Exploring the effect of intravenous lipid emulsion in acute methamphetamine toxicity

Objective(s): The increasing use of methamphetamine (METH) in the last decades has made it the second most abused drug. Advancs in the area of intravenous lipid emulsion (ILE) have led to its potential application in the treatment of poisoning. The present study aims to investigate the potential role of ILE as an antidote for acute METH poisoning. Materials and Methods: Two groups of six male rats were treated by METH (45 mg/kg), intraperitoneally. Five to seven min later, they received an infusion of 18.6 ml/kg ILE 20% through the tail vein or normal saline (NS). Locomotor and behavioral activity was assessed at different time after METH administration. Body temperature and survival rates were also evaluated. Brain and internal organs were then removed for histological examination and TUNEL assay. Results: ILE therapy for METH poisoning in rats could prevent rats mortalities and returned the METH-induced hyperthermia to normal rates (

Molecular Crosslinking of Calcium-Silicate-Hydrate with Organosilanes: Toward Engineering of Cementitious Matrices with High Thermal Resistance

This research work is focused on the development of a novel approach aiming at improving the thermal resistance of cementitious matrices. The proposed approach utilizes the molecular scale crosslinking phenomena in which the galleries of inorganic calcium-silicate-hydrate (C-S-H), the main product of cement hydration, are directly bridged with bis-organosilanes molecules. The crosslinking with organic molecules provides C-S-H with attributes imperative for the reduced thermal conductivity e.g. contrast in vibrational densities of states and reduced particle density, which can positively alter the insulation potential of cement-based matrices at a macro-scale. It is postulated, that such a solution can be successfully realized at an engineering scale in the form of a high concentration seed admixture applied to hydrating cement mixtures. The results reported in this document demonstrate the feasibility of the proposed crosslinking approach; the organic-inorganic C-S-H gels of controlled stoichiometry can be successfully realized via sol-gel processing and using bis-alkoxysilanes of various lengths of the alkyl chain. The results of the extensive experimental campaign show that the novel organic-inorganic gels have layered turbostratic molecular structure with certain similarities to C-S-H precipitating in hydrating cement paste. The organic molecules' chain length controls the interlayer distance, which shows little to no shrinkage upon dehydration up to 105 °C. However, the structure of hybrid C-S-H becomes distorted in the basal plane, in which dimer and trimer Si-polyhedra structures condense on a 2D hexagonal Ca-polyhedra layer. Cross-linked C-S-H gels display plate-like morphology with a tendency toward stacking into agglomerates at a larger scale. Hybrid organic-inorganic C-S-H gels exhibit significantly lower intrinsic conductivity than the inorganic one and the magnitude of the observed reduction is related to the molecular size of the bis-organosilanes. The largest reduction was achieved for the organic-inorganic C-S-H incorporating the longest chain organic molecules (nCH2=8). The results of isothermal calorimetry confirmed the positive action of inorganic and hybrid C-S-H seeds on the hydration of Type II cement system. Both types of seed incorporation to the cement system lead to the increased intensity on the onset of the hydration reaction as well as higher heat rates of main hydration peaks. Also, higher total heat of hydration resulted from the incorporation of the C-S-H seeds, thus indicating the enhanced formation of hydration products; hybrid C-S-H seeds displayed superior activity in comparison to the inorganic seed. XRD test results revealed that the addition of both types of seeds did not alter the types of hydration products formed in the hardened paste; especially in the case of hybrid seeded cement system, it was observed that seeds have been efficiently accommodated in the system as a whole and tend to be chemically stable, which also supported by thermal analysis

Simulation of Stock Market by Concerning Structural Characteristics of Tehran Stock Exchange

Simulation has been increasingly applied in social sciences and economics in the two last decades. Agent Based Simulation (ABS) provides the opportunity of creation of an artificial environment for many agents to have interaction in a computer. In this paper, concerning ABS literature and the new characteristics, Tehran Stock Exchange has been simulated. Primary tests show that this model is capable of reproducing the existing statistical identifications in time-series of prices and returns in International and Tehran stock markets

Learning to rank with click-through features in a reinforcement learning framework

Purpose - Learning to rank algorithms inherently faces many challenges. The most important challenges could be listed as high-dimensionality of the training data, the dynamic nature of Web information resources and lack of click-through data. High dimensionality of the training data affects effectiveness and efficiency of learning algorithms. Besides, most of learning to rank benchmark datasets do not include click-through data as a very rich source of information about the search behavior of users while dealing with the ranked lists of search results. To deal with these limitations, this paper aims to introduce a novel learning to rank algorithm by using a set of complex click-through features in a reinforcement learning (RL) model. These features are calculated from the existing click-through information in the data set or even from data sets without any explicit click-through information. Design/methodology/approach - The proposed ranking algorithm (QRC-Rank) applies RL techniques on a set of calculated click-through features. QRC-Rank is as a two-steps process. In the first step, Transformation phase, a compact benchmark data set is created which contains a set of click-through features. These feature are calculated from the original click-through information available in the data set and constitute a compact representation of click-through information. To find most effective click-through feature, a number of scenarios are investigated. The second phase is Model-Generation, in which a RL model is built to rank the documents. This model is created by applying temporal difference learning methods such as Q-Learning and SARSA. Findings - The proposed learning to rank method, QRC-rank, is evaluated on WCL2R and LETOR4.0 data sets. Experimental results demonstrate that QRC-Rank outperforms the state-of-the-art learning to rank methods such as SVMRank, RankBoost, ListNet and AdaRank based on the precision and normalized discount cumulative gain evaluation criteria. The use of the click-through features calculated from the training data set is a major contributor to the performance of the system. Originality/value - In this paper, we have demonstrated the viability of the proposed features that provide a compact representation for the click through data in a learning to rank application. These compact click-through features are calculated from the original features of the learning to rank benchmark data set. In addition, a Markov Decision Process model is proposed for the learning to rank problem using RL, including the sets of states, actions, rewarding strategy and the transition function

Learning to rank: new approach with the layered multi-population genetic programming on click-through features

Users\u27 click-through data is a valuable source of information about the performance of Web search engines, but it is included in few datasets for learning to rank. In this paper, inspired by the click-through data model, a novel approach is proposed for extracting the implicit user feedback from evidence embedded in benchmarking datasets. This process outputs a set of new features, named click-through features. Generated click-through features are used in a layered multi-population genetic programming framework to find the best possible ranking functions. The layered multi-population genetic programming framework is fast and provides more extensive search capability compared to the traditional genetic programming approaches. The performance of the proposed ranking generation framework is investigated both in the presence and in the absence of explicit click-through data in the utilized benchmark datasets. The experimental results show that click-through features can be efficiently extracted in both cases but that more effective ranking functions result when click-through features are generated from benchmark datasets with explicit click-through data. In either case, the most noticeable ranking improvements are achieved at the tops of the provided ranked lists of results, which are highly targeted by the Web users

Integration of data fusion and reinforcement learning techniques for the rank-aggregation problem

Rank-aggregation or combining multiple ranked lists is the heart of meta-search engines in web information retrieval. In this paper, a novel rank-aggregation method is proposed, which utilizes both data fusion operators and reinforcement learning algorithms. Such integration enables us to use the compactness property of data fusion methods as well as the exploration and exploitation capabilities of reinforcement learning techniques. The proposed algorithm is a two-steps process. In the first step, ranked lists of local rankers are combined based on their mean average precisions with a variety of data fusion operators such as optimistic and pessimistic ordered weighted averaging (OWA) operators. This aggregation provides a compact representation of the utilized benchmark dataset. In the second step, a Markov decision process (MDP) model is defined for the aggregated data. This MDP enables us to apply reinforcement learning techniques such as Q-learning and SARSA for learning the best ranking. Experimentations on the LETOR4.0 benchmark dataset demonstrates that the proposed method outperforms baseline rank-aggregation methods such as Borda Count and the family of coset-permutation distance based stage-wise (CPS) rank-aggregation methods on P@n and NDCG@n evaluation criteria. The achieved improvement is especially more noticeable in the higher ranks in the final ranked list, which is usually more attractive to Web users

Evaluating the Effects and Safety of Intravenous Lipid Emulsion on Haloperidol-Induced Neurotoxicity in Rabbit

There are many reports on the effect of intravenous lipid emulsion (ILE) as an antidote in drugs related toxicities. We determined the effects of ILE on neurotoxicity of haloperidol (HA), a highly lipophilic antipsychotic, as a model of antipsychotics poisoning. We used six groups of five male rabbits. Two groups received distilled water intravenously followed by infusions of either 18 mL/kg of normal saline or ILE 20%, after 30 minutes. The third group received 18 mL/kg of normal saline after HA (2.6 mg/kg) administration. The three other groups received ILE 20% solution (6, 12, and 18 mL/kg) following HA injection. Catalepsy scores, temperature, pupil size, and mortality rate were measured at 0, 0.5, 1, 2, 3, 4, 8, and 24 hours after HA administration began. Blood and tissue samples were taken from all animals at 24 hours or at death time for biochemical, cell count, and pathological studies. ILE reversed cataleptic scores, miotic pupils, and hypothermia of HA intoxication much faster than normal saline ( < 0.001). Biochemical complications and mortality rate of the animals were significantly higher in the HA + 18 mL/Kg ILE group. ILE reversed sings of HA neurotoxicity; however, synergistic effect of high dose of ILE and HA increased complications and mortality

Does Allopurinol Prevent Post Endoscopic Retrograde Cholangio- Pancreatography Pancreatitis? A Randomized Double Blind Trial

"nPost endoscopic retrograde cholangiopancreatography (ERCP) pancreatitis is a frequent complication either for diagnosis or treatment of pancreatobiliary diseases. A number of pharmacological agents have been tried for prevention or alleviation of the complication. Allopurinol with free radical scavenger property has been considered as an effective prophylactic agent in some clinical trials. Administration of allopurinol in these trials was done in a long period before doing ERCP. Hence allopurinol converts to oxupurinol in the liver rapidly; it seems that clinical judgment about the net effect of allopurinol on prevention of post ERCP pancreatitis is doubtful. In this randomized double blind clinical trial, effect of allopurinol on prevention or alleviation of clinical and laboratory signs of pancreatitis has been evaluated in 74 patients undergoing ERCP. Results showed that there is not any difference between allopurinol and placebo in occurrence and severity of post ERCP pancreatitis (P=0.97). Also there is not any significant difference in amylase rises between 2 groups in 8 and 16 hours after ERCP (P=0.947, 0.287 respectively). Beneficial effects of allopurinol in some of the previous studies may be attributed to its active metabolite (oxypurinol). Further studies recommended about the net effect of allopurinol and oxypurinol in the complication