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    Heterogeneity of damage between segments of rat liver after inflow-outflow obstruction
    (Elsevier Science Inc, 2006) Polat, E.; Topaloğlu, S.; Sökmensüer, C.; Topaloğlu, E.; Avşar, F. M.; Özel, H.; Hengirmen, S.
    Background. Total vascular exclusion (TVE) causes warm liver ischernia. The complete explanation of the events during inflow and outflow obstruction of the liver during selective TVE has not yet been studied. The aim of this study was to investigate the liver injury caused by inflow-outflow obstruction in the rat liver. Materials and methods. Forty Wistar-Albino rats were divided into four groups. Liver inflow occlusion (groups A and C) or inflow-outflow occlusion (groups B and D) was applied for 30 minutes. Samples were collected at the end of the ischemia period. We examined oxidative injury in the liver tissue and liver histopathology. Results. Oxidative stress and histopathologic alterations were more prominent with TVE application. Significant alterations were shown in hepatic superoxide dismutase, glutathione, and glutathione S-transferase levels. Central segments of the rat liver were affected significantly from inflow occlusion, whereas dome segments were significantly damaged from inflow-outflow occlusion. Conclusions. Inflow-outflow occlusion of the liver caused more tissue damage compared with inflow occlusion. The pattern of distribution of the damage due to TVE seemed different from other well-known ischemia-reperfusion injuries.
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    The interaction of fibroblast and endothelial cells with electrospun nanofibrous Nap-FFGRGD reinforced pcl peripheral nerve regeneration conduits
    (Wiley-Blackwell, 2014) Polat, E.; Meifeng, Z.; Kong, D.; Turk, M.; Piskin, E.
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    Long-term predictive value of cardiac biomarkers in patients with COVID-19 infection
    (Verduci Publisher, 2022) Sabanoglu, C.; Inanc, I. H.; Polat, E.; Peker, S. A.
    OBJECTIVE: Several studies have investigated the association between cardiac biomarkers and short-term prognosis in the COVID-19 infection. However, the data on the pre-dictive value of cardiac biomarkers to predict long-term prognosis in COVID-19 infection are limited. We aimed at determining the relationship between N-terminal brain-type natriuretic peptide (NT-proBNP) and high-sensitivity cardiac troponin I (hs-TnI) as cardiac biomarkers and in-hospital/ long-term outcomes in COVID-19 infection.PATIENTS AND METHODS: The study included a total of 916 patients with confirmed COVID-19 infection. The primary outcome was in-hospital and 1-year mortality. The secondary outcome was intensive care need at admission or the need to be transferred to the intensive care unit later on.RESULTS: The study included 498 (54.4%) males and 418 (45.6%) females with a mean age of 55.1 & PLUSMN;18.5 years. The patients with known heart failure (HF), COVID-19-related HF, acute renal failure (ARF), chronic kidney disease (CKD), diabetes mellitus, hypertension, coronary artery dis-ease (CAD), chronic obstructive pulmonary dis-ease (COPD)/asthma, high CO-RADS score (& GE; 4), lower EF, higher hs-TnI, and NT-proBNP levels had increased in-hospital and 1-year mortality. After multivariate analysis, NT-proBNP, hs-TnI, CKD, ARF, diabetes mellitus, and CAD were independent predictors of in-hospital and 1-year mortali-ty. After ROC analysis, NT-proBNP cut-off levels of 1022.50 (sensitivity 87.5%, specificity 87.1%) and 1008 (sensitivity 88.6%, specificity 88.0%) were found to predict in-hospital and 1-year mortality, respectively. Hs-TnI cut-off levels of 49.6 (sensitivity 88.6%, specificity 88.9%) and 34.10 (sensitivity 83.8%, specificity 84.1%) were found to predict in-hospital and 1-year mortality, respectively.CONCLUSIONS: The current study suggests that NT-proBNP and hs-TnI can be used as valuable cardiac biomarkers to predict short-term and long-term parameters in COVID-19 infection.
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    A multiobject tracking framework for interactive multimedia applications
    (Ieee-Inst Electrical Electronics Engineers Inc, 2004) Yeasin, M.; Polat, E.; Sharma, R.
    Automatic initialization and tracking of multiple people and their body parts is one of the first steps in designing interactive multimedia applications. The key problems in this context are robust detection and tracking of people and their body parts in an unconstrained environment. This paper presents an integrated framework to address detection and tracking of multiple objects in a computationally efficient manner. In particular, a neural network-based face detector was employed to detect faces and compute person specific statistical model for skin color from the face regions. A probabilistic model was proposed to fuse the color and motion information to localize the moving body parts (hands). Multiple hypothesis tracking (MHT) algorithm was adopted to track face and hands. In real world scenes extracted features (face and hands) usually contain spurious measurements that create unconvincing trajectories and needless computations. To deal with this problem a path coherence function was incorporated along with MHT to reduce the number of hypotheses, which in turn reduces the computational cost and improves the structure of trajectories. The performance of the framework was validated using experiments on synthetic and real sequence of images.