BioMed research international
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MIDAS is a valid and reliable short questionnaire for assessment of headache related disability. Linguistic validation of Persian MIDAS and assessment of psychometric properties between tension type headache (TTH) and migraine were the aims of this study. ⋯ Persian MIDAS is a valid and reliable questionnaire for migraine and TTH that can differentiate between episodic headache and chronic headache.
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Septic encephalopathy (SE) is a common complication of severe sepsis. Increased concentrations of circulating soluble adhesion molecules are reported in septic patients. This study aimed to determine whether serum adhesion molecules are associated with SE. ⋯ Septic encephalopathy implies higher mortality in nontraumatic, nonsurgical patients with severe sepsis. VCAM-1 level on presentation is a more powerful predictor of SE in these patients than lactate concentration and other adhesion molecules on admission.
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To compare the modifications in lipids between patients with rheumatoid arthritis (RA) receiving etanercept plus methotrexate (ETA + MTX) versus methotrexate (MTX) and their relationship with serum levels of tumor necrosis factor-alpha (TNF-α). ⋯ HDL-C levels increased significantly following treatment with ETA + MTX, without a relationship with decrease of TNF-α.
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Ischemia/reperfusion (I/R) injury is a major cause of acute renal failure and allograft dysfunction in kidney transplantation. ROS/inflammatory cytokines are involved in I/R injury. 2-Methoxyestradiol (2ME2), an endogenous metabolite of estradiol, inhibits inflammatory cytokine expression and is an antiangiogenic and antitumor agent. We investigated the inhibitory effect of 2ME2 on renal I/R injury and possible molecular actions. ⋯ 2ME2 reduces renal I/R injury in mice because it inhibits the expression of ROS and proinflammatory cytokines and induces antiapoptotic proteins.
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The aim of this paper was to develop a computer assisted tissue classification (granulation, necrotic, and slough) scheme for chronic wound (CW) evaluation using medical image processing and statistical machine learning techniques. The red-green-blue (RGB) wound images grabbed by normal digital camera were first transformed into HSI (hue, saturation, and intensity) color space and subsequently the "S" component of HSI color channels was selected as it provided higher contrast. Wound areas from 6 different types of CW were segmented from whole images using fuzzy divergence based thresholding by minimizing edge ambiguity. ⋯ The performance of the wound area segmentation protocol was further validated by ground truth images labeled by clinical experts. It was observed that SVM with 3rd order polynomial kernel provided the highest accuracies, that is, 86.94%, 90.47%, and 75.53%, for classifying granulation, slough, and necrotic tissues, respectively. The proposed automated tissue classification technique achieved the highest overall accuracy, that is, 87.61%, with highest kappa statistic value (0.793).