Fasciola and Schistosoma parasites are of public health and economic importance. However, most inter‑ventions target human disease control, neglecting animal reservoir hosts. A cross‑sectional study was conducted at Lake Mburo National Park (LMNP) livestock‑wildlife interface, to determine the prevalence of Fasciola and Schis‑ tosoma parasites in cattle and wild mammals and assess watering points’ potential as breeding sites for aquatic snail‑vectors. Animals in ranch‑lands were tracked along transects, and fresh faecal samples collected. In LMNP, samples came from animal paths and grazing areas. Parasite eggs were concentrated using the formal‑ether sedi‑ mentation method and examined microscopically. Animal watering points were surveyed for 30 min to collect snail vectors, and water physicochemical conditions recorded. Differences in prevalence and snail abundance between sites were assessed using chi‑square test and odds ratios were computed from binary logistic regression. Fasciola  parasites were prevalent among buffaloes (79.6%), waterbucks (54.1%), impalas (51.6%), and cattle (45.1%), but were not detected in baboons and topis. Animals foraging in ranch‑lands were more likely to contract liver‑flukes (38.9%, OR = 3.374, CI: 1.73–6.561) than those in LMNP. Schistosoma bovis was only detected in cattle (2.2%) and buffaloes (2.2%). Watering points, especially valley dams (66.7%) in ranch‑lands, harbored more snails on aver‑ age. Shared grazing and water points could increase risk of parasite cross‑transmission between livestock and wild mammals, as each could be reservoir for the other complicating disease control. We recommend targeted mollus‑ ciciding and fencing off open water sources to reduce contact with snail‑infested habitats on farmlands.

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Aseptic meningitis is a serious neurological disorder that poses diagnostic challenges due to overlapping symptoms with other conditions and the limitations of conventional diagnostic techniques. Existing approaches often rely on invasive procedures or subjective interpretation of medical images, leading to delays or misdiagnosis. The condition leads to high mortality, especially amongst individuals with Human Immunodeficiency Virus (HIV), and predicting the incidence of disease‑related complications remains challenging, with the value of brain magnetic resonance imaging (MRI) not yet fully explored. To address this, we used a convolutional neural network (CNN) to investigate the complementary contribution of brain MRI to conventional prognostic determinants. A hybrid approach was developed, integrating CNN‑based autonomous feature extraction and recognition with K‑Means clustering for efficient segmentation of medical images. Quantitatively, the proposed model achieved a test AUC of 84.1% ± 2.6, accuracy of 81.3% ± 2.7, F1‑score of 79.2% ± 2.4, and balanced accuracy of 77.3% ± 2.3, consistently outperforming ResNet50, DenseNet121, U‑Net, Vision Transformer, and Swin‑UNet across all evaluation metrics. The framework offers a fast, accurate, and non‑invasive decision‑support tool designed to assist clinicians in the timely identification and management of aseptic meningitis, ultimately improving patient care and outcomes

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