Most of the modulating effects of cannabinoids on pain are through putative cannabinoid CB1 and CB2 receptors. However, the involvement of other receptors is also suggested. Cannabinoid compounds with analgesic activity such as palmitoylethanolamide (PEA) show low affinity to CB1 and CB2 receptors, yet selectively activate GPR55 receptors. The objective of the present study was to evaluate the possible role of spinal CB1 and GPR55 receptors on antinociceptive activity of PEA in formalin test as well as in the spinal expression of IL1-β in rat. Intrathecal (i.t.) administration of PEA (1, 10 μg) significantly decreased both pain-related scores in formalin test and IL1-β expression in rat spinal cord. Pretreatment of rats with low doses of CB1 receptor antagonist/GPR55 receptor agonist AM251 (10, 100 ng; i.t.), did not attenuated the effect of PEA, yet even significantly increased the effect of PEA on IL1-β expression in rat spinal cord. Interestingly, i.t. administration of low doses of AM251 per se significantly decreased both pain related behavior and spinal IL1-β expression in formalin test. These findings suggest the possible involvement of receptors other than CB1 receptors in spinal pain pathways, such as GPR55, in pain modulating activity of cannabinoids.
Aim Smoking is a significant source of oxidative stress. Also, the disruption in equilibrium between free-oxygen radicals and antioxidants plays a pivotal role in the progression of periodontal inflammation. Green tea with antioxidant potential might have an effect on periodontal disease. This study evaluated the periodontal status in different groups of smokers compared with nonsmokers and investigates the association with salivary antioxidant levels after a period of green tea consumption. Materials and methods In this interventional study, 60 healthy males, including 20 light and 20 heavy cigarette smokers and 20 control nonsmokers, participated. Periodontal status was determined by the Community Periodontal Index of Treatment Needs (CPITN) at the beginning of the study. All participants were asked to consume a total of two cups of green tea (4 gm) per day. Total antioxidant capacity (TAC) of saliva was measured at baseline and after 21 days. Statistical evaluation was done by Statistical Package for the Social Sciences (SPSS) version 21 software. Results The Pearson correlation coefficient between CPITN and salivary TAC showed significant correlation in light and heavy smokers (p = 0.001 and p = 0.003 respectively). Changes in salivary TAC from baseline to day 21, after green tea consumption, in subjects with CPITN ≤ 18 were 139 (± 61.5), and 66.16 (± 67.37) in subjects with CPITN > 18. There was a significant interaction effect between time of TAC evaluation and patients' periodontal state (p = 0.009). Conclusion This study confirmed the association between periodontal status and smoking, and the association with salivary antioxidant capacity. A significant alteration in TAC of whole saliva in cases with clinical periodontal problems after green tea consumption was indicated. Clinical significance Considering the safety and availability of green tea, it can be used as a preventive or supplementary treatment in periodontal problems, especially in smokers, after further investigation. How to cite this article Bakhtiari S, Azimi S, Mansouri Z, Kruger E, Tennant M, Namdari M. Periodontal Status and Relation with Salivary Total Antioxidant Capacity after Green Tea Consumption in Smokers. Int J Experiment Dent Sci 2017;6(2):61-64.
BackgroundPET/CT images combining anatomic and metabolic data provide complementary information that can improve clinical task performance. PET image segmentation algorithms exploiting the multi‐modal information available are still lacking.PurposeOur study aimed to assess the performance of PET and CT image fusion for gross tumor volume (GTV) segmentations of head and neck cancers (HNCs) utilizing conventional, deep learning (DL), and output‐level voting‐based fusions.MethodsThe current study is based on a total of 328 histologically confirmed HNCs from six different centers. The images were automatically cropped to a 200 × 200 head and neck region box, and CT and PET images were normalized for further processing. Eighteen conventional image‐level fusions were implemented. In addition, a modified U2‐Net architecture as DL fusion model baseline was used. Three different input, layer, and decision‐level information fusions were used. Simultaneous truth and performance level estimation (STAPLE) and majority voting to merge different segmentation outputs (from PET and image‐level and network‐level fusions), that is, output‐level information fusion (voting‐based fusions) were employed. Different networks were trained in a 2D manner with a batch size of 64. Twenty percent of the dataset with stratification concerning the centers (20% in each center) were used for final result reporting. Different standard segmentation metrics and conventional PET metrics, such as SUV, were calculated.ResultsIn single modalities, PET had a reasonable performance with a Dice score of 0.77 ± 0.09, while CT did not perform acceptably and reached a Dice score of only 0.38 ± 0.22. Conventional fusion algorithms obtained a Dice score range of [0.76–0.81] with guided‐filter‐based context enhancement (GFCE) at the low‐end, and anisotropic diffusion and Karhunen–Loeve transform fusion (ADF), multi‐resolution singular value decomposition (MSVD), and multi‐level image decomposition based on latent low‐rank representation (MDLatLRR) at the high‐end. All DL fusion models achieved Dice scores of 0.80. Output‐level voting‐based models outperformed all other models, achieving superior results with a Dice score of 0.84 for Majority_ImgFus, Majority_All, and Majority_Fast. A mean error of almost zero was achieved for all fusions using SUVpeak, SUVmean and SUVmedian.ConclusionPET/CT information fusion adds significant value to segmentation tasks, considerably outperforming PET‐only and CT‐only methods. In addition, both conventional image‐level and DL fusions achieve competitive results. Meanwhile, output‐level voting‐based fusion using majority voting of several algorithms results in statistically significant improvements in the segmentation of HNC.
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