Intraperitoneal administration of Telmisartan prevents postsurgical adhesion band formation

Background Angiotensin II receptor blockers (ARBs) have a potential role in reducing inflammation and fibrosis. We have integrated systems and molecular biology approaches to investigate the therapeutic potential of ARBs in preventing postsurgical adhesion band formation. Material and methods we have followed the ARRIVE guidelines point by point during experimental studies. Telmisartan (1 and 9 mg/kg), valsartan (1 and 9 mg/kg), and losartan (1 and 10 mg/kg) were administered intraperitoneally in different groups of male albino Wistar rat. After 7 d of treatment, macroscopic evidence and score of fibrotic bands based on scaling methods was performed. Moreover, the anti-inflammatory and antifibrosis effects of telmisartan on reduction of fibrotic bands were investigated by using histopathology, ELISA, and real-time polymerase chain reaction methods. Results Telmisartan, but not losartan or valsartan, prevented the frequency as well as the stability of adhesion bands. Telmisartan appears to elicit anti-inflammatory responses by attenuating submucosal edema, suppressing proinflammatory cytokines, decreasing proinflammatory cell infiltration, and inhibiting oxidative stress at the site of peritoneal surgery. We also showed that telmisartan prevents fibrotic adhesion band formation by reducing excessive collagen deposition and suppression of profibrotic genes expression at the peritoneum adhesion tissues. Conclusions These results support the potential application of telmisartan in preventing postsurgical adhesion band formation by inhibiting key pathologic responses of inflammation and fibrosis in postsurgery patients

Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

The global burden of cancer attributable to risk factors, 2010–19: a systematic analysis for the Global Burden of Disease Study 2019

BACKGROUND: Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. METHODS: The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk–outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. FINDINGS: Globally, in 2019, the risk factors included in this analysis accounted for 4·45 million (95% uncertainty interval 4·01–4·94) deaths and 105 million (95·0–116) DALYs for both sexes combined, representing 44·4% (41·3–48·4) of all cancer deaths and 42·0% (39·1–45·6) of all DALYs. There were 2·88 million (2·60–3·18) risk-attributable cancer deaths in males (50·6% [47·8–54·1] of all male cancer deaths) and 1·58 million (1·36–1·84) risk-attributable cancer deaths in females (36·3% [32·5–41·3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20·4% (12·6–28·4) and DALYs by 16·8% (8·8–25·0), with the greatest percentage increase in metabolic risks (34·7% [27·9–42·8] and 33·3% [25·8–42·0]). INTERPRETATION: The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden

Investigating the energy consumption in different operations of oilseed productions in Iran

The energy consumption in different operations of soybean, canola and sunflower productions in Golestan province of Iran was investigated. This study also focused sketches the environmental footprints of energy use in oilseed production. For these purpose Inquiries on 319 oilseed farms were conducted in 2009/10 production period. The results revealed that soybean gave the highest operational energy input (22235 MJ ha -1 ); while, total operational energy for canola and sunflower was relatively low as 8317 and 6013 MJ ha -1 , respectively. Irrigation operation consumed the highest share of total operational energy in soybean and sunflower productions; it was mainly in the form of electricity energy; however, in canola production, the tillage operation was the most intensive energy consumer, followed by harvesting practice. From this study it was found that increasing energy use efficiency of water pumping systems by good repair and maintenance and employing improved tillage and harvesting practices, such as low till agriculture, could be the pathways to make oilseed productions more environmental friendly and thus reduce their environmental footprints

Sensitivity analysis of agrochemical energy inputs and their environmental impacts in rapeseed production

Abstract: Agrochemicals, especially fertilizers, are the key energetic inputs in rapeseed production in Iran. In this study the relationship between agrochemicals energy inputs and rapeseed yield was investigated and the sensitivity of energy inputs on output level was analyzed using the Marginal Physical Productivity (MPP) method and partial regression coefficients of the Cobb-Douglas production function. Also, the environmental impacts of usage agrochemical energy inputs had investigated. Data were collected from 130 randomly selected rapeseed farms in Golestan province of Iran. The results of regression model estimation showed that nitrogen, herbicides, fungicides and insecticides energy inputs had the significant impacts on yield; while the impacts of phosphate, potassium and sulfur were not significant. Moreover, the herbicides, insecticides and potassium energy inputs were negatively contributed to yield; indicating that rapeseed producers have applied an excess use of these inputs, resulting in an inverse effect on yield as well as imposing risks to natural resources and environmental health. The study showed that, optimal fertilizer and chemical energy use by growing leguminous pastures or dray-land crops in rotation with rapeseed and employing integrated pest management can maximize yield and help to reduce the environmental footprints of food production

Development and Evaluation of Combined Adaptive Neuro-Fuzzy Inference System and Multi-Objective Genetic Algorithm in Energy, Economic and Environmental Life Cycle Assessments of Oilseed Production

Energy consumption, economics, and environmental impacts of canola production were assessed using a combined technique involving an adaptive neuro-fuzzy inference system (ANFIS) and a multi-objective genetic algorithm (MOGA). Data were collected from canola farming enterprises in the Mazandaran province of Iran and were used to test the application of the combined modeling algorithms. Life cycle assessment (LCA) for one ha functional unit of canola production from cradle to farm gate was conducted in order to evaluate the impacts of energy, materials used, and their environmental emissions. MOGA was applied to maximize the output energy and benefit-cost ratio, and to minimize environmental emissions. The combined ANFIS-MOGA technique resulted in a 6.2% increase in energy output, a 144% rise in the benefit-cost ratio, and a 19.8% reduction in environmental emissions from the current canola production system in the studied region. A comparison of ANFIS-MOGA with the data envelopment analysis approach was also conducted and the results established that the former is a better system than the latter because of its ability to generate optimum conditions that allow for the assessment of a combination of parameters such as energy, economic, and environmental impacts of agricultural production systems