Kampala International University, Uganda

As a matter of fact, although RCA is inferior to that of NA (natural aggregate) in terms of performance, RCA is currently being used extensively in concrete production as a sustainable solution due to global increasing construction waste. To overcome these limitations, several mechanisms and supplementary cementitious materials (SCMs) have been investigated in the recent past. This study investigates the combined effect of natural fiber coconut (0%, 0.5%,1%,1.5% and 2%) and activated fly ash (mechanically and chemically) on performance of recycled concrete aggregate (RCA). Four concrete compositions were studied: a control group with varying RCA percentages and no fly ash, second group with 30% inactive fly ash, third group with 30% mechanically activated fly ash, and a fourth group with 30% chemically activated fly ash. The findings revealed that the addition of 30% chemically activated fly ash with 1.5% CF increased compressive strength by 25%, tensile strength by 17% for 100% RCA mix, While strength losses for the same mix are 7.18% after one month and 22.14% after three months of acid exposure. Scanning electron microscopy further validates the enhanced packing density and effective crack filling in the optimized mixes, highlighting their superior performance. This approach holds significant potential for advancing sustainable development pathways in high-performance structural concrete, particularly in regions prioritizing green building solutions.

Anti-malarial drug resistance poses a significant challenge to global malaria control efforts, necessitating a deeper understanding of the evolutionary dynamics underlying the emergence and spread of resistance. This study explores how evolutionary theory provides a framework for elucidating the molecular mechanisms and genetic variation within parasite populations that drive resistance evolution. Drawing on recent research findings, we discuss the role of natural selection, genetic diversity, and fitness costs in shaping resistance phenotypes. Additionally, we highlight the implications of evolutionary insights for antimalarial drug policy, treatment guidelines, and future research directions. By integrating evolutionary biology principles with molecular epidemiology, this review aims to inform strategies for combating antimalarial drug resistance and advancing malaria treatment efforts. Using evolutionary theory to understand the dynamics of antimalarial drug resistance at the molecular level, we explored the influence of genetic variation within parasite populations on the emergence and spread of resistance. Antimalarial drug resistance poses a formidable challenge to global malaria control. By applying evolutionary theory to understand the dynamics of resistance emergence and spread at the molecular level, researchers can develop more effective strategies for surveillance, prevention, and treatment of drug-resistant malaria. Abbreviations: ACTs = artemisinin-based combination therapies, GWAS = genome-wide association studies, k13 = mutations in the kelch13, pfcrt = Plasmodium falciparum chloroquine resistance transporter, pfmdr = multidrug resistance protein 1, sWGA = selective whole genome amplification.

Strontium Zirconate (SrZrO₃) is a well-known perovskite-type material that has generated significant interest in materials research due to its unique structural and functional features. In addition, it has appeared as a potential photocatalyst in the realm of environmental remediation and energy conversion. The electronic structure and structural geometry of the SrZrO3 crystal were computed employing the five functionals of GGA, including GGA with PBE, GGA with RPBE, GGA with PW91, GGA with WC, and GGA with PBEsol, as well as DFT + U using by computational approaches. Next, to improve the photocatalytic activity with reduced band gap, the doping by 4%, 8%, and 12% of Ge atoms in substituting Zr atoms has the empirical formula: SrZr0.96Ge0.04O3, SrZr0.92Ge0.08O3 and SrZr0.88Ge0.12O3, respectively. Secondly, GGA with PBE method conveyed almost overlapping band gap (3.72 eV) with the experimental value at 3.72 eV for standard, SrZrO3 crystal. As a result, it was used for calculation of the density of state (DOS), the partial density of state (PDOS), and optical properties. At last, the absorption ability regarding their photocatalytic activity against methylene blue (MB) dye was assessed and calculated. First of all, the band gaps by the most accurate method of GGA with PBE are at 3.72, 2.43, 2.18, and 1.20 eV for SrZrO3, SrZr0.96Ge0.04O3, SrZr0.92Ge0.08O3 and SrZr0.88Ge0.12O3, respectively. Secondly, having the sharp peak for all crystals in valence band (VB), they are considered as p-type semiconductor materials, creating holes in the VB thereby enabling more hydroxyl free radical for photocatalysis. Doping showed no effect on absorbance at photon energies greater than 4.0 eV, but it can have an effect at lower photon energies, which is more supportive of band gap or electronic structure. In case of absorption, SrZr0.88Ge0.12O3 illustrates the highest photocatalytic activity against MB dye, and have a larger surface energy.

Sickle cell anemia (SCA) is a genetic blood disorder characterized by recurrent pain episodes, chronic complications, and significant emotional and physical stress. This review article explores effective strategies for managing both the emotional and physical aspects of stress in SCA patients. A comprehensive literature search was conducted across multiple databases, including PubMed, Scopus, and Google Scholar, using keywords such as “sickle cell anemia,” “stress management,” “psychological support,” and “pain management.” Emotional stress in SCA arises from chronic pain, frequent hospitalizations, and disease uncertainty, leading to conditions such as anxiety and depression. Effective management of emotional stress involves a combination of psychological counseling, cognitive-behavioral therapy (CBT), and support groups, which help patients develop coping strategies and address the mental health challenges of living with a chronic illness. This review evaluates various psychological interventions and their impact on patient outcomes, emphasizing the need for integrated mental health support in the management of SCA. Physical stress in SCA is primarily due to acute vaso-occlusive crises and chronic pain, which require effective pain management and preventive measures. The review explores pharmacological treatments, such as opioids and hydroxyurea, as well as non-pharmacological approaches, including physical therapy and lifestyle modifications. Additionally, the article discusses innovative therapies like gene therapy and stem cell transplantation, which hold promise for long-term disease management.

Diabetes mellitus is a long-term metabolic condition marked by chronically high levels of glucose in the blood due to insufficient synthesis of insulin or impaired insulin function. Throughout history, several civilizations have used traditional medicinal plants to treat and control diabetes. This paper offers a thorough review, to date, of the antidiabetic natural compounds of medicinal plants origin, specifically highlighting their reported modes or mechanisms of action and therapeutic possibilities, with a view to fast-tracking the possibility of their transition to pharmaceutical products for human use. Phytochemicals such as flavonoids, alkaloids, terpenoids, and glycosides have notable antidiabetic effects. They have been shown to control blood glucose levels via many processes, including boosting insulin secretion, improving insulin sensitivity, blocking glucose absorption in the \gastrointestinal-tract, and regulating carbohydrate metabolism. Unlike earlier reviews, this current one, in addition to the chemistry and proposed mechanisms of action, also x-rays the effectiveness and safety of these natural chemicals by combining evidences from laboratory research, animal experiments, and clinical trials. Several substances, such as Demethoxycurcumin, Trigonelline, and Ginsenosides, have progressed to clinical trials, while others like Allicin and Polypeptide-p have been used at the bedside. Several substances, including Bisdemethoxycurcumin, Vicine, and Gymnemosides, are now in the preclinical trial phase, suggesting that research is still ongoing and there is promise for their future medicinal application. The review highlights the evolution of chemicals in diabetes management, highlighting their potential to improve treatment. It suggests incorporating plant-derived compounds into traditional treatments, reducing dependence on drugs and reducing adverse effects.

Sickle Cell Anemia (SCA) is driven by the polymerization of hemoglobin S (HbS), where the nucleation process plays a central role in initiating sickling episodes. Advances in structural biology and computational modeling have significantly deepened our understanding of this process. High-resolution crystallography has elucidated the structural changes in deoxygenated HbS that promote nucleation, revealing critical interactions between valine-substituted β-globin chains. Cryo-electron microscopy (cryo-EM) has provided detailed visualizations of early-stage polymerization, capturing the formation of small HbS aggregates, which are essential for understanding the dynamics of nucleation in physiological conditions. Additionally, computational modeling has offered valuable insights into the kinetics of HbS nucleation, enabling the prediction of polymer formation under varying oxygen tensions. Molecular dynamics simulations have been instrumental in identifying key factors that modulate nucleation, such as intracellular HbS concentration, pH, and ionic strength. These simulations also suggest that heterogeneous nucleation, facilitated by cellular surfaces or macromolecules, may accelerate the sickling process, highlighting potential therapeutic targets for disrupting this interaction. Together, these techniques have led to new opportunities for innovative treatments. For instance, voxelotor, a drug developed using structural insights, binds to HbS and prevents its deoxygenation, reducing nucleation rates. Other strategies, such as CRISPR-based gene editing and allosteric modulators, are emerging as potential therapeutic avenues for altering nucleation kinetics, offering hope for more effective treatments to mitigate the clinical severity of SCA.

The increasing demand for sustainable energy solutions has highlighted the need to optimize solar power generation systems. While solar power has been extensively studied, the influence of local wind flow on solar irradiance and power generation remains underexplored. This study addresses this gap by developing a differential model that incorporates both solar irradiance and wind flow effects to enhance the prediction of solar power generation across various regions in Uganda. Key qualitative findings suggest that regions with higher wind flow significantly enhance solar power efficiency, revealing potential opportunities for optimizing solar facility locations. Numerical findings show that the northern region yielded the highest solar power generation ($132.8 \,{\text{Wm}}^{-2}$), followed closely by the eastern ($132.7 \, {\text{Wm}}^{-2}$), western ($127.2 \, {\text{Wm}}^{-2}$), and central ($119.6 \,{\text{Wm}}^{-2}$) regions. Error analysis using the RMSE indicator confirms the validity of the model with values of 0.9701, 0.8215, and 6.4186 for the northern, central, and western regions, respectively. This work proposes an integrated approach to solar power generation, considering both solar irradiance and wind flow effects, with the potential to identify optimal deployment sites for solar facilities. Consequently, the study suggests deploying solar facilities in regions with higher solar power distribution and transmitting energy to areas with sparse distribution. Further studies are needed to conduct a comprehensive assessment of solar potential in varying environmental conditions.

Background Chronic obstructive pulmonary disease (COPD) is a significant global health issue, worsened by pollution and modernisation. Ensifentrine (EFT), a new dual inhibitor of phosphodiesterase PDE3 and PDE4, is being developed for inhalation to target airway inflammation, bronchodilation, and ciliary function in COPD treatment. Objective This study aims to develop and validate a new quantification method for Ensifentrine, as no previous techniques are available, by integrating analytical quality-by-design (AQbD) and green analytical chemistry (GAC) principles. Methods An AQbD framework, utilizing Design-expert® software and a central composite design, optimized the RP-UPLC method. The optimized conditions involved isocratic separation on an ACQUITY UPLC HSS C18 SB column at ambient temperature, with a mobile phase of 0.01 N KH2PO4 (pH 5.4) and acetonitrile (66.4:33.6 v/v), a flow rate of 0.27 mL/min, and PDA detection at 272.0 nm. Results The statistical analysis confirmed the model’s significance and normal distribution. The method, validated according to ICH guidelines, showed good linearity (r² = 0.9997) over a range of 3.75–22.5 μg/mL, with an LOD of 3.3 μg/mL and LOQ of 10 μg/mL. It was successfully applied to bulk materials and pharmaceutical formulations with statistical comparisons. Green chemistry assessment The greenness of the developed method was evaluated using tools such as ComplexMoGAPI, AGREE, BAGI, Green certificate-modified Eco-scale, and ChlorTox Scale. Additionally, the EVG method evaluation tool was also used to assess environmental impact, with the results shown in a radar chart. Conclusion This study presents a sensitive and robust RP-UPLC method for quantifying Ensifentrine, combining AQbD and GAC principles. The method, validated according to ICH guidelines, also ensures environmental sustainability. This approach sets a precedent for future analytical method development in pharmaceutical sciences with a focus on sustainability. Graphical abstract

Exposure to heavy metals significantly contributes to insulin resistance , a major factor in type 2 diabetes. This study investigated the antioxidant and therapeutic potential of ethanol leaf extract of Cnidoscolus aconitifolius in mitigating heavy metal-induced insulin resistance, oxidative stress and inflammation in albino rats. Thirty rats were divided into five groups: Groups I and II received normal saline and a lead-mercury mixture, respectively, while Groups III, IV and V were treated with the extract (200, 400 and 600 mg/kg) for four weeks after exposure. The extract, rich in catechin, rutin, gallic acid and kaempferol, exhibited strong antioxidant activity. Heavy metal exposure induced hepatic insulin resistance, dyslipidaemia, oxidative stress and inflammation, as shown by elevated HOMA-IR values. Extract treatment reversed these effects in a dose-dependent manner, restoring insulin sensitivity via oxidative stress and inflammation modulation. This highlights the potential of C. aconitifolius as a nutraceutical for heavy metal-induced metabolic disorders.

The main objective of the present study was to improve the solubility and dissolution rate of felodipine (FLD), a drug that does not dissolve well in water, using a self-nanoemulsifying drug delivery system (SNEDDS). Many analyses have been performed in the laboratory using different oils, non-ionic surfactants, and water-soluble co-solvents to prepare FLD-loaded SNEDDS. It involves measurements of viscosity, refractive index, and droplet size. Solubility studies revealed the best way to load drugs, and pseudo-ternary phase diagrams showed the right amounts of surfactant and co-surfactant for preparing the nanoemulsion. An in vitro dissolution study showed that SNEDDS worked better than pure FLD, releasing over 95% of FLD within 20 min. SNEDDS loaded with felodipine are a good option for developing new oral medicines because they can hold more drugs, dissolve better, and dissolve more quickly. This new SNEDDS technology shows promise for improving the performance of drugs that do not dissolve well, which could lead to better therapeutic results.

Introduction: This research assessed the impact of adding Moringa oleifera leaf and seed to the diet of laying hens on their nutritional status and reproductive performance. Methods: Twenty one (21) week-old laying hens were randomly divided into seven groups of three birds. The control group was fed Layers’ Mash, whereas the experimental groups were given a control diet supplemented with 5%, 10%, or 20% Moringa oleifera leaves and seeds. This feeding lasted six weeks. Eggs, meat and blood samples were analysed for nutritional quality evaluation and effect on some common reproductive hormones using appropriate standard protocols. Results: The present study revealed statistically significant (P < 0.05) differences in the internal and exterior properties of chicken eggs across groups. Compared with those in the control group, the concentrations of serum follicle stimulating hormone (FSH) and luteinising hormone were found to increase at some supplementation doses. Compared with those in the control group, significant (P < 0.05) increases in the serum total protein, albumin, ferritin, vitamins, and certain minerals were detected at different concentrations. Compared with those in the control group, significant (p < 0.05) changes in the dry matter content, energy content, ash content, ether extract content, albumen height of the bird meat and haematological parameters were detected. Conclusion: The addition of Moringa oleifera leaf and seed to the diet of laying birds increases their nutritional condition and reproductive performance, making it highly recommended.

Cancer remains a significant health issue, resulting in around 10 million deaths per year, particularly in developing nations. Demographic changes, socio-economic variables, and lifestyle choices are responsible for the rise in cancer cases. Despite the potential to mitigate the adverse effects of cancer by early detection and the implementation of cancer prevention methods, several nations have limited screening facilities. In oncology, the use of artificial intelligence (AI) represents a transformative advancement in cancer diagnosis, prognosis, and treatment. The use of AI in biomarker discovery improves precision medicine by uncovering biomarker signatures that are essential for early detection and treatment of diseases within vast and diverse datasets. Deep learning and machine learning diagnostics are two examples of AI technologies that are changing the way biomarkers are made by finding patterns in large datasets and making new technologies that make it possible to deliver accurate and effective therapies. Existing gaps include data quality, algorithmic transparency, and ethical concerns around privacy, among others. The advancement of biomarker discovery methodologies with AI seeks to transform cancer by improving patient survival rates through enhanced early diagnosis and targeted therapy. This commentary aims to clarify how AI is improving the identification of novel biomarkers for optimal early diagnosis, focused treatment, and improved clinical outcomes, while also addressing certain obstacles and ethical issues related to the application of artificial intelligence in oncology. Data from reputable scientific databases such as PubMed, Scopus, and ScienceDirect were utilized.

The integration of big data analytics and machine learning (ML) into hematology has ushered in a new era of precision medicine, offering transformative insights into disease management. By leveraging vast and diverse datasets, including genomic profiles, clinical laboratory results, and imaging data, these technologies enhance diagnostic accuracy, enable robust prognostic modeling, and support personalized therapeutic interventions. Advanced ML algorithms, such as neural networks and ensemble learning, facilitate the discovery of novel biomarkers and refine risk stratification for hematological disorders, including leukemias, lymphomas, and coagulopathies. Despite these advancements, significant challenges persist, particularly in the realms of data integration, algorithm validation, and ethical concerns. The heterogeneity of hematological datasets and the lack of standardized frameworks complicate their application, while the "black-box" nature of ML models raises issues of reliability and clinical trust. Moreover, safeguarding patient privacy in an era of data-driven medicine remains paramount, necessitating the development of secure and ethical analytical practices. Addressing these challenges is critical to ensuring equitable and effective implementation of these technologies. Collaborative efforts between hematologists, data scientists, and bioinformaticians are pivotal in translating these innovations into real-world clinical practice. Emphasis on developing explainable artificial intelligence models, integrating real-time analytics, and adopting federated learning approaches will further enhance the utility and adoption of these technologies. As big data analytics and ML continue to evolve, their potential to revolutionize hematology and improve patient outcomes remains immense. Abbreviations: AI = artificial intelligence, CNNs = convolutional neural networks, DL = deep learning, EHR = electronic health record, ML = machine learning, NLP = Natural Language Processing

Introduction Rectovaginal fistula (RVF) is a complex debilitating condition that results from several etiologies, obstetric trauma being the most common. Occasionally RVF closure is non-successful. The objective of this study is to develop a predictive score to identify predictors of failure of surgical closure of obstetric RVF (FSCORVF) in the Democratic Republic of the Congo. Methods This was an analytical cross-sectional study conducted on 268 patients with obstetric RVF who have received surgical management. We proceeded with a bivariate and then multivariate analysis. Score discrimination was assessed using the ROC curve and C-index and score calibration was done according to the Hosmer–Lemeshow test. Results Surgical closure of RVF failed in 12.31% of cases (33/268). After logistic modelling, five criteria emerged as predictive factors of FSCORVF (LUSSY Score): the presence of moderate/severe fibrosis (aOR: 36.25; 95% CI: 1.88–699.37), combined RVF with other type of fistula (aOR: 61.41; 95% CI: 8.78–429.72), fistula size > 3 cm (aOR: 82.45; 95% CI: 10.48–648.58), per-operative hemorrhage (aOR: 27.84;; 95% CI: 5.08–152.47) and postoperative infection (aOR: 1161.35; 95% CI: 46.89–28765.47). A score of 0 to 22 was obtained with a value ≤ 9 points indicating a low risk of FSCORVF, a value between 10 and 12 defining a moderate risk and the value ≥ 13 points corresponding to a high risk of FSCORVF. The area under the ROC curve of the score is 0.9744 with a sensitivity of 90.91%, a specificity of 97.87%, a positive predictive value of 85.71% and a negative predictive value of 98.71%. Conclusion This study identified predictive factors for FSCORVF in the DRC, grouped in the LUSSY score. Complex fistulas (fistula size > 3 cm, severe fibrosis, combined fistulas) require advanced surgical routes different from the transvaginal and the transperineal ones used in the present study. Prevention of intraoperative hemorrhage and postoperative infections requires rigorous preparation, appropriate antibiotic prophylaxis, and strict postoperative follow-up.

Background The low use of self-injectable contraception, coupled with the recognition that many individuals need support beyond training to use self-care technologies successfully, suggests the need for innovative programming. We describe the participatory human-centered design process we used in two districts of Uganda to develop a community-based peer support intervention to improve women’s agency to make and act on contraceptive decisions and help diffuse self-injectable contraception. Methods The design team included multi-disciplinary researchers from Uganda and the United States, representatives of local community-based organizations and village health teams, and local women of reproductive age. The research group conducted 21 in-depth interviews, 12 observations, and six focus group discussions to understand women’s social support needs, contraceptive-seeking experiences, and communication channels. From these data, the design team derived insights into needs and opportunities to improve contraceptive agency and support self-injection use among interested women, spurring a creative idea-generation process to develop a large set of potential solutions. We collectively prioritized the most promising ideas into an integrated, theoretically informed intervention and subsequently prototyped, tested, and refined the intervention. Results Design insights included: women value information from experienced peers and want support to navigate uneven partner dynamics, conflicting contraceptive information, concerns about contraceptive-related side effects, and unreliable contraception services. The final intervention—called I-CAN (English), Nsobola (Lusoga), An Atwero (Langi)—engages experienced self-injection users as ‘mentors’ to support other women (‘mentees’) they recruit in community-based settings. Mentors provide informational, instrumental, appraisal, and emotional support tailored to the individual needs of mentees. This support is designed to improve mentees’ knowledge, consciousness of their rights related to contraception, self-efficacy, and perceived control related to contraceptive decision-making, self-injection self-efficacy, contraceptive access, and ability to act on preferences. Conclusions Our iterative human-centered design process incorporated diverse, lived experiences and scientific expertise and resulted in a peer support intervention with the potential to fill an important gap in contraception service delivery in Uganda. Our approach demonstrates that creating complex interventions to prioritize support for women’s agency related to contraception in line with a human rights-based approach and to spread new contraceptive methods is feasible.

Among the several sources of chromium pollution in groundwater, the most significant source is the effluents produced by tannery operations. The vast majority of the sub-urban communities rely on ground water as their primary source of providing drinking water. Being exposed to chromium for an extended period of time is harmful to human beings. With careful attention given to the optimization of costs, the prime objective of this study is to measure groundwater pollution, as well as to adopt relevant procedures and to create suitable ways for either preventing chromium contamination or reducing it to extremely low levels. Five different sites were chosen to gather bore well samples, which were then analyzed for seven different essential criteria. Onion peel is utilized as the adsorbent material for the extraction of chromium from groundwater. A column study was conducted to assess the efficiency of the adsorbent in eliminating chromium from groundwater. This study aims to assess the impact of bed height on the efficiency of chromium adsorption using onion peels in a fixed bed column. An astonishing 89% of the chromium at a depth of 9 cm adsorbent that was present in the groundwater samples was successfully removed by the use of a fixed bed column that included onion peel as an adsorbent, as shown by the findings. Additionally, it was determined that the impact of various parameters such as chromium concentration, agitation time, pH and adsorbent dosage. The outcome showed that the water that was treated using this procedure was fit for human consumption.

The traditional evaluation of compressive strength through repeated experimental works can be resource-intensive, time-consuming, and environmentally taxing. Leveraging advanced machine learning (ML) offers a faster, cheaper, and more sustainable alternative for evaluating and optimizing concrete properties, particularly for materials incorporating industrial wastes and steel fibers. In this research work, a total of 166 records were collected and partitioned into training set (130 records = 80%) and validation set (36 records = 20%) in line with the requirements of data partitioning and sorting for optimal model performance. These data entries represented ten (10) components of the steel fiber reinforced concrete such as C, W, FAg, CAg, PL, SF, FA, Vf, FbL, and FbD, which were applied as the input variables in the model and Cs, which was the target. Advanced machine learning techniques were applied to model the compressive strength (Cs) of the steel fiber reinforced concrete such as “Semi-supervised classifier (Kstar)”, “M5 classifier (M5Rules), “Elastic net classifier (ElasticNet), “Correlated Nystrom Views (XNV)”, and “Decision Table (DT)”. All models were created using 2024 “Weka Data Mining” software version 3.8.6. Also, accuracies of developed models were evaluated by comparing sum of squared error (SSE), mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), Error (%), Accuracy (%) and coefficient of determination (R²), correlation coefficient (R), willmott index (WI), Nash–Sutcliffe efficiency (NSE), Kling–Gupta efficiency (KGE) and symmetric mean absolute percentage error (SMAPE) between predicted and calculated values of the output. At the end, machine learning has been found to be a transformative approach that enhances the efficiency, cost-effectiveness, and sustainability of evaluating compressive strength in industrial wastes-based concrete reinforced with steel fiber. Among the models reviewed, Kstar and DT emerge as the most practical for achieving precise and sustainable results. Their adoption can significantly reduce environmental impacts and promote the sustainable use of industrial by-products in construction. The sensitivity of the input variables on the compressive strength of industrial wastes-based concrete reinforced with steel fiber produced 36% from C, 71% from W, 70% from FAg, 60% from CAg, 34% from PL, 5% from SF, 33% from FA, 67% from Vf, 5% from FbL, and 61% from 61%. Fiber Volume Fraction (Vf) (67%) high sensitivity suggests that steel fiber content greatly impacts crack resistance and tensile strength. Steel Fiber Orientation (61%) indicates the importance of fiber alignment in distributing stresses and enhancing structural integrity.