Efficacy of Iranian traditional medicine in the treatment of epilepsy

Epilepsy is a brain disorder which affects about 50 million people worldwide. Ineffectiveness of the drugs in some cases and the serious side effects and chronic toxicity of the antiepileptic drugs lead to use of herbal medicine as a form of complementary and alternative medicine. In this review modern evidences for the efficacy of antiepileptic medicinal plants in Traditional Iranian Medicine (TIM) will be discussed. For this purpose electronic databases including PubMed, Scopus, Sciencedirect, and Google Scholar were searched for each of the antiepileptic plants during 1970-February 2013.Anticonvulsant effect of some of the medicinal plants mentioned in TIM like Anacyclus pyrethrum, Pimpinella anisum, Nigella sativa, and Ferula gummosa was studied with different models of seizure. Also for some of these plants like Nigella sativa or Piper longum the active constituent responsible for antiepileptic effect was isolated and studied. For some of the herbal medicine used in TIM such as Pistacia lentiscus gum (Mastaki), Bryonia alba (Fashra), Ferula persica (Sakbinaj), Ecballium elaterium (Ghesa-al Hemar), and Alpinia officinarum (Kholanjan) there is no or not enough studies to confirm their effectiveness in epilepsy. It is suggested that an evaluation of the effects of these plants on different epileptic models should be performed. © 2013 Mehri Abdollahi Fard and Asie Shojaii

A machine learning model to predict heart failure readmission: toward optimal feature set

BackgroundHospital readmissions for heart failure patients remain high despite efforts to reduce them. Predictive modeling using big data provides opportunities to identify high-risk patients and inform care management. However, large datasets can constrain performance.ObjectiveThis study aimed to develop a machine learning based prediction model leveraging a nationwide hospitalization database to predict 30-day heart failure readmissions. Another objective of this study is to find the optimal feature set that leads to the highest AUC value in the prediction model.Material and methodsHeart failure patient data was extracted from the 2020 Nationwide Readmissions Database. A heuristic feature selection process incrementally incorporated predictors into logistic regression and random forest models, which yields a maximum increase in the AUC metric. Discrimination was evaluated through accuracy, sensitivity, specificity and AUC.ResultsA total of 566,019 discharges with heart failure diagnosis were recognized. Readmission rate was 8.9% for same-cause and 20.6% for all-cause diagnoses. Random forest outperformed logistic regression, achieving AUCs of 0.607 and 0.576 for same-cause and all-cause readmissions respectively. Heuristic feature selection resulted in the identification of optimal feature sets including 20 and 22 variables from a pool of 30 and 31 features for the same-cause and all-cause datasets. Key predictors included age, payment method, chronic kidney disease, disposition status, number of ICD-10-CM diagnoses, and post-care encounters.ConclusionThe proposed model attained discrimination comparable to prior analyses that used smaller datasets. However, reducing the sample enhanced performance, indicating big data complexity. Improved techniques like heuristic feature selection enabled effective leveraging of the nationwide data. This study provides meaningful insights into predictive modeling methodologies and influential features for forecasting heart failure readmissions

Comparison of estimation capabilities of the artificial neural network with the wavelet neural network in lipase-catalyzed synthesis of triethanolamine-based esterquats cationic surfactant

In this study, estimation capabilities of the artificial neural network (ANN) and the wavelet neural network (WNN) based on genetic algorithm were investigated in a synthesis process. An enzymatic reaction catalyzed by Novozym 435 was selected as the model synthesis process. The conversion of enzymatic reaction was investigated as a response of five independent variables; enzyme amount, reaction time, reaction temperature, substrates molar ratio and agitation speed in conjunction with an experimental design. After training of the artificial neurons in ANN and WNN, using the data of 30 experimental points, the products were used for estimation of the response of the 18 experimental points. Estimated responses were compared with the experimentally determined responses and prediction capabilities of ANN and WNN were determined. Performance assessment indicated that the WNN model possessed superior predictive ability than the ANN model, since a very close agreement between the experimental and the predicted values was obtained

Glycerolysis of palm fatty acid distillate for biodiesel feedstock under different reactor conditions

This paper deals with the comparative study on glycerolysis of palm fatty acid distillate (PFAD) in a solvent free system at different reaction conditions in an attempt to get maximum degree of FFA% reduction for biodiesel feedstock. Initially, optimization of varied reaction parameters was performed under all the different reaction conditions using artificial neural network (ANN) based on the genetic algorithm (GA). It has been found that the reduction of acidity varies with varying reaction conditions with maximum reaction rate observed in case of reaction carried-out in open reactor system with inert gas flow, followed by the reaction in open reactor system without inert gas flow and then in case of reaction under the close reactor system. In the most favorable case, 1.5 mgKOH/gPFAD of FFA (free fatty acid) was achieved after 90 min of reaction time with an excess glycerol of 4% at 220 °C. The results from the ANN model show good agreement with experimental results. Thus, the glycerolysis in open reactor system with inert gas flow (N2) option is much-preferred option compared to acid esterification for the same biodiesel plant capacity, particularly for high-FFA feedstocks

Artificial neural network modelling of photodegradation in suspension of manganese doped zinc oxide nanoparticles under visible-light irradiation

The artificial neural network (ANN) modeling of m-cresol photodegradation was carried out for determination of the optimum and importance values of the effective variables to achieve the maximum efficiency. The photodegradation was carried out in the suspension of synthesized manganese doped ZnO nanoparticles under visible-light irradiation. The input considered effective variables of the photodegradation were irradiation time, pH, photocatalyst amount, and concentration of m-cresol while the efficiency was the only response as output. The performed experiments were designed into three data sets such as training, testing, and validation that were randomly splitted by the software’s option. To obtain the optimum topologies, ANN was trained by quick propagation (QP), Incremental Back Propagation (IBP), Batch Back Propagation (BBP), and Levenberg-Marquardt (LM) algorithms for testing data set. The topologies were determined by the indicator of minimized root mean squared error (RMSE) for each algorithm. According to the indicator, the QP-4-8-1, IBP-4-15-1, BBP-4-6-1, and LM-4-10-1 were selected as the optimized topologies. Among the topologies, QP-4-8-1 has presented the minimum RMSE and absolute average deviation as well as maximum R-squared. Therefore, QP-4-8-1 was selected as final model for validation test and navigation of the process. The model was used for determination of the optimum values of the effective variables by a few three-dimensional plots. The optimum points of the variables were confirmed by further validated experiments. Moreover, the model predicted the relative importance of the variables which showed none of them was neglectable in this work

Effect of oral Utrogestan in comparison with Cetrotide on preventing luteinizing hormone surge in IVF cycles: A randomized controlled trial

Background: Oral progesterone is recommended as an alternative to gonadotropin-releasing hormone (GnRH) agonists and antagonists to prevent luteinizing hormone (LH) surge in assisted reproductive technology (ART) cycles. However, there are little data regarding its use. Objective: We aimed to compare the effect of oral Utrogestan and Cetrotide (a GnRH antagonist) on preventing LH surge in ART cycles. Materials and Methods: In this randomized clinical trial, 100 infertile women undergoing ART who received recombinant follicle-stimulating hormone (FSH) at 150- 225 IU/day were randomly assigned to receive either Utrogestan 100 mg twice a day (case group) or GnRH antagonist protocol (control group) from cycle day 3 until the trigger day. Triggering was performed with 10,000 IU hCG) when there were at least three mature follicles. Viable embryos were cryopreserved for transfer in the next cycle for both groups. The number of oocytes retrieved and transferred embryos were compared between groups. Results: The case group had significantly higher progesterone levels on triggering day, more follicles of >14 mm with higher maturity, and more oocytes retrieved with a higher rate of embryos transferred. A small increase in the pregnancy rate was observed in the case group, with no significant between-group differences. The most important result was the lack of premature LH surge in either group upon serum LH assessment on the triggering day. Conclusion: Utrogestan is an alternative treatment that could reduce the LH surge rate and increase the ART outcomes including the number of oocytes retrieved and transferred embryos compared with GnRH agonists and antagonists. Key words: In vitro fertilization, Premature luteinization, Utrogestan

Photodegradation of p-cresol in aqueous Mn(1%) doped zinc oxide suspension.

Photodegradation of p-cresol was carried out by a 1.0wt% Mn doped ZnO under visible light irradiation. The amount of photocatalyst, concentration of p-cresol and pH were studied as variables. The residue of p-cresol and mineralisation was measured using a UV-visible spectrophotometer and TOC analyzer, respectively. The intermediate was detected by UPLC. The results showed the amount of photocatalyst and concentration of p-cresol was 1.5g/L and 35 ppm respectively. P-cresol photodegradation was favorable in the pH 6-9 range. The detected intermediate was 4-hydroxy-benzaldehyde. TOC studies show that 74% of total organic carbon was removed from solution during irradiation time. Reusability shows no significant reduction in photocatalytic performance in photodegrading p-cresol. This study indicates that 1.0 wt% Mn doped ZnO can remove p-cresol from wastewater under visible light irradiation, and being more economic than UV irradiation could be applied on an industrial scale

Statistical optimization of process parameters for lipase-catalyzed synthesis of triethanolamine-based esterquats using response surface methodology in 2-liter bioreactor

Lipase-catalyzed production of triethanolamine-based esterquat by esterification of oleic acid (OA) with triethanolamine (TEA) in n-hexane was performed in 2 L stirred-tank reactor. A set of experiments was designed by central composite design to process modeling and statistically evaluate the findings. Five independent process variables, including enzyme amount, reaction time, reaction temperature, substrates molar ratio of OA to TEA, and agitation speed, were studied under the given conditions designed by Design Expert software. Experimental data were examined for normality test before data processing stage and skewness and kurtosis indices were determined. The mathematical model developed was found to be adequate and statistically accurate to predict the optimum conversion of product. Response surface methodology with central composite design gave the best performance in this study, and the methodology as a whole has been proven to be adequate for the design and optimization of the enzymatic process

The first experience of ex-vivo lung perfusion (EVLP) in Iran: An effective method to increase suitable lung for transplantation

Background: Although lung transplantation is a well-accepted treatment for end-stage lung diseases patients, only 15-20 of the brain-dead donors' lungs are usable for transplantation. This results in high mortality of candidates on waiting lists. Ex-vivo lung perfusion (EVLP) is a novel method for better evaluation of a potential lung for transplantation. Objective: To report the first experience of EVLP in Iran. Methods: The study included a pig in Vienna Medical University, Vienna, Austria, and 4 humans in Masih Daneshvari Hospital, Tehran, Iran. All brain-dead donors from 2013 to 2015 in Tehran were evaluated for EVLP. Donors without signs of severe chest trauma or pneumonia, with poor oxygenation were included. Results: An increasing trend in difference between the pulmonary arterial pO2 and left atrial pO2, an increasing pattern in dynamic lung compliance, and a decreasing trend in the pulmonary vascular resistance, were observed. Conclusion: The initial experience of EVLP in Iran was successful in terms of important/critical parameters. The results emphasize on some important considerations such as precisely following standard lung harvesting and monitoring temperature and pressure. EVLP technique may not be a cost-effective option for low-income countries at first glance. However, because this is the only therapeutic treatment for end-stage lung disease, it is advisable to continue working on this method to find alternatives with lesser costs