ArticleMycoses2026
Diagnostic Performance of Sōna GM LFA in Pulmonary Infections Caused by Different Aspergillus Species.
Article in Mycoses, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundAlthough Aspergillus fumigatus remains the primary cause of invasive aspergillosis, other species-including emerging cryptic ones-are increasingly identified in clinical settings. These species often exhibit distinct antifungal susceptibility profiles, emphasising the importance of species-level identification for effective patient management. Galactomannan, once considered specific to A. fumigatus, is now widely used for aspergillosis diagnosis regardless of species. However, its performance in detecting non-fumigatus infections remains insufficiently characterised.
objectiveTo evaluate the diagnostic performance of the Sōna GM LFA (IMMY) in BAL samples from patients with pulmonary infections attributed to different Aspergillus spp., including cryptic species.
methodsWe evaluated the diagnostic performance of the Sōna galactomannan LFA (IMMY) in bronchoalveolar lavage samples from 49 patients (15% paediatric) with probable invasive aspergillosis or COVID-associated pulmonary aspergillosis, confirmed by microscopy and culture.
resultsThe etiologic agents comprised A. fumigatus sensu stricto (46.9%), A. terreus ss. (16.3%), A. flavus ss. (16.3%), A. niger ss. (6.1%), A. nidulans (4.1%) and five cryptic species (10.2%). Galactomannan index values varied across species and clinical group, with particularly low readings (0.09-0.31) in six cases (five involving cryptic species), suggesting reduced assay sensitivity for cryptic taxa. Paediatric samples also showed lower galactomannan levels compared to adults.
conclusionsThis study highlights the limitations of galactomannan assays in detecting cryptic Aspergillus infections, showing consistently low galactomannan levels in BAL. Multicenter studies are needed to validate findings and refine clinical algorithms.
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