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Öğe A case of tracheobronchial amyloidosis presenting with acute myeloid leukemia(European Publishing, 2023) Demir, Şükrü Egemen; Kansu, Abdullah; Gemici, Ali İhsan; Özöver Çelik, İremLocalized tracheobronchial amyloidosis (TBA) is a rare form of pulmonary amyloidosis that is characterized by amyloidosis accumulation in the trachea and main bronchus submucosa. TBA is usually localized in the lung and is not associated with systemic amyloidosis. Although patients may be asymptomatic at first, they may develop dyspnea, recurrent cough, and hemoptysis attacks as the lesions narrow the tracheobronchial tree. Histochemical examination of biopsies taken with flexible bronchoscopy after thorax CT findings is usually used to make the diagnosis. There has never been a case reported in which TBA and acute myeloid leukemia (AML) coexisted. We report the first case of TBA in a patient diagnosed with AML.Öğe A single center cohort of 40 severe COVID-19 patients who were treated with convalescent plasma(Tübitak Scientific & Technical Research Council Turkey, 2020) Gemici, Aliihsan; Bilgen, Hülya; Erdoğan, Cem; Kansu, Abdullah; Olmuşçelik, Oktay; Beköz, Hüseyin Saffet; Dinleyici, Rümeysa; Mert, Ali; Sevindik, Ömür GökmenBackground/aim: A SARS-Cov2 infection which was first arised from Wuhan in December 2019 and named as COVID-19. Still there lacks either a specific treatment or a vaccine to treat COVID-19. Convalescent plasma (CP) was previously used successfully to treat SARS-CoV-1 and MERS infections. Health authority in Turkey has published a guideline to integrate this promising option in the treatment process of patients who are prone to high risk of developing severe COVID-19.Materials and Methods: Forty consecutive patients who had received CP at our center were included in the study. Demographics, COVID-19 specific parameters, biomarkers to detect the severity of COVID-19 infection and outcome variables were collected retrospectively. The correlation between outcome variables and the independent predictors of the outcome were reported.Results: Median age of the patients was 57.5 and 72.5% were male. At least one COVID-19 PCR test was confirmed to be positive in 75% of patients. Remaining 25% had a Chest-CT which was reported to be compatible with an ongoing COVID-19. All patients (100%) were classified as having severe COVID-19 infection. Over a half of the patients harbored an oxygen saturation of less than 90 despite of a continuous 5 L/min support of O-2. 82.5% of the patients had a need for mechanical ventilation and 45.5% had a need for invasive mechanical ventilation. Nine out of 10 patients who have received CP outside ICU have totally recovered from COVID-19 at a median of 9 days, and a half of the patients who needed invasive mechanical ventilation were successfully free of mechanical ventilation support and managed to recover from COVID-19.Conclusion: According to the results of this study, CP is an efficient conjunct to conventional therapy against COVID-19 with a favorable safety profile.Öğe Asbestos-related Diseases in Turkey: caused not only by naturally occurring fibers but also by industrial exposures(American Thoracic Society, 2019) Bayram, Mehmet; Özkan, Didem; Hayat, Esat; Bilgin, Mehmet; Mehdi, Elnur; Bilgin, Sabriye Şennur; Akkoyunlu, Muhammed Emin; Okyaltırık, Fatmanur; Kansu, AbdullahMany publications have been devoted to the epidemiology of asbestos exposure in Turkey. However, they have all focused on environmental exposure to “naturally occurring asbestos” (natural component of soils or rocks)”, ignoring potential industrial asbestos exposure(1-4). Yet, many industrial activities involved occupational asbestos exposure during Turkey’s industrialization, including construction, shipbuilding, automotive manufacturing, and others. Between 1900 and 2003, 1.2 million tons of asbestos were used in Turkish industry(5). The use of asbestos was officially banned in 2010.Öğe One-year follow-up evaluation of radiological and respiratory findings and functional capacity in COVID-19 survivors without comorbidities(NLM (Medline), 2023) Ogün, Hamza; Gül, Merve; Akkoyunlu, Yasemin; Hayat, Esat; Gökbulut, Nuran; Sümbül, Bilge; Başel Karaçöp, Handan; Yurtsever, İsmail; Yabacı, Ayşegül; Kansu, Abdullah; Okyaltırık, FatmanurThe aim of this study was to assess clinical findings, radiological data, pulmonary functions and physical capacity change over time and to investigate factors associated with radiological abnormalities after coronavirus disease 2019 (COVID-19) in non-comorbid patients. This prospective cohort study was conducted between April 2020 and June 2020. A total of 62 symptomatic in non-comorbid patients with COVID-19 pneumonia were included in the study. At baseline and the 2nd, 5th and 12th months, patients were scheduled for follow-up. Males represented 51.6% of the participants and overall mean age was 51.60?±?12.45 years. The percentage of patients with radiological abnormalities at 2 months was significantly higher than at 5 months (P?Öğe Postural stability and fall risk in patients with obstructive sleep apnea: A cross-sectional study(Springer Heidelberg, 2021) Yılmaz Gökmen, Gülhan; Gürses, H. Nilgün; Zeren, Melih; Özyılmaz, Semiramis; Kansu, Abdullah; Akkoyunlu, Muhammed EminPurpose Nocturnal hypoxia and daytime sleepiness resulting from fragmented sleep may impair the ability of postural stability in subjects with OSA. This study investigates the effect of disease severity on postural stability and whether or not it poses a fall risk in individuals with obstructive sleep apnea (OSA). Methods Forty-nine patients with OSA diagnosed by all-night polysomnography (apnea-hypopnea index (AHI) >= 5) and aged 51.4 +/- 7.2 years were included in the study. The patients were divided into two groups as severe OSA (AHI >= 30, n = 24) and non-severe OSA (5 <= AHI <= 30, n = 25). All patients were subjected to testing for postural stability (PS), limits of stability (LOST), and the stability index for fall risk (fall risk SI) with the Biodex Balance System (R). Daytime sleepiness was assessed using the Epworth Sleepiness Scale (ESS). Biodex measurements and daytime sleepiness were compared between severe and non-severe OSA groups. Univariate analysis was conducted to explore if AHI, ESS score, lowest SaO(2) (%), sleep stages (%), or total arousal index predict postural stability scores. Results Overall and anterior-posterior PS indices were higher in the severe OSA group (p < 0.05). Dynamic PS and fall risk indices did not differ between groups. AHI and lowest SaO(2) (%) were found to be an independent predictor for both overall PS (r = 0.300 and r = 0.286, respectively) and fall risk SI (r = 0.296 and r = 0.374, respectively), whereas stage N1 (%) and stage N3 (%) were an independent predictor for overall LOST score (r = -0.328 and r = 0.298, respectively) (p < 0.05). Conclusion Static postural stability of individuals with severe OSA is worse than those with non-severe OSA. Static postural stability worsens, and fall risk increases as AHI increases and the lowest SaO(2) decreases in individuals with OSA. On the other hand, dynamic postural stability worsens as stage N1 (%) sleep increases and stage N3 (%) sleep decreases. While nocturnal hypoxia indicators such as AHI and lowest SaO(2) are associated with static postural stability, sleep structure-related variables are associated with dynamic stability. Including postural stability assessments in the clinical practice for OSA may help addressing workplace accidents or tendency to fall.Öğe Predicting severe respiratory failure in patients with covid-19: a machine learning approach(2024) Ceylan, Bahadır; Olmuşçelik, Oktay; Karaalioğlu, Banu; Şahin, Meyha; Aydın, Selda; Yılmaz, Ezgi; Dumlu, Rıdvan; Kapmaz, Mahir; Çiçek, Yeliz; Kansu, Abdullah; Düger, Mustafa; Mert, AliBackground/Objectives: Studies attempting to predict the development of severe respiratory failure in patients with a COVID-19 infection using machine learning algorithms have yielded different results due to differences in variable selection. We aimed to predict the development of severe respiratory failure, defined as the need for high-flow oxygen support, continuous positive airway pressure, or mechanical ventilation, in patients with COVID-19, using machine learning algorithms to identify the most important variables in achieving this prediction. Methods: This retrospective, cross-sectional study included COVID-19 patients with mild respiratory failure (mostly receiving oxygen through a mask or nasal cannula). We used XGBoost, support vector machines, multi-layer perceptron, k-nearest neighbor, random forests, decision trees, logistic regression, and naïve Bayes methods to accurately predict severe respiratory failure in these patients. Results: A total of 320 patients (62.1% male; average age, 54.67 ± 15.82 years) were included in this study. During the follow-ups of these cases, 114 patients (35.6%) required high-level oxygen support, 67 (20.9%) required intensive care unit admission, and 43 (13.4%) died. The machine learning algorithms with the highest accuracy values were XGBoost, support vector machines, k-nearest neighbor, logistic regression, and multi-layer perceptron (0.7395, 0.7395, 0.7291, 0.7187, and 0.75, respectively). The method that obtained the highest ROC-AUC value was logistic regression (ROC-AUC = 0.7274). The best predictors of severe respiratory failure were a low lymphocyte count, a high computed tomography score in the right and left upper lung zones, an elevated neutrophil count, a small decrease in CRP levels on the third day of admission, a high Charlson comorbidity index score, and a high serum procalcitonin level. Conclusions: The development of severe respiratory failure in patients with COVID-19 could be successfully predicted using machine learning methods, especially logistic regression, and the best predictors of severe respiratory failure were the lymphocyte count and the degree of upper lung zone involvement.Öğe Use of geological maps in detecting asbestos-related diseases; a new region in Anatolia(2024) Ogun, Hamza; Kansu, Abdullah; Eğri Kansu, Zeynep; Bayram, MehmetObjective: To examine the potential relationship between the presence of asbestos-related diseases (ARDs) in the region of Kastamonu, Turkey. Methods: The birthplaces of patients with ARDs and control subjects diagnosed between 2008 and 2019 and identified in a tertiary hospital in Istanbul. Soil samples were taken from plaster surfaces and quarries. The analysis was done with transmission electron microscopy. Results: Of 307 participants, 55 (17,9%) with ARDs . Patients had a mean age of 68±11 years. Residential proximity to ophiolites increased ARD incidence by 6.2% per km (p=0.003). Birthplaces were identified as being inside an ophiolitic unit, or if they weren't, the Google Earth software was used to determine the beeline distance between the settlement's center and the edge of the closest ophiolitic unit. The appropriate threshold for this case is 12.75 kilometers, with 75% sensitivity and 87% specificity. Conclusion: ARDs due to naturally occurring asbestos (NOA) are present in hitherto unknown places. Geological maps including ophiolites can help locate these places.
