Research Topic: fungal diagnosis

Three cases of easy to be misdiagnosed tinea of vellus hair

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This case report describes three patients (ages 2-24) who had skin infections caused by a fungus called Microsporum canis that initially was misdiagnosed as eczema or other simple skin conditions. The infections involved fine vellus hair and required prolonged combined treatment with both oral and topical antifungal medications to fully resolve. The key lesson is that proper fungal testing and extended treatment similar to scalp ringworm protocols are necessary for treating these easily-missed infections.

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Cryptococcus neoformans/gattii and Histoplasma capsulatum var. capsulatum infections on tissue sections: Diagnostic pitfalls and relevance of an integrated histomolecular diagnosis

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This research addresses the challenge of identifying two common fungal infections (cryptococcosis and histoplasmosis) when examining tissue samples under a microscope. The study found that these infections often look similar and can be confusing for pathologists. The researchers discovered that looking for specific fungal features, particularly ‘dented-looking’ yeasts, can help differentiate cryptococcosis from histoplasmosis. When standard laboratory tests on fresh tissue aren’t available, using molecular testing (genetic analysis) on preserved tissue samples can successfully identify the fungus in about 75% of cases.

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Unmasking Tinea Incognito: Case Study, Insights Into the Pathogenesis, and Recommendations

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A 29-year-old woman was incorrectly treated for psoriasis with steroids, which made her fungal infection worse instead of better. When doctors finally diagnosed her with a fungal infection (tinea incognito), they found it was caused by a common dermatophyte fungus. This case highlights how steroid creams can hide fungal infections and make them spread, and shows why recognizing this condition is important for proper treatment.

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Inappropriate treatment of pulmonary aspergillosis caused by Aspergillus flavus in susceptible pediatric patients: a case series

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This case series describes three children in Iran who developed serious lung infections caused by a fungal organism called Aspergillus flavus. Tragically, two of the three children died because their infections were diagnosed too late and they did not receive appropriate antifungal medications. The study emphasizes that children with weakened immune systems need quick diagnosis and proper antifungal treatment to survive these dangerous fungal infections.

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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 using two deep learning models to automatically identify fungal infections in microscope images. The system analyzes fluorescence-stained samples to detect fungal spores, hyphae, and mycelium with accuracy matching experienced doctors. This automated approach can significantly reduce the time clinicians spend examining samples and help prevent misdiagnosis, especially in hospitals with fewer specialist technicians.

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Fungal sepsis in a 7-month-old female: diagnosis through peripheral blood smear

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A 7-month-old girl with persistent fever that did not respond to antibiotics was found to have a yeast infection in her blood through examination of a blood sample under a microscope. This discovery was unusual because such infections are typically difficult to spot without special culture tests. After treatment with the antifungal medication fluconazole, the child recovered completely. This case shows how simple microscopy can help diagnose serious fungal infections in areas where advanced laboratory testing is not available.

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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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