Anxiety as a consequence of modern dietary pattern in adults in Tehran-Iran

Food intake patterns in relation to mental health have already been revealed. To investigate the relationship between processed food consumption behavior and anxiety disorder, a cross sectional study was conducted. Overall, 1782 young adults aged 18-35 years were randomly selected using cluster sampling method from 22 districts of Tehran Iran in 2011. Diet assessment was done using a 24 hour recall questionnaire in two times with a week interval. Anxiety level was determined using the validated Speilburger test (Persian version). A proportional odds regression model was used to assess the effect of processed food consumption on anxiety variables. A significant statistical difference was found between men and women in terms of processed food consumption (p<0.001). Adjusting for age, total calorie intake, gender, body mass index, socioeconomic status, and history of sedative drug consumption as well as mental health disorders, the proportional odds regression model showed a significant relationship between increased consumption of processed foods and anxiety (OR = 4.73, 95 CI: 2.89-12.54 for state and OR = 4.91, 95 CI: 2.88-13.99 for trait). Identification, modification and adjusting incorrect food patterns in the community could be considered as valuable steps to turn down nutritional-based health difficulties. (C) 2012 Elsevier Ltd. All rights reserved

Linking resilience and robustness and uncovering their trade-offs in coupled infrastructure systems

Robustness and resilience are concepts in systems thinking that have grown in importance and popularity. For many complex social-ecological systems, however, robustness and resilience are difficult to quantify and the connections and trade-offs between them difficult to study. Most studies have either focused on qualitative approaches to discuss their connections or considered only one of them under particular classes of disturbances. In this study, we present an analytical framework to address the linkage between robustness and resilience more systematically. Our analysis is based on a stylized dynamical model that operationalizes a widely used conceptual framework for social-ecological systems. The model enables us to rigorously delineate the boundaries of conditions under which the coupled system can be sustained in a long run, define robustness and resilience related to these boundaries, and consequently investigate their connections. The results reveal the trade-offs between robustness and resilience. They also show how the nature of such trade-offs varies with the choice of certain policies (e.g., taxation and investment in public infrastructure), internal stresses, and uncertainty in social-ecological settings.</p

Adenosine receptor mediates nicotine-induced antinociception in formalin test

In this study, the effect of adenosine receptor agents on nicotine induced antinociception, in formalin test, has been investigated. Intraperitoneal (i.p.) administration of different doses of nicotine (0.1, 1, 10 and 100 μg kg -1) induced a dose-dependent antinociception in mice, in the both first and second phases of the test. Adenosine receptor antagonist, theophylline (5, 10, 20 and 80 mg kg-1, i.p.) also induced antinociception in the both phases, while a dose of the drug (40 mg kg-1, i.p.) did not induce any response. Theophylline reduced antinociception induced by nicotine in both phases of formalin test. The A2 receptor agonist, 5�-N-ethylcarboxamide adenosine (NECA; 1 and 5 μg kg-1, i.p.) also produced antinociception, which was reversed with different doses of theophylline (5, 10, 20 and 40 mg kg-1, i.p.). But administration of the adenosine receptor agonist, NECA did not potentiate the response of nicotine. It is concluded that adenosine system may be involved in modulation of antinociception induced by nicotine. © 2004 Elsevier Ltd. All rights reserved

Lateral wedge insoles for reducing biomechanical risk factors for medial knee osteoarthritis progression : a systematic review and meta-analysis

Objective Lateral wedge insoles are intended to reduce biomechanical risk factors of medial knee osteoarthritis (OA) progression, such as increased knee joint load; however, there has been no definitive consensus on this topic. The aim of this systematic review and meta-analysis was to establish the within-subject effects of lateral wedge insoles on knee joint load in people with medial knee OA during walking. Methods Six databases were searched from inception until February 13th 2015. Included studies reported on the acute biomechanical effects of lateral wedge insoles in people with medial knee osteoarthritis during walking. Primary outcomes of interest relating to the biomechanical risk of disease progression were the 1st and 2nd peak external knee adduction moment (EKAM) and knee adduction angular impulse (KAAI). Eligible studies were pooled using random-effects meta-analysis. Results Eighteen studies were included with a total of 534 participants. Lateral wedge insoles resulted in a small but statistically significant reduction in the 1st peak EKAM (SMD: -0.19; 95% CI -0.23 − -0.15) and 2nd peak EKAM (SMD: -0.25; 95% CI -0.32 − -0.19) with a low level of heterogeneity (I2 = 5% and 30%, respectively). There was a favourable but small reduction in the KAAI with lateral wedge insoles (SMD: -0.14; 95% CI -0.21 − -0.07, I2 =31%). Risk of methodological bias scores (Quality Index) ranged from 8 to 13 out of 16. Conclusions Lateral wedge insoles cause small reductions in the EKAM and KAAI in people with medial knee OA during walking. At present, they appear ineffective at attenuating structural changes in people with medial knee OA as a whole and may be better suited to targeted use in biomechanical phenotypes associated with larger reductions in knee load

Assessment of the association between body composition and risk of non-alcoholic fatty liver

Non-alcoholic fatty liver disease (NAFLD) is defined as the condition of fat accumulation in the liver. This cross-sectional study aimed to investigate the relationship between body composition and fatty liver and determine of cut-off point for predicting NAFLD. Samples were selected from the nutrition clinic from 2016 to 2017 in Tehran, Iran. The liver steatosis was calculated using the CAP score through the FiroScan� and body composition was measured using the dual-energy X-ray absorptiometry scan method. A total of 2160 patients participated in this study, 745 (34.5) subjects had NAFLD. We found that fat-free tissue was inversely and fat tissue was directly correlated with the risk of NAFLD in almost all factors and the risk of developing NAFLD increases if the total fat exceeds 32.23 and 26.73 in women and men and abdominal fat exceeds 21.42 and 13.76 in women and men, respectively. Finally, we realized that the total fat percent had the highest AUC (0.932 for men and 0.917 for women) to predict the risk of NAFLD. Overall, the likelihood of NAFLD development rose significantly with increasing the amount of total fat and abdominal fat from the cut-off point level. Copyright: © 2021 Ariya et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

CovidCTNet: an open-source deep learning approach to diagnose covid-19 using small cohort of CT images

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method; however, its accuracy in detection is only ~70�75. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80�98, but similar accuracy of 70. To enhance the accuracy of CT imaging detection, we developed an open-source framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95 compared to radiologists (70). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership. © 2021, The Author(s)