbers of increasingly complex patients. Large language models (LLMs) may be helpful for this type of clinical decision support, but their real-world performance and safety remain uncertain. The objective of this study was to evaluate feasibility, usability, and potential benefits and risks of an LLM-based assistant for postoperative neurosurgical care in the Gambia. A prospective, single-arm implementation study was conducted at the Edward Francis Small Teaching Hospital. A convenience sample of 4 medical officers (MOs) and 5 nurses assigned to the neurosurgical service participated. A prompted GPT-4o Turbo was deployed on OpenAI Pro accounts to support performance. Usability, helpfulness, and safety were the primary outcomes. Cost-effectiveness was a secondary outcome. Participants completed 75 LLM-assisted interactions on 9 postoperative neurosurgery patients. Usability metrics indicated a moderately high cognitive workload, marginally acceptable usability of the LLM system, and high perceived ease of use. Management plan quality improved in 45 of 75 mock rounds interactions (60%), with a mean improvement of 8.5% (P < .001) on mock rounds scoring rubrics. The improvement was greater for MOs (21.0% change) than nurses (6.5% change). In hypothetical case dilemmas, MO plan accuracy improved by 22.7% (P = .001), and critical errors declined from 33.3% to 0%. Fourteen care changes for 9 patients were attributed to LLM suggestions, including 6 that potentially prevented major morbidity. No unsafe outputs were detected. Exploratory cost analysis suggested potential savings from clinical care changes exceeded the labor costs involved in LLM use. LLM use was associated with improved plan quality without observed safety concerns, while also prompting clinically meaningful care changes. Larger, controlled studies are needed to determine generalizability, durability of benefit, and patient-centered outcomes.