National Primary Health Care Development Agency

AI-based emotion recognition has emerged as a critical component of affect-aware educational technologies, particularly in online, large-scale, and technology-mediated learning environments where direct observation of learners’ emotional states is limited. By leveraging facial expressions, speech, physiological signals, and interaction data, emotion-aware systems aim to support adaptive instruction, learner engagement, and instructional decision-making. Despite rapid technical progress, the integration of emotion recognition into authentic educational practice remains constrained by dataset limitations, cultural bias, weak pedagogical alignment, and unresolved ethical concerns. This article presents a critical and theory-informed review of recent advances in AI-based emotion recognition in education, with particular emphasis on deep learning architectures and multimodal fusion approaches, while highlighting persistent challenges and future research directions.Purpose:This study aims to critically examine the current state of AI-based emotion recognition in educational contexts by analyzing dominant modeling paradigms, sensing modalities, application domains, and ethical implications, in order to identify key gaps between technical development and pedagogical practice.Methods/Study design/approach:The study adopts a narrative literature review approach grounded in educational and affective computing theories. Representative and influential studies were analyzed across facial, speech, physiological, and multimodal emotion recognition systems, with emphasis on deep learning, attention-based, and transformer architectures. The review synthesizes findings related to system performance, educational applications, dataset characteristics, and ethical considerations.Result/Findings:The findings indicate that multimodal emotion recognition systems generally outperform unimodal approaches in detecting learning-relevant affective states such as engagement, frustration, and confusion. Advances in deep learning, particularly attention-based and transformer models, improve temporal modeling and robustness. However, most systems rely on non-educational datasets, demonstrate limited cultural generalizability, and prioritize recognition accuracy over pedagogical interpretability and long-term educational impact. Ethical challenges related to privacy, consent, bias, and emotional surveillance remain insufficiently addressed.Novelty/Originality/Value:This study provides an integrative and pedagogically grounded synthesis that bridges affective computing research with educational theory and governance perspectives. By reframing AI-based emotion recognition as a decision-support mechanism rather than an automated control system, the article offers a conceptual foundation for responsible, culturally inclusive, and ethically aligned deployment of emotion-aware AI in education, serving as a reference for future interdisciplinary research and policy development.

Microbiological contamination, particularly from unspecified spoilage bacteria such as Salmonella sp., Vibrio spp., Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli, has raised concerns. This study aims to evaluate the effect of transportation on chemical and microbiological status of frozen imported fish purchased in Wukari markets, Taraba State. Proximate composition analysis and microbial assays were conducted. The highest protein content was recorded in frozen marine fish samples from Marmara market, followed by New market and Old market respectively. The bacterial count in New market was 1.94 x 104, Old market it was 0.97 x 104, and Marmara market was 1.1 x 103. The fungal count in New market was 5.0 x 103, Old market it was 4.0103, and Marmara market was 4.1 x 103. Staphylococcus aureus, Pseudomonas aeruginosa, and E. coli were isolated from both New market and Old market, with a percentage occurrence of 37.50%; however, E. coli and S. aureus were present in Marmara market with a percentage occurrence of 25%. Nonetheless, the results obtained from the sampled fish species fell within a safe range for human consumption.

The COVID-19 pandemic posed a major public health challenge with varying devastating effects on the global community. Vaccination is the most effective measure in combating this pandemic. A major challenge, especially in resource-limited settings, was the shortage of the vaccines. However, evidence by WHO in the use of fractionated doses in combating yellow fever prompted the evaluation of the safety and immunogenicity of fractionated doses of COVID-19 vaccines (ChAd0x1, AD26.COV2.S, and BNT162B2) among Nigerian adults (18-65years). This is a multi-center, non-inferiority randomized, triple-blind trial involving 1812 COVID-19 vaccine naïve participants. Six hundred four eligible participants will be randomized into each of the three vaccine arms of ChAd0x1, AD26.COV2.S, and BNT162B2 COVID-19 vaccine. 151 participants will be randomized each into the 25% and 50% fractionated dose and 300 in the standard full dose arm for the ChAd0x1 and AD26.COV2.S group. In the BNT162B2 group, 201 participants will be randomized into the 50% fractionated dose and 403 into the standard dose arms. The efficacy (immunogenicity) will be evaluated by collecting blood samples at baseline (day 0) and day 28 after immunization to determine the geometric mean fold rise (GMFR) of ≥ 2.5 within the range of 20% non-inferiority to the standard dose of anti-SARS-CoV-2 S antibody response detected by ELISA termed as seroconversion. Safety and adverse events will be evaluated through active evaluation during the first 72h of vaccine administration and on days 14 and 28 of clinic visits. The trial will also assess secondary immunogenicity variables of 50% plaque plaalite reduction neutralization by antibodies against SARS-CoV-2 variants (α, δ, and κ) and cytokines production (IFNα, γ, and IL-2 producing CD4 and CD8 T lymphocytes). The proposed trial will allow for a comprehensive understanding of the immunologic responses and safety profile of fractional doses compared to full doses of COVID-19 vaccines among Nigerian adults. In addition, this will guide policy implementation for prompt vaccination of the population to maximize access to the limited vaccines available during the COVID-19 pandemic or future epidemics/pandemics. The study is registered at the Pan African Clinical Trials Registry (PACTR), a primary registry in the WHO International Clinical Trial Platforms. PACTR202206754734018. Registered on June 1, 2022, Accessible at: https://pactr.samrc.ac.za/ Trial Sponsor: Open Philanthropy.

Malaria remains a major global health challenge due to its high morbidity and mortality, further complicated by growing antimalarial drug resistance. Natural products are being increasingly explored as potential sources of new therapies, with malarial proteases emerging as promising targets due to their essential roles in parasite development, invasion, egress, and hemoglobin degradation. This study evaluates the inhibitory potential of five compounds, quercetrin (DG1), dihydrostilbene (DG2), 4′-methoxy-isoliquiritigenin (DG3), stigmasterol (DG4), and quercetin (DG5), isolated from Globimetula oreophila leaves, using in silico docking against Plasmodium falciparum enzymes. Targets included falcipain-2 and falcipain-3 (cysteine proteases), SERA5 (hemoglobin-processing enzyme), PfDHFR-TS (bifunctional enzyme), and PfCDPK2 (kinase). Docking revealed strong binding affinities through hydrogen bonds, van der Waals forces, and hydrophobic interactions. DG4 showed a high affinity for PfDHFR (-10.3 kcal/mol), comparable to cycloguanil (-10.7 kcal/mol), while DG1 bound firmly to falcipain-2 (-7.9 kcal/mol), falcipain-3 (-7.5 kcal/mol), and PfCDPK2 (-9.0 kcal/mol). Binding to SERA5 ranged from -6.0 to -6.8 kcal/mol. These findings suggest that the tested compounds may act as inhibitors of vital P. falciparum enzymes, holding promise for the development of antimalarial drugs.

This study examined the concentrations and toxicological risks of four priority heavy metals lead (Pb), cadmium (Cd), arsenic (As), and mercury (Hg) in blue crabs (Callinectes sapidus) collected from oil-affected creeks in Rivers State, Nigeria. A total of 90 crabs were gathered from Bodo, Ataba, and Okpoka creeks, and metal analysis was conducted using Microwave Plasma Atomic Emission Spectroscopy (MP-AES 4200) after acid digestion. One-way ANOVA revealed significant site-related variations for all metals (p < 0.001) and significant gender effects for Pb and Hg (p < 0.05), with Tukey’s post-hoc tests indicating that crabs from Ataba and Bodo contained significantly higher concentrations than those from Okpoka (p < 0.05). Human health risk indices, including Estimated Daily Intake (EDI), Target Hazard Quotient (THQ), Hazard Index (HI), and Lifetime Cancer Risk (LCR), were calculated for both adult and child consumers based on mean metal concentrations. Results indicated that mean concentrations of Pb, Cd, As, and Hg in crab tissues exceeded permissible limits set by the World Health Organisation and the Food and Agriculture Organisation (WHO/FAO), with the highest values recorded in Ataba and Bodo creeks. THQ and HI values for Pb and As surpassed the acceptable threshold of 1 in both adult and child groups, signalling potential non-carcinogenic health risks. LCR values for As and Pb also exceeded the United States Environmental Protection Agency (USEPA) acceptable range (10⁻⁶–10⁻⁴), suggesting carcinogenic potential with prolonged exposure. These findings highlight critical environmental and public health concerns linked to seafood consumption from oil-contaminated ecosystems in the Niger Delta and underscore the need for continuous ecological monitoring, stricter seafood safety regulations, and targeted community health awareness programmes.

Streamflow modelling is regarded as a crucial part of managing and planning water resources. Water resources engineers face a variety of challenges when predicting streamflow. These difficulties are caused by complex natural processes that involve non-linearity, non-stationarity, and randomness. This research investigates the application of machine learning (ML)-based models for forecasting streamflow discharge (Q) using input variables, including temperature and rainfall. The study attains the essential stationarity required for precise modelling using Augmented Dickey-Fuller tests, data normalization, and transformation. In contrast, unit root tests identify initial-level non-stationarity and call for first-differencing. Correlation matrix analysis identifies relevant input combinations. The result findings are supported by statistical metrics including mean squared error (MSE), mean absolute error (MAE), and Pearson correlation coefficient (PCC). Notably, in terms of prediction accuracy, the Gaussian Process Regression (GPR) GPR-M3 model stands out as a notable performer, with a low MAE value of 0.034 in the calibration phase and 0.027 in the verification phase. The success of these techniques is further supported by first and second-order ensemble algorithms, with some models reaching a perfect PCC score during both the calibration and verification phases. The study emphasizes the significance of preprocessing, model selection, and ensemble procedures in improving the accuracy of streamflow prediction models. In addressing complex nonlinear interactions, artificial intelligence (AI)- based models are valuable tools for both technical applications and practical understanding.

ABSTRACTDifferent modification techniques have been utilized to improve the properties of starch and enhance the suitability of its films for packaging applications. This includes crosslinking, which involves the formation of molecular linkages between starch molecules. This review extensively examines various reports on crosslinking modification of starch particularly, the effect of different crosslinking agents such as epichlorohydrin, glutaraldehyde, phosphorus oxychloride, sodium trimonophosphate, sodium tripolyphosphate, citric acid, oxidized sucrose, tartaric acid, malonic acid, and photo‐crosslinking on the properties of starch and starch‐based films. Furthermore, it evaluates the outcomes of these crosslinking modifications, outlines existing trends, and identifies some knowledge gaps based on published findings.

The food industry has always been at the forefront of technological innovation, from the advent of mechanized equipment in the early 20th century to the introduction of computerized systems in recent decades. The present era is strongly driven by technology. Particularly in the food sector, technologies such as intelligent packaging, RFID, smart labels, artificial intelligence (AI), Big Data, and Internet of Things (IoT) are proffering potential solutions and fostering creation of smarter traceability systems. Today, AI is utilized in various aspects of food processing and packaging, from quality control and defect detection to supply chain management and smart packaging solutions. These advancements in integration have led to more efficient processes, reduced costs, better compliance with safety regulations, and tremendously increased production capabilities and enhanced food safety standards. This review offers a comprehensive overview of recent advancements and trends in AI applications in food processing and packaging. This review also stresses current advances in the application of RFID tags and traceability sensors in food packaging.

Background: Endophytes are ubiquitous microbes that colonize plants’ tissues without causing any harm to the host plants, but rather, they confer several adaptable characteristics on them. They could be of bacterial, fungal, algal, archaeal and actinomycetes origins. Endophytes also produce useful bioactive metabolites some of which are potent antimicrobials. In this study, bacterial endophytes were isolated from the leaves of Datura stramonium and Sida acuta, both of which are important medicinal plants. Methodology: Freshly cut leaves were collected, rinsed and sequentially sterilized. The sterilized leaves were then cut into smaller pieces and incubated onto extract-seeded nutrient agar medium augmented with antifungal drug. Pure distinct bacterial colonies were morphologically and biochemically characterized. Molecular characterization of the strains was done using the 16S rRNA while phylogenetic evolutionary analysis was carried out using the maximum likelihood method and Tamura-Nei model with MEGA 11 software.Results: A total of 7 bacterial endophytes were isolated and identified, 3 from D. stramonium and 4 from S. acuta. 16S rDNA genome sequencing of the strains revealed thatisolates from D. stramonium were Pseudomonas aeruginosa, Atlantibacter hermannii and Enterobacter roggenkampiiwhile those isolatedfrom S. acuta were identified as Pseudomonas aeruginosa, Pseudomonas monteilii, Enterobacter pseudoroggenkampii and Aeromonas veronii. Conclusion: The outcome of this research has indicated that numerous strains of endophytic bacteria inhabit leaves of plants. It is therefore essential to exploit these endophytic bacteria for their potentials to produce bioactive metabolites and other valuable antimicrobial products.

Background: Piroxicam is a non-steroidal anti-inflammatory drug (NSAID) that is used to manage mild to moderate pain, arthritis, and other inflammatory conditions. There is a dearth of simple, cost-effective, and reliable colorimetric methods for the routine determination of piroxicam in pharmaceutical formulations. The study was aimed at developing a colorimetric method for quantifying piroxicam based on charge-transfer complexation between piroxicam and picric acid. Methods: The method is based on the formation of a charge-transfer complex between piroxicam an electron donor and picric acid as the electron acceptor. The yellow coloured product formed was quantified at the absorption maximum of 420 nm. The effect of various experimental conditions (reaction time, solvent type and reagent concentration) on complex formation were also investigated. The method was validated as per ICH guidelines. The accuracy was evaluated using recovery studies while precision was evaluated on intra-day and inter-day basis. The limit of detection (LOD), limit of quantification (LOQ) and molar absorptivity were determined and the method was compared with the official titrimetric assay method for the drug. Results: The optimized conditions for complex formation were found to involve the use of Dichloromethane as solvent, a reaction time of 10 minutes and 2.5 ml of 0.001% picric acid solution. 2 Beer's law was obeyed in the concentration range of 20 – 100 μg/ml (r = 0.994). The method showed good precision with inter-day precision in the range of 0.03 – 0.91 % RSD while intra-day precision was from 0.02 – 0.85 % RSD. A comparison of the proposed method with the official method revealed that they produce comparable analytical results. Conclusion: The developed method was applied successfully to the analysis of piroxicam in capsule formulation with good accuracy and precision and without any interference from the excipients. As such, the newly developed method can serve as a useful technique for the quality assessment of piroxicam.