Comparison of two different antibiotic regimens for the prophylaxisis of cases with preterm premature rupture of membranes: a randomized clinical trial

Objectives: The aim of the study was to assess the effect of 1 g ampicillin prophylactic dosage whether it is as effective as the dosage of 2 g to prevent maternal and neonatal morbidity in a randomized manner. Materials and methods: One hundred and fourty eight singleton pregnant women with preterm premature rupture of membranes between 21 and 33 weeks of gestation were followed-up during the study period in our institution. We com­pared the efficacy of two different different dosages of ampicillin. The study population was randomized into 2 groups. In the group 1, 1 g of intravenous ampicillin was given every 6 hours. In the group 2, 2 g of intravenous ampicillin was given every 6 hours. Results: There was no significant difference between groups interms of fetal complications (RDS, icterus, mortality, sepsis, transient tachypnea of newborn and the pneumonia), rate of intensive care unit admission, fetal gender, fever, rate of clinical chorioamnionitis, high white blood cell count and the CRP, rate of cases < 30 weeks (p > 0.05). There was a significant differ­ence between the groups for the rate of previous preterm premature rupture of membranes history, steroid administration and the need for tocolysis (p < 0.05). Conclusions: Although antibiotics seems to be innocent, several side effects have been introduced. It is reasonable to use the lowest dosages in shortest period in order to minimize these unwanted effects

Classification of Hematoxylin and Eosin Images Using Local Binary Patterns and 1-D SIFT Algorithm†

In this paper, Hematoxylin and Eosin (H&E) stained liver images are classified by using both Local Binary Patterns (LBP) and one dimensional SIFT (1-D SIFT) algorithm. In order to obtain more meaningful features from the LBP histogram, a new feature vector extraction process is implemented for 1-D SIFT algorithm. LBP histograms are extracted with different approaches and concatenated with color histograms of the images. It is experimentally shown that,with the proposed approach, it possible to classify the H&E stained liver images with the accuracy of 88 %

Vision Based Single Stroke Character Recognition For Wearable Computing

We describe a method for recognizing the regular characters drawn by hand gestures or by a pointer on the forearm of the user captured by a head mounted camera for wearable computing. We assume that each character is drawn by a single stroke and in an isolated manner as in Graffiti. Recognition is performed by a bank of finite state machines whose input is the chain code of the hand drawn character

Wavelet merged multi-resolution super-pixels and their applications on fluorescent MSC images

A new multi-resolution super-pixel based algorithm is proposed to track cell size, count and motion in Mesenchymal Stem Cells (MSCs) images. Multi-resolution super-pixels are obtained by placing varying density seeds on the image. The density of the seeds are determined according to the local high frequency components of the MSCs image. In this way a multi-resolution super-pixels decomposition of the image is obtained. A second contribution of the paper is novel decision rule for merging similar neighboring super-pixels. An algorithm based on well known wavelet decomposition is developed and applied to the histograms of neighboring super pixels to exploit similarity. The proposed algorithm is experimentally shown to be successful in segmenting and tracking cells in MSCs images

DETECTION OF CANCER STEM CELLS IN MICROSCOPIC IMAGES BY USING REGION COVARIANCE AND CODIFFERENCE METHOD

This paper presents a cancer stem cell detection method using region covariance and codifference method. It focuses on detection of Cancer Stem Cell (CSC) in microscopic images which are stained with CD13 marker. Features of CSC images are extracted by using both covariance method and its multiplication free version codifference method and these features are fed into a Support Vector Machine (SVM) for classification. Experimental results are presented

DETECTION OF CANCER STEM CELLS IN MICROSCOPIC IMAGES BY USING REGION COVARIANCE AND CODIFFERENCE METHOD

This paper presents a cancer stem cell detection method using region covariance and codifference method. It focuses on detection of Cancer Stem Cell (CSC) in microscopic images which are stained with CD13 marker. Features of CSC images are extracted by using both covariance method and its multiplication free version codifference method and these features are fed into a Support Vector Machine (SVM) for classification. Experimental results are presented

MULTI-RESOLUTION SUPER-PIXELS AND THEIR APPLICATIONS ON FLUORESCENT MESENCHYMAL STEM CELLS IMAGES USING 1-D SIFT MERGING

A new multi-resolution super-pixel based algorithm is proposed to track cell size, count and motion in Mesenchymal Stem Cells (MSCs) images. Multi-resolution super-pixels are obtained by placing varying density seeds on the image. The density of the seeds are determined according to the local high frequency components of the MSCs image. In this way a multi-resolution super-pixels decomposition of the image is obtained. A second contribution of the paper is novel decision rule for merging similar neighboring super-pixels. One-dimensional version of the well known scale invariant feature transform (SIFT) is developed and applied to the histograms of the neighboring super-pixels to determine similar regions. The proposed algorithm is experimentally shown to be successful in segmenting and tracking cells in MSCs images

The Reliability and Validity of "Dokuz Eylul University Meniere's Disease Disability Scale"

WOS: 000456115400027PubMed ID: 29283101OBJECTIVE: Meniere's Disease (MD) is a chionic, non-life threatening inner ear disease, with attacks of disabling vertigo, progressive hearing loss, and tinnitus as the major symptoms. All three symptoms, separately or in combination, cause great distress and have a considerable impact on the quality of life of the patients.The aims of this study were to develop a disease-specific quality of life survey for patients with MD and to analyze the relationships between the audiovestibular findings and the survey. MATERIALS and METHODS: Following Ear-Nose-Throat examination and audiovestibular tests, the Dokuz Fylul University Meniere's Disease Disability Scale (DEU-MDDS) and Turkish version of the Dizziness Handicap Inventory (DHI-T) weie administered to 93 patients with definite MD. Reliability and validity analyses of the scale weie performed. RESULTS: There were 45 (48.4%) male and 48 (51.6%) female patients and the mean age was 48.9 +/- 12.1 years. Cronbach's alpha was 0.92 and intraclass correlation coefficients of the DEU-MMDS were significant (p0.05). CONCLUSION:The MDDS is a valid and reliable scale as a disease-specific quality of life questionnaire for patients with MD

The role of SPIO-enhanced MRI in the detection of malignant liver lesions

The aim of this study was to evaluate the efficacy of superparamagnetic iron oxide (SPIO)-enhanced magnetic resonance imaging (MRI) in the detection of malignant liver tumors. MRI, using fast spin-echo T-2-weighted and gradient-echo T-1-weighted imagings before and after SPIO infusion, was performed in 32 patients with known or suspected hepatic lesions. Statistical analysis was performed using lesion-by-lesion analysis. SPIO-enhanced T-2-weighted MRI showed results comparable to those of unenhanced T-2-weighted MRI in the detection of focal liver lesions. (C) 2006 Elsevier Inc. All rights reserved