Introduction: Candida auris is emerging as an important cause of candidemia and deep seated candidal infection. We compared the susceptibility results of bloodstream Candida auris isolates by Vitek 2 with Sensititre YeastOne (SYO) method.
Methods: Forty-seven C.
Background: There is a scarcity of studies evaluating the microbial profile, antimicrobial susceptibility, and prevalence of MDR/XDR pathogens causing medical device-associated infections (MDAIs). The present study was sought in this regard.
Materials And Methods: An ambispective-observational, site-specific, surveillance-based study was performed for a period of 2 years in the intensive care unit (ICU) and high dependency unit (HDU) (medicine/surgery) of a Tertiary-care University Hospital.
We present the case of a drug reaction with eosinophilia and systemic symptoms (DRESS) manifesting multi-organ dysfunction syndrome (MODS) that led to death in an elderly patient during the intensive phase of antitubercular therapy (ATT). A 74-year-old male developed skin rash (morbilliform), patchy erythematous macules, pustular-purpuric nonblanching spots, fever, lymphadenopathy, liver dysfunction, leukocytosis, and eosinophilia during intensive phase of ATT (ATT: day 45). Laboratory tests revealed hypereosinophilia (eosinophils; 10500/μL), hyperacute fulminant hepatic failure (aspartate transaminase/alanine transaminase; 1444/1375 IU/L, total bilirubin; 11.
View Article and Find Full Text PDFNeoantigens are peptides derived from non-synonymous mutations presented by human leukocyte antigens (HLAs), which are recognized by antitumour T cells. The large HLA allele diversity and limiting clinical samples have restricted the study of the landscape of neoantigen-targeted T cell responses in patients over their treatment course. Here we applied recently developed technologies to capture neoantigen-specific T cells from blood and tumours from patients with metastatic melanoma with or without response to anti-programmed death receptor 1 (PD-1) immunotherapy.
View Article and Find Full Text PDFBackground: Vocal biomarker-based machine learning approaches have shown promising results in the detection of various health conditions, including respiratory diseases, such as asthma.
Objective: This study aimed to determine whether a respiratory-responsive vocal biomarker (RRVB) model platform initially trained on an asthma and healthy volunteer (HV) data set can differentiate patients with active COVID-19 infection from asymptomatic HVs by assessing its sensitivity, specificity, and odds ratio (OR).
Methods: A logistic regression model using a weighted sum of voice acoustic features was previously trained and validated on a data set of approximately 1700 patients with a confirmed asthma diagnosis and a similar number of healthy controls.