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Dermatophytosis is a superficial fungal infection with an ever-increasing number of patients. Culture-based mycology remains the most commonly used diagnosis, but it takes around four weeks to identify the causative agent. Therefore, routine clinical laboratories need rapid, high throughput, and accurate species-specific analytical methods for diagnosis and therapeutic management. Based on these requirements, we investigated the feasibility of DendrisCHIP® technology as an innovative molecular diagnostic method for the identification of a subset of 13 pathogens potentially responsible for dermatophytosis infections in clinical samples. This technology is based on DNA microarray, which potentially enables the detection and discrimination of several germs in a single sample. A major originality of DendrisCHIP® technology is the use of a decision algorithm for probability presence or absence of pathogens based on machine learning methods. In this study, the diagnosis of dermatophyte infection was carried out on more than 284 isolates by conventional microbial culture and DendrisCHIP®DP, which correspond to the DendrisCHIP® carrying oligoprobes of the targeted pathogens implicated in dermatophytosis. While convergence ranging from 75 to 86% depending on the sampling procedure was obtained with both methods, the DendrisCHIP®DP proved to identify more isolates with pathogens that escaped the culture method. These results were confirmed at 86% by a third method, which was either a specific RT-PCR or genome sequencing. In addition, diagnostic results with DendrisCHIP®DP can be obtained within a day. This faster and more accurate identification of fungal pathogens with DendrisCHIP®DP enables the clinician to quickly and successfully implement appropriate antifungal treatment to prevent the spread and elimination of dermatophyte infection. Taken together, these results demonstrate that this technology is a very promising method for routine diagnosis of dermatophytosis. [ABSTRACT FROM AUTHOR]

Objectives: We aimed to assess the performance of the Novodiag® Stool Parasites (NSP) assay in the diagnosis of the most common intestinal protozoan and microsporidia infections. Methods: A panel of 167 selected stool samples was retrospectively analysed with the NSP assay and compared to routine microscopy and qPCR methods for the detection of pathogenic protozoa and microsporidia. Results: Whereas specificity was high for all protozoa and microsporidia, NSP sensitivity was strongly dependent on the comparative method used as reference. When compared to microscopic methods, NSP sensitivity was high (96.7 to 100%) for Blastocystis hominis, Entamoeba histolytica and Cyclospora cayetanensis but was lower for Giardia intestinalis (85.2%) and ≤50% for Cystoisospora belli and Dientamoeba fragilis. In comparison to conventional qPCR, the NSP assay demonstrated lower sensitivity characteristics dependent on parasite loads, reaching 60 to 70% for G. intestinalis, D. fragilis, Cryptosporidium spp. and E. histolytica. Sensitivity was 100% for Enterocytozoon bieneusi, but none of the five samples containing Encephalitozoon spp. were detected. Conclusions: The overall performance of the NSP assay in the diagnosis of gastrointestinal protozoa and microsporidia seems to be better than or equivalent to that observed with microscopic methods but inferior to that obtainable with classical targeted qPCR. [ABSTRACT FROM AUTHOR]

While COVID-19-associated pulmonary aspergillosis is now well described in developed countries, COVID-19-associated mucormycosis (CAM) has seemed to remain quite rare in Europe. A retrospective study was performed between March 2020 to September 2021 among COVID-19 adult patients in the intensive care unit (ICU) at Toulouse Hospital (Southern France). PCR screening on respiratory samples, which target Aspergillus or Mucorales DNA, were performed, and the number of fungal detections was evaluated monthly during the study period. During the 19 months of the study, 44 (20.3%) COVID-19 ICU patients had a positive PCR for Aspergillus, an overall rate in keeping with the incidence of ICU COVID-19 patients. Ten patients (7.1%) had a positive Mucorales PCR over the same period. Surprisingly, 9/10 had a positive Mucor/Rhizopus PCR in August-September 2021, during the fourth Delta SARS-CoV-2 variant wave. Epidemic investigations have identified a probable environmental cause linked to construction works in the vicinity of the ICU (high levels of airborne spores due to the mistaken interruption of preventive humidification and summer temperature). Even if CAM are apparently rare in Europe, a cluster can also develop in industrialised countries when environmental conditions (especially during construction work) are associated with a high number of COVID-19 patients in the ICU. [ABSTRACT FROM AUTHOR]

The host lymphocyte response is decisive in Pneumocystis pneumonia (PCP) pathophysiology but little is known of the specific roles of lymphocyte subpopulations in this fungal infection. Peripheral NK, NKT, B, TCD4+ and TCD8+ subpopulations were compared by immunophenotyping between 20 patients diagnosed with PCP (PCP(+)] and 20 uninfected immunosuppressed patients (PCP(-)). Among PCP(+) subjects, the lymphocyte populations were also compared between surviving and deceased patients. Low B cell count (<40 cells/μL) was more frequent in PCP(+) than in PCP(-) patients (p = 0.03), while there was no difference for the TCD4 count. Among the PCP(+) group, the 7 deceased patients had lower Th1 (p = 0.02) and Tc1 (p = 0.03) populations, higher Th2 response (p = 0.03), higher effector TCD8 (p < 0.01), lower central memory TCD8 (p = 0.04) and reduced NK cells (p = 0.02) compared with the 13 survivors. Th1/Th2 ratio < 17, CD8 Tc1 < 44%, effector TCD8 < 25%, central memory TCD8 < 4%, NK cells < 50 cells/μL and total lymphocytes < 0.75 G/L were associated with a higher risk of mortality (p = 0.003, p = 0.007, p = 0.0007, p = 0.004, p = 0.02 and p = 0.019, respectively). The traditional analysis of TCD4 and TCD8 populations may be insufficient in the context of PCP. It could be completed by using B cells to predict the risk of PCP, and by using lymphocyte subpopulations or total lymphocyte count, which are easy to obtain in all health care facilities, to evaluate PCP prognosis. [ABSTRACT FROM AUTHOR]