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Fusarium spp. are widespread environmental fungi as well as pathogens that can affect plants, animals and humans. Yet the epidemiology of human fusariosis is still cloudy due to the rapidly evolving taxonomy. The Mass Spectrometry Identification database (MSI) has been developed since 2017 in order to allow a fast, accurate and free-access identification of fungi by matrix-assisted laser desorption ionization—time of flight (MALDI-TOF) mass spectrometry. Taking advantage of the MSI database user network, we aim to study the species distribution of Fusarium spp. isolates in an international multicenter prospective study. This study also allowed the assessment of the abilities of miscellaneous techniques to identify Fusarium isolates at the species level. The identification was performed by PCR-sequencing and phylogenic-tree approach. Both methods are used as gold standard for the evaluation of mass spectrometry. Identification at the species complex was satisfactory for all the tested methods. However, identification at the species level was more challenging and only 32% of the isolates were correctly identified with the National Center for Biotechnology Information (NCBI) DNA database, 20% with the Bruker MS database and 43% with the two MSI databases. Improvement of the mass spectrometry database is still needed to enable precise identification at the species level of any Fusarium isolates encountered either in human pathology or in the environment.

HIV surveillance requires monitoring of new HIV diagnoses and differentiation of incident and older infections. In 2008, Switzerland implemented a system for monitoring incident HIV infections based on the results of a line immunoassay (Inno-Lia) mandatorily conducted for HIV confirmation and type differentiation (HIV-1, HIV-2) of all newly diagnosed patients. Based on this system, we assessed the proportion of incident HIV infection among newly diagnosed cases in Switzerland during 2008-2013.Inno-Lia antibody reaction patterns recorded in anonymous HIV notifications to the federal health authority were classified by 10 published algorithms into incident (up to 12 months) or older infections. Utilizing these data, annual incident infection estimates were obtained in two ways, (i) based on the diagnostic performance of the algorithms and utilizing the relationship 'incident = true incident + false incident', (ii) based on the window-periods of the algorithms and utilizing the relationship 'Prevalence = Incidence x Duration'. From 2008-2013, 3'851 HIV notifications were received. Adult HIV-1 infections amounted to 3'809 cases, and 3'636 of them (95.5%) contained Inno-Lia data. Incident infection totals calculated were similar for the performance- and window-based methods, amounting on average to 1'755 (95% confidence interval, 1588-1923) and 1'790 cases (95% CI, 1679-1900), respectively. More than half of these were among men who had sex with men. Both methods showed a continuous decline of annual incident infections 2008-2013, totaling -59.5% and -50.2%, respectively. The decline of incident infections continued even in 2012, when a 15% increase in HIV notifications had been observed. This increase was entirely due to older infections. Overall declines 2008-2013 were of similar extent among the major transmission groups.Inno-Lia based incident HIV-1 infection surveillance proved useful and reliable. It represents a free, additional public health benefit of the use of this relatively costly test for HIV confirmation and type differentiation.

Detection of HIV-1 p24 antigen permits early identification of primary HIV infection and timely intervention to limit further spread of the infection. Principally, HIV screening should equally detect all viral variants, but reagents for a standardised test evaluation are limited. Therefore, we aimed to create an inexhaustible panel of diverse HIV-1 p24 antigens.We generated a panel of 43 recombinantly expressed virus-like particles (VLPs), containing the structural Gag proteins of HIV-1 subtypes A-H and circulating recombinant forms (CRF) CRF01_AE, CRF02_AG, CRF12_BF, CRF20_BG and group O. Eleven 4th generation antigen/antibody tests and five antigen-only tests were evaluated for their ability to detect VLPs diluted in human plasma to p24 concentrations equivalent to 50, 10 and 2 IU/ml of the WHO p24 standard. Three tests were also evaluated for their ability to detect p24 after heat-denaturation for immune-complex disruption, a pre-requisite for ultrasensitive p24 detection.Our VLP panel exhibited an average intra-clade p24 diversity of 6.7%. Among the 4th generation tests, the Abbott Architect and Siemens Enzygnost Integral 4 had the highest sensitivity of 97.7% and 93%, respectively. Alere Determine Combo and BioRad Access were least sensitive with 10.1% and 40.3%, respectively. Antigen-only tests were slightly more sensitive than combination tests. Almost all tests detected the WHO HIV-1 p24 standard at a concentration of 2 IU/ml, but their ability to detect this input for different subtypes varied greatly. Heat-treatment lowered overall detectability of HIV-1 p24 in two of the three tests, but only few VLPs had a more than 3-fold loss in p24 detection.The HIV-1 Gag subtype panel has a broad diversity and proved useful for a standardised evaluation of the detection limit and breadth of subtype detection of p24 antigen-detecting tests. Several tests exhibited problems, particularly with non-B subtypes.

Tests for recent infections (TRIs) are important for HIV surveillance. We have shown that a patient's antibody pattern in a confirmatory line immunoassay (Inno-Lia) also yields information on time since infection. We have published algorithms which, with a certain sensitivity and specificity, distinguish between incident (< = 12 months) and older infection. In order to use these algorithms like other TRIs, i.e., based on their windows, we now determined their window periods.We classified Inno-Lia results of 527 treatment-naïve patients with HIV-1 infection < = 12 months according to incidence by 25 algorithms. The time after which all infections were ruled older, i.e. the algorithm's window, was determined by linear regression of the proportion ruled incident in dependence of time since infection. Window-based incident infection rates (IIR) were determined utilizing the relationship 'Prevalence = Incidence x Duration' in four annual cohorts of HIV-1 notifications. Results were compared to performance-based IIR also derived from Inno-Lia results, but utilizing the relationship 'incident = true incident + false incident' and also to the IIR derived from the BED incidence assay.Window periods varied between 45.8 and 130.1 days and correlated well with the algorithms' diagnostic sensitivity (R(2) = 0.962; P