Stressors contribute to thrombosis and insulin resistance. Since obesity-related adipose inflammation is also involved in these pathological states, we assumed that stress correlates with adipose inflammation. Male mice were subjected to 2-week intermittent restraint stress. Expression of plasma lipids, monocyte/macrophage markers (CD11b, CD68, and F4/80), proinflammatory cytokines (monocyte chemoattractant protein-1 [MCP-1], tumor necrosis factor-α, and interleukin-6), adiponectin, heat shock protein 70.1 (HSP70.1), and coagulation factors (plasminogen activation inhibitor-1 [PAI-1] and tissue factor [TF]) in blood and inguinal white adipose tissue (WAT) was determined using immunohistochemistry, enzyme-linked immunosorbent assay, and RT-PCR, respectively. Glucose metabolism was assessed by glucose tolerance tests (GTTs) and insulin tolerance tests, and expression of insulin receptor substrate-1 (IRS-1) and glucose transporter 4 (GLUT4) in WAT. To examine effects of MCP-1 blockade, animals were treated with control or neutralizing antibody, or transplanted with control or 7ND (dominant-negative form of MCP-1)-overexpressing adipose-derived stromal cells (ADSCs). Stress increased monocyte accumulation, free fatty acids, proinflammatory cytokine, and HSP70.1 and reduced adiponectin. Adipose stromal cells highly expressed MCP-1. The stress-induced adipose inflammation increased PAI-1 and TF but did not give rise to thrombus formation. Without any changes in GTT, stress worsened insulin sensitivity and decreased IRS-1 and GLUT4 in WAT. Neutralizing antibody and 7ND-ADSCs reversed stress-induced adipose inflammation, procoagulant state, and insulin resistance. Stress evoked adipose inflammation to increase coagulation factors and impair insulin sensitivity through adipose-derived MCP-1.

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We have developed an apparatus to measure the anteroposterior stability of the knee to forces of up to 250 N, applied at 20 degrees of flexion. We measured anterior laxity at 200 N, anterior stiffness at 50 N and total laxity at +/- 200 N. A study of cadaveric knees revealed that the soft tissues surrounding the bones had a significant influence on the force-displacement curve, and emphasised that differences between injured and normal pairs of knees are much more important than the absolute values of the parameters. In 61 normal volunteers we found no significant left to right differences in anterior laxity at 200 N and anterior stiffness at 50 N. In 92 patients with unilateral anterior cruciate deficiency there were significant differences (p less than 0.0005) in anterior laxity, anterior stiffness and total laxity, the injured-normal differences averaging 6.7 mm, 1.3 N/mm, and 8.1 mm respectively.

It can be concluded that prosthodontic treatment may improve the nutritional status of institutionalized elderly.

Most of the mechanical systems in industries are made to run through induction motors (IM). To maintain the performance of the IM, earlier detection of minor fault and continuous monitoring (CM) are required. Among IM faults, bearing faults are considered as indispensable because of its high probability incidence nature. CM mainly depends upon signal processing and fault detection techniques. In recent decades, various methods have been involved in detecting the bearing fault using machine learning (ML) algorithms. Additionally, the role of artificial intelligence (AI), a growing technology, has also been used in fault diagnosis of IM. Taking the necessity of minor fault detection and the detailed study about the role of ML and AI to detect the bearing fault, the present study is performed. A comprehensive study is conducted by considering various diagnosis methods from ML and AI for detecting a minor bearing fault (hole and scratch). This study helps in understanding the difference between the diagnosis approach and their effectiveness in detecting an IM bearing fault. It is accomplished through FFT (fast Fourier transform) analysis of the load current and the extracted features are used to train the algorithm. The application is extended by comparing the result of ML and AI, and then explaining the specific purpose of use.