Category Archives: Publications

Mycotoxins, toxic secondary metabolites produced by filamentous fungi, pose significant threats to global food safety, public health, and agricultural sustainability. This review summarizes the classification, biosynthesis, chemistry, and mechanisms of action of these compounds, and highlights their global prevalence and the serious health consequences of both acute and chronic exposure. Despite decades of research, substantial gaps remain in effective surveillance, prevention, and risk management. Traditional control and detection strategies, although valuable, are often limited by their sensitivity, high costs, and inadequate field applicability. Addressing these gaps, this review emphasizes the potential of emerging technologies, particularly the integration of artificial intelligence (AI) and machine learning (ML) with advanced sensing platforms, to revolutionize mycotoxin detection. These innovations offer enhanced precision, real-time monitoring, and predictive modelling capabilities, paving the way for proactive food safety systems. By critically evaluating current knowledge and exploring future-oriented solutions, this review highlights the urgent need for interdisciplinary approaches that integrate molecular insights, biotechnological advancements, and digital technologies. Finally, we emphasize that adopting these novel strategies is essential to overcoming the silent yet profound global impact of mycotoxins.https://www.mdpi.com/2309-608X/11/12/840

Over the past three decades, dengue disease incidence has significantly increased worldwide, creating serious public health concerns. The principal mosquito vector, Aedes aegypti, exhibits resistance to commonly used insecticides, reducing the efficacy of vector control measures. Thus, the necessity for alternate strategies is critical. Using bioinsecticides such as entomopathogenic fungi (EPF) is one such strategy. This study details the evaluation of mycoparasitic Trichoderma atroviride and entomopathogenic Aspergillus niger against pyrethroid-resistant and pyrethroid-susceptible Ae. aegypti populations. 

Molecular identification of the isolated entomopathogenic fungal strains was done using ITS-rDNA sequence data. Larvicidal and adulticidal assays were performed using different spore concentrations of fungal species. Pupal emergence was assessed from the survived larvae of larvicidal assays.

 Larvicidal assays revealed the highest mortality of 60% for T. atroviride after 9 days of exposure when compared with the highest mortality of 52% for A. niger after 6 days of exposure. No significant difference was observed between the pyrethroid-resistant and pyrethroid-susceptible mosquito colonies, suggesting a lack of connection between prior resistance status and EPF pathogenicity. No pupal mortality was observed, although pupal duration was prolonged. Both EPF strains exhibited 100% mortality in adulticidal assays, signifying the potential use of the two fungal species as adulticides.

The findings have implications for the possible use of A. niger and T. atroviride as potential bioinsecticides against the control of Ae. aegypti.

https://www.frontiersin.org/journals/cellular-and-infection-microbiology/articles/10.3389/fcimb.2025.1502579/full

Reliance on visual inspection for Maize leaf disease identification proves unreliable, often resulting in inappropriate pesticide application and associated health hazards. Food security requires precise and automated disease detection methods to save time and prevent crop losses. Although several studies have used deep and machine learning to detect plant leaf diseases from different perspectives, most of them require numerous training parameters or have low classification accuracy. Furthermore, a model that was developed for one region of the world might not be appropriate for another due to distinctions in morphology and other aspects. In this context, an application for mobile phones was developed that recognizes and classifies maize leaf diseases using a CNN-based pretrained VGG16 architecture. The model can detect northern corn leaf blight, common rust, and gray leaf spots in maize leaves in tropical climates. A total of 3024 images were used to generate the underlying model, including publicly available and field-collected images. The established model uses fewer training parameters to attain a training accuracy of 95.16% and a testing accuracy of 93%. The model provides farmers with an early warning system for early detection of plant diseases, enabling them to take preventive measures before significant production deficits occur.https://www.tandfonline.com/doi/full/10.1080/01140671.2024.2385813

Melanommataceous species exhibit high diversity with a cosmopolitan distribution worldwide and show a prominent saprobic lifestyle. In this study, we explored five saprobic species collected from plant litter substrates from terrestrial habitats in China and Thailand. A combination of morphological characteristics and multi-locus phylogenetic analyses was used to determine their taxonomic classifications. Maximum Likelihood and Bayesian Inference analyses of combined LSU, SSU, ITS and tef1-α sequence data were used to clarify the phylogenetic affinities of the species. Byssosphaeria poaceicola and Herpotrichia zingiberacearum are introduced as new species, while three new host records, Bertiella fici, By. siamensis and Melanomma populicola are also reported from litter of Cinnamomum verum, Citrus trifoliata and Fagus sylvatica, respectively. Yet, despite the rising interest in the melanommataceous species, there is a considerable gap in knowledge on their host associations and geographical distributions. Consequently, we compiled the host-species associations and geographical distributions of all the so far known melanommataceous species