The Diagnosis of Developmental Dysplasia of the Hip From Hip Ultrasonography Images With Deep Learning Methods.

BACKGROUND: Hip ultrasonography is very important in the early diagnosis of developmental dysplasia of the hip. The application of deep learning-based medical image analysis to computer-aided diagnosis has the potential to provide decision-making support to clinicians and improve the accuracy and efficiency of various diagnostic and treatment processes. This has encouraged new research and development efforts in computer-aided diagnosis. The aim of this study was to evaluate hip sonograms using computer-assisted deep-learning methods. METHODS: The study included 376 sonograms evaluated as normal according to the Graf method, 541 images with dysplasia and 365 images with incorrect probe position. To classify the developmental hip dysplasia ultrasound images, transfer learning was applied with pretrained VGG-16, ResNet-101, MobileNetV2 and GoogLeNet networks. The performances of the networks were evaluated with the performance parameters of accuracy, sensitivity, specificity, precision, F1 score, and AUC (area under the ROC curve). RESULTS: The accuracy, sensitivity, specificity, precision, F1 score, and AUC results obtained by testing the VGG-16, ResNet-101, MobileNetV2, and GoogLeNet models showed performance >80%. With the pretrained VGG-19 model, 93%, 93.5%, 96.7%, 92.3%, 92.6%, and 0.99 accuracy, sensitivity, specificity, precision, F1 score, and AUC results were obtained, respectively. CONCLUSION: In this study, in addition to the ultrasonography images of dysplastic and healthy hips, images were also included of probe malpositioning, and these images were able to be successfully evaluated with deep learning methods. On the sonograms, which provided criteria appropriate for evaluation, successful differentiation could be made of healthy hips and dysplastic hips. LEVEL OF EVIDENCE: Level-IV; diagnostic studies.

Deep learning methods in the diagnosis of sacroiliitis from plain pelvic radiographs.

OBJECTIVES: The aim of this study is to develop a computer-aided diagnosis method to assist physicians in evaluating sacroiliac radiographs. METHODS: Convolutional neural networks, a deep learning method, were used in this retrospective study. Transfer learning was implemented with pre-trained VGG-16, ResNet-101 and Inception-v3 networks. Normal pelvic radiographs (n = 290) and pelvic radiographs with sacroiliitis (n = 295) were used for the training of networks. RESULTS: The training results were evaluated with the criteria of accuracy, sensitivity, specificity and precision calculated from the confusion matrix and AUC (area under the ROC curve) calculated from ROC (receiver operating characteristic) curve. Pre-trained VGG-16 model revealed accuracy, sensitivity, specificity, precision and AUC figures of 89.9%, 90.9%, 88.9%, 88.9% and 0.96 with test images, respectively. These results were 84.3%, 91.9%, 78.8%, 75.6 and 0.92 with pre-trained ResNet-101, and 82.0%, 79.6%, 85.0%, 86.7% and 0.90 with pre-trained inception-v3, respectively. CONCLUSIONS: Successful results were obtained with all three models in this study where transfer learning was applied with pre-trained VGG-16, ResNet-101 and Inception-v3 networks. This method can assist clinicians in the diagnosis of sacroiliitis, provide them with a second objective interpretation and also reduce the need for advanced imaging methods such as magnetic resonance imaging.

Automated Classification of Rheumatoid Arthritis, Osteoarthritis, and Normal Hand Radiographs with Deep Learning Methods.

Rheumatoid arthritis and hand osteoarthritis are two different arthritis that causes pain, function limitation, and permanent joint damage in the hands. Plain hand radiographs are the most commonly used imaging methods for the diagnosis, differential diagnosis, and monitoring of rheumatoid arthritis and osteoarthritis. In this retrospective study, the You Only Look Once (YOLO) algorithm was used to obtain hand images from original radiographs without data loss, and classification was made by applying transfer learning with a pre-trained VGG-16 network. The data augmentation method was applied during training. The results of the study were evaluated with performance metrics such as accuracy, sensitivity, specificity, and precision calculated from the confusion matrix, and AUC (area under the ROC curve) calculated from ROC (receiver operating characteristic) curve. In the classification of rheumatoid arthritis and normal hand radiographs, 90.7%, 92.6%, 88.7%, 89.3%, and 0.97 accuracy, sensitivity, specificity, precision, and AUC results, respectively, and in the classification of osteoarthritis and normal hand radiographs, 90.8%, 91.4%, 90.2%, 91.4%, and 0.96 accuracy, sensitivity, specificity, precision, and AUC results were obtained, respectively. In the classification of rheumatoid arthritis, osteoarthritis, and normal hand radiographs, an 80.6% accuracy result was obtained. In this study, to develop an end-to-end computerized method, the YOLOv4 algorithm was used for object detection, and a pre-trained VGG-16 network was used for the classification of hand radiographs. This computer-aided diagnosis method can assist clinicians in interpreting hand radiographs, especially in rheumatoid arthritis and osteoarthritis.

Detection of hip osteoarthritis by using plain pelvic radiographs with deep learning methods.

OBJECTIVE: The incidence of osteoarthritis is gradually increasing in public due to aging and increase in obesity. Various imaging methods are used in the diagnosis of hip osteoarthritis, and plain pelvic radiography is the first preferred imaging method in the diagnosis of hip osteoarthritis. In this study, we aimed to develop a computer-aided diagnosis method that will help physicians for the diagnosis of hip osteoarthritis by interpreting plain pelvic radiographs. MATERIALS AND METHODS: In this retrospective study, convolutional neural networks were used and transfer learning was applied with the pre-trained VGG-16 network. Our dataset consisted of 221 normal hip radiographs and 213 hip radiographs with osteoarthritis. In this study, the training of the network was performed using a total of 426 hip osteoarthritis images and a total of 442 normal pelvic images obtained by flipping the raw data set. RESULTS: Training results were evaluated with performance metrics such as accuracy, sensitivity, specificity, and precision calculated by using the confusion matrix. We achieved accuracy, sensitivity, specificity and precision results at 90.2%, 97.6%, 83.0%, and 84.7% respectively. CONCLUSION: We achieved promising results with this computer-aided diagnosis method that we tried to develop using convolutional neural networks based on transfer learning. This method can help clinicians for the diagnosis of hip osteoarthritis while interpreting plain pelvic radiographs, also provides assistance for a second objective interpretation. It may also reduce the need for advanced imaging methods in the diagnosis of hip osteoarthritis.

Corneal thickness and endothelial changes in long-term hydroxychloroquine use.

Objective: To determine possible associations between long-term HCQ use and corneal changes in patients who used HCQ for at least 3 years. Materials and methods: The study included 62 healthy controls and 62 consecutive patients who used HCQ for the treatment of rheumatologic disease and were referred to the ophthalmology department between August 2018 and November 2018 for HCQ retinal toxicity screening. Central corneal thickness (CCT), corneal endothelial cell density (ECD), the coefficient of variation (CV) of cell size, and the percentage of hexagonal cells (HEX%) were measured to evaluate changes in the cornea. Results: The mean age of the patient group and control group was 50.10 ± 10.91 and 50.53 ± 10.67 years, respectively. The mean ECD was 2742 ± 347 (cells/mm2) in the patient group and 2875 ± 188 cells/mm2 in the control group. There was a significant difference between groups (p = 0.01). The mean CCT was 567.05 ± 32.35 µm in the patient group and 540.15 ± 38.50 µm in the control group. CCT was significantly higher in the patient group compared with control group (p < 0.001). There was no significant difference between groups in terms of mean CV and HEX values (p > 0.05). Conclusions: Patients using long-term HCQ demonstrated lower ECD and higher CCT than the control group. However, the CV of cell sizes and the HEX % values were not significantly different from the controls.

Plasma thrombin-activatable fibrinolysis inhibitor (TAFI) antigen levels in acromegaly patients in remission

Background/aim: Acromegaly is associated with increased morbidity andmortality, mostly due to cardiovascular complications.Plasma thrombin-activatable fibrinolysis inhibitor (TAFI) antigen levels are associated with coagulation/fibrinolysis and inflammation. Plasma TAFI may play a role in arterial thrombosis in cardiovascular diseases. In this study, it was aimed to evaluate the thrombin-activatable fibrinolysis inhibitor (TAFI) antigen and homocysteine levels in patients with acromegaly and healthy control subjects. Materials and methods: Plasma TAFI antigen and homocysteine levels in 29 consecutive patients with acromegaly and 26 age-matched healthy control subjects were measured. All patients included in the study were in remission. The TAFIa/ai antigen in the plasma samples was measured using a commercially available ELISA kit. Results: Routine biochemical parameters, fasting blood glucose, prolactin, thyroid stimulating hormone, total-cholesterol, low density lipoprotein cholesterol, triglyceride, and homocysteine levels were similar in the 2 groups (P > 0.05), whereas the plasma TAFI antigen levels were significantly elevated in the acromegalic patients (154.7 ± 94.0%) when compared with the control subjects (107.2 ± 61.6%) (P = 0.033). No significant correlation was identified by Pearson's correlation test between the plasma TAFI antigen and homocysteine levels (r = 0.320, P = 0.250). Conclusion: A significant alteration in the plasma TAFI antigen levels was detected in acromegaly. Increased plasma TAFI antigen levels might aggravate prothrombotic and thrombotic events in patients with acromegaly.

The factors considered as trigger for the attacks in patients with familial Mediterranean fever.

Although the inflammatory cascade of familial Mediterranean fever (FMF) is partially understood, triggering factors of those attacks has not been studied well. It is supposed that physical stresses such as cold exposure, tiredness and emotional stresses could provoke attacks. This study is aimed to survey the factors regarded as triggering the attacks in patients with FMF and their relationship with MEFV gene mutations. Clinical findings and genetic mutations (consist of M694V, M694I, M680I, V726A, E148Q) of patients were recorded. Patients were questioned about cold exposure, emotional stress, tiredness, long-lasting standing, long-duration travel, starvation, high intake of food, trauma, and infection as triggering factors for the attacks with both serositis and musculoskeletal pain. The study is comprised of 275 FMF patients (male/female: 177/98). The most common triggering factors for the attacks with serositis were cold exposure (59.3 %), emotional stress (49.8 %), tiredness (40.0 %) and menstruation (33.7 % in females). Long-lasting standing (78.8 %), long-duration travel (64.1 %) and tiredness (47.8 %) were the triggering factors for the attacks with musculoskeletal symptoms. The relationships between MEFV mutations and triggering factors were found as M694V allele with starvation, E148Q allele with high intake of food and V726A allele with long-duration travel. The attacks with serositis seem to be triggered by those factors to which whole body exposed, whereas the attacks with musculoskeletal complaints seem to be triggered by those factors to which regional or local part of body exposed. Since the number of alleles was small, a clear conclusion for a relationship between a particular gene variant and a specific trigger was not made.

Heart-type fatty acid binding protein level in familial Mediterranean fever.

OBJECTIVES: Familial Mediterranean fever (FMF) is an autosomal recessive disorder and the most frequent periodic syndrome characterized by recurrent attacks of polyserositis. Heart-type fatty acid-binding protein (h-FABP) is an intracellular molecule engaged in the transport of fatty acids through the myocardial cytoplasm and a rapid marker of myocardial injury. FMF is an autoinflammatory disease characterized by ongoing inflammatory activity. Inflammation also plays an important role in the development and progression of atherosclerosis in some rheumatic diseases. We aimed to investigate markers of atherosclerosis in patients with FMF by the measurement of serum h-FABP and malondialdehyde levels (MDA). STUDY DESIGN: Forty consecutive patients with FMF and twenty healthy volunteers were selected to participate in the study. The diagnosis of FMF was based on Tel-Hashomer criteria. Serum h-FABP and MDA levels were determined to examine the association. RESULTS: The mean h-FABP level in FMF patients was significantly higher than the normal population (4.89±0.83 vs. 3.06±2.13 ng/ml, p<0.01). The mean platelet volume was significantly higher in FMF patients than in the normal group (8.87±0.99 vs. 8.22±0.45 fl, p=0.04). Serum MDA levels were the same between the groups (1.08±0.66 vs. 1.08 ± 0.33 nmol/ml, p=0.99). h-FABP and MDA levels were the same in FMF patients with an acute attack and during an attack free period. CONCLUSION: Our results show that h-FABP increases in patients with FMF. Higher h-FABP levels may lead to increased atherosclerotic propensity in FMF, independent of the oxidative stress status of these patients.

The diagnosis of femoroacetabular impingement can be made on pelvis radiographs using deep learning methods.

OBJECTIVES: The aim of this study was to evaluate diagnostic ability of deep learning models, particularly convolutional neural network models used for image classification, for femoroacetabular impingement (FAI) using hip radiographs. MATERIALS AND METHODS: Between January 2010 and December 2020, pelvic radiographs of a total of 516 patients (270 males, 246 females; mean age: 39.1±3.8 years; range, 20 to 78 years) with hip pain were retrospectively analyzed. Based on inclusion and exclusion criteria, a total of 888 hip radiographs (308 diagnosed with FAI and 508 considered normal) were evaluated using deep learning methods. Pre-trained VGG-16, ResNet-101, MobileNetV2, and Inceptionv3 models were used for transfer learning. RESULTS: As assessed by performance measures such as accuracy, sensitivity, specificity, precision, F-1 score, and area under the curve (AUC), the VGG-16 model outperformed other pre-trained networks in diagnosing FAI. With the pre-trained VGG-16 model, the results showed 86.6% accuracy, 82.5% sensitivity, 89.6% specificity, 85.5% precision, 83.9% F1 score, and 0.92 AUC. CONCLUSION: In patients with suspected FAI, pelvic radiography is the first imaging method to be applied, and deep learning methods can help in the diagnosis of this syndrome.

Use of deep learning methods for hand fracture detection from plain hand radiographs.

BACKGROUND: Patients with hand trauma are usually examined in emergency departments of hospitals. Hand fractures are frequently observed in patients with hand trauma. Here, we aim to develop a computer-aided diagnosis (CAD) method to assist physicians in the diagnosis of hand fractures using deep learning methods. METHODS: In this study, Convolutional Neural Networks (CNN) were used and the transfer learning method was applied. There were 275 fractured wrists, 257 fractured phalanx, and 270 normal hand radiographs in the raw dataset. CNN, a deep learning method, were used in this study. In order to increase the performance of the model, transfer learning was applied with the pre-trained VGG-16, GoogLeNet, and ResNet-50 networks. RESULTS: The accuracy, sensitivity, specificity, and precision results in Group 1 (wrist fracture and normal hand) dataset were 93.3%, 96.8%, 90.3%, and 89.7%, respectively, with VGG-16, were 88.9%, 94.9%, 84.2%, and 82.4%, respectively, with Resnet-50, and were 88.1%, 90.6%, 85.9%, and 85.3%, respectively, with GoogLeNet. The accuracy, sensitivity, specificity, and precision results in Group 2 (phalanx fracture and normal hand) dataset were 84.0%, 84.1%, 83.8%, and 82.8%, respectively, with VGG-16, were 79.4%, 78.5%, 80.3%, and 79.7%, respectively, with Resnet-50, and were 81.7%, 81.3%, 82.1%, and 81.3%, respectively, with GoogLeNet. CONCLUSION: We achieved promising results in this CAD method, which we developed by applying methods such as transfer learning, data augmentation, which are state-of-the-art practices in deep learning applications. This CAD method can assist physicians working in the emergency departments of small hospitals when interpreting hand radiographs, especially when it is difficult to reach qualified colleagues, such as night shifts and weekends.

Diagnosis of osteoarthritic changes, loss of cervical lordosis, and disc space narrowing on cervical radiographs with deep learning methods.

OBJECTIVES: In this study, we aimed to differentiate normal cervical graphs and graphs of diseases that cause mechanical neck pain by using deep convolutional neural networks (DCNN) technology. MATERIALS AND METHODS: In this retrospective study, the convolutional neural networks were used and transfer learning method was applied with the pre-trained VGG-16, VGG-19, Resnet-101, and DenseNet-201 networks. Our data set consisted of 161 normal lateral cervical radiographs and 170 lateral cervical radiographs with osteoarthritis and cervical degenerative disc disease. RESULTS: We compared the performances of the classification models in terms of performance metrics such as accuracy, sensitivity, specificity, and precision metrics. Pre-trained VGG-16 network outperformed other models in terms of accuracy (93.9%), sensitivity (95.8%), specificity (92.0%), and precision (92.0%) results. CONCLUSION: The results of this study suggest that the deep learning methods are promising support tool in automated control of cervical graphs using the DCNN and the exclusion of normal graphs. Such a supportive tool may reduce the diagnosis time and provide radiologists or clinicians to have more time to interpret abnormal graphs.

Anti-cyclic citrullinated peptides positivity rate in patients with familial Mediterranean fever.

OBJECTIVES: To investigate the prevalence and levels of anti-cyclic citrullinated peptide antibodies (anti-CCP) in patients with familial Mediterranean fever (FMF) with and without arthritis. METHODS: Eighty-three patients with FMF and 43 healthy controls were included in the study. Thirty seven FMF patients had a history of arthritis, and 46 patients did not. Serum antibodies directed to the anti-CCP were assessed with a commercial enzyme-linked immunosorbent assay (ELISA) kit. Values <20U were considered negative, between 20 and 39U low, 40-99U moderate, and >100U high positive. RESULTS: Positivity rate of anti-CCP in the whole FMF group (14.5%) was three-fold higher than the control group (4.7%). However, the difference failed to achieve a statistically significant level (p=0.09). Anti-CCP levels were 21±30.1 in patients with arthritis and 13.1±10.3 in the non arthritic group (p<0.05). Anti-CCP positivity rates were 10/37 (27%) in patients with arthritis and 2/46 (4.3%) in patients without arthritis (p<0.005). Five FMF patients with arthritis (13.5%) had moderate-high anti-CCP levels (>40U/ml). Anti-CCP levels were between 20-39U/ ml in 2FMF patients without arthritis and in 2 healthy controls. Anti-CCP positivity rate is higher in FMF patients with arthritis (27%) than healthy controls (4.7%) (p<0.005). CONCLUSIONS: Anti-CCP prevalence is higher in FMF patients with arthritis than without arthritis, and that a significant proportion of FMF patients with arthritis (13.5%) had moderate-high titers of anti-CCP. Therefore, anti-CCP antibodies may not be a reliable indicator to differentiate between FMF arthritis and rheumatoid arthritis.

Demographic, clinical and mutational characteristics of Turkish familial Mediterranean fever patients: results of a single center in Central Anatolia.

Clinical and genetic findings of familial Mediterranean fever (FMF) may be variable in different populations. Environmental factors may also affect phenotypic features of FMF. In this study, we investigated demographic, clinical and mutational features of FMF patients who were treated in a single reference hospital in Turkey. Two hundred and sixty patients (169 females, 91 males, mean age 30.44 +/- 10.29 years) were included in this study. All patients were evaluated regarding MEFV gene mutations. The mean age of disease onset was 17.21 +/- 8.66 years (range 2-40 years). The mean duration between the disease onset and diagnosis was 9.39 +/- 8.92 years. Seventy percent of patients had symptoms before 20 years of age (early onset FMF). Arthritis and erysipelas like erythema (ELE) were more common, and the mean duration between the disease onset and diagnosis was longer in early onset FMF patients. The frequency of attacks per year, and disease severity score (DSS) was higher in early onset patients. Homozygote mutation of M694V was detected in 37 (20.2%) and 4 (5.2%) patients in early onset FMF and adult onset FMF groups, respectively (p < 0.05). Histological diagnosis of amyloidosis was established in 7 patients (2.7%). The age of disease onset was earlier, and arthritis and ELE were more frequent, and DSS was higher in patients with M694V/M694V mutation. In conclusion, mean delay to diagnosis in our FMF population is quite high. Early and adult onset forms may differ regarding some clinical, molecular and prognostic characteristics. Disease activity was higher in patients with homozygote mutation of M694V.

Detection of rheumatoid arthritis from hand radiographs using a convolutional neural network.

INTRODUCTION: Plain hand radiographs are the first-line and most commonly used imaging methods for diagnosis or differential diagnosis of rheumatoid arthritis (RA) and for monitoring disease activity. In this study, we used plain hand radiographs and tried to develop an automated diagnostic method using the convolutional neural networks to help physicians while diagnosing rheumatoid arthritis. METHODS: A convolutional neural network (CNN) is a deep learning method based on a multilayer neural network structure. The network was trained on a dataset containing 135 radiographs of the right hands, of which 61 were normal and 74 RA, and tested it on 45 radiographs, of which 20 were normal and 25 RA. RESULTS: The accuracy of the network was 73.33% and the error rate 0.0167. The sensitivity of the network was 0.6818; the specificity was 0.7826 and the precision 0.7500. CONCLUSION: Using only pixel information on hand radiographs, a multi-layer CNN architecture with online data augmentation was designed. The performance metrics such as accuracy, error rate, sensitivity, specificity, and precision state shows that the network is promising in diagnosing rheumatoid arthritis.

Familial Mediterranean fever with chronic ascites: a case report and a review of literature.

Familial Mediterranean fever (FMF) is an autosomal recessive inflammatory disease especially seen in Turks, Sephardic Jews, Armenians, and Arabs. Peritoneal and pleural inflammation, arthritis, erysipelas-like erythema, and arthralgia are well-known features of FMF. A small amount of peritoneal fluid collection can be seen during peritoneal attacks in FMF patients, but chronic ascites is a rather rare complication. We herein report a female FMF patient who developed chronic ascites. She was compound heterozygote for M694V/M680I mutation of the MEFV gene. Aspiration of the ascites fluid revealed a small amount of erythrocytes and mesothelial cells. After dose adjustment of colchicine the amount of ascites decreased. In conclusion, FMF should be considered in the differential diagnosis of chronic ascites in populations where the disease is endemic.

The detailed assessment of left and right ventricular functions by tissue Doppler imaging in patients with familial Mediterranean fever.

In the contrary to other rheumatologic disorders, there have been limited numbers of studies investigating the cardiac involvement in patients with familial Mediterranean fever (FMF), although the disease may carry a potential for cardiovascular disorders because of sustained inflammation during its course. In the present study, we used high usefulness tissue Doppler echocardiography for detailed analysis of cardiac changes in FMF patients. The study population included 30 patients with FMF (11 men, 19 women; mean age, 35 +/- 7 years, mean disease duration, 15.4 +/- 7.6 years) and 30 healthy subjects as controls (12 men, 18 women; mean age, 33 +/- 7 years). The diagnosis of FMF was established according to the Tell-Hashomer criteria. Left and right ventricular functions were measured using echocardiography comprising standard two-dimensional, M-mode, and conventional Doppler as well as tissue Doppler imaging. The conventional echocardiographic paratemeters were similar apart from left ventricular relaxation time was longer (107 +/- 25 vs 85 +/- 10 ms, p < 0.001, respectively) in patients with FMF. According to the tissue Doppler measurements, while systolic velocities of both ventricles were not different, diastolic filling velocities of left ventricle including E'(m) (12.6 +/- 3.4 vs 14.7 +/- 3.3 cm/s, p = 0.04), A'(m) (10.1 +/- 2.6 vs 8.6 +/- 2.0 cm/s, p = 0.015), and E'(m)/ A'(m) (1.24 +/- 0.4 vs 1.71 +/- 0.5 cm/s, p = 0.012) values were statistically different between the groups. Left ventricular myocardial performance indices and right ventricular diastolic functions were found similar between two groups. In addition, there were no significant correlations between the disease duration, clinical features, and echocardiographic parameters. In conclusion, we have demonstrated that although systolic functions were comparable in the patients and controls, left ventricular diastolic function indices were impaired in FMF patients by using tissue Doppler analysis.

Vascular endothelial growth factor and carotid intima-media thickness in patients with Behçet's disease.

There is morphologic evidence of subclinical atherosclerosis in Behçet's disease (BD) patients obtained by high-resolution B-mode ultrasonography (US). Vascular endothelial growth factor (VEGF) is a potent angiogenetic factor and a marker for endothelial dysfunction. VEGF could contribute to the pathological events in BD. VEGF could also be an important factor in the progression of atherosclerosis. In this study, we investigated whether there is correlation between intima-media thickness (IMT) of the carotid arteries and serum VEGF levels in BD patients and healthy controls. Twenty-one patients with BD (male/female: 15/6, mean age: 35.8 +/- 8.6 years) were individually matched to control subjects on the basis of age (within 3 years) and sex. Carotid IMT of the subjects was measured by high-resolution B-mode US. Mean IMT values of common carotid arteries were 0.86 +/- 0.18 mm for patients with BD, and 0.57 +/- 0.14 mm for healthy controls (p < 0.001). Mean VEGF levels were 130.41 +/- 58.28 pg/ml for patients with BD and 82.69 +/- 25.03 pg/ml for healthy controls (p < 0.001). There was no correlation between VEGF levels in the control group and in the BD group, but there was a significant correlation between VEGF levels and mean carotid IMT in the whole group (r = 0.317, p < 0.05). In conclusion, elevation of VEGF appears as a feature of the inflammatory reaction during the course of BD, not a direct determinant of subclinical atherosclerosis. On the other hand, the significant correlation between carotid IMT and serum VEGF levels in the whole group suggests that association between VEGF levels and carotid IMT warrants further investigation with larger sample sizes.

Severe arterial thrombophilia associated with a homozygous MTHFR gene mutation (A1298C) in a young man with Klinefelter syndrome.

Klinefelter syndrome (KS) is the most common sex chromosome disorder in men. It may be associated with an increased risk for venous thrombosis and thromboembolism, which is partially explained by hypofibrinolysis due to androgen deficiency. Additional genetic or acquired thrombophilic states have been shown in KS patients complicated with venous thrombosis as isolated case reports. Arterial thrombotic events had not been previously reported in KS. In this study, a young man with KS who developed acute arterial thrombosis during testosterone replacement therapy is presented. He was homozygous for the A1298C mutation of the methylenetetrahydrofolate reductase (MTHFR) gene.

Spontaneous and simultaneous rupture of both Achilles tendons and pathological fracture of the femur neck in a patient receiving long-term hemodialysis.

Spontaneous rupture of the Achilles tendons in hemodialysis patients is a rare complication. The majority of these patients have additional predisposing factors, such as previous use of fluoroquinolone antibiotics or corticosteroids. Spontaneous rupture of bilateral Achilles tendons without any of those predisposing factors is exceptional. In this paper, we report a 43-year-old woman who had been undergoing regular hemodialysis for 16 years. She developed bilateral spontaneous and simultaneous rupture of the Achilles tendons. She also developed pathological fracture of right femur neck 3 years later. Based on previous cases of tendon ruptures in uremic patients and the lack of history for the use of corticosteroids or fluoroquinolones, we believe that secondary hyperparathyroidism is the predisposing factor in this patient.

Assessment of atrial conduction in patients with scleroderma by tissue Doppler echocardiography and P wave dispersion.

BACKGROUND: Atrial conduction abnormalities in patients with scleroderma have not been evaluated in terms of P wave duration, P wave dispersion (P(d)) and electromechanical coupling measured by tissue Doppler echocardiography. METHODS: Twenty-four patients with scleroderma and 24 control subjects underwent resting electrocardiogram (ECG), M mode and tissue Doppler echocardiography. The P wave duration was calculated in all leads of the surface ECG. The difference between the maximum (P(max)) and minimum P wave duration was calculated and defined as P(d). Interatrial and intraatrial electromechanical delays were measured with tissue Doppler tissue echocardiography. RESULTS: The left ventricular dimensions, fractional shortening, and left atrial diameter did not differ between the patients and the controls. P(d) and P(max) were significantly higher in patients with scleroderma compared with controls: 51 +/- 17 versus 28 +/- 7 ms (p < 0.01) and 109 +/- 10 versus 93 +/- 6 ms (p < 0.01), respectively. There was a delay between the onset of the P wave on surface ECG and the onset of the late diastolic wave (A wave; PA) obtained by tissue Doppler echocardiography in patients with scleroderma compared with controls measured at lateral septal annulus (lateral PA; 122 +/- 8 vs. 105 +/- 7 ms, p = 0.001), septal mitral annulus (104 +/- 11 vs. 93 +/- 10 ms, p = 0.01) and tricuspid annulus (right ventricular PA; 71 +/- 9 vs. 64 +/- 7 ms, p = 0.05). Interatrial conduction time (lateral PA - right ventricular PA) was delayed in patients with scleroderma compared with controls (88 +/- 13 vs. 76 +/- 11 ms, p = 0.01). A positive correlation was detected between interatrial electromechanical delay (lateral PA - right ventricular PA) and P(d) (r = 0.5, p = 0.03). CONCLUSION: Atrial conduction abnormalities as estimated with P(d) and P(max) are significantly higher in patients with scleroderma compared with controls. There is a delay in both intraatrial and interatrial electromechanical coupling intervals in patients with scleroderma.