Emergence of Candida auris in Brazil in a COVID-19 Intensive Care Unit.
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ABSTRACT:
SUBMITTER: de Almeida JN
PROVIDER: S-EPMC8002986 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundCandida auris a frequently multidrug-resistant yeast species that poses a global health threat due to its high potential for hospital outbreaks. While C. auris has become endemic in parts of Asia and Africa, transmissions have so far rarely been reported in Western Europe except for Great Britain and Spain. We describe the first documented patient-to-patient transmission of C. auris in Germany in a COVID-19 intensive care unit (ICU) and infection control measures implemented to prevent further spread of the pathogen.MethodsIdentification of C. auris was performed by MALDI-TOF and confirmed by internal transcribed spacer (ITS) sequencing. Antifungal susceptibility testing was carried out. We conducted repeated cross-sectional examinations for the presence of C. auris in the patients of the affected ICU and investigated possible routes of transmission.ResultsThe index patient had been transferred to Germany from a hospital in Northern Africa and was found to be colonised with C. auris. The contact patient developed C. auris sepsis. Infection prevention and control (IPC) measures included strict isolation of the two C. auris patients and regular screening of non-affected patients. No further case occurred during the subsequent weeks. Reusable blades used in video laryngoscope-guided intubation were considered as the most likely vehicle of transmission.ConclusionsIn view of its high risk of transmission, vigilance regarding C. auris colonisation in patients referred from endemic countries is crucial. Strict and immediate IPC measures may have the potential to prevent C. auris outbreaks.
| S-EPMC9115290 | biostudies-literature
Project description:Candida auris has recently emerged as a multidrug-resistant yeast implicated in various healthcare-associated invasive infections and hospital outbreaks. In the current study, we report the first five intensive care unit (ICU) cases affected by C. auris isolates in Greece, during October 2020-January 2022. The ICU of the hospital was converted to a COVID-19 unit on 25 February 2021, during the third wave of COVID-19 in Greece. Identification of the isolates was confirmed by Matrix Assisted Laser Desorption Ionization Time of Flight mass spectroscopy (MALDI-TOF]. Antifungal susceptibility testing was performed by the EUCAST broth microdilution method. Based on the tentative CDC MIC breakpoints, all five C. auris isolates were resistant to fluconazole (≥32 μg/mL), while three of them exhibited resistance to amphotericin B (≥2 μg/mL). The environmental screening also revealed the dissemination of C. auris in the ICU. Molecular characterization of C. auris clinical and environmental isolates was performed by MultiLocus Sequence Typing (MLST) of a set of four genetic loci, namely ITS, D1/D2, RPB1 and RPB2, encoding for the internal transcribed spacer region (ITS) of the ribosomal subunit, the large ribosomal subunit region and the RNA polymerase II largest subunit, respectively. MLST analysis showed that all isolates possessed identical sequences in the four genetic loci and clustered with the South Asian clade I strains. Additionally, PCR amplification and sequencing of the CJJ09_001802 genetic locus, encoding for the "nucleolar protein 58" that contains clade-specific repeats was performed. Sanger sequence analysis of the TCCTTCTTC repeats within CJJ09_001802 locus also assigned the C. auris isolates to the South Asian clade I. Our study confirms that C. auris is an emerging yeast pathogen in our region, especially in the setting of the ongoing COVID-19 worldwide pandemic. Adherence to strict infection control is needed to restrain further spread of the pathogen.
| S-EPMC9964037 | biostudies-literature
Project description:Coronavirus disease 2019 (COVID-19) can lead to multiorgan damage and fatal outcomes. MicroRNAs (miRNAs) are detectable in blood, reflecting cell activation and tissue injury. We performed small RNA-Seq in healthy controls (N=11), non-severe (N=18) and severe (N=16) COVID-19 patients
2021-11-17 | GSE176498 | GEO
Project description:Global healthcare systems are challenged by the COVID-19 pandemic. There is a need to optimize allocation of treatment and resources in intensive care, as clinically established risk assessments such as SOFA and APACHE II scores show only limited performance for predicting the survival of severely ill COVID-19 patients. Comprehensively capturing the host physiology, we speculated that proteomics in combination with new data-driven analysis strategies could produce a new generation of prognostic discriminators. We studied two independent cohorts of patients with severe COVID-19 who required intensive care and invasive mechanical ventilation. SOFA score, Charlson comorbidity index and APACHE II score were poor predictors of survival. Instead, using plasma proteomes quantifying 302 plasma protein groups at 387 timepoints in 57 critically ill patients on invasive mechanical ventilation, we found 14 proteins that showed trajectories different between survivors and non-survivors. A proteomic predictor trained on single samples obtained at the first time point at maximum treatment level (i.e. WHO grade 7) and weeks before the outcome, achieved accurate classification of survivors (AUROC 0.81, n=49). We tested the established predictor on an independent validation cohort (AUROC of 1.0, n=24). The majority of proteins with high relevance in the prediction model belong to the coagulation system and complement cascade. Our study demonstrates that predictors derived from plasma protein levels have the potential to substantially outperform current prognostic markers in intensive care.
2022-03-03 | PXD029009 | Pride
Project description: Not available
| S-EPMC7334133 | biostudies-literature
Project description:ObjectivesTo describe the first outbreak of Candida auris in Brazil, including epidemiological, clinical and microbiological data.MethodsAfter the first Candida auris-colonised patient was diagnosed in a COVID-19 ICU at a hospital in Salvador, Brazil, a multidisciplinary team conducted a local C. auris prevalence investigation. Screening cultures for C. auris were collected from patients, healthcare workers and inanimate surfaces. Risk factors for C. auris colonisation were evaluated, and the fungemia episodes that occurred after the investigation were also analysed and described. Antifungal susceptibility of the C. auris isolates was determined, and they were genotyped with microsatellite analysis.ResultsAmong body swabs collected from 47 patients, eight (n = 8/47, 17%) samples from the axillae were positive for C. auris. Among samples collected from inanimate surfaces, digital thermometers had the highest rate of positive cultures (n = 8/47, 17%). Antifungal susceptibility testing showed MICs of 0.5 to 1 mg/L for AMB, 0.03 to 0.06 mg/L for voriconazole, 2 to 4 mg/L for fluconazole and 0.03 to 0.06 mg/L for anidulafungin. Microsatellite analysis revealed that all C. auris isolates belong to the South Asian clade (Clade I) and had different genotypes. In multivariate analysis, having a colonised digital thermometer was the only independent risk factor associated with C. auris colonisation. Three episodes of C. auris fungemia occurred after the investigation, with 30-day attributable mortality of 33.3%.ConclusionsEmergence of C. auris in Salvador, Brazil, may be related to local C. auris clade I closely related genotypes. Contaminated axillary monitoring thermometers may facilitate the dissemination of C. auris reinforcing the concept that these reusable devices should be carefully cleaned with an effective disinfectant or replaced by other temperature monitoring methods.
| S-EPMC8242760 | biostudies-literature
Project description: Not available
| S-EPMC7520507 | biostudies-literature
Project description:BackgroundForecasting models for intensive care occupancy of coronavirus disease 2019 (COVID-19) patients are important in the current pandemic for strategic planning of patient allocation and avoidance of regional overcrowding. They are often trained entirely on retrospective infection and occupancy data, which can cause forecast uncertainty to grow exponentially with the forecast horizon.MethodologyWe propose an alternative modeling approach in which the model is created largely independent of the occupancy data being simulated. The distribution of bed occupancies for patient cohorts is calculated directly from occupancy data from "sentinel clinics". By coupling with infection scenarios, the prediction error is constrained by the error of the infection dynamics scenarios. The model allows systematic simulation of arbitrary infection scenarios, calculation of bed occupancy corridors, and sensitivity analyses with respect to protective measures.ResultsThe model was based on hospital data and by adjusting only two parameters of data in the Aachen city region and Germany as a whole. Using the example of the simulation of the respective bed occupancy rates for Germany as a whole, the loading model for the calculation of occupancy corridors is demonstrated. The occupancy corridors form barriers for bed occupancy in the event that infection rates do not exceed specific thresholds. In addition, lockdown scenarios are simulated based on retrospective events.DiscussionOur model demonstrates that a significant reduction in forecast uncertainty in occupancy forecasts is possible by selectively combining data from different sources. It allows arbitrary combination with infection dynamics models and scenarios, and thus can be used both for load forecasting and for sensitivity analyses for expected novel spreading and lockdown scenarios.
| S-EPMC7856858 | biostudies-literature
Project description:COVID-19 has impacted millions of patients across the world. Molecular testing occurring now identifies the presence of the virus at the sampling site: nasopharynx, nares, or oral cavity. RNA sequencing has the potential to establish both the presence of the virus and define the host's response in COVID-19. Single center, prospective study of patients with COVID-19 admitted to the intensive care unit where deep RNA sequencing (> 100 million reads) of peripheral blood with computational biology analysis was done. All patients had positive SARS-CoV-2 PCR. Clinical data was prospectively collected. We enrolled fifteen patients at a single hospital. Patients were critically ill with a mortality of 47% and 67% were on a ventilator. All the patients had the SARS-CoV-2 RNA identified in the blood in addition to RNA from other viruses, bacteria, and archaea. The expression of many immune modulating genes, including PD-L1 and PD-L2, were significantly different in patients who died from COVID-19. Some proteins were influenced by alternative transcription and splicing events, as seen in HLA-C, HLA-E, NRP1 and NRP2. Entropy calculated from alternative RNA splicing and transcription start/end predicted mortality in these patients. Current upper respiratory tract testing for COVID-19 only determines if the virus is present. Deep RNA sequencing with appropriate computational biology may provide important prognostic information and point to therapeutic foci to be precisely targeted in future studies.
| S-EPMC9491252 | biostudies-literature