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Background/Objectives: Vaccine development for the prevention of ASF has been very challenging due to the extensive genetic and largely unknown antigenic diversity. Inactivated vaccines, using different inactivation methods and a variety of adjuvants, have been consistently inefficacious. Historically, animals recovering from an infection with an attenuated virus became protected from the development of a clinical disease caused by an antigenically related strain. Therefore, immunization of susceptible animals with attenuathe ted virus strains has become a common method of vaccination with the first two commercially available vaccines based on recombinant live-attenuated viruses (LAVs). An important limitation is that the efficacy of the LAV is restricted to those strains that are antigenically related and, in most cases, only provide protection against homologous strains. Due to the unknown antigenic heterogeneity among all ASFV field isolates, the development of broad-spectrum vaccines is a challenge. Besides the anecdotal data, there is not a large amount of information describing patterns of cross-protection between different ASFV strains. Methods: We evaluated the cross-protection induced by the ASFV live-attenuated vaccine ASFV-G-ΔI177L against different biotypes of ASFV and compared their genomic sequences to determine potential genetic mutations that could cause the lack of cross-protection. Results: Results presented here demonstrate different patterns of protection when ASFV-G-ΔI177L vaccinated pigs were challenged with six different ASFV field isolates belonging to different biotypes. Conclusions: The presence of cross-protection cannot be predicted solely by the classical methodology for genotyping-based B646L ORF only. Biotyping, considering the entire virus proteome, appears to be a more promising prediction tool, although additional gathering of experimental data will be necessary to fully validate it; until then, the presence of cross-protection needs to be confirmed in efficacy trials challenging vaccinated animals.

Background A cross sectional study was conducted to detect and characterize species of porcine reproductive and respiratory syndrome virus (PRRSv) identified from slaughtered pigs in Lira district, northern Uganda. The study was conducted from March to September 2019 in three selected slaughter slabs. Pigs brought for slaughter were randomly sampled. At necropsy, lungs were extracted from the thoracic cavity and examined for pneumonic lesions. Seventy-three (73) pigs with gross lung lesions were sampled, from which one hundred and one (101) tissue samples were taken. A real-time reverse transcriptase PCR (RT-qPCR) was used to characterize PRRSv species. Results A total of 20 samples tested positive for PRRSv. The respective prevalence of PRRSv type 1 and type 2 were 24.65% ( n  = 18) and 2.73% ( n  = 2) respectively. Of the pigs sampled ( n  = 73), only two pigs, 2.73% ( n  = 2) tested positive to both species. The likelihood of PRRSv detection decreased with pig age, but increased with gross pneumonic pathology. Conclusions This study demonstrated dual circulation of both species in northern Uganda. The association between PRRSv and lung pathology suggests that it may be an important cause of lung disease in pigs in Uganda and hence loss of production. This calls for further investigations on potential economic impacts of PRRSv on pig productivity. These findings contribute to discussions about the need of surveillance and possible vaccination strategies against PRRSv in Uganda.

Q fever is a neglected zoonotic disease of global concern. In Kenya, it ranks among the top priority zoonoses, yet data on its prevalence and associated risk factors in smallholder dairy farms are limited. To estimate the seroprevalence and molecular prevalence of Coxiella burnetii and identify risk factors for exposure in dairy cattle on smallholder farms in Kenya. A cross-sectional study was conducted on 448 smallholder dairy farms across Nandi County and nearby dairy-producing areas. Serum (n = 1829), vaginal swabs (n = 1783) and preputial swabs (n = 52) were collected. Animal and herd-level data on Q fever risk factors were recorded. An indirect IgG ELISA was used for antibody detection, whereas real-time polymerase chain reaction (PCR) (quantitative PCR [qPCR]) was performed on the swab samples. The animal-level seroprevalence of C. burnetii was 8.7% (95% CI: 7.4-10.0), whereas herd-level seroprevalence was 29.0% (95% CI: 24.8-33.2). Molecular prevalence detected by qPCR was 1.5% (95% CI: 0.9-2.0). Cows with a history of abortion had increased odds of seropositivity (OR = 1.73, 95% CI: 1.14-2.61). Cattle on farms where dogs were kept-whether fed placentas or not-had lower odds of seropositivity compared to farms without dogs. This study confirms circulation of Q fever in smallholder dairy herds in Kenya. Investigating environmental persistence, farm management, interactions between susceptible hosts and sociocultural aspects of dog ownership in smallholder settings is essential to unravel the local transmission dynamics of Q fever. These findings highlight the need for coxiellosis control strategies, including improved biosecurity, surveillance and evaluation of vaccination approaches that have been successfully applied in other endemic settings.

Background Wildebeest associated malignant catarrhal fever (WA-MCF) is a fatal disease of cattle. Outbreaks are seasonal and associated with close interaction between cattle and calving wildebeest. In Kenya, WA-MCF has a dramatic effect on cattle-keepers who lose up to 10% of their cattle herds per year. The objective of this study was to report the impact of WA-MCF on a commercial ranch and assess the performance of clinical diagnosis compared to laboratory diagnosis as a disease management tool. A retrospective study of WA-MCF in cattle was conducted from 2014 to 2016 at Kapiti Plains Ranch Ltd., Kenya. During this period, 325 animals showed clinical signs of WA-MCF and of these, 123 were opportunistically sampled. In addition, 51 clinically healthy animals were sampled. Nested polymerase chain reaction (PCR) and indirect enzyme linked immunosorbent assay (ELISA) were used to confirm clinically diagnosed cases of WA-MCF. A latent class model (LCM) was used to evaluate the diagnostic parameters of clinical diagnosis and the tests in the absence of a gold standard. Results By PCR, 94% (95% C.I. 89–97%) of clinically affected animals were positive to WA-MCF while 63% (95% C.I. 54–71%) were positive by indirect ELISA. The LCM demonstrated the indirect ELISA had poor sensitivity 63.3% (95% PCI 54.4–71.7%) and specificity 62.6% (95% PCI 39.2–84.9%) while the nested PCR performed better with sensitivity 96.1% (95% PCI 90.7–99.7%) and specificity 92.9% (95% PCI 76.1–99.8%). The sensitivity and specificity of clinical diagnosis were 99.1% (95% PCI 96.8–100.0%) and 71.5% (95% PCI 48.0–97.2%) respectively. Conclusions Clinical diagnosis was demonstrated to be an effective method to identify affected animals although animals may be incorrectly classified resulting in financial loss. The study revealed indirect ELISA as a poor test and nested PCR to be a more appropriate confirmatory test for diagnosing acute WA-MCF. However, the logistics of PCR make it unsuitable for field diagnosis of WA-MCF. The future of WA-MCF diagnosis should be aimed at development of penside techniques, which will allow for fast detection in the field.

•Wildebeest-associated malignant catarrhal fever is a fatal disease of cattle.•Seasonal disease caused by alcelaphine herpesvirus-1 with incidence up to 10%•Randomised placebo trial for the efficacy of alcelaphine herpesvirus-1 C500 vaccine.•The vaccine efficacy for controlling malignant catarrhal fever in cattle over 80%•Safe and effective novel method for controlling malignant catarrhal fever in cattle. Wildebeest-associated malignant catarrhal fever (WA-MCF), a fatal disease of cattle caused by alcelaphine herpesvirus 1 (AlHV-1), is one of the most important seasonal diseases of cattle in wildebeest endemic areas, with annual incidence reaching 10%. Here we report efficacy of over 80% for a vaccine based on the attenuated AlHV-1 C500 strain, in preventing fatal WA-MCF in cattle exposed to natural wildebeest challenge. The study was conducted at Kapiti Plains Ranch Ltd, south-east of Nairobi, Kenya. In 2016, 146 cattle were selected for a randomised placebo-controlled trial. Cattle were stratified according to breed and age and randomly assigned to groups given vaccine or culture medium mixed with Emulsigen®. Cattle received prime and boost inoculations one month apart and few adverse reactions (n = 4) were observed. Indirect ELISA demonstrated that all cattle in the vaccine group developed a serological response to AlHV-1. The study herd was grazed with wildebeest from one month after booster vaccination. Three cattle, two that received vaccine and one control, succumbed to conditions unrelated to WA-MCF before the study ended. Twenty-five cattle succumbed to WA-MCF; four of the remaining 71 cattle in the vaccine group (5.6%) and 21 of the remaining 72 control cattle (29.2%; χ2 = 13.6, df = 1, p < 0.001). All of the WA-MCF affected cattle were confirmed by PCR to be infected with AlHV-1 and in 23 cases exhibited histopathology typical of WA-MCF. Vaccine efficacy was determined to be 80.6% (95% CI 46.5–93.0%). Hence, the AlHV-1 C500 vaccine is a safe and potentially effective novel method for controlling WA-MCF in cattle. The implementation of this vaccine may have significant impacts on marginalised cattle keeping communities.

Young children are particularly vulnerable to foodborne disease due to their immature immune systems. Safe food hygiene behaviours by caregivers can potentially reduce this disease burden. Here, we evaluate the potential for a locally designed intervention to improve caregivers’ food hygiene behaviour in a peri-urban, low-income area of Nairobi, Kenya. In this cluster-randomised proof-of-concept trial, 50 community health volunteers (CHVs) were randomly assigned to intervention or control arm (1:1). 101 households under the CHV’s catchment (2-3/CHV), with at least one child aged 6–24 months, participated. Caregivers in intervention households (n = 50) received the CHV-delivered food hygiene intervention. The control arm (n-51) received no intervention. Blinding was not possible due to the nature of the intervention. Our primary outcome was the proportion of caregivers observed to practice all five pre-specified food hygiene behaviours, four weeks post intervention delivery. Secondary outcomes assessed the five observed behaviours individually plus a sixth behaviour—the proportion of caregivers who report always boiling the child’s drinking water. We found no between-arm difference in the proportion of caregivers practising all five observed behaviours. However individually, five behaviours were significantly improved. Specifically, caregivers in the intervention arm had higher odds of washing their hands before feeding the child (adjusted odds ratio (aOR) = 7.40, 95%CI 1.85, 29.62) and before preparing the child’s food (aOR = 7.05, 95%CI 1.52, 32.71), washing the child’s hands before eating (aOR = 21.57, 95%CI 1.15, 405.93) and heating the child’s food (aOR = 4.03, 95%CI 1.27, 12.85) and drinking water (aOR = 12.82, 95%CI 2.54, 64.77) to boiling. There was no effect on cleaning and storage of feeding utensils. This study offers promising preliminary evidence that a CHV-led intervention targeting caregivers of young children can improve their food hygiene behaviour. Our findings warrant further research to refine the intervention and undertake larger scale trials to explore the intervention’s potential impact more comprehensively. Trial registration: This trial was registered with Open Science Framework: osf.io/eu5kf .

In the era of evidence based practice (EBP), health care delivery should be grounded on new or validated knowledge and evidence from research. The aim of the study was to assess research utilization by nurses and the influencing factors at Kenyatta National Hospital (KNH), the largest teaching hospital in Kenya. The study employed a descriptive design that utilized both quantitative and qualitative methods of data collection. It incorporated the Barriers to Research Utilization Scale. It was conducted in six specialized care areas at KNH. Data was collected using questionnaires, Focus Group Discussion and in-depth interviews. Data was analyzed using SPSS version 13 and qualitative data analyzed using themes. The study found that 20.6% of the nurses were participating in research related to their work and 53.6% of these were implementing research findings to practice. Over 2/3 (70.5%) of the respondents were basing their evidence for practice on the knowledge gained during their nursing school. The three greatest barriers to research utilization were that research reports are not readily available (68.7%), unclear implications for practice (66.5%) and inadequate facilities for implementation (66.4%). It is recommended that sensitization trainings on nursing research/ utilization of findings in nursing practice be established to create awareness, motivate and enhance nurses' abilities and also facilities should be provided to enable implementation.

Smallholder farmers are the mainstay of the agricultural economies of sub-Saharan Africa (SSA), where they produce several crops, predominantly centered on maize. Smallholder productivity remains limited resulting from a range of confounding factors, but a primary cause is loss from pests and diseases, particularly insects. To improve productivity, recommendations for the mitigation of crop loss globally include early-warning and management systems for in-season indigenous pests. There are many early-warning systems in temperate regions; however, such systems are poorly established in Africa. This is in part due to the need for a combination of pest modeling, data handling and dissemination infrastructure, capacity, and resource provision. While each of these components is progressing in Africa, the means to successfully deploy such systems remain limited. To bridge this, the development of the Pest Risk Information Service (PRISE) began in 2017 for farmers in SSA. Implemented in Kenya, Ghana, Malawi, and Zambia, PRISE developed temperature-driven phenology models for major maize, bean, and tomato pests. Using downscaled and processed Earth Observation data to drive the models, PRISE partnered with African national agencies to communicate pre- and in-season pest alerts that forecast the time to act against key insect pests. Alerts were designed to be integrated into country-specific Good Agricultural Practice (GAP) recommendations to provide a complementary package to agricultural stakeholders. End line studies with farmers showed that those who received information about the target crops including PRISE pest forecasts, generally reported better outcomes in terms of reduced losses and increased incomes compared with farmers who did not.

Introduction: Existing medical LLM benchmarks largely reflect examination syllabi and disease profiles from high income settings, raising questions about their validity for African deployment where malaria, HIV, TB, sickle cell disease and other neglected tropical diseases (NTDs) dominate burden and national guidelines drive care. Methodology: We systematically reviewed 31 quantitative LLM evaluation papers (Jan 2019 May 2025) identifying 19 English medical QA benchmarks. Alama Health QA was developed using a retrieval augmented generation framework anchored on the Kenyan Clinical Practice Guidelines. Six widely used sets (AfriMedQA, MMLUMedical, PubMedQA, MedMCQA, MedQAUSMLE, and guideline grounded Alama Health QA) underwent harmonized semantic profiling (NTD proportion, recency, readability, lexical diversity metrics) and blinded expert rating across five dimensions: clinical relevance, guideline alignment, clarity, distractor plausibility, and language/cultural fit. Results: Alama Health QA captured >40% of all NTD mentions across corpora and the highest within set frequencies for malaria (7.7%), HIV (4.1%), and TB (5.2%); AfriMedQA ranked second but lacked formal guideline linkage. Global benchmarks showed minimal representation (e.g., sickle cell disease absent in three sets) despite large scale. Qualitatively, Alama scored highest for relevance and guideline alignment; PubMedQA lowest for clinical utility. Discussion: Quantitative medical LLM benchmarks widely used in the literature underrepresent African disease burdens and regulatory contexts, risking misleading performance claims. Guideline anchored, regionally curated resources such as Alama Health QA and expanded disease specific derivatives are essential for safe, equitable model evaluation and deployment across African health systems.

Large Language Models(LLMs) hold promise for improving healthcare access in low-resource settings, but their effectiveness in African primary care remains underexplored. We present a methodology for creating a benchmark dataset and evaluation framework focused on Kenyan Level 2 and 3 clinical care. Our approach uses retrieval augmented generation (RAG) to ground clinical questions in Kenya's national guidelines, ensuring alignment with local standards. These guidelines were digitized, chunked, and indexed for semantic retrieval. Gemini Flash 2.0 Lite was then prompted with guideline excerpts to generate realistic clinical scenarios, multiple-choice questions, and rationale based answers in English and Swahili. Kenyan physicians co-created and refined the dataset, and a blinded expert review process ensured clinical accuracy, clarity, and cultural appropriateness. The resulting Alama Health QA dataset includes thousands of regulator-aligned question answer pairs across common outpatient conditions. Beyond accuracy, we introduce evaluation metrics that test clinical reasoning, safety, and adaptability such as rare case detection (Needle in the Haystack), stepwise logic (Decision Points), and contextual adaptability. Initial results reveal significant performance gaps when LLMs are applied to localized scenarios, consistent with findings that LLM accuracy is lower on African medical content than on US-based benchmarks. This work offers a replicable model for guideline-driven, dynamic benchmarking to support safe AI deployment in African health systems.