fungal diagnosis – mycolab.ai

Molecular Identification of Candida Species among Iranian Patients: Pursuing Candida auris

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Researchers in Iran studied 136 hospital patients to identify different types of Candida fungal infections using a molecular laboratory technique called PCR-RFLP. They found that while Candida albicans remained the most common species, other Candida species were increasingly prevalent in hospitalized patients. The study developed a cost-effective diagnostic method that could help developing countries identify these fungal infections faster and more accurately, though the dangerous multidrug-resistant Candida auris was not found in their samples.

The Impact of the Fungal Priority Pathogens List on Medical Mycology: A Northern European Perspective

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Fungal infections affect over a billion people worldwide but are often overlooked in healthcare. The World Health Organization recently created a priority list of dangerous fungi to help doctors and researchers focus their efforts. This review discusses how this list can improve diagnosis, testing, and treatment of fungal infections across Europe, while also highlighting the need for better training and awareness among healthcare workers.

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.

Identification of Challenging Dermatophyte Species Using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry

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This study shows that MALDI-TOF mass spectrometry is an effective method for quickly identifying skin fungal infections caused by dermatophytes. By combining commercial reference databases with a custom library created from local isolates, researchers achieved 90.7% accurate identification compared to only 16.1% using the commercial database alone. This improved method could help doctors diagnose and treat fungal skin infections more quickly and accurately in clinical laboratories.

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.

First Step on the Way to Identify Dermatophytes Using Odour Fingerprints

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Researchers have developed a new method to quickly identify fungal skin infections caused by dermatophytes by detecting the unique smells (volatile compounds) they produce. Instead of waiting days or weeks for culture-based tests, this approach uses advanced chemical analysis to create a fingerprint of the fungus based on its odor. The study analyzed 47 different dermatophyte strains and found that each species and even individual strains have distinctive chemical signatures, which could one day allow doctors to diagnose infections rapidly using portable devices similar to electronic noses.

Diagnostic Capacity for Fungal Infections in Tertiary Hospitals in Nigeria and Ghana – An Onsite Baseline Audit of 9 Sites

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Researchers audited nine hospital laboratories in Nigeria and Ghana to assess their ability to diagnose fungal infections. They found that most laboratories lack basic equipment like microscopes and trained staff for fungal testing. The study revealed critical gaps in infrastructure and resources needed to identify serious fungal diseases, highlighting the need for investment in laboratory capacity building in African healthcare systems.

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.

Evaluating the Potential of Galactosaminogalactan as a Diagnostic Target for Invasive Aspergillosis

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Researchers developed a new test to detect Aspergillus fungal infections using an antibody that recognizes a fungal component called GAG. The test worked very well in mouse studies, reliably detecting the fungus in blood and other body fluids. However, when tested with samples from infected patients, the test could not find GAG in the blood or other fluids, though it could see the fungus in lung tissue. This suggests either patients don’t produce enough GAG in their bloodstream, or something in human biology interferes with detection.

Disseminated subcutaneous sporotrichosis in an immunocompetent patient: A case report and a systematic literature review

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A 74-year-old man developed painful red masses on his arm and hand after being stuck by a tree branch. Tests identified the infection was caused by a fungus called Sporothrix schenckii. Despite having a normal immune system, the infection had spread across his arm in an unusual pattern. Treatment with an antifungal medication called itraconazole completely healed the lesions within four months without any scarring.

Research Keyword: fungal diagnosis