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Background
Polycystic ovary syndrome (PCOS) is related to metabolic syndrome, insulin resistance, and cardiovascular metabolic syndromes. This is particularly true for individuals with central and abdominal obesity because visceral abdominal adipose tissue (VAAT) and epicardial adipose tissue (EAT) produce a large number of proinflammatory and proatherogenic cytokines. The present study aimed to determine whether there are changes in VAAT and EAT levels which were considered as indirect predictors for subclinical atherosclerosis in lean patients with PCOS.Methods
The clinical and demographic characteristics of 35 patients with PCOS and 38 healthy control subjects were recorded for the present study. Additionally, the serum levels of various biochemical parameters were measured and EAT levels were assessed using 2D-transthoracic echocardiography.Results
There were no significant differences in mean age (p?=?0.056) or mean body mass index (BMI) (p?=?0.446) between the patient and control groups. However, the body fat percentage, waist-to-hip ratio, amount of abdominal subcutaneous adipose tissue, and VAAT thickness were higher in the PCOS patient group than in the control group. The amounts of EAT in the patient and control groups were similar (p?=?0.384). EAT was correlated with BMI, fat mass, waist circumference, and hip circumference but not with any biochemical metabolic parameters including the homeostasis model assessment of insulin resistance index or the levels of triglycerides, low-density lipoprotein cholesterol, and high-density lipoprotein (HDL) cholesterol. However, there was a small positive correlation between the amounts of VAAT and EAT. VAAT was directly correlated with body fat parameters such as BMI, fat mass, and abdominal subcutaneous adipose thickness and inversely correlated with the HDL cholesterol level.Conclusions
The present study found that increased abdominal adipose tissue in patients with PCOS was associated with atherosclerosis. Additionally, EAT may aid in the determination of the risk of atherosclerosis in patients with PCOS because it is easily measured.Antiphospholipid syndrome (APS) is a systemic autoimmune disorder characterized by arterial and/ or venous thrombosis accompanied by persistently elevated levels of antiphospholipid antibodies (aPLs). The aim of this study is to evaluate the pulmonary manifestations of APS and compare the levels of aPLs in patients with and without pulmonary involvement. We retrospectively reviewed the files of patients with the diagnosis of APS between October 2010 and May 2017. Demographic data, clinical, radiological and laboratory findings were recorded. The study included 67 patients (56 female/11 male) with a mean age of 39?±?13 years. Pulmonary manifestations such as parenchymal and/or vascular involvement were seen in 12 (17.9%) patients. The patients with and without pulmonary manifestations were not significantly different in terms of age (p?=?0.46), comorbidities (p?=?0.48) and APS duration (p?=?0.66). Acute pulmonary thromboembolism (PE) was determined in 11 (16.4%), alveolar hemorrhage in 2 (3%) patients. Four patients with acute PE (36%) developed chronic thromboembolic pulmonary hypertension (CTEPH). One patient developed both CTEPH and diffuse alveolar hemorrhage after acute PE during follow up. Antiphosholipid antibody IgM was highly positive in patients with PE compared to patients without PE (p?=?0.005). Other antibodies and lupus anticoagulant were not significantly different in patients with and without PE. None of the patients were deceased due to pulmonary manifestations of APS. PE was the most common pulmonary manifestation of APS. The development of CTEPH was high among APS patients. Patients with APS should be closely followed for the onset of PE and CTEPH.
相似文献Hypophysitis is a heterogeneous condition that includes inflammation of the pituitary gland and infundibulum, and it can cause symptoms related to mass effects and hormonal deficiencies. We aimed to evaluate the potential role of machine learning methods in differentiating hypophysitis from non-functioning pituitary adenomas.
MethodsThe radiomic parameters obtained from T1A-C images were used. Among the radiomic parameters, parameters capable of distinguishing between hypophysitis and non-functioning pituitary adenomas were selected. In order to avoid the effects of confounding factors and to improve the performance of the classifiers, parameters with high correlation with each other were eliminated. Machine learning algorithms were performed with the combination of gray-level run-length matrix-low gray level run emphasis, gray-level co-occurrence matrix-correlation, and gray-level co-occurrence entropy.
ResultsA total of 34 patients were included, 17 of whom had hypophysitis and 17 had non-functioning pituitary adenomas. Among the 38 radiomics parameters obtained from post-contrast T1-weighted images, 10 tissue features that could differentiate the lesions were selected. Machine learning algorithms were performed using three selected parameters; gray level run length matrix-low gray level run emphasis, gray-level co-occurrence matrix-correlation, and gray level co-occurrence entropy. Error matrices were calculated by using the machine learning algorithm and it was seen that support vector machines showed the best performance in distinguishing the two lesion types.
ConclusionsOur analysis reported that support vector machines showed the best performance in distinguishing hypophysitis from non-functioning pituitary adenomas, emphasizing the importance of machine learning in differentiating the two lesions.
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