Demographic Factors, Duration and Costs of Hospitalization, and Causes of Death in Patients Intoxicated with Opioids and Amphetamines

Background: Intoxications are medical emergencies and among the significant causes of morbidity and mortality worldwide. In recent years, prevalence of intoxication with opioids and stimulants, such as amphetamines, is increasing particularly among young people. In this study, we investigated demographic factors, duration of hospitalization, costs of hospitalization, and cause of death in patients intoxicated with amphetamines and opioids.Materials and Methods: This study was a prospective descriptive–analytic study. Sampling method was census, and Subjects were patients intoxicated with amphetamines and opioids, alone or combined, who referred to toxicology ward of Ali-Asghar hospital in Isfahan, from October 2009 to April 2010.Results: During 6 months, among 2325 subjects, 419 patients used opioids, 98 patients used amphetamines, and 25 patients used both of them. The mean age of patients in the three groups was not significantly different. Most patients were male in all groups. The most common route of intoxication was orally in opioid group and inhalation in amphetamine group. The most common cause of intoxication was intentional attempt. Vital signs at admission were normal in three groups, but the average of heart rate, body temperature, respiratory rate and blood pressure, was slightly higher in the amphetamine group than the opioid group. Duration and cost of hospitalization were not significantly different between groups. Four patients were died totally and the outcome was not significantly different between groups. The mean age and duration of hospitalization were significantly higher in died compared to living patients.Conclusion: Opioids and amphetamines accounted for high percentages of intoxication, especially in young single men with self-employed job. Therefore, control and prevention of opioids and amphetamines consumption are important ways to reduce this kind of intoxication in this group

Phenolic metabolism and antioxidant activity during endodormancy of Kiwifruit buds

Bud dormancy is an adaptability process in woody plants that enables them to survive in unfavorable conditions. In the present study, the phenols, antioxidant capacity, and activity of three enzymes were evaluated during endodormancy phases in two Hayward and Tomuri cultivars and two female and male Golden genotypes of kiwifruit buds. The buds were collected from ten-year-old own-rooted vines from the end of October 2015 until the end of January 2016 in the north of Iran. The results revealed that phenols, antioxidant capacity (RSA), phenylalanine ammonia-lyase (PAL), and polyphenol oxidase (PPO) activities of buds significantly increased at the beginning of endodormancy and subsequently decreased at the end of the endodormancy. The POD activity increased in Hayward and Tomuri from the onset of endodormancy and continued for two weeks after the endodormancy release. The total phenol had a positive and significant correlation with RSA and PAL enzyme activity. Furthermore, higher antioxidant capacity and phenols in both male and female Golden genotypes were attributed to the higher PAL enzyme activity in both genotypes. This study proposes that the RSA%, PAL activity, and phenol concentration could be employed as a biomarker to indicate bud dormancy phases in kiwifruit.

Sb2Se3 Thin Film Growth by Solution Atomic Layer Deposition

We establish solution atomic layer deposition sALD for the controlled growth of pure Sb2Se3 thin films under mild conditions, namely, room temperature and atmospheric pressure. Upscaling this process yields Sb2Se3 thin films with high homogeneity over large area 4 amp; 8243; substrates. Annealing of the initially amorphous material leads to highly crystalline and smooth Sb2Se3 thin films. Removing the constraints of thermal stability and sufficient volatility in sALD compared to traditional gas phase ALD opens up a broad choice of precursors and allows us to examine a wide range of Se2 precursors, of which some exhibit facile synthetic routes and allow us to tune their reactivity for optimal experimental ease of use. Moreover, we demonstrate that the solvent used in sALD represents an additional, attractive tool to influence and tailor the reactivity at the liquid solid interface between the precursors and the surfac

Mathematical modelling of clostridial acetone-butanol-ethanol fermentation

Clostridial acetone-butanol-ethanol (ABE) fermentation features a remarkable shift in the cellular metabolic activity from acid formation, acidogenesis, to the production of industrial-relevant solvents, solventogensis. In recent decades, mathematical models have been employed to elucidate the complex interlinked regulation and conditions that determine these two distinct metabolic states and govern the transition between them. In this review, we discuss these models with a focus on the mechanisms controlling intra- and extracellular changes between acidogenesis and solventogenesis. In particular, we critically evaluate underlying model assumptions and predictions in the light of current experimental knowledge. Towards this end, we briefly introduce key ideas and assumptions applied in the discussed modelling approaches, but waive a comprehensive mathematical presentation. We distinguish between structural and dynamical models, which will be discussed in their chronological order to illustrate how new biological information facilitates the ‘evolution’ of mathematical models. Mathematical models and their analysis have significantly contributed to our knowledge of ABE fermentation and the underlying regulatory network which spans all levels of biological organization. However, the ties between the different levels of cellular regulation are not well understood. Furthermore, contradictory experimental and theoretical results challenge our current notion of ABE metabolic network structure. Thus, clostridial ABE fermentation still poses theoretical as well as experimental challenges which are best approached in close collaboration between modellers and experimentalists

Predicting the wheel rolling resistance regarding important motion parameters using the artificial neural network

Introduction: Rolling resistance is one of the most substantial energy losses when the wheel moves on soft soil. Rolling resistance value optimization will help to improve energy efficiency. Accurate modeling of the interaction soil-tire is an important key to this optimization and has eliminated the need for costly field tests and has reduced the time required to test. Rolling resistance will change because of the tire and wheel motion parameters and characteristics of the ground surface. Some tire design parameters are more important such as the tire diameter, width, tire aspect ratio, lugs form, inflation pressure and mechanical properties of tire structure. On the other hand, the soil or ground surface characteristics include soil type; moisture content and bulk density have an important role in this phenomenon. In addition, the vertical load and the wheel motion parameters such as velocity and tire slip are the other factors which impact on tire rolling resistance. According to same studies about the rolling resistance of the wheel, the wheel is significantly affected by the dynamic load. Tire inflation pressure impacted on rolling resistance of tires that were moving on hard surfaces. Studies showed that the rolling resistance of tires with low inflation pressure (less than 100 kPa) was too high. According to Zoz and Griss researches, increasing the tire pressure increases rolling resistance on soft soil but reduces the rolling resistance of on-road tires and tire-hard surface interaction. Based on these reports, the effect of velocity on tire rolling resistance for tractors and vehicles with low velocity (less than 5 meters per second) is usually insignificant. According to Self and Summers studies, rolling resistance of the wheel is dramatically affected by dynamic load on the wheel. Artificial Neural Network is one of the best computational methods capable of complex regression estimation which is an advantage of this method compared with the analytical and statistical methods. It is expected that the neural network can more accurately predict the rolling resistance. In this study, the neural network for experimental data was trained and the relationship among some parameters of velocity, dynamic load and tire pressure and rolling resistance were evaluated. Materials and Methods: The soil bin and single wheel tester of Biosystem Engineering Mechanics Department of Urmia University was used in this study. This soil bin has 24 m length, 2 m width and 1 m depth including a single-wheel tester and the carrier. Tester consists of four horizontal arms and a vertical arm to vertical load. The S-shaped load cells were employed in horizontal arms with a load capacity of 200 kg and another 500 kg in the vertical arm was embedded. The tire used in this study was a general pneumatic tire (Good year 9.5L-14, 6 ply) In this study, artificial neural networks were used for optimizing the rolling resistance by 35 neurons, 6 inputs and 1 output choices. Comparison of neural network models according to the mean square error and correlation coefficient was used. In addition, 60% of the data on training, 20% on test and finally 20% of the credits was allocated to the validation and Output parameter of the neural network model has determined the tire rolling resistance. Finally, this study predicts the effects of changing parameters of tire pressure, vertical load and velocity on rolling resistance using a trained neural network. Results and Discussion: Based on obtained error of Levenberg- Marquardt algorithm, neural network with 35 neurons in the hidden layer with sigmoid tangent function and one neuron in the output layer with linear actuator function were selected. The regression coefficient of tested network is 0.92 which seems acceptable, considering the complexity of the studied process. Some of the input parameters to the network are speed, pressure and vertical load which their relationship with the rolling resistance is discussed. The results indicated that in general trend of changes, the velocity is not affected by rolling resistance. Rolling resistance increases when tire pressure decreases. This is due to energy consumption for creating deflection on the body of the tire at the lower levels of tire inflation pressure. Another variable parameter is the vertical load on the wheel and its logical relation with rolling resistance using neural network. The results showed that increasing the vertical load increases the rolling resistance. Conclusions: The major purpose of this study was the feasibility of using learning algorithms for interaction between wheel and soil. The parameters of the wheel when clashes with soil are not stochastic and in spite of their complexity follow a specific model, certainly. Artificial neural network trained with a correlation coefficient of 0.92 relatively had a good performance in education, testing and validation parts. To validate the network results, the impact of some factors on the extraction process such as velocity, load and inflation pressure was simulated. The main objective of this article is comparing the network performance with basic principles and other scientific reports. In this regard, the predictions by trained neural network indicated that rolling resistance is independent of the velocity of the wheel. On the other hand, rolling resistance decreases by increasing tire inflation pressure which is a general trend similar to other studies and reports in the same mechanical condition of the soil tested. Rolling resistance changes are directly proportional to load vertical variations on the wheel in terms of quantity and quality, similar to experimental models such as Wismer and Luth

Atopic diseases: Risk factor in developing adverse reaction to intravenous N-Acetylcysteine

Background: N-acetylcysteine (NAC) is the choice treatment for acetaminophen overdose. The main side effect of intravenous NAC therapy is anaphylaxis or anaphylactoid reactions. We investigated the prevalence of anaphylactoid or anaphylaxis reactions to IV-NAC therapy in acetaminophen poisoned patients with atopic disease.
 Methods: A case series antrograde and descriptive–analytic study was done on acetaminophen poisoned patients who treated with IV-NAC from September 2003 to September 2004 in Isfahan, Iran.
 Results: Of 173 infused IV-NAC patients, 77 patients (44.5%) developed an anaphylactoid reaction. Its side effects was nausea and vomiting (n=49, 63.15%), flashing (n=23, 30.26%), bronchospasm (n=20, 26.31%), vertigo (n=18, 23.68%), skin rash (n=25, 32.36%) and hypotension (n=12, 15.75%). Also, 71 patients (41%) had history of atopic disease. Atopic diseases were asthma (n=12, 6.9%), atopic dermatitis (n=7, 4%), allergic rhinitis (n=5, 2.8%) and allergic conjunctivitis (n=1, 0.5%). Among 71 atopic patients, 59 patients (83.13 %) developed side effects to NAC. There was a relation between previous history of atopic disease and anaphylactoid reaction to NAC.
 Conclusions: We report substantially higher incidence of anaphylactoid reactions to IV-NAC than previous studies. Different atopic diseases must be considered as a risk factor in the development of side effects to IV-NAC-therapy.
 Keywords: Poisoning, Acetaminophen, Anaphylactoid reaction, N-acetylcysteine, Atopic diseas