cutaneous fungal infections

Clinical experience of primary subcutaneous mycoses in Shanghai: a retrospective analysis

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Researchers in Shanghai studied 33 patients with deep skin fungal infections that had become increasingly common in the area. They identified 13 different fungal species causing these infections, most commonly Candida parapsilosis, Trichophyton rubrum, and Sporothrix schenckii. Patients were treated with antifungal medications tailored to the specific fungus and its drug sensitivity, with most patients recovering completely, though some experienced relapses, emphasizing the importance of long-term follow-up care.

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.

qPCR-Based Molecular Detection of Trichophyton indotineae by Targeting Divergent Sequences

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Trichophyton indotineae is a dangerous fungal infection that causes ringworm and is increasingly resistant to common antifungal treatments. Scientists developed a rapid blood test-like diagnostic tool called qPCR that can accurately identify this specific fungus in less than 2 hours for just a few dollars. The test was created by comparing the genetic codes of different fungal species to find unique fingerprints that distinguish T. indotineae from similar-looking fungi.

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

mycolabadmin

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.

Disease: cutaneous fungal infections

Clinical experience of primary subcutaneous mycoses in Shanghai: a retrospective analysis

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

qPCR-Based Molecular Detection of Trichophyton indotineae by Targeting Divergent Sequences

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