Publications by authors named "Nuran Sari"

Objectives: Solid-organ transplant recipients have high rates of invasive fungal infections. Candida species are the most commonly isolated fungi. Our aim was to identify risk factors, clinical presentations, and outcomes of candidemia in solid-organ transplant recipients.

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Objectives: Bloodstream infections caused by carbapenem-resistant bacteria have increased globally. Solid-organ transplant recipients are more prone to these infections. This study aimed to compare the clinical courses of carbapenem-susceptible and carbapenem-resistant Enterobacteriaceae bloodstream infections and to identify risk factors for carbapenem resistance in solid-organ transplant recipients.

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Objectives: Herpes zoster infections can be complicated and mortal in solid-organ transplant recipients. In our study, we investigated herpes zoster infections in solid-organ transplant recipients.

Materials And Methods: UntilJune 2022, our center has performed 3342 kidney, 708 liver, and 148 heart transplants.

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Objective: Candidemia is the most common form of invasive candidiasis, and it is associated with end-organ involvement, prolonged hospitalization, increased mortality, and higher healthcare costs. Candidemia can lead to metastatic heart and ocular infections. This study aimed to define the incidence, characteristics, and mortality of candidemia episodes and compare the data with our center's previous results.

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Objectives: Vaccination against SARS-CoV-2 may reduce COVID-19 mortality and complications in solidorgan transplant recipients, and we evaluated the associated antibody responses and adverse effects in this high-risk population.

Materials And Methods: This prospective observational study (April-June 2021) included 10 liver and 38 kidney transplant recipients who received 2 vaccine doses (Sinovac, n = 31; or BioNTech, n = 17) and 56 healthy adults (Sinovac), all of whom provided 3 blood samples (prevaccination, 4 weeks after first dose, and 4-6 weeks after second dose) for quantitative tests (Abbott Quant assay forimmunoglobulin G antibodies against SARS-CoV-2 spike protein). Type I error was α = .

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Objectives: The present study aimed to develop a clinical decision support tool to assist coronavirus disease 2019 (COVID-19) diagnoses with machine learning (ML) models using routine laboratory test results.

Methods: We developed ML models using laboratory data (n = 1,391) composed of six clinical chemistry (CC) results, 14 CBC parameter results, and results of a severe acute respiratory syndrome coronavirus 2 real-time reverse transcription-polymerase chain reaction as a gold standard method. Four ML algorithms, including random forest (RF), gradient boosting (XGBoost), support vector machine (SVM), and logistic regression, were used to build eight ML models using CBC and a combination of CC and CBC parameters.

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Vaccines have been seen as the most important solution for ending the coronavirus disease 2019 (COVID-19) pandemic. The aim of this study is to evaluate the antibody levels after inactivated virus vaccination. We included 148 healthcare workers (74 with prior COVID-19 infection and 74 with not).

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The epidemiological and antifungal susceptibility data for 35 episodes of candidemia in intensive care units (ICU) in 2007 were evaluated by prospective active surveillance. The incidence of fungaemia was 39.1 cases per 1000 ICU admissions and 2.

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Mucor spp. are rarely pathogenic in healthy adults, but can cause fatal infections in patients with immuosuppression and diabetes mellitus. Documented mucor fungemia is a very rare condition in the literature.

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