Lifestyle assessment in two age groups of ischemic stroke: A cross-sectional study in Iran

  Background: Healthy lifestyle factors are associated with a lower risk of stroke. The current study aimed to describe lifestyle-related risk factors in ischemic stroke.   Methods: In this cross-sectional study patients with ischemic stroke in two age groups assessed for lifestyle. Demographic characteristics (age, sex, BMI, marital status, educational level, job type as low or full stress, living area), lifestyle habits, and past medical history in two age groups collected in the structured form by researchers. Chi-square (Fisher's exact) test for assessment of the statistical difference between categorical variables applied. Also, a multivariate logistic regression model was used to predict possible life-threatening lifestyles which can lead to stroke under the age of 50 (odds ratio, 95% confidence interval). All statistical tests were two-tailed and were performed with the use of PASW Statistics for Windows, Version 18.0. Chicago: SPSS Inc. P values <0.05 were regarded as significant.   Results: Totally, 11.2% of ischemic stroke cases were 50≥ years old. In the  multivariable logistic regression model higher BMI (P=0.02, OR =1.5, 95%CI=1.2 – 4.3), smoking (P<0.001, OR=1.8, 95%CI=1.08 – 2.56), alcohol drinking (P<0.001, OR=1.6, 95%CI=1.01 – 3.87), hookahs consumption (P<0.001, OR=1.2, 95%CI= 1.1 – 3.5) were predicting factors for ischemic stroke incidence in age ≤50 and only appropriate diet (low fat, sugar, salt, high fruits and  vegetables) (P=0.01, OR= 0.7, 95% CI= 0.04-0.87) was  preventive factors against stroke in age ≤50 years in compare with over 50.   Conclusion: Based on this survey many lifestyle factors effects the incidence of ischemic stroke in any age group. Therefore, periodic monitoring and effective in educating healthy people should be planned

Repetative Transcranial Magnetic Stimulator (rTMS) Characterization and How to Develop the Functionalities for Treating Neural Disorders

Background: Repetative transcranial magnetic stimulation (rTMS) is an important non-invasive technique with several protocols to treat a wide variety of neural disorders. This method utilizes a strong power supply to discharge high currents in a single or dual flat spiral coil with specific characterizations. It makes a magnetic field that promotes neuroplasticity by applying the field distribution on the appropriate brain zone and requires adjusting time and frequency relating to intervention protocols. Aim: This study investigates components of an rTMS machine to describe development approaches to increase performance, specifically in the binary mode of recovering proportionally with brain and heart signals. Methods: The proposed method achieves an rTMS and probe-set coil prototypes whose performance is approved with some statistical modelings and experiments analysis. Results: Results show that the physical properties of the coil are proportional to the power supply effect and the magnetic field distribution in front of the probe set. Conclusion: By clarifying the mechanism of oscillator switching modes and the location of the processing unit in rTMS, this paper is directed to utilize external sensors to create a smart stimulator with touch EEG or ECG signals through the most accurate intervention

Seroepidemiological study of Toxoplasma gondii infection in a population of Iranian epileptic patients

Epilepsy is one of the most common neurologic disorders. Underlying cause of epilepsy is unknown in 60 % of the patients. Toxoplasma gondii is an intracellular parasite which is capable of forming tissue cysts in brain of chronically infected hosts including humans. Some epidemiological studies suggested an association between tox- oplasmosis and acquisition of epilepsy. In this study we determined seroprevalence of latent Toxoplasma infection in a population of Iranian epileptic patients. Participants were classified in three groups as Iranian epileptic patients (IEP, n = 414), non-epileptic patients who had other neurologic disorders (NEP, n = 150), and healthy people without any neurologic disorders (HP, n = 63). The presence of anti-Toxoplasma IgG antibodies and IgG titer in the sera were determined by ELISA method. Anti-T. gondii IgG seroprevalence obtained 35.3 %, 34.7 % and 38.1 % in IEP, NEP and HP, respectively. The seroprevalence rate was not significantly different among the three groups (P = 0.88). Anti-T. gondii IgG titer was 55.7 ± 78, 52.4 ± 74 and 69.7 ± 92 IU/ml in IEP, NEP and HP, respectively. There was not any statistically significant difference in the antibody titer between the study groups (P = 0.32). The rate of T. gondii infection in epileptic patients was not higher than non-epileptic patients and healthy people in the Iranian population

Developing a novel prediction model in opioid overdose using machine learning; a pilot analytical study

Abstract Background and Aims The opioid epidemic has extended to many countries. Data regarding the accuracy of conventional prediction models including the Simplified Acute Physiologic Score (SAPS) II and acute physiology and chronic health evaluation (APACHE) II are scarce in opioid overdose cases. We evaluate the efficacy of adding quantitative electroencephalogram (qEEG) data to clinical and paraclinical data in the prediction of opioid overdose mortality using machine learning. Methods In a prospective study, we collected clinical/paraclinical, and qEEG data of 32 opioid‐poisoned patients. After preprocessing and Fast Fourier Transform analysis, absolute power was computed. Also, SAPS II was calculated. Eventually, data analysis was performed using SAPS II as a benchmark at three levels to predict the patient's course in comparison with SAPS II. First, the qEEG data set was used alone, secondly, the combination of the clinical/paraclinical, SAPS II, qEEG datasets, and the SAPS II‐based model was included in the pool of classifier models. Results Seven out of 32 (22%) died. SAPS II (cut‐off of 50.5) had a sensitivity/specificity/positive/negative predictive values of 85.7%, 84.0%, 60.0%, and 95.5% in predicting mortality, respectively. Adding majority voting on random forest with qEEG and clinical data, improved the model sensitivity, specificity, and positive and negative predictive values to 71.4%, 96%, 83.3%, and 92.3% (not significant). The model fusion level has 40% less prediction error. Conclusion Considering the higher specificity and negative predictive value in our proposed model, it could predict survival much better than mortality. The model would constitute an indicator for better care of opioid poisoned patients in low resources settings, where intensive care unit beds are limited