Research Keyword: machine learning

Drug repurposing to fight resistant fungal species: Recent developments as novel therapeutic strategies

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This editorial highlights the growing problem of fungal infections that resist current treatments, causing millions of deaths worldwide each year. Researchers are finding new ways to fight these resistant infections by repurposing existing drugs in new combinations and discovering novel compounds from natural sources. The collection of studies presented shows promising results using combinations like minocycline with antifungal drugs, natural compounds like baicalin, and AI technology to predict resistance patterns. These innovative approaches offer hope for better treatment options for patients suffering from serious fungal infections.

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Automatic classification of fungal-fungal interactions using deep learning models

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Researchers developed an artificial intelligence system that automatically analyzes images of fungi growing together to identify which ones can fight off disease-causing fungi. Instead of having humans manually look at thousands of plate images, which is time-consuming and subjective, their computer vision system can classify the outcomes with 95% accuracy. This automation tool could help scientists quickly find beneficial fungi that could replace chemical pesticides in agriculture, supporting the goal of sustainable and more environmentally friendly farming.

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Application of ATR-FTIR and FT-NIR spectroscopy coupled with chemometrics for species identification and quality prediction of boletes

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Researchers developed a fast and non-destructive method to identify different types of edible boletes and assess their nutritional quality by analyzing their amino acid content. Using special spectroscopy techniques combined with computer analysis, they achieved perfect accuracy in identifying five bolete species and could predict the amino acid content that contributes to flavor and nutrition. This breakthrough provides consumers with better protection against accidentally purchasing toxic mushroom species that look similar to edible ones, while helping food producers quickly assess quality without lengthy lab testing.

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Deep learning application to hyphae and spores identification in fungal fluorescence images

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Researchers developed an artificial intelligence system that can automatically identify fungal infections in microscope images as accurately as experienced doctors. The system uses two different AI models working together to spot fungal spores, thread-like hyphae, and mycelium in fluorescence images. This technology could significantly reduce the time doctors spend analyzing samples and help ensure more accurate diagnoses, especially in hospitals with fewer experienced specialists.

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