Genetic diversity of Aspergillus species isolated from onychomycosis and Aspergillus hongkongensis sp. nov., with implications to antifungal susceptibility testing.

Thirteen Aspergillus isolates recovered from nails of 13 patients (fingernails, n=2; toenails, n=11) with onychomycosis were characterized. Twelve strains were identified by multilocus sequencing as Aspergillus spp. (Aspergillus sydowii [n=4], Aspergillus welwitschiae [n=3], Aspergillus terreus [n=2], Aspergillus flavus [n=1], Aspergillus tubingensis [n=1], and Aspergillus unguis [n=1]). Isolates of A. terreus, A. flavus, and A. unguis were also identifiable by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. The 13th isolate (HKU49(T)) possessed unique morphological characteristics different from other Aspergillus spp. Molecular characterization also unambiguously showed that HKU49(T) was distinct from other Aspergillus spp. We propose the novel species Aspergillus hongkongensis to describe this previously unknown fungus. Antifungal susceptibility testing showed most Aspergillus isolates had low MICs against itraconazole and voriconazole, but all Aspergillus isolates had high MICs against fluconazole. A diverse spectrum of Aspergillus species is associated with onychomycosis. Itraconazole and voriconazole are probably better drug options for Aspergillus onychomycosis.

Lipid mediators of inflammation as novel plasma biomarkers to identify patients with bacteremia.

OBJECTIVES: Rapid diagnostic tests for bacteremia are important for early treatment to improve clinical outcome. We sought to identify plasma biomarkers that can identify patients with bacteremia using an untargeted global metabolomic analysis. METHODS: Plasma metabolomic profiles were analyzed for 145 adult patients with (cases) and without (controls) bacteremia using ultra-high-performance liquid chromatography/quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF-MS). All metabolites were compared between cases and controls using a 2-tier filtering approach, and each metabolite underwent receiver operating characteristic (ROC) curve analysis. Individual metabolites that distinguish between cases and controls were characterized. Subgroup analysis was performed to identify metabolites with prognostic significance. RESULTS: After 2-tier filtering, 128 molecular features were identified to be potential biomarkers that could distinguish cases from controls. Five metabolites had an area under the ROC curve (AUC) of >0.8 in ROC curve analysis, including a sphingolipid, an acylcarnitine, a fatty acid ester, and 2 glycerophosphocholines. These metabolites could distinguish cases from controls in the unsupervised hierarchical clustering analysis. Subgroup analysis of bacteremic patients showed that the level of trans-2,3,4-trimethoxycinnamate was lower in fatal than non-fatal cases. CONCLUSIONS: Plasma lipid mediators of inflammation can distinguish bacteremia cases from non-bacteremia controls. These biomarkers may be used as targets for rapid test in clinical practice.