Correlation of Hepatitis C Antibody Levels in Gingival Crevicular Fluid and Saliva of Hepatitis C Seropositive Hemodialysis Patients

Search for hepatitis C virus (HCV) in body fluids other than blood is important when assessing possible nonparenteral routes of viral transmission. However, the role of oral fluids in HCV transmission remains controversial. Our aim was to compare the prevalence of HCV antibody (HCV Ab) levels in saliva, and gingival crevicular fluid (GCF) of HCV seropositive hemodialysis patients. Serum, saliva and GCF samples were collected from thirty-nine patients. Samples were analyzed for HCV Ab using the Ortho HCV 3.0 SAVe enzyme-linked immunosorbent assay (ELISA). HCH Ab levels in saliva and GCF of all HCV-seropositive patients were statistically compared. Reported here are the results of the study designed to determine the correlation between HCV-RNA positivity in serum and the detection of antibodies in GCF and saliva. One hundred percent (100%) of the 39 patients have antibodies to HCV in their serum, 15.4% have antibodies to HCV in GCF, and saliva found out. HCV Ab seropositivity in GCF and saliva was significantly correlated (kappa = 0.462; P < .001). This study supports the concept that GCF may be a significant source of HCV in saliva

White blood cell count to mean platelet volume ratio: A novel and promising prognostic marker for ST-segment elevation myocardial infarction

Background: Increased white blood cell (WBC) count is associated with increased mortality in patients with ST-segment elevation myocardial infarction (STEMI). We aimed to evaluate predictive value of admission WBC to mean platelet volume (MPV) ratio (WMR) on prognosis in patients undergoing primary percutaneous coronary intervention (pPCI) for STEMI. Methods: A total of 2,603 consecutive patients with STEMI who underwent pPCI were recruited for the study. Follow-up data were obtained from digital records, patient files or by telephone interview with patients, family members, or primary care physicians. Results: WMR has the highest area under receiver operating characteristic (ROC) curve and pairwise comparisons of the ROC curves revealed that WMR has the higher discriminative ability for long-term mortality than WBC, MPV, red blood cell distribution with (RDW), WBC-MPV combination, and platelet to lymphocyte ratio and neutrophil to lymphocyte ratio (PLR-NLR) combination in patients undergoing pPCI for STEMI (a WMR value of 1,653.47 was also found as threshold value for mortality with 75.4% sensitivity and 87.3% specificity by ROC curve analysis). Conclusions: Higher WMR value on admission was associated with worse outcomes in patients with STEMI and independently better predicted the long-term mortality than other complete blood count components, such as MPV, RDW, PLR-NLR and WBC-MPV combinations

The ratio of contrast volume to glomerular filtration rate predicts in-hospital and six-month mortality in patients undergoing primary angioplasty for ST-elevation myocardial infarction

Background: The aim of this study is to determine the impact of ratio of contrast volume to glomerular filtration rate (V/GFR) on development of contrast-induced nephropathy (CIN) and long-term mortality in patients with ST-segment elevation acute myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PCI). Methods: A total of 645 patients with STEMI undergoing primary PCI was prospectively enrolled. CIN was defined as an absolute increase in serum creatinine &gt; 0.5 mg/dL or a relative increase &gt; 25% within 48 h after PCI. The study population was divided into tertiles based on V/GFR. A high V/GFR was defined as a value in the third tertile (&gt; 3.7). Results: Patients in tertile 3 were older, had higher rate of smoking, diabetes mellitus and CIN, lower left ventricular ejection fraction, hemoglobin, and systolic and diastolic blood pressure compared to tertiles 1 and 2 (p &lt; 0.05). V/GFR was found an independent predictor of in-hospital and 6-month mortality. We found 2 separate values of V/GFR for 2 different end points. While the ratio of 3.6 predicted in-hospital mortality with 78% sensitivity and 82% specificity, the ratio of 3.3 predicted 6-month mortality with 71% sensitivity and 76% specificity. Survival rate decreases as V/GFR increases both for in-hospital and during 6-month follow-up. Diabetes mellitus and multivessel disease were other predictors of in-hospital mortality. Conclusions: High V/GFR level is associated with increased in-hospital and long-term mortality in patients with STEMI undergoing primary PCI.

Parametric pattern recognation methods based on multi dimensional gauss processes and arrhythmias of heart are determined by these methods

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 1993Thesis (M.Sc.) -- İstanbul Technical University, Institute of Science and Technology, 1993Bu tezde, çeşitli bilim dallarında önemli uygulama alanları bulan "Sekil Tanıma" ve "öğrenme" konuları ince¬ lenmekte ve bu kavramlar yardımıyla kalp işaretler indeki biçim bozukluklarını belirlemek için yeni bir yöntem ge- l işti r iImiştir. Tezin ikinci bölümünde kalp işaretlerinin temel ö- zellikleri, algılanması ve bu işaretlerdeki çeşitli bo¬ zukluklar anlatılmıştır. Üçüncü bölümde, parametrik şekil tanıma ve öğrenme yöntemleri ayrıntılarıyla verilmektedir. Ayrıca, pratik¬ te hesaplama ve gerçekleştirme yönünden oluşan problemler incelenmekte ve bu problemlerin çözüm yolları araştırıl¬ maktadır. Dördüncü bölümde, Parametrik şekil tanıma teorisin¬ den yararlanarak, şekil sınıflarının, ortalamaları ve ko- varyans matrisleri bilinmeyen Gauss dağılımları ile be¬ lirlendiği bir şekil tanıma problemi çözülmektedir. Son bölümde, Elektrokardiogram işaretleri Paramet¬ rik Sekil Tanıma yöntemiyle sınıflanarak; Normal ve Arit- mili kalp dalga biçimleri bu yöntem yardımıyla belirlen¬ miştir.In the last -Few years, "Pattern Recognat ion" and "Learning" techniques which have many applications in various -Fields o-F science, have developed a great deal. These -Fields of application are over the problems o-F handwriting recognation, Elektroanse-Fologram and Elektro- kardiogram analysis, statistical communication systems analysis etc. Pattern recognation is event in which patterns or objects which have similar properties or relations are recognized and classi-Fied by some special -Features and measured properties. Pattern class is a term used -For sets o-F patterns which have common properties. For example every letters o-F the alphabet can be represented as a pattern class. Pattern recognation systems are usually made up o-F two parts. The -First part is the selection o-F necessary properties or the measurements. This is called -Features extraction. The -Features which do not have the exact properties have high error. The second part is classifi cation by these -Features. If the features which determine the pattern classes are defined by probablity distribution rules, the classi fication is done by statistical decision theory. This type of pattern recognation is called parametric pattern recognation. Non parametric pattern recognation technigues are used when the parametric model is not sufficient. In this way of classification, every division of feature VI space is defined by a pattern class and a linear discriminant -Function is de-Fined -For every division o-F.features space. In the both of the methods of classification, it is necessary to determine the real values of the parameters which define the pattern classes by the sample values. In figure 1 The block diagram of pattern recognation system is given. PATTERNS FEATURE SELECTION FEATURE EXTRACTION CLASSIFICATION LEARNING DECISION ? Figure 1: Block Diagram of Pattern Recognation System The parametric pattern. recognation method is chosen in this thesis. The probablity rules are used for recognizing the patterns. Priori information is used for making a decision in parametric pattern recognition theory. This term gives information about the pattern classes, probability of their activeness, and the distri bution of the sets. The Bayes decision rule are developed by priori information. The most important properties of Bayes rule is to use the prior information. Also it minimizes the error in pattern recognation. A variable x from the active class w± is defined by the conditional probability which is : Pt(x) = P(X=x/Wi) i=l,2,..,N VII The Posteriori probability term is used to maintain easiness in calculations. This is the priori probability term which varies by the results o-f measurements. It is given by : P i=l,2..N Basic Bayes is then j = l,2,..,N (j=|=k) P(w*/X=x) > P(wj/X = x) -> Six) = d* and the rule -for the learning process is : k=l,2,..,N (k:f=j> P(wj/x,y) > P(wR/x,y) - > Six) = d. Two problems appear as the learning process is done on computers. These problems are : 1-) The memory problem which appears as various posteri ori probability distributions are created in every learning step 2-) The need -For many calculations -for creating new posteriori probability -Functions in every step If these problems have the some mathematical repre- sations o-F posteriori probability distribution, the de termination o-F these types of finite number of parameters will be enough. The function which satisfy this property is called reproducing distribution family. This distri bution family is defined by sufficient statistic. In Section Four, the parametric learning and pattern recognition technigues where the general theory is given and the computer simulations of the model is done. VÎİÎ The -Fun deme ntal model parameters are: 1-) The pattern recognation system is based on two classes as w0 and Wi. 2-) The mean value au, covariance matrix E± are Gauss distribution parameters. Distribution is de-fined by: Wi : Fi % NKUijEi > i=0, 1 3-) The öi parameter where the real value is unknown o-f the Ft distribution is de-fined by 6i=9(«M2i). Zt is the inverse o-f the covariance matrix Zi=E, 4-) Y1 = (Yii, Yi2,.., Yoi ), is the represatations o-f the n4 number of learning samples taken from the pattern class Wi. These samples are the Gauss distributions which have Ui as mean value and Ei as covariance matrix. They are statistically independent IM-dimen- sional-random variables. 5-) xk=(xi, x2,.., x*) is the k, IM-dimensional variable. 6-) P(wo)=P(w!)=l/2 is given Since the learning process is the same -For every class, the "i" index will be dropted. The probability density -Function o-F the learning samples is: 1 T -1 Pvk(yk)=.expC-l/2(y^-M>.E.Yüksek LisansM.Sc

Onyx: A new Canvas-based tool for visualizing biomedical and health ontologies

Ontologies provide formal, machine-readable, and human-interpretable representations of domain knowledge. Therefore, ontologies have come into question with the development of Semantic Web technologies. People who want to use ontologies need an understanding of the ontology, but this understanding is very difficult to attain if the ontology user lacks the background knowledge necessary to comprehend the ontology or if the ontology is very large. Thus, software tools that facilitate the understanding of ontologies are needed. Ontology visualization is an important research area because visualization can help in the development, exploration, verification, and comprehension of ontologies. This paper introduces the design of a new ontology visualization tool, which differs from traditional visualization tools by providing important metrics and analytics about ontology concepts and warning the ontology developer about potential ontology design errors. The tool, called Onyx, also has advantages in terms of speed and readability. Thus, Onyx offers a suitable environment for the representation of large ontologies, especially those used in biomedical and health information systems and those that contain many terms. It is clear that these additional functionalities will increase the value of traditional ontology visualization tools during ontology exploration and evaluation.Öztürk, Ö., Açıkgöz, H. G. (2019), Onyx: A New Canvas-Based Tool For Visualizing Biomedical And Health Ontologies, New York: Wiley

Septumun adenoid kistik karsinomu

https://www.kbb.org.tr/Custom/Upload/Document/13.-Türk%20Rinoloji-5.-Ulusal-Otoloji-Nörootoloji-1.-Ulusal-Bas-Boyun-Cerrahisi-Kongresi.pd

Is it safe to use long-acting G-CSF for febrile neutropenia prophylaxis in COVID-19 pandemic?

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