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BACKGROUND: One of the reasons for tolerance to morphine is increased oxidative stress and dysfunction of cell mitochondria in the hippocampus. Venlafaxine and calcium channel blockers can protect mitochondrial function. The investigation of the role of mitochondrial damage and oxidative stress in the simultaneous use of venlafaxine and calcium channel blockers on the acute analgesic effects of morphine and the induction of tolerance to its effects in mice was assessed. METHOD: In this experimental study, to induce tolerance to morphine, NMRI mice were treated with 50 mg/kg morphine for three consecutive days and 5 mg/kg morphine on the fourth day. Venlafaxine (20 mg/kg) alone or in combination with calcium channel blockers, nimodipine (10 mg/kg), and diltiazem (40 mg/kg) was administered 30 min before morphine, and the hot plate test was used. Then, hippocampal mitochondria were isolated by differential centrifugation method, and the levels of mitochondrial dehydrogenase activity, mitochondrial membrane potential, mitochondrial ROS production rate, as well as the content of glutathione and malondialdehyde in hippocampal mitochondria, were measured. RESULTS: The administration of venlafaxine-nimodipine and venlafaxine-diltiazem increased morphine's acute analgesic effects (P < 0.05) and reduced the induction and expression of tolerance to the analgesic effects of morphine (P < 0.05). Morphine significantly decreased MTT and GSH and increased MDA, mitochondrial membrane damage, and ROS compared to the control group (P < 0.01). Injection of venlafaxine-nimodipine and also venlafaxine-diltiazem 30 min before morphine can improve these alterations (P < 0.05). DISCUSSION AND CONCLUSION: Our data showed that the simultaneous use of venlafaxine with calcium channel blockers could increase the acute analgesic effects of morphine and reduce the induction and expression of tolerance to it. Also, the preventive and protective roles of simultaneous administration of venlafaxine and calcium channel blockers on morphine-induced mitochondrial oxidative stress and damage during the tolerance test were achieved.

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Background: Alzheimer's Disease (AD) is one of the most prevalent chronic neurodegenerative disorders. The present study aims to better understand the mechanism by which Citrus aurantium (C. aurantium) and Lavandula angustifolia (L. angustifolia) hydro-alcoholic extracts were used to treat AD and anti-oxidant issues in a laboratory model. Methods: 15 male Wistar rats, weighing 250±20 gr, aged 6-8 weeks, were used. Amyloids in the brain were found and identified using the shuttle box and Congo red test. ELISA testing for norepinephrine and serotonin, Superoxide Dismutase (SOD), Malondialdehyde (MDA), and Real-time PCR for expression of the APP and GLT1 genes were done. Results: The shuttle box test demonstrated that AD produces behavioral harm, since it significantly reduces passive avoidance learning. The Congo red test revealed that the AD models had much more amyloid beta in their brain tissue than the control. Norepinephrine levels were also decreased by using both extracts in test group. Treatment with both extracts led to a substantial rise in SOD activity and fall in MDA concentration. Conclusion: The gene expression data indicated that the relative expression of the APP and GLT1 genes was shown to be lower in the groups treated with both extracts. C. aurantium and L. angustifolia may therefore offer a multi-target treatment strategy for AD, which calls for more research in this area.

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Fat-water magnetic resonance (MR) images allow automated noninvasive analysis of morphological properties and fat fractions of vertebral bodies (VBs) and intervertebral discs (IVDs) that constitute an important part of human biomechanical systems. In this paper, we propose a fully automated approach for simultaneously segmenting multiple VBs and IVDs on fat-water MR images without prior localization or geometry estimation. This method involved a hierarchical random forest (HRF) classifier and a hierarchical conditional random field (HCRF) that encoded a multi-resolution image pyramid based on a set of multiscale local and contextual features. The HRF classifier employed penalized multivariate linear discriminants and SMOTEBagging to handle limited and imbalanced training data with large feature dimension. The HCRF estimated optimum labels according to their spatial and hierarchical consistencies by using the layer-wise significant features determined over the trained HRF classifier. To handle variable sample numbers at different resolutions, resolution-specific hyperparameters were used. This method was trained and evaluated for segmenting 15 thoracic and lumbar VBs and their IVDs on fat-water MR images of a subset of a large cohort data set. It was further evaluated for segmenting seven IVDs of the lower spine on fat-water images of a public grand challenge. These evaluations revealed the comparable accuracy of this method with the state-of-the-art while requiring less computational burden due to a simultaneous localization and segmentation.

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OBJECTIVES: To evaluate and compare conventional T1-weighted 2D turbo spin echo (TSE), T1-weighted 3D volumetric interpolated breath-hold examination (VIBE), and two-point 3D Dixon-VIBE sequences for automatic segmentation of visceral adipose tissue (VAT) volume at 3 Tesla by measuring and compensating for errors arising from intensity nonuniformity (INU) and partial volume effects (PVE). MATERIALS AND METHODS: The body trunks of 28 volunteers with body mass index values ranging from 18 to 41.2 kg/m2 (30.02 ± 6.63 kg/m2) were scanned at 3 Tesla using three imaging techniques. Automatic methods were applied to reduce INU and PVE and to segment VAT. The automatically segmented VAT volumes obtained from all acquisitions were then statistically and objectively evaluated against the manually segmented (reference) VAT volumes. RESULTS: Comparing the reference volumes with the VAT volumes automatically segmented over the uncorrected images showed that INU led to an average relative volume difference of -59.22 ± 11.59, 2.21 ± 47.04, and -43.05 ± 5.01 % for the TSE, VIBE, and Dixon images, respectively, while PVE led to average differences of -34.85 ± 19.85, -15.13 ± 11.04, and -33.79 ± 20.38 %. After signal correction, differences of -2.72 ± 6.60, 34.02 ± 36.99, and -2.23 ± 7.58 % were obtained between the reference and the automatically segmented volumes. A paired-sample two-tailed t test revealed no significant difference between the reference and automatically segmented VAT volumes of the corrected TSE (p = 0.614) and Dixon (p = 0.969) images, but showed a significant VAT overestimation using the corrected VIBE images. CONCLUSION: Under similar imaging conditions and spatial resolution, automatically segmented VAT volumes obtained from the corrected TSE and Dixon images agreed with each other and with the reference volumes. These results demonstrate the efficacy of the signal correction methods and the similar accuracy of TSE and Dixon imaging for automatic volumetry of VAT at 3 Tesla.

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