University of Sri Jayewardenepura

Dengue, an Aedes mosquito-borne viral infection, is on the rise with climate and demographic change. In 2023, WHO declared dengue its highest grade of emergency, following the largest number of cases and deaths in recorded history. This emergency highlighted the absence of safe and effective dengue-specific treatments as a gap in the product landscape for care for people with dengue. To accelerate development of dengue-specific treatments for wide implementation and access in all dengue-endemic countries, WHO conducted a landscape analysis of the dengue therapeutics pipeline and convened a global expert and public consultation to develop target product profiles for treatments for non-severe and severe dengue. These target product profiles provide strategic guidance for product developers, regulators, procurement agencies, and funders on the intended use, target populations, and key product characteristics of dengue-specific treatments required to meet public health needs, setting clear targets to drive development of dengue drugs accessible to those who need them the most.

The protection of crops from pests is essential for sustainable agriculture and global food security. Traditional pest identification techniques that are based on visual examination are time-consuming and prone to errors. Recent developments in the field of artificial intelligence (AI) and specifically deep learning (DL) enabled pests to be detected accurately and automatically. This review systematically examines DL-based approaches, including Convolutional Neural Networks (CNNs), Transformer models, ensemble methods, and Graph Neural Networks (GNNs), for pest classification, with an emphasis on benchmark datasets, model architectures, and evaluation metrics. Transformer models, such as GNViT, achieved 99.52% accuracy and a 90.9% F1-score on the IP102 dataset, which is approximately 10% higher than the CNNs. The Vision Transformer (ViT) model achieved 96.7% accuracy on PlantVillage. The ensemble model, like GAEnsemble, achieved excellent accuracies of 98.81% and 95.16% on D0 and SCD, respectively. CNN models had relatively lower performance on the IP102, and the GNNs showed poor performance (below 60%). This paper discusses prospective methodological enhancements, current limitations, and future prospects for developing scalable, understandable, and multi-domain pest classification systems.

Fungal nail infections (onychomycosis) remain challenging to treat due to prolonged therapy, poor cure rates, and safety concerns associated with oral antifungal agents. Although topical formulations are the preferred treatment option, their clinical effectiveness is constrained by the dense keratinized nail barrier. While formulation-driven strategies and device-based therapies have expanded the therapeutic approaches for onychomycosis, their clinical efficacy remains suboptimal, pointing towards the need for pre-clinical evaluation of their performance using more clinically relevant models during the development stage. Nail-integrated model systems provide a platform for evaluating the efficacy of novel antifungal topical treatments in a controlled disease-mimicking environment, thereby enhancing the likelihood of clinical success. This review critically evaluates in vitro and ex vivo models, including keratin-supplemented media, keratin biomembranes, animal hooves, and human nails, focusing on their physiological relevance, advantages and inherent limitations. Ex vivo experimental model designs reported in recent studies are discussed to spotlight their application in evaluating topical formulations and device-based interventions. Ex vivo human nail models, when rationally designed to reflect the structural and pathological architecture of onychomycosis, offer robust, clinically predictive platforms for formulation and device optimization, dose and treatment frequency and comparative efficacy assessment. Infected animal hooves serve as a surrogate for human nails and provide reproducible experimental models for screening the antifungal efficacy under standardized conditions. The available ex vivo data support the utility of these models in predicting clinical performance of antifungal formulations and devices during pre-clinical development; however, broader industrial adoption will require standardized and harmonized protocols, as well as further clinical validation.

Ocean acidification (OA) and nutrient enrichment can separately or together threaten coral reefs by reducing calcification efficiency and increasing physiological stress, ultimately weakening reef resilience. Therefore, the study evaluates the prevailing OA level over the Sri Lankan coral reef areas using the aragonite saturation state (ΩAr) and assesses the nitrate (NO3−), and phosphate (PO43−) concentrations over the coral sites. The study was conducted on coral reefs on the eastern coast (EC), southern coast (SC), northern coast (NC), and west coast (WC) of Sri Lanka from April to June 2024. A total of 63 seawater samples were collected around each coastal site for analysis. The ΩAr were supersaturated (ΩAr > 1) and ranged from 2.98 ± 0.04 to 4.92 ± 0.12. Throughout the study period, the study sites had ΩAr values exceeding 2.92 ± 0.16, indicating that the nation's corals were resilient to deterioration, and the comparative analysis demonstrates that these sites were not vulnerable to OA. However, the NC exhibited significantly (P < 0.05) the lowest ΩAr values (3.2 ± 0.64), positioning the regions near the lower bound of optimal calcification conditions. While ΩAr values indicate low OA stress during sampling, elevated NO3− concentrations (2 – 5 μmol L−1) in SC (2.19 ± 1.28 µmol L−1) and WC (3.52 ± 1.48 µmol L−1) may exacerbate coral bleaching during thermal stress events, representing a co-stressor rather than OA effect. Coral bleaching HotSpot (HS) identification emphasizes how spatially distributed HS are from January to June. The OA risk assessment confirmed that climate change will bring high risk to the coral calcification, reproduction, and damage to the breeding ground, which impact on the ecology and economy of Sri Lanka.

The municipal solid waste management sector is a nationally significant greenhouse gas source in Sri Lanka, yet decision makers lack comprehensive, city-level life-cycle assessment of full waste management chains. This study quantifies and compares greenhouse gas emissions and mitigation potential of alternative waste management scenarios for Colombo and Kandy, supporting nationally determined contributions (NDC) 3.0. Using IPCC 2021 GWP100 V1.03 as the impact assessment method, six scenarios were assessed, including business-as-usual, recycling, composting, confined cover windrow composting, anaerobic digestion, refuse-derived fuel production, incineration, pyrolysis, co-processing in cement kilns, open dumping, and sanitary landfilling. The business-as-usual scenario, dominated by open dumping, resulted in the highest greenhouse gas emissions in both Colombo and Kandy. In contrast, the integrated waste management approach (Scenario 3), combining anaerobic digestion, confined cover windrow composting, refuse-derived fuel production, and enhanced recycling, converted both cities from net emitters to net carbon sinks. Over the projection period of 2026-2035, this transition is expected to deliver substantial cumulative emission reductions, contributing significantly toward achieving NDC 3.0 waste sector targets in Sri Lanka despite the relatively small share of national baseline emissions in the sector. These findings highlight the strong mitigation potential of integrated waste management systems for advancing low-carbon urban strategies.