An in vitro Comparative study upon the Hemolytic, Thrombogenic, Coagulation parameters and Stability properties of the Hemiscorpius lepturus Venom

Hemiscorpius lepturus belonging to Hemiscorpiidae family is the most venomous of all types of scorpion existing in south west of Iran causing hemoglobinuria and dermal lesions by envenomation. We compare the hemolytic pattern upon time in different domestic animals upon time according to their different sphingomyelin contents. In addition other in vitro hematologic parameters, platelet lysis, coagulation changes and finally preservative factors (temperature, pH, protases) are discussed. The hemolytic activity was inhibited significantly by heating at 100 °C for 60 minutes (26%) and reached 38% via incubation with papain (10U/ml) while retained over a pH range of 4-11. Horses and sheep have the lower (61%) and upper (100%) rate of hemolysis. Calcium and magnesium ions could increase rate of hemolysis and EDTA solution had significantly decresing effect. The venom significantly changed in vitro coagulation factors (PT and APTT) from base line levels and had no effect on platelet lysis. It seems that our venom belongs to metalloproteinases due to potentiation effects of bivalent cations (calcium and magnesium) and ghost cell formation in our study indicatiing hemoglobin efflux

Barriers to women entering surgical careers: a global study into medical student perceptions

Background Barriers to female surgeons entering the field are well documented in Australia, the USA and the UK, but how generalizable these problems are to other regions remains unknown. Methods A cross-sectional survey was developed by the International Federation of Medical Students' Associations (IFMSA)'s Global Surgery Working Group assessing medical students' desire to pursue a surgical career at different stages of their medical degree. The questionnaire also included questions on students' perceptions of their education, resources and professional life. The survey was distributed via IFMSA mailing lists, conferences and social media. Univariate analysis was performed, and statistically significant exposures were added to a multivariate model. This model was then tested in male and female medical students, before a further subset analysis by country World Bank income strata. Results 639 medical students from 75 countries completed the survey. Mentorship [OR 3.42 (CI 2.29–5.12) p = 0.00], the acute element of the surgical specialties [OR 2.22 (CI 1.49–3.29) p = 0.00], academic competitiveness [OR 1.61 (CI 1.07–2.42) p = 0.02] and being from a high or upper-middle-income country (HIC and UMIC) [OR 1.56 (CI 1.021–2.369) p = 0.04] all increased likelihood to be considering a surgical career, whereas perceived access to postgraduate training [OR 0.63 (CI 0.417–0.943) p = 0.03], increased year of study [OR 0.68 (CI 0.57–0.81) p = 0.00] and perceived heavy workload [OR 0.47 (CI 0.31–0.73) p = 0.00] all decreased likelihood to consider a surgical career. Perceived quality of surgical teaching and quality of surgical services in country overall did not affect students' decision to pursue surgery. On subset analysis, perceived poor access to postgraduate training made women 60% less likely to consider a surgical career [OR 0.381 (CI 0.217–0.671) p = 0.00], whilst not showing an effect in the men [OR 1.13 (CI 0.61–2.12) p = 0.70. Concerns about high cost of training halve the likelihood of students from low and low-middle-income countries (LICs and LMICs) considering a surgical career [OR 0.45 (CI 0.25–0.82) p = 0.00] whilst not demonstrating a significant relationship in HIC or UMIC countries. Women from LICs and LMICs were 40% less likely to consider surgical careers than men, when controlling for other factors [OR 0.59 CI (0.342–1.01 p = 0.053]. Conclusion Perceived poor access to postgraduate training and heavy workload dissuade students worldwide from considering surgical careers. Postgraduate training in particular appears to be most significant for women and cost of training an additional factor in both women and men from LMICs and LICs. Mentorship remains an important and modifiable factor in influencing student's decision to pursue surgery. Quality of surgical education showed no effect on student decision-making

Experimental Evaluation of Mouse Hind Paw Edema Induced by Iranian Naja oxiana Venom

Iranian Naja oxiana (the Elapidae family) known as cobra snake inhabits in the northwestern part of Iran. This study aimed to evaluate the edematogenic potency of the crude venom with intraplantar injection into mice. Additionally, the inhibitory effects of three different drugs (i.e., promethazine, dexamethasone, and piroxicam) on paw edema were examined. Moreover, the gelatinase activity of this venom was assessed using the zymography method. Paw edema was induced by the intraplantar injection of different concentrations of the venom (0.5-5 μg dissolved in 50 μl of normal saline) into the mice (six in each group). It was estimated through the measurement of the increase in the paw thickness (%) with a digital caliper. The paws were pretreated and the rate of changes was measured after the venom injection. Pathological findings in the treated paws were evaluated with hematoxylin and eosin staining. Paw thickness reached its maximum amount within 5 min and resolved after 1 h. This venom had no gelatinase activity using the zymography method ruling out its role in edema. It caused non-hemorrhagic diffuse edema with the infiltration of inflammatory cells (i.e., leukocytes and lymphocytes) in the dermis. Intraperitoneal pretreatment with drugs significantly inhibited the venom-induced (1 μg/paw) edema; however, all the mice died unexpectedly a day after piroxicam injection. This in vitro and in vivo preliminary study demonstrated for the first time that N. oxiana venom-induced non-hemorrhagic edema in a short time. Dexamethasone (phospholipase A2 inhibitor; 1 mg/kg) and promethazine (H1 inhibitor; 5 mg/kg) decreased the venom-induced edema (p <0.001). It is suggested to carry out further studies to identify different mediators in venom-induced edema formation

An in vitro Comparative study upon the Hemolytic, Thrombogenic, Coagulation parameters and Stability properties of the Hemiscorpiuslepturus Venom

Hemiscorpius lepturus belonging to Hemiscorpiidae family is the most venomous of all types of scorpion existing in south west of Iran causing hemoglobinuria and dermal lesions by envenomation. We compare the hemolytic pattern upon time in different domestic animals upon time according to their different sphingomyelin contents. In addition other in vitro hematologic parameters, platelet lysis, coagulation changes and finally preservative factors (temperature, pH, protases) are discussed. The hemolytic activity was inhibited significantly by heating at 100 °C for 60 minutes (26%) and reached 38% via incubation with papain (10U/ml) while retained over a pH range of 4-11. Horses and sheep have the lower (61%) and upper (100%) rate of hemolysis. Calcium and magnesium ions could increase rate of hemolysis and EDTA solution had significantly decresing effect. The venom significantly changed in vitro coagulation factors (PT and APTT) from base line levels and had no effect on platelet lysis. It seems that our venom belongs to metalloproteinases due to potentiation effects of bivalent cations (calcium and magnesium) and ghost cell formation in our study indicatiing hemoglobin efflux

SVM and ANFIS Models for precipitaton Modeling (Case Study: GonbadKavouse)

Introduction: In recent years, according to the intelligent models increased as new techniques and tools in hydrological processes such as precipitation forecasting. ANFIS model has good ability in train, construction and classification, and also has the advantage that allows the extraction of fuzzy rules from numerical information or knowledge. Another intelligent technique in recent years has been used in various areas is support vector machine (SVM). In this paper the ability of artificial intelligence methods including support vector machine (SVM) and adaptive neuro fuzzy inference system (ANFIS) were analyzed in monthly precipitation prediction. Materials and Methods: The study area was the city of Gonbad in Golestan Province. The city has a temperate climate in the southern highlands and southern plains, mountains and temperate humid, semi-arid and semi-arid in the north of Gorganroud river. In total, the city's climate is temperate and humid. In the present study, monthly precipitation was modeled in Gonbad using ANFIS and SVM and two different database structures were designed. The first structure: input layer consisted of mean temperature, relative humidity, pressure and wind speed at Gonbad station. The second structure: According to Pearson coefficient, the monthly precipitation data were used from four stations: Arazkoose, Bahalke, Tamar and Aqqala which had a higher correlation with Gonbad station precipitation. In this study precipitation data was used from 1995 to 2012. 80% data were used for model training and the remaining 20% of data for validation. SVM was developed from support vector machines in the 1990s by Vapnik. SVM has been widely recognized as a powerful tool to deal with function fitting problems. An Adaptive Neuro-Fuzzy Inference System (ANFIS) refers, in general, to an adaptive network which performs the function of a fuzzy inference system. The most commonly used fuzzy system in ANFIS architectures is the Sugeno model since it is less computationally exhaustive and more transparent than other models. A consequent membership function (MF) of the Sugeno model could be any arbitrary parameterized function of the crisp inputs, most like lya polynomial. Zero and first order polynomials were used as consequent MF in constant and linear Sugeno models, respectively. In addition, the defuzzification process in Sugeno fuzzy models is a simple weighted average calculation. The fuzzy space was divided via grid partitioning according to the number of antecedent MF, and each fuzzy region was covered with a fuzzy rule. Results Discussion: The statistical results showed that in first structure determination coefficient values for both the training and test was not good performance in precipitation prediction so that ANFIS and SVM had determination coefficient of 0.67 and 0.33 in training phase and 0.45 and 0.40 in test phase. Also the error RMSE values showed that both models had failed to predict precipitation in first structure. The results of second structure in precipitation prediction showed that determination coefficient of ANFIS at training and testing was 0.93 and 0.87 respectively and RMSE was 7.06 and 9.28 respectively. MBE values showed that the ANFIS underestimated at training phase and overestimated at test phase. Determination coefficient of SVM at training and testing was 0.89 and 0.91 respectively and RMSE was 9.28 and 5.59 respectively. SVM underestimated precipitation at train phase and overestimated it at test phase. ANFIS and SVM modeled precipitation using precipitation gauging stations with reasonable accuracy. Determining coefficient in the test phase was almost the same for ANFIS and SVM but the RMSE error of SVM model was about 20% lower than the ANFIS. The coefficient of determination and error values indicated SVM had greater accuracy than ANFIS. ANFIS overestimated precipitation for less than 20 mm but for higher values of uniformly distributed around the 1:1. SVM underestimated precipitation for more than 90 mm precipitation due to the low number of data in the training phase, which made this model, did not train well. When meteorological parameters were introduced as input, minimum determination coefficient and maximum error in the test phase occurred while humidity parameters were removed. By removing any of the parameters of temperature, pressure and wind speed the error values and coefficient of determination in test phase was approximately equal. Conclusion: The potential of the support vector machine (SVM) and neuoro fuzzy inference system (ANFIS) in monthly precipitation pattern were analyzed. In order to model, two data sets were used containing meteorological parameters (temperature, humidity, pressure and wind speed) and the stations precipitation. The results showed that the simulated precipitation using meteorological parameters by ANFIS and SVM had low accuracy. Precipitation forecasting using stations precipitation in the region had good accuracy by ANFIS and SVM. Comparing the results of this study showed the high efficiency of SVM in simulating precipitation. This method can be successfully used in modeling precipitation to increase efficiency of precipitation modelling