Association of Paraoxonase-1 Genotype and Phenotype with Angiogram Positive Coronary Artery Disease

Funding Information: This study was supported by Mashhad and Isfahan University of Medical Sciences. The authors would like to thank technicians of Sina, Sadi, Ghaem catheterization laboratory and technicians of Isfahan Alzahra genetics laboratory.Peer reviewedPublisher PD

Predicting working days for secondary tillage and planting operation in fall

Introduction The working day is an important component in selection and analysis of farm machinery systems. The number of working days is affected by various factors such as climate, soil characteristics and type of operation. Daily soil moisture models based on weather long-term data and soil characteristics were almost used for calculating probability of working days. The goal of this study was to develop a simulation model to predict the number of working days for secondary tillage and planting operation in fall at 50, 80 and 90% probability levels. Materials and Methods A Simulation model was developed using 21 years weather data and soil characteristics for calculate daily soil moisture content in Research Station of Ferdowsi University of Mashhad. So soil moisture was calculated using daily soil water equation for top 25 centimeter of soil depth. Moisture equal or lower than 85% of soil field capacity and precipitation lower than 4 millimeter (local data) were considered as soil workability criteria. Then the working days were determined for secondary tillage and planting operation at 50, 80 and 90% probability levels in falls. The number of days at 50% probability was the mean over 21 years and the number of days at 80% and 90% were determined for each two weeks period as the average number of working days minus the product of t value and standard deviation of those numbers. Model Evaluation Evaluation of model included a comparison of predicted and the observed the number of working days in Research Station of Ferdowsi University of Mashhad during 2002-2010 and sensitivity analysis was implemented to test the effect of changes in soil workability criterion (80, 90, 95 and 100% of soil field capacity), drainage coefficient (25 % decrease and increase) and soil field capacity (40% increase) on simulation results. Results and Discussion Comparison of predicted and observed days showed that correlation coefficient was 0.998 and the difference between the simulated data and observed data was not significant at the 5% level. Results from sensitivity analysis in Table 3 showed that when soil workability, drainage coefficient and field capacity increased, the number of working days increased, but model sensitivity was very low to drainage coefficient and soil field capacity. In general, the most important factor is precipitation in this weather conditions. The number of working days for secondary tillage and planting operation for each period in fall are shown in Table 4. Conclusions A simulation model was developed for predicting the number of working days for secondary tillage and planting operation in fall. This model was based on weather long-term data and soil characteristics for the Research Station of Ferdowsi University of Mashhad. The most important factor was precipitation and the model had low sensitivity to drainage coefficient and soil field capacity. The number of working days in 50%, 80% and 90% probability levels for period of ten days was on average 9.94, 9.21, 8.57 days for 23th September to 22th October and 9.77, 8.02, 6.41 days for 23th October to 21th November and 9.68, 7.48 and 5.24 for 22th November to 21th December, respectively

Does Selection of Variety Affect the Exergy Flow of Agricultural Production? Rice Production System in Italy

Exergy analysis is receiving considerable attention as an approach to be applied for making decision toward moving to a sustainable and energy-efficient food supply chain. This study focuses on how selection of variety affects the exergy flow of a crop production system (rice production). In this regard, 9 varieties of rice were investigated in Italy, the largest rice producer in Europe. Sensitivity analysis of inputs consumption and the exergy management scenarios of the most sensitive inputs are also provided in this study. The results indicated that the cumulative exergy consumption value of the investigated rice varieties ranges from 11,682 MJha-1 to 15,541 MJha-1. Chemical fertilizers and diesel fuel consumption were the biggest contributors to the total energy consumption in all investigated varieties. Luna variety, with the cumulative degree of perfection value of 3.87 and renewability indicator of 0.74, was identified as the most exergy efficient variety of rice in Italy

Nutritional adequacy in critically ill patients: Result of PNSI study

Background & aims: Critically ill patients are provided with the intensive care medicine to prevent further complications, including malnutrition, disease progression, and even death. This study was intended to assess nutritional support and its' efficacy in the Intensive Care Units (ICUs) of Iran. Methods: This cross-sectional study assessed 50 ICU's patients out of 25 hospitals in the 10 major regions of Iran's health system and was performed using the multistage cluster sampling design. The data were collected from patient's medical records, ICU nursing sheets, patients or their relatives from 2017 to 2018. Nutritional status was investigated by modified NUTRIC score and food frequency checklist. Results: This study included 1321 ICU patients with the mean age of 54.8 ± 19.97 years, mean mNUTRIC score of 3.4 ± 2.14, and malnutrition rate of 32.6. The mean time of first feeding was the second day and most of patients (66) received nutrition support, mainly through enteral (57.2) or oral (37) route during ICU stay. The patients received 59.2 ± 37.78 percent of required calorie and 55.5 ± 30.04 percent of required protein. Adequate intake of energy and protein was provided for 16.2 and 10.7 of the patients, respectively. The result of regression analysis showed that the odds ratio of mNUTRIC score was 0.85 (95 confidence interval CI = 0.74�0.98) and APACHE II was 0.92 (95%CI = 0.89�0.95) for the prediction of energy deficiency. Nutrition intake was significantly different from patient's nutritional requirements both in terms of energy (p < 0.001) and protein (p < 0.001). Also, mean mNUTRIC score varied notably (p = 0.011) with changing in energy intake, defined as underfeeding, adequate feeding, and overfeeding. Conclusion: The present findings shown that, provided nutritional care for ICU patients is not adequate for their requirements and nutritional status. © 2020 Elsevier Ltd and European Society for Clinical Nutrition and Metabolis