Gender Specificity of Genistein Treatment in Penicillin-Induced Epileptiform Activity in Rats

We investigated gender-dependent differences of genistein (isoflavone phytoestrogen) treatment in a penicillin-induced experimental epilepsy rat model. Twenty-eight adult Wistar Albino rats (14 females and 14 males) were devided into four groups, control and genisteintreatmed males and females. Genistein (100 µg/kg, i.p) or saline was given during 15 days before the electrocorticography (ECoG) recordings. The epileptiform activity was induced by penicillin G potassium solt (500 IU, i.c) injections into the left somatomotor cortex. Significant differences among the groups were found in the latency to onset of epileptiform activity. This value in the female control group was significantly longer than the latencies in the male control, male genistein, and female genistein groups (respectively, P = 0.002, 0.015, and 0.032). There were no significant differences regarding the spike/wave frequencies and amplitudes in epileptiform activity between female/male genistein and control groups within all observation intervals (P > 0.05). Thus, genistein exerts a proconvulsant effect in the penicillin-induced epilepsy model, and the effect demonstrates the clear gender specificity related to the specificity of hormonal backgrounds in males and females.Ми досліджували залежні від статі відмінності впливу ґеністеїну (ізофлавоноїдного фітоестрогена) в умовах індукованої пеніциліном експериментальної моделі епілепсії у щурів. 28 дорослих щурів лінії Вістар (14 самиць і 14 самців) були поділені на чотири групи – контрольних та лікованих ґеністеїном самців і самиць. Ґеністеїн (100 мкг/кг, внутрішньоочеревинно) або фізіологічний розчин уводився тваринам протягом 15 діб, після чого у них відводились електрокортикограма (ЕКоГ). Епілептиформна активність індукувалась ін’єкцією пеніциліну G калієвої солі (500 МО) в ліву соматомоторну кору. Істотні міжгрупові відмінності були виявлені щодо латентного періоду початку епілептиформної активності (P = 0.013). Ця величина в контрольній групі самиць була істотно більшою, ніж аналогічні значення в контрольній групі самців та групах самців і самиць, лікованих ґеністеїном (P = 0.002, 0.015 та 0.032 відповідно). Не було виявлено істотних відмінностей щодо частоти комплексів пік/хвиля та амплітуди епілептиформної активності у всіх чотирьох груп у межах інтервалу спостережень (P > > 0.05). Зроблено висновок, що ґеністеїн впливає на пеніцилініндуковану модель епілепсії як проконвульсант; відповідні ефекти демонструють значну гендерну специфіку, очевидно, залежну від гормонального фону в самців і самиць

Evaluation of apertura piriformis and related cranial anatomical structures through computed tomography: golden ratio

Background: The purpose of study was to evaluate normal morphometric measurements of piriform aperture (PA) by limiting the age range in genders to show the morphometry of the relevant and close proximal cranial structures; and also to investigate whether these are in compliance with the golden ratio. Materials and methods: Our study was performed on 83 (42 female, 41 male) multidetector computed tomography images obtained from patients. A total of 14 morphological measurements were performed including the height of PA, the width of PA and 12 cranial structures; and these measurements were evaluated for compliance with the golden ratio. The differences of 14 parameters between the genders and age groups, and also the interaction of these two factors were analysed. Results: In our morphometric study, significant difference between the genders was found in all measurements except for the distance between vertex and rhinion (V~Rh), between rhinion and right foramen supraorbitalis (Rh~FSOR), between rhinion and left FSO (Rh~FSOL), and the width of PA on the level between the right and left foramen infraorbitalis (PAW~FIO) with the difference valid for both age subgroups (p < 0.05). When the differences between the age subgroups were evaluated, there was significant difference only at the widest distance of cranium (CW; p = 0.008); and it was observed that the average has increased with age in both genders. When the golden ratio was examined, the ratio of the distance between anterior nasal spine and nasion to the height of piriform aperture (NSA~N:PAH) was found to be within the limits of the golden ratio in males (p = 0.074). No golden ratio has been found in females. Conclusions: In our study, significant differences were detected between genders in all parameters of PA and in some parameters of the close cranial structures in the age group we examined. The effect of age was detected only in the CW parameter, and the PA and close cranial structures were not affected. In our study, the averages of the morphometric measurements of 13 parameters of young adults were determined. The PA and surrounding cranial structures are important for the area and related surgical procedures; however, gender differences must be considered in this respect. In addition to this, in the PA, which is the anterior limit of the skeletal nose in males, the NSA~N:PAH ratio having the ideal golden ratio limits is valuable in aesthetical terms and due to its position of the PA in the face

Feedback Control as a Framework for Understanding Tradeoffs in Biology

Control theory arose from a need to control synthetic systems. From regulating steam engines to tuning radios to devices capable of autonomous movement, it provided a formal mathematical basis for understanding the role of feedback in the stability (or change) of dynamical systems. It provides a framework for understanding any system with feedback regulation, including biological ones such as regulatory gene networks, cellular metabolic systems, sensorimotor dynamics of moving animals, and even ecological or evolutionary dynamics of organisms and populations. Here we focus on four case studies of the sensorimotor dynamics of animals, each of which involves the application of principles from control theory to probe stability and feedback in an organism's response to perturbations. We use examples from aquatic (electric fish station keeping and jamming avoidance), terrestrial (cockroach wall following) and aerial environments (flight control in moths) to highlight how one can use control theory to understand how feedback mechanisms interact with the physical dynamics of animals to determine their stability and response to sensory inputs and perturbations. Each case study is cast as a control problem with sensory input, neural processing, and motor dynamics, the output of which feeds back to the sensory inputs. Collectively, the interaction of these systems in a closed loop determines the behavior of the entire system.Comment: Submitted to Integr Comp Bio

Detecting imipenem resistance in Acinetobacter baumannii by automated systems (BD Phoenix, Microscan WalkAway, Vitek 2); high error rates with Microscan WalkAway

<p>Abstract</p> <p>Background</p> <p>Increasing reports of carbapenem resistant <it>Acinetobacter baumannii </it>infections are of serious concern. Reliable susceptibility testing results remains a critical issue for the clinical outcome. Automated systems are increasingly used for species identification and susceptibility testing. This study was organized to evaluate the accuracies of three widely used automated susceptibility testing methods for testing the imipenem susceptibilities of <it>A. baumannii </it>isolates, by comparing to the validated test methods.</p> <p>Methods</p> <p>Selected 112 clinical isolates of <it>A. baumanii </it>collected between January 2003 and May 2006 were tested to confirm imipenem susceptibility results. Strains were tested against imipenem by the reference broth microdilution (BMD), disk diffusion (DD), Etest, BD Phoenix, MicroScan WalkAway and Vitek 2 automated systems. Data were analysed by comparing the results from each test method to those produced by the reference BMD test.</p> <p>Results</p> <p>MicroScan performed true identification of all <it>A. baumannii </it>strains while Vitek 2 unidentified one strain, Phoenix unidentified two strains and misidentified two strains. Eighty seven of the strains (78%) were resistant to imipenem by BMD. Etest, Vitek 2 and BD Phoenix produced acceptable error rates when tested against imipenem. Etest showed the best performance with only two minor errors (1.8%). Vitek 2 produced eight minor errors(7.2%). BD Phoenix produced three major errors (2.8%). DD produced two very major errors (1.8%) (slightly higher (0.3%) than the acceptable limit) and three major errors (2.7%). MicroScan showed the worst performance in susceptibility testing with unacceptable error rates; 28 very major (25%) and 50 minor errors (44.6%).</p> <p>Conclusion</p> <p>Reporting errors for <it>A. baumannii </it>against imipenem do exist in susceptibility testing systems. We suggest clinical laboratories using MicroScan system for routine use should consider using a second, independent antimicrobial susceptibility testing method to validate imipenem susceptibility. Etest, whereever available, may be used as an easy method to confirm imipenem susceptibility.</p

Identifying risk factors for blood culture negative infective endocarditis: An international ID-IRI study

Background: Blood culture-negative endocarditis (BCNE) is a diagnostic challenge, therefore our objective was to pinpoint high-risk cohorts for BCNE. Methods: The study included adult patients with definite endocarditis. Data were collected via the Infectious Diseases International Research Initiative (ID-IRI). The study analysing one of the largest case series ever reported was conducted across 41 centers in 13 countries. We analysed the database to determine the predictors of BCNE using univariate and logistic regression analyses. Results: Blood cultures were negative in 101 (11.65 %) of 867 patients. We disclosed that as patients age, the likelihood of a negative blood culture significantly decreases (OR 0.975, 95 % CI 0.963–0.987, p &lt; 0.001). Additionally, factors such as rheumatic heart disease (OR 2.036, 95 % CI 0.970–4.276, p = 0.049), aortic stenosis (OR 3.066, 95 % CI 1.564–6.010, p = 0.001), mitral regurgitation (OR 1.693, 95 % CI 1.012–2.833, p = 0.045), and prosthetic valves (OR 2.539, 95 % CI 1.599–4.031, p &lt; 0.001) are associated with higher likelihoods of negative blood cultures. Our model can predict whether a patient falls into the culture-negative or culture-positive groups with a threshold of 0.104 (AUC±SE = 0.707 ± 0.027). The final model demonstrates a sensitivity of 70.3 % and a specificity of 57.0 %. Conclusion: Caution should be exercised when diagnosing endocarditis in patients with concurrent cardiac disorders, particularly in younger cases

Adjusting the effect of baseline differences between groups in trials with which have two or more groups [Iki veya daha fazla gruplu denemelerde gruplar arasindaki başlangiç degerleri farkliliginin etkisinin düzeltilmesi]

Objective: In many clinical and experimental trials, researchers assess the effect of treatment by measuring the value of a continuous variable before and after the treatment. If there is an imbalance in baseline values between groups, some statistical comparisons may result with mistakes in estimation of the treatment effect. The aim of this study was to explain which statistical methods were more suitable in the estimation of the treatment effect when there was an imbalance for the baseline values between groups. Material and Methods: Different statistical methods, which are used in estimation of treatment effects, were briefly explained and were applied to a hypothetical data set, which had significant differences between groups according to baseline values of the related variable. In addition, a limited simulation study for several conditions was carried out to determine suitable statistical methods. Results: Baseline values were different between two groups and correlation was low between baseline and follow up values of related variable in each group for hypothetical data set. In this condition, comparison of simple differences between baseline and follow up values was the best method for the estimation of treatment effect. In the simulation study, the power of the test for simple differences was higher (85%) than the value in the analysis of covariance (40%) when correlations were low and sample sizes were small in each group. Moreover, the powers of these two tests were high and similar to each other, when sample sizes were moderate. When the correlation was high, the powers of both tests were high in both small and moderate sample sizes. Conclusion: The presence of a significant difference should be sought between groups according to baseline values of the related variable even though groups are randomly assigned. In addition, the degree of the correlation between baseline and follow up values should be taken into consideration. When significant differences exist between baseline values and the correlation is low, we suggest that the classical methods should be used to determine the significance of the effect; however, when the correlation is high, covariance analysis is a suitable method. Copyright © 2009 by Türkiye Klinikleri