Automated differential diagnosis method for iron deficiency anemia and beta thalassemia trait based on iterative Chi2 feature selector

INTRODUCTION

The differential diagnosis of anemia is an important issue for hematology laboratories. We aimed at investigating the performance of a powerful computer-based model to aid diagnosis.

MATERIALS AND METHODS

Our work presents a new feature selection-based automated disease diagnosis model. To create a testbed, a new corpus is collected retrospectively. Our data sets contain beta thalassemia trait, iron deficiency anemia, and healthy groups. Our presented automated ailment classification model consists iterative chi2 (IChi2) feature selection and classification phases. The used data set includes 25 features, and IChi2 selects the 20 most valuable of them. These are forwarded to 24 traditional classifiers.

RESULTS

In this work, two data sets have been used to test our proposal. In the classification phase of this model, 24 shallow classifiers have been used and the best accurate classifiers are Medium Gaussian Support Vector Machine (MGSVM) and Coarse Tree (CT) for the first and second data sets, respectively. These classifiers have been attained 97.48% and 99.73% classification accuracies using the first and second data sets, consecutively. These results are calculated using 10-fold cross-validation. Moreover, hold-out validation has been used in this work, and the results are given in the experiments.

CONCLUSION

Our results denoted the success of IChi2-based classification model for diagnosis on the laboratory data set. We have found a new and robust model to differentiate iron deficiency anemia and beta thalassemia trait. This model may be beneficial for rational laboratory use.

Automated COVID-19 and Heart Failure Detection Using DNA Pattern Technique with Cough Sounds

COVID-19 and heart failure (HF) are common disorders and although they share some similar symptoms, they require different treatments. Accurate diagnosis of these disorders is crucial for disease management, including patient isolation to curb infection spread of COVID-19. In this work, we aim to develop a computer-aided diagnostic system that can accurately differentiate these three classes (normal, COVID-19 and HF) using cough sounds. A novel handcrafted model was used to classify COVID-19 vs. healthy (Case 1), HF vs. healthy (Case 2) and COVID-19 vs. HF vs. healthy (Case 3) automatically using deoxyribonucleic acid (DNA) patterns. The model was developed using the cough sounds collected from 241 COVID-19 patients, 244 HF patients, and 247 healthy subjects using a hand phone. To the best our knowledge, this is the first work to automatically classify healthy subjects, HF and COVID-19 patients using cough sounds signals. Our proposed model comprises a graph-based local feature generator (DNA pattern), an iterative maximum relevance minimum redundancy (ImRMR) iterative feature selector, with classification using the k-nearest neighbor classifier. Our proposed model attained an accuracy of 100.0%, 99.38%, and 99.49% for Case 1, Case 2, and Case 3, respectively. The developed system is completely automated and economical, and can be utilized to accurately detect COVID-19 versus HF using cough sounds.

Feed-forward LPQNet based Automatic Alzheimer's Disease Detection Model

BACKGROUND

Alzheimer's disease (AD) is one of the most commonly seen brain ailments worldwide. Therefore, many researches have been presented about AD detection and cure. In addition, machine learning models have also been proposed to detect AD promptly.

MATERIALS AND METHOD

In this work, a new brain image dataset was collected. This dataset contains two categories, and these categories are healthy and AD. This dataset was collected from 1070 subjects. This work presents an automatic AD detection model to detect AD using brain images automatically. The presented model is called a feed-forward local phase quantization network (LPQNet). LPQNet consists of (i) multilevel feature generation based on LPQ and average pooling, (ii) feature selection using neighborhood component analysis (NCA), and (iii) classification phases. The prime objective of the presented LPQNet is to reach high accuracy with low computational complexity. LPQNet generates features on six levels. Therefore, 256 × 6 = 1536 features are generated from an image, and the most important 256 out 1536 features are selected. The selected 256 features are classified on the conventional classifiers to denote the classification capability of the generated and selected features by LPQNet.

RESULTS

The presented LPQNet was tested on three image datasets to demonstrate the universal classification ability of the LPQNet. The proposed LPQNet attained 99.68%, 100%, and 99.64% classification accuracy on the collected AD image dataset, the Harvard Brain Atlas AD dataset, and the Kaggle AD dataset. Moreover, LPQNet attained 99.62% accuracy on the Kaggle AD dataset using four classes.

CONCLUSIONS

Moreover, the calculated results from LPQNet are compared to other automatic AD detection models. Comparisons, results, and findings clearly denote the superiority of the presented model. In addition, a new intelligent AD detector application can be developed for use in magnetic resonance (MR) and computed tomography (CT) devices. By using the developed automated AD detector, new generation intelligence MR and CT devices can be developed.

Automatic COVID-19 Detection Using Exemplar Hybrid Deep Features with X-ray Images

COVID-19 and pneumonia detection using medical images is a topic of immense interest in medical and healthcare research. Various advanced medical imaging and machine learning techniques have been presented to detect these respiratory disorders accurately. In this work, we have proposed a novel COVID-19 detection system using an exemplar and hybrid fused deep feature generator with X-ray images. The proposed Exemplar COVID-19FclNet9 comprises three basic steps: exemplar deep feature generation, iterative feature selection and classification. The novelty of this work is the feature extraction using three pre-trained convolutional neural networks (CNNs) in the presented feature extraction phase. The common aspects of these pre-trained CNNs are that they have three fully connected layers, and these networks are AlexNet, VGG16 and VGG19. The fully connected layer of these networks is used to generate deep features using an exemplar structure, and a nine-feature generation method is obtained. The loss values of these feature extractors are computed, and the best three extractors are selected. The features of the top three fully connected features are merged. An iterative selector is used to select the most informative features. The chosen features are classified using a support vector machine (SVM) classifier. The proposed COVID-19FclNet9 applied nine deep feature extraction methods by using three deep networks together. The most appropriate deep feature generation model selection and iterative feature selection have been employed to utilise their advantages together. By using these techniques, the image classification ability of the used three deep networks has been improved. The presented model is developed using four X-ray image corpora (DB1, DB2, DB3 and DB4) with two, three and four classes. The proposed Exemplar COVID-19FclNet9 achieved a classification accuracy of 97.60%, 89.96%, 98.84% and 99.64% using the SVM classifier with 10-fold cross-validation for four datasets, respectively. Our developed Exemplar COVID-19FclNet9 model has achieved high classification accuracy for all four databases and may be deployed for clinical application.

Automated ASD detection using hybrid deep lightweight features extracted from EEG signals

BACKGROUND

Autism spectrum disorder is a common group of conditions affecting about one in 54 children. Electroencephalogram (EEG) signals from children with autism have a common morphological pattern which makes them distinguishable from normal EEG. We have used this type of signal to design and implement an automated autism detection model.

MATERIALS AND METHOD

We propose a hybrid lightweight deep feature extractor to obtain high classification performance. The system was designed and tested with a big EEG dataset that contained signals from autism patients and normal controls. (i) A new signal to image conversion model is presented in this paper. In this work, features are extracted from EEG signal using one-dimensional local binary pattern (1D_LBP) and the generated features are utilized as input of the short time Fourier transform (STFT) to generate spectrogram images. (ii) The deep features of the generated spectrogram images are extracted using a combination of pre-trained MobileNetV2, ShuffleNet, and SqueezeNet models. This method is named hybrid deep lightweight feature generator. (iii) A two-layered ReliefF algorithm is used for feature ranking and feature selection. (iv) The most discriminative features are fed to various shallow classifiers, developed using a 10-fold cross-validation strategy for automated autism detection.

RESULTS

A support vector machine (SVM) classifier reached 96.44% accuracy based on features from the proposed model.

CONCLUSIONS

The results strongly indicate that the proposed hybrid deep lightweight feature extractor is suitable for autism detection using EEG signals. The model is ready to serve as part of an adjunct tool that aids neurologists during autism diagnosis in medical centers.

Application of substitution box of present cipher for automated detection of snoring sounds

BACKGROUND AND PURPOSE

Snoring is one of the sleep disorders, and snoring sounds have been used to diagnose many sleep-related diseases. However, the snoring sound classification is done manually which is time-consuming and prone to human errors. An automated snoring sound classification model is proposed to overcome these problems.

MATERIAL AND METHOD

This work proposes an automated snoring sound classification method using three new methods. These methods are maximum absolute pooling (MAP), the nonlinear present pattern, and two-layered neighborhood component analysis, and iterative neighborhood component analysis (NCAINCA) selector. Using these methods, a new snoring sound classification (SSC) model is presented. The MAP decomposition model is applied to snoring sounds to extract both low and high-level features. The presented model aims to attain high performance for SSC problem. The developed present pattern (Present-Pat) uses substitution box (SBox) and statistical feature generator. By deploying these feature generators, both textural and statistical features are generated. NCAINCA chooses the most informative/valuable features, and these selected features are fed to k-nearest neighbor (kNN) classifier with leave-one-out cross-validation (LOOCV). The Present-Pat based SSC system is developed using Munich-Passau Snore Sound Corpus (MPSSC) dataset comprising of four categories.

RESULTS

Our model reached an accuracy and unweighted average recall (UAR) of 97.10 % and 97.60 %, respectively, using LOOCV. Moreover, a nocturnal sound dataset is used to show the universal success of the presented model. Our model attained an accuracy of 98.14 % using the used nocturnal sound dataset.

CONCLUSIONS

Our developed classification model is ready to be tested with more data and can be used by sleep specialists to diagnose the sleep disorders based on snoring sounds.

An automated COVID-19 detection based on fused dynamic exemplar pyramid feature extraction and hybrid feature selection using deep learning

The new coronavirus disease known as COVID-19 is currently a pandemic that is spread out the whole world. Several methods have been presented to detect COVID-19 disease. Computer vision methods have been widely utilized to detect COVID-19 by using chest X-ray and computed tomography (CT) images. This work introduces a model for the automatic detection of COVID-19 using CT images. A novel handcrafted feature generation technique and a hybrid feature selector are used together to achieve better performance. The primary goal of the proposed framework is to achieve a higher classification accuracy than convolutional neural networks (CNN) using handcrafted features of the CT images. In the proposed framework, there are four fundamental phases, which are preprocessing, fused dynamic sized exemplars based pyramid feature generation, ReliefF, and iterative neighborhood component analysis based feature selection and deep neural network classifier. In the preprocessing phase, CT images are converted into 2D matrices and resized to 256 × 256 sized images. The proposed feature generation network uses dynamic-sized exemplars and pyramid structures together. Two basic feature generation functions are used to extract statistical and textural features. The selected most informative features are forwarded to artificial neural networks (ANN) and deep neural network (DNN) for classification. ANN and DNN models achieved 94.10% and 95.84% classification accuracies respectively. The proposed fused feature generator and iterative hybrid feature selector achieved the best success rate, according to the results obtained by using CT images.

A dynamic center and multi threshold point based stable feature extraction network for driver fatigue detection utilizing EEG signals

Driver fatigue is the one of the main reasons of the traffic accidents. The human brain is a complex structure, whose function can be evaluated with electroencephalogram (EEG). Automated driver fatigue detection utilizing EEG decreases the incidence probability of related traffic accidents. Therefore, devising an appropriate feature extraction technique and selecting a competent classification method can be considered as the crucial part of the effective driver fatigue detection. Therefore, in this study, an EEG-based intelligent system was devised for driver fatigue detection. The proposed framework includes a new feature generation network, which is implemented by using texture descriptors, for fatigue detection. The proposed scheme contains pre-processing, feature generation, informative features selection and classification with shallow classifiers phases. In the pre-processing, discrete cosine transform and fast Fourier transform are used together. Moreover, dynamic center based binary pattern and multi threshold ternary pattern are utilized together to create a new feature generation network. To improve the detection performance, we utilized discrete wavelet transform as a pooling method, in which the functional brain network-based feature describing the relationship between fatigue and brain network organization. In the feature selection phase, a hybrid three layered feature selection method is presented, and benchmark classifiers are used in the classification phase to demonstrate the strength of the proposed method. In the experiments, the proposed framework achieved 97.29% classification accuracy for fatigue detection using EEG signals. This result reveals that the proposed framework can be utilized effectively for driver fatigue detection.

A novel Covid-19 and pneumonia classification method based on F-transform

Nowadays, Covid-19 is the most important disease that affects daily life globally. Therefore, many methods are offered to fight against Covid-19. In this paper, a novel fuzzy tree classification approach was introduced for Covid-19 detection. Since Covid-19 disease is similar to pneumonia, three classes of data sets such as Covid-19, pneumonia, and normal chest x-ray images were employed in this study. A novel machine learning model, which is called the exemplar model, is presented by using this dataset. Firstly, fuzzy tree transformation is applied to each used chest image, and 15 images (3-level F-tree is constructed in this work) are obtained from a chest image. Then exemplar division is applied to these images. A multi-kernel local binary pattern (MKLBP) is applied to each exemplar and image to generate features. Most valuable features are selected using the iterative neighborhood component (INCA) feature selector. INCA selects the most distinctive 616 features, and these features are forwarded to 16 conventional classifiers in five groups. These groups are decision tree (DT), linear discriminant (LD), support vector machine (SVM), ensemble, and k-nearest neighbor (k-NN). The best-resulted classifier is Cubic SVM, and it achieved 97.01% classification accuracy for this dataset.

An automated snoring sound classification method based on local dual octal pattern and iterative hybrid feature selector

In this research, a novel snoring sound classification (SSC) method is presented by proposing a new feature generation function to yield a high classification rate. The proposed feature extractor is named as Local Dual Octal Pattern (LDOP). A novel LDOP based SSC method is presented to solve the low success rate problems for Munich-Passau Snore Sound Corpus (MPSSC) dataset. Multilevel discrete wavelet transform (DWT) decomposition and the LDOP based feature generation, informative features selection with ReliefF and iterative neighborhood component analysis (RFINCA), and classification using k nearest neighbors (kNN) are fundamental phases of the proposed SSC method. Seven leveled DWT transform, and LDOP are used together to generate low, medium, and high levels features. This feature generation network extracts 4096 features in total. RFINCA selects 95 the most discriminative and informative ones of these 4096 features. In the classification phase, kNN with leave one out cross-validation (LOOCV) is used. 95.53% classification accuracy and 94.65% unweighted average recall (UAR) have been achieved using this method. The proposed LDOP based SSC method reaches 22% better result than the best of the other state-of-the-art machine learning and deep learning-based methods. These results clearly denote the success of the proposed SSC method.

An automated Residual Exemplar Local Binary Pattern and iterative ReliefF based COVID-19 detection method using chest X-ray image

Coronavirus is normally transmitted from animal to person, but nowadays it is transmitted from person to person by changing its form. Covid-19 appeared as a very dangerous virus and unfortunately caused a worldwide pandemic disease. Radiology doctors use X-ray or CT images for the diagnosis of Covid-19. It has become crucial to help diagnose such images using image processing methods. Therefore, a novel intelligent computer vision method to automatically detect the Covid-19 virus was proposed. The proposed automatic Covid-19 detection method consists of preprocessing, feature extraction, and feature selection stages. Image resizing and grayscale conversion are used in the preprocessing phase. The proposed feature generation method is called Residual Exemplar Local Binary Pattern (ResExLBP). In the feature selection phase, a novel iterative ReliefF (IRF) based feature selection is used. Decision tree (DT), linear discriminant (LD), support vector machine (SVM), k nearest neighborhood (kNN), and subspace discriminant (SD) methods are chosen as classifiers in the classification phase. Leave one out cross-validation (LOOCV), 10-fold cross-validation, and holdout validation are used for training and testing. In this work, SVM classifier achieved 100.0% classification accuracy by using 10-fold cross-validation. This result clearly has shown that the perfect classification rate by using X-ray image for Covid-19 detection. The proposed ResExLBP and IRF based method is also cognitive, lightweight, and highly accurate.

An exemplar pyramid feature extraction based humerus fracture classification method

Humerus fracture have been widely seen disease in the orthopedic clinics and classification of them is a hard process for orthopedist. The main aim of the proposed method is to classify humerus fracture by using a naïve and multileveled method. We collected a novel humerus fracture X-ray image dataset. This dataset consists of 115 images. In this paper, a novel stable feature extraction method is presented to classify humerus fractures. This method is called exemplar pyramid method and it is inspired by exemplar facial expression recognition methods. To classify humerus fractures, X-ray images were employed as input. In this study, X-ray images are resized to 512 × 512 sized image. Then, the used humerus fracture images are divided into 64 × 64 size of exemplars. To create levels, maximum pooling which has been mostly used in deep networks is used and four levels are created. Histogram of oriented gradients (HOG) and local binary pattern (LBP) are employed for feature generation. The most discriminative ones of the generated and concatenated features are selected by using ReliefF and Neighborhood Component Analysis (NCA) based two levelled feature selector (RFNCA). To emphasize success of the proposed exemplar pyramid model based feature generation, four conventional classifiers are chosen for classification and the proposed exemplar pyramid model achieved 99.12% classification accuracy by using leave one out cross validation (LOOCV). Results and tests clearly illustrates success of the proposed exemplar pyramid model based humerus fracture classification method. The results also shown that the proposed exemplar pyramid model achieved higher classification rate than Orthopedist specialized in shoulder.

A survival classification method for hepatocellular carcinoma patients with chaotic Darcy optimization method based feature selection

Survey is one of the crucial data retrieval methods in the literature. However, surveys often contain missing data and redundant features. Therefore, missing feature completion and feature selection have been widely used for knowledge extraction from surveys. We have a hypothesis to solve these two problems. To implement our hypothesis, a classification method is presented. Our proposed method consists of missing feature completion with a statistical moment (average) and feature selection using a novel swarm optimization method. Firstly, an average based supervised feature completion method is applied to Hepatocellular Carcinoma survey (HCC). The used HCC survey consists of 49 features. To select meaningful features, a chaotic Darcy optimization based feature selection method is presented and this method selects 31 most discriminative features of the completed HCC dataset. 0.9879 accuracy rate was obtained by using the proposed chaotic Darcy optimization-based HCC survival classification method.

A Tunable-Q wavelet transform and quadruple symmetric pattern based EEG signal classification method

Electroencephalography (EEG) signals have been widely used to diagnose brain diseases for instance epilepsy, Parkinson's Disease (PD), Multiple Skleroz (MS), and many machine learning methods have been proposed to develop automated disease diagnosis methods using EEG signals. In this method, a multilevel machine learning method is presented to diagnose epilepsy disease. The proposed multilevel EEG classification method consists of pre-processing, feature extraction, feature concatenation, feature selection and classification phases. In order to create levels, Tunable-Q wavelet transform (TQWT) is chosen and 25 frequency coefficients sub-bands are calculated by using TQWT in the pre-processing. In the feature extraction phase, quadruple symmetric pattern (QSP) is chosen as feature extractor and extracts 256 features from the raw EEG signal and the extracted 25 sub-bands. In the feature selection phase, neighborhood component analysis (NCA) is used. The 128, 256, 512 and 1024 most significant features are selected in this phase. In the classification phase, k nearest neighbors (kNN) classifier is utilized as classifier. The proposed method is tested on seven cases using Bonn EEG dataset. The proposed method achieved 98.4% success rate for 5 classes case. Therefore, our proposed method can be used in bigger datasets for more validation.

Automated Parkinson's disease recognition based on statistical pooling method using acoustic features

Parkinson's disease is one of the mostly seen neurological disease. It affects to nervous system and hinders people's vital activities. The majority of Parkinson's patients lose their ability to speak, write and balance. Many machine learning methods have been proposed to automatically diagnose Parkinson's disease using acoustic, hand writing and gaits. In this study, a statistical pooling method is proposed to recognize Parkinson's disease using the vowels. The used Parkinson's disease dataset contains the features of vowels. In the proposed method, the features of dataset are increased by applying statistical pooling method. Then, the most weighted features are selected from increased feature vector by using ReliefF. The classification is applied using the most weighted feature vector obtained. In the proposed method, Support Vector Machine (SVM) and K Nearest Neighbor (KNN) algorithms are used. The success rate was calculated as 91.25% and 91.23% with by using SVM and KNN respectively. The proposed method has two main contributions. The first is to obtain new features from the Parkinson's acoustic dataset using the statistical pooling method. The second one is the selection of the most significant features from the many feature vectors obtained. Thus, successful results were obtained for both KNN and SVM algorithms. The comparatively results clearly show that the proposed method achieved the best success rate among the selected state-of-art methods. Considering the proposed method and the results obtained, it proposed method is successful for Parkinson's disease recognition.

A new Watermarking System based on Discrete Cosine Transform (DCT) in color biometric images

This paper recommend a biometric color images hiding approach An Watermarking System based on Discrete Cosine Transform (DCT), which is used to protect the security and integrity of transmitted biometric color images. Watermarking is a very important hiding information (audio, video, color image, gray image) technique. It is commonly used on digital objects together with the developing technology in the last few years. One of the common methods used for hiding information on image files is DCT method which used in the frequency domain. In this study, DCT methods in order to embed watermark data into face images, without corrupting their features.

EULAR evidence-based recommendations for the diagnosis of knee osteoarthritis

Objective

To develop evidence-based recommendations for the diagnosis of knee osteoarthritis (OA).

Methods

The multidisciplinary guideline development group, representing 12 European countries, generated 10 key propositions regarding diagnosis using a Delphi consensus approach. For each recommendation, research evidence was searched systematically. Whenever possible, the sensitivity, specificity and likelihood ratio were calculated for individual diagnostic indicators and a diagnostic ladder was developed using Bayes' method. Secondary analyses were undertaken to test directly the recommendations using multiple predictive models in two populations from the UK and the Netherlands. Strength of recommendation was assessed by the EULAR visual analogue scale.

Results

Recommendations covered the definition of knee OA and its risk factors, subsets, typical symptoms and signs, the use of imaging and laboratory tests and differential diagnosis. Three symptoms (persistent knee pain, limited morning stiffness and reduced function) and three signs (crepitus, restricted movement and bony enlargement) appeared to be the most useful. Assuming a 12.5% background prevalence of knee OA in adults aged ≥45 years, the estimated probability of having radiographic knee OA increased with increasing number of positive features, to 99% when all six symptoms and signs were present. The performance of the recommendations in the study populations varied according to the definition of knee OA, background risk and number of tests applied.

Conclusion

10 key recommendations for diagnosis of knee OA were developed using both research evidence and expert consensus. Although there is no agreed reference standard, thorough clinical assessment alone can provide a confident rule-in diagnosis.

Prevalence of rheumatoid arthritis in Antalya, Turkey

The aim of this study was to evaluate the prevalence of rheumatoid arthritis (RA) in Antalya, Turkey. A cross-sectional study was performed face-to-face using a structured interview. Subjects were asked whether they had arthritis at present or previously. Subjects suspected of having RA were invited to the hospital for physical examination and laboratory investigations. Diagnosis of RA was confirmed if the patient fulfilled 1987 American College of Rheumatology (ACR) criteria for RA. A total of 3173 subjects were interviewed. The diagnosis of RA was established in 12 subjects. The prevalence of RA was determined as 0.38% [95% confidence interval (CI): 0.16-0.59]. The mean age was 49.92+/-11.56 years in subjects with RA and greater than that of other subjects (p<0.001). Of 12 subjects with RA, 9 had previously been diagnosed with the disease. Rheumatoid factor was detected in the sera of eight subjects. RA is less frequent in Turkey than in Northern Europe. Different genetic and environmental factors may have a role in this result.

The association of milk consumption with the occurrence of symptomatic knee osteoarthritis

OBJECTIVE

To evaluate the association of nutritional factors with symptomatic knee OA.

METHODS

This cross-sectional study was performed face-to-face using a structured interview. Individuals who had a diagnosis of symptomatic knee OA and were aged 50 years or over were included in this study. The frequency of consumption of dairy products, meat/poultry, fish, cereals, vegetables, tea and coffee were also determined. The diagnosis of knee OA was made clinically or clinically and radiologically according to the ACR criteria for knee OA.

RESULTS

A total of 655 subjects was interviewed. The frequency of symptomatic knee OA was significantly lower in daily milk consumers (p < 0.05). Tea consumption was also inversely associated with symptomatic knee OA (p < 0.05), although other nutritional elements showed no significant relationship with OA.

CONCLUSION

Milk consumption may have beneficial effects on symptomatic knee OA.

The prevalence of symptomatic knee and distal interphalangeal joint osteoarthritis in the urban population of Antalya, Turkey

The aim of this cross-sectional study was to estimate the prevalence and risk factors of symptomatic knee and distal interphalangeal (DIP) joint osteoarthritis (OA) in the elderly (> or =50 years of age) urban population of Antalya, Turkey. According to the 1997 national census, Antalya's population was 508,840. By random cluster sampling, 655 individuals aged 50 years or more were interviewed face-to-face and subjected to structured interviews regarding knee pain, worsening pain on exertion, and the gelling phenomenon. They were also asked about performing namaz (a fundamental act of worship in Islam performed five times a day), smoking, type of residence, type of toilet, work style, and duration of walking per day. They were also questioned about swelling in DIP joints. In the case of suspicion of knee OA, the individuals were invited to the hospital for further evaluation by physical examination and direct roentgenogram. The diagnosis of knee OA was based on clinical or clinical and radiographic findings. The prevalence of symptomatic knee OA was determined as 14.8% in the population aged 50 years or over. Advanced age, female sex, namaz, and type of residence were found to be associated with knee OA. The rate of symptomatic knee OA was significantly lower in smokers and those walking more than 2 h per day. Female sex was also strongly associated with OA DIP joints. OA of DIP joints was found significantly associated with symptomatic knee OA. The latter is a major health problem in the elderly population, especially in about one fourth of women aged 50 years or over. These data suggest that advanced age, female sex, and type of residence are risk factors.

Vegetable oils used as vitamin E vehicle affect the electrical activity of the rat heart

The aim of this study is to define the possible effects of vegetable oils used as vitamin E vehicle on the electrical activity of the rat heart. To test the possible effects of vitamin E vehicles we studied the effect of i.p. injected corn oil, hazelnut oil or peanut oil on the action potential parameters recorded in both papillary and left atrial muscle strips. Four experimental groups were used. The control group was injected (i.p.) with distilled water, while the three remaining groups received injections of corn oil, hazelnut oil, or peanut oil for five weeks (in a dose of 0.4 ml/kg/day--minimum amount of oil in which vitamin E could be dissolved). We used borosilicated (15-20 megaohms) capillary electrodes and intracellular action potentials (AP) were recorded in isolated papillary and left atrium muscle strips. While administration of three different types of vegetable oil had no significant effect on AP parameters of papillary muscle, they significantly prolonged the repolarization phase of AP in atrial strips. These results show that vegetable oils used as vitamin E vehicles may alter the electrical activity of the heart in a tissue-dependent manner. The present data indicate that the possible effect of vegetable oil vehicles should be kept in mind while evaluating the possible effects of in vivo vitamin E administration.

The evaluation of sacroiliitis using 99mTc-nanocolloid and 99mTc-MDP scintigraphy

The role of imaging studies in the evaluation of patients with sacroiliitis is controversial. We aimed to evaluate the role of nanocolloid and bone scintigraphy in patients with sacroiliitis and to investigate the clinical relevance of imaging findings. Thirty-two patients with clinically sacroiliac disease (nine females, 23 males, aged 22-47 years) underwent scintigraphic and radiographic examinations and all imaging studies were performed within 2 weeks. Twenty-five subjects were also included as a control group (10 females, 15 males, aged 20-51 years) for quantitative analysis of the bone scan. The quantitative analysis was done by using regions of interest drawn over the right and left sacroiliac (SI) joint and sacrum (S) and SI/S ratios were calculated. Abnormal uptake was defined as an uptake higher than the mean +/- 2 SD of the control SI/S values. Bone scintigraphy was performed using a three-phase technique and single photon emission computed tomography (SPECT). Nanocolloid scintigraphy (NS) was performed 1 h later, after administration of 370 MBq 99mTc-nanocolloid, and evaluated visually. Each of the scintigraphic examinations was performed on separate days within the same week. Sensitivity values were 25%, 47%, 69% and 97%, and specificity values were 80%, 90%, 95% and 90% in quantitative bone scanning (QBS), 99mTc-NS, planar and SPECT bone imaging, respectively, when the clinical findings were considered as the 'gold standard'. Our results showed that bone SPECT scanning was more sensitive than planar imaging, but planar imaging was the most specific method. SPECT was also the most associated technique with clinical findings. 99mTc-NS was neither specific nor sensitive enough in the detection of sacroiliitis although it could be helpful for the confirmation of inflammation.

Quantitative sacroiliac joint scintigraphy in normal subjects and patients with sacroiliitis

The aim of this study is to determine the sacroiliac index (SII) of healthy subjects and to compare these values with patients having sacroiliitis (SI). Quantitative sacroiliac scintigraphy (QSS) was performed with Tc-99m hydroxy methylene diphosphonate (HMDP) and whole sacroiliac joint-to-sacrum ratio was calculated as a SII by the region of interest (ROI) method. Forty-seven nonarthritic healthy subjects and 13 patients with SI were studied. Effects of aging, gender and laterality on SII were evaluated in 47 healthy subjects. The sacroiliac index was higher in men than women (p < 0.05). SII did not change significantly in aged men, but it decreased significantly in aged women (p < 0.05). Eleven of 13 patients with SI had a higher SII than healthy subjects (> mean + 2SD). In the other two cases by using small ROIs, SIIs were found to be higher than the normal range. Our results suggest that QSS is a sensitive method for the diagnosis of early stage SI and every institution should establish its own normal SII.

Primary fibromyalgia and allergy

Primary fibromyalgia (PF) has attracted much interest since the 80's. There are many controversies as to whether it is a true disease or not and many studies are carried on. In this study 32 patients which were accepted as PF were examined for some frequent symptoms and allergy and compared with controls. Migraine, irritable bowel syndrome, sleep disturbance and morning stiffness were investigated and found to be 40.6%, 12.5%, 71.9%, 68.8% respectively. Sleep disturbance and morning stiffness showed a positive correlation. Allergy background of PF patients was found frequently when compared with an age and sex matched control group. Though serum IgE levels were found elevated in PF group, they were not statistically significant. Allergic skin tests which could not be performed in the control group, were positive in 10 of 15 PF patients.

HLA B27 and clinical features in Reiter's syndrome

HLA B27 and other clinical findings were investigated in 18 Turkish patients with Reiter's syndrome (mean age 35.8 +/- 8.09). Male/female ratio was 2/1. All 18 patients were seronegative, 12 (66.6%) presenting with an asymmetrical oligoarticular arthritis. Radiological sacroiliitis and enthesopathy was found in 9 (50%) and 7 (45.6%) patients respectively. HLA B27 was present in 11 (61.1%) patients.