Central University

Escherichia coli (E. coli) is a leading cause of bloodstream infections (BSIs) globally, with multidrug-resistant (MDR) strains complicating treatment outcomes. In South Africa, genomic data on such isolates remain scarce. To elucidate the genomic landscape of E. coli isolates from bloodstream infections (BSIs) collected over a one-year period in the uMgungundlovu District, South Africa, with a focus on the resistance and virulence genes, mobile genetic elements (MGEs), genetic synteny, sequence types (STs), and phylogenomic context. Twenty-five non-duplicate E. coli isolates were recovered from blood cultures and subjected to antimicrobial susceptibility testing. All twelve MDR isolates underwent whole-genome sequencing and bioinformatic analysis. The MDR isolates comprised six distinct STs, notably high-risk international lineages ST131 (33.3%) and ST69 (25.0%), spanning five phylogroups, predominantly B2 and D. Eight O: H serotypes were identified, with O25:H4 and O45:H16 being most frequent. CH-typing revealed dominant CH types CH40-30 (ST131) and CH35-27 (ST69) and fimH alleles such as fimH30 and fimH27. The isolates encoded β-lactamases, including blaCTX-M-15, blaTEM-1B and blaOXA-1, frequently co-located with MGEs. Notably, blaCTX-M-15 was chromosomally integrated within a Tn3–ISEcp1 transposon unit, while blaTEM-1B and blaOXA-1 were associated with diverse plasmid-associated syntenic architectures flanked by IS1, IS91, or integron-associated regions. Other antibiotic resistance genes were detected, conferring resistance to aminoglycosides (aph(3’)-Ia, aac(3)-IIa, aac(3)-IIe, aadA1, aadA2), sulfonamides (sul1, sul2) and trimethoprim (dfrA variants). A diverse array of virulence genes was identified, associated with adhesion, iron acquisition, serum resistance, and toxin production. Phylogenomic clustering revealed close relationships between local ST131/ST69 isolates and counterparts across Africa. The study identifies diverse MDR E. coli clones circulating in bloodstream infections, notably high-risk lineages ST131, ST69 and ST10, with complex resistance and virulence gene arsenals facilitated by MGEs. These insights reinforce the imperative for genomic surveillance to guide infection control and antibiotic stewardship in high-burden settings.

Cancer continues to pose a serious public health problem globally, and ongoing research into other therapy modalities has included the utilization of botanicals from traditional medicine. Objective:This review systematically compiles and critically evaluates herbs traditionally used in cancer treatment, focusing on their ethnobotanical relevance and available experimental evidence to highlight their potential as alternative or complementary therapeutic options in cancer management. Methodology: Literature was accessed from ScienceDirect, PubMed, Scopus, Google Scholar, SpringerLink, and Web of Science between 2000 and 2024, with a focus on research reporting anticancer activity supported by in vitro or in vivo evidence. Results: A total of 198 plant species across 73 families were found, Apiaceae (12.6%), Lamiaceae (8.1%), and Liliaceae (6.1%) were the best represented. Leaves contributed 41.9% of the plant material used, followed by roots (24.2%) and bark (14.1%), indicating a shift towards sustainable collection. The phytochemical content showed flavonoids (23.7%) as the dominant phytochemical, followed by alkaloids (17.2%), terpenes (12.4%), and saponins (9.6%), which are pro-apoptotic, cell cycle arrest-inducing, and tumor cell proliferation inhibitors. Most notably, 38% of the plants tested were strongly active against breast cancer cell lines, and 24% and 19% against liver and colorectal cancer cell lines, respectively. Conclusion: This study highlights the enormous pharmacological potential of traditional medicines and provides a rationale for future drug development and integrative oncology research. Overall, this study offers a thorough data repository for the scientific community engaged in cutting-edge cancer treatment research.

Accurate building footprint is a fundamental element of large-scale base maps, which serve as critical inputs for urban planning, infrastructure development, environmental monitoring, and disaster management. While building footprint extraction and geometric regularization have been widely studied, their combined application for automated, large-scale basemap generation using very-high-resolution satellite imagery has received limited attention. To address this gap, this study proposes an integrated framework that leverages deep learning and geometric regularization to efficiently extract and refine building footprints for large-scale base maps. The framework first enhances spectral, spatial, and textural features of very-high-resolution satellite imagery through pan-sharpening, NDVI computation, GLCM-based texture analysis, and PCA. A Mask R-CNN model is then trained on multi-band imagery to segment building footprints, followed by geometric regularization to simplify and align polygons along dominant structural orientations. Object-based evaluation on ground-truth buildings demonstrates high performance, with 97.6% precision, 91.6% recall, and a 94.5% F1-score. The proposed systematic framework substantially reduces production time compared to manual stereo-plotting, requiring less than an hour per 5.29 km2 map sheet in operational production, representing a more than 35-fold efficiency gain. While minor geometric inaccuracies and merged adjacent buildings persist, the methodology offers a robust, scalable, and efficient approach to support large-scale base map production.

There are extant studies on the knowledge, attitudes and practices of air pollution in sub-Saharan Africa and Ghana, however, studies among commercial drivers and traders in transport stations in Ghana are sparse. This study examined the mediation effect of attitude on the association between knowledge and practice toward air pollution among two high-risk workers in the Greater Accra Metropolitan Area, Ghana. A population-based cross-sectional study was conducted among commercial drivers and traders between November 2023 and June 2024. A consecutive sampling technique was used to select 1011 participants (drivers [n = 619, 61.2%] and traders [n = 392, 38.8%]). A structured questionnaire was used, including questions on knowledge (9 items), attitude (7 items) and practices (5 items) toward air pollution. Statistical analysis (descriptive, hierarchical linear regression and partial least squares structural equation modelling) was performed using Stata 17/MP. The primary sources of air pollution information for drivers and traders were social media and radio. The PLS-SEM revealed a significant direct effect of knowledge for both drivers/traders (β = 0.23/0.26, p < 0.001). Moreover, attitude partially mediated the relationship between knowledge and practices, with a direct significant effect observed for drivers/traders (β = 0.10/0.02, p < 0.001). HLR further confirmed that knowledge strongly predicted protective practices for both drivers (β = 0.23, p < 0.001) and traders (β = 0.26, p < .001. Higher educational level consistently predicted better knowledge and protective practices but old age had an inverse relationship with protective behaviour toward air pollution. Although a cross-sectional design precludes causality, attitude partially mediated the association between knowledge and practices, but the effect was more substantial among drivers than traders. The findings have implications for social and traditional media education and attitudinal change campaigns to effectively reduce air pollution exposure risk among these high-risk occupational groups.

Abstract Kilonovae are the scientifically rich—but observationally elusive—optical transient phenomena associated with compact binary mergers. Only a handful of events have been discovered to date, all through multiwavelength (gamma-ray) and multimessenger (gravitational-wave) signals. Given their scarcity, it is important to maximise the discovery possibility of new kilonova events. To this end, we present our follow-up observations of the gravitational-wave signal S250818k—a plausible binary neutron star merger at a distance of 237 ± 62 Mpc. Pan-STARRS tiled 286 and 318 deg 2 (32% and 34% of the 90% sky localisation region) within 3 and 7 days of the GW signal, respectively. ATLAS covered 65% of the sky map within 3 days, but with lower sensitivity. These observations uncovered 47 new transients; however, none were deemed to be linked to S250818k. We undertook an expansive follow-up campaign of AT2025ulz, the purported counterpart to S250818k. The griz -band light curve, combined with our redshift measurement ( z = 0.0849 ± 0.0003), all indicate that SN2025ulz is a type IIb supernova and thus not the counterpart to S250818k. We rule out the presence of an AT2017gfo-like kilonova within ≈27% of the distance posterior sampled by our Pan-STARRS pointings (≈9.1% across the total 90% 3D sky localisation). We demonstrate that early observations are optimal for probing the distance posterior of the 3D gravitational-wave sky map, and that SN2025ulz was a plausible kilonova candidate for ≲5 days, before ultimately being ruled out.

STEM (Science, Technology, Engineering, and Mathematics) is essential for the development of 21st-century skills, particularly in a world driven by technological innovation. However, in vulnerable school contexts, access to meaningful STEM experiences remains limited. This study addresses this issue through the design and implementation of a didactic strategy in a public high school in Bogotá, Colombia, based on two educational resources: the BioSen electronic board, which is compatible with Arduino technology and designed to acquire physiological signals such as electrocardiography (ECG), electromyography (EMG), electrooculography (EOG), and body temperature; and the Space Exploration instructional guide, which is organized around contextualized learning missions. This study employed a quasi-experimental mixed-methods design that combined pre–post perception questionnaires, unstructured classroom observations, and a contextualized knowledge test administered to three student groups. Findings demonstrate that after eight weeks of implementation with upper secondary students, the strategy had a positive impact on the development of 21st-century skills, such as creativity, computational thinking, and critical thinking. These skills were assessed through a mixed quasi-experimental design combining perception questionnaires, qualitative observations, and knowledge evaluations. Unlike the control groups, students who participated in the intervention adjusted their self-perceptions when facing real-world challenges and showed progress in the application of key competencies. Overall, the results support the effectiveness of integrating low-cost biomedical tools with gamified STEM instruction to enhance higher-order thinking skills and student engagement in vulnerable educational contexts.

Mental health conditions among youths are increasing rapidly, taking into consideration their biological, psychological and social development in the time of technological advancement with its associated challenges. Therefore, this study examined the psychometric properties of eight mental health scales among Ghanaian youth. A total of 708 youths (62.1% females; 10–29 years) from junior high schools, senior high schools and a university were recruited to respond to measures on depression, anxiety, somatic symptoms, obsessive–compulsive symptoms, insomnia, smartphone application-based addiction, internet addiction, life satisfaction, stress and cognitive fatigue. Confirmatory factor analysis (CFA) and Pearson’s r were used to analyse the data. The findings indicated acceptable CFA fit for all scales (comparative fit index [CFI] &gt;0.9, Tucker–Lewis index [TLI] &gt;0.9, root mean square error of approximation [RMSEA] &lt;0.08 and standardized root mean square residual [SRMR] &lt;0.08), and internal reliability was satisfactory (Cronbach’s α = 0.774–0.868 and McDonald’s ω = 0.775–0.870). Correlation analyses showed significant relationships between all the measures except for life satisfaction and internet addiction, and stress and life satisfaction. Both the CFA indices and correlation analyses indicate that all the mental health measures demonstrate acceptable initial evidence of reliability and construct validity.

The rapid expansion of personalized healthcare has positioned wearable electrocardiogram (ECG) monitoring systems as a pivotal solution for continuous cardiac assessment. However, achieving real-time signal classification under strict power and resource constraints remains a core challenge for edge-based deployment. While convolutional neural networks (CNNs) offer excellent feature extraction capabilities, their intensive computational and memory demands impede efficient implementation on lightweight hardware platforms. To overcome these limitations, we propose a hybrid hardware accelerator integrating a binarized ReActNet backbone for low-overhead feature extraction with an XGBoost decision tree classifier that provides interpretable and hardware-efficient inference. The ReActNet module leverages binary convolution operations to reduce arithmetic complexity, while the XGBoost component replaces dense layers, enhancing model transparency and reducing logic resource usage. To further streamline deployment, we introduce a generative hardware synthesis methodology based on large language models (LLMs), enabling automatic translation of trained XGBoost models into synthesizable VHDL code from their JSON representation. This LLM-assisted workflow significantly reduces development time, eliminates manual RTL coding, and promotes modular, reusable classifier generation adaptable to evolving clinical models. The proposed system is evaluated through RTL-level simulation and synthesis targeting low-power, resource-constrained FPGA platforms, demonstrating its architectural feasibility for real-time ECG classification at the edge. These findings highlight a unified and scalable framework that fuses binary neural networks, interpretable classifiers, and generative AI techniques to accelerate hardware-software co-design and broaden the deployment of edge AI in wearable health applications.

Abstract Diarrhoea continues to be one of the leading causes of death among children under five in Ghana preceded by malaria and pneumonia. We aimed to assess mothers' knowledge, attitudes, and practices (KAP) regarding the prevention and management of diarrhoea in children under five years of age in the Lower Manya Krobo Municipality, Ghana. A descriptive cross-sectional design was employed, utilizing a convenience sampling technique to select 116 mothers attending health facilities in the municipality. Data were collected using a structured questionnaire and analyzed using the Statistical Package for Social Sciences (SPSS) version 23. The study found that majority of the mothers were aged 24–29 years (41%). Over 52% of them demonstrated good knowledge of diarrhoea. Approximately 45% identified contaminated food and water as the main cause of diarrhoea, with 26% recognizing dehydration as a major complication. Regarding practices, 94% restricted food intake during diarrhoea episodes, with 50% discontinuing breastfeeding. Additionally, 57% self-medicated with antibiotics for non-bloody diarrhoea. While mothers exhibited good knowledge of diarrhoea, negative attitudes and suboptimal practices were prevalent, particularly concerning breastfeeding and appropriate treatment. Health promotion programs should focus on enhancing attitudes and practices, emphasizing the importance of continued breastfeeding and appropriate medical treatment during diarrhoea episodes.

Abstract We present the X-ray polarization observation of G21.5−0.9, a young Galactic supernova remnant (SNR), conducted with the Imaging X-ray Polarimetry Explorer (IXPE) in 2023 October, with a total livetime of approximately 837 ks. Using different analysis methods, such as a space-integrated study of the entire region of the pulsar wind nebula (PWN) and a space-resolved polarization map, we detect significant polarization from the PWN at the center of the SNR, with an average polarization degree of ∼10% oriented at ∼33° (north through east). No significant energy-dependent variation in polarization is observed across the IXPE band (2–8 keV). The polarization map, corrected for the effect of polarization leakage, reveals a consistent pattern in both degree and angle, with little change across the nebula. Our findings indicate the presence of a highly polarized central torus, suggesting low levels of turbulence at particle acceleration sites. Unlike Vela, but similar to the Crab Nebula, we observe substantial differences between radio and X-ray polarization maps. This suggests a clear separation in energy of the emitting particle populations and hints at an important, yet poorly understood, role of instabilities in the turbulence dynamics of PWNe.