Publications by authors named "L Ozdemir"

Alpha-1 antitrypsin deficiency (AATD) is a rare autosomal co-dominant disease caused by mutations in the SERPINA1 gene. The alleles most frequently associated with AATD are protease inhibitors S and Z. Here, we report on a 35-year-old woman diagnosed with Kartagener's syndrome and subsequently referred for bronchiectasis testing.

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Many mechanisms are thought to play a role in the pathogenesis of the COVID-19 pandemic, which started in 2019 and affected the whole world. It has been claimed that a deficiency in the immune system can significantly affect the severity of COVID-19 disease. It is important that the levels of essential elements and vitamin D are at certain levels for the healthy functioning of the immune system.

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The study aimed to explore the protective effect of mask use against respiratory tract viral agents during the pandemic. The study included patients with a COVID-19 negative test who were hospitalized in the pulmonary disease clinic with the diagnoses of asthma attack, chronic obstructive pulmonary disease (COPD) exacerbation, and pneumonia in two periods: during mandatory mask use (October 2021 - May 2022) and after the mask mandate was lifted (October 2022 - May 2023). Combined nose and throat swab samples taken from the patients were evaluated for viral agents by using the PCR test method.

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Aim: In cases where standardized maximum uptake (SUVmax) values in positron emission tomography (PET-CT) were not sufficient to differentiate mediastinal lymphadenopathy and masses from malignant or benign, the contribution of Hounsfield unit (HU) values in thorax computed tomography to the diagnosis was evaluated.

Material Method: The study was conducted by evaluating the data of 182 patients between 2019 and 2023. HU values on non-contrast thorax computed tomography and PET-CT SUV values of biopsied masses and lymph nodes were compared with histopathological diagnoses.

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Background: Risk stratification for patients undergoing coronary artery bypass surgery (CABG) for left main coronary artery (LMCA) disease is essential for informed decision-making. This study explored the potential of machine learning (ML) methods to identify key risk factors associated with mortality in this patient group.

Methods: This retrospective cohort study was conducted on 866 patients from the Gulf Left Main Registry who presented between 2015 and 2019.

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