Caspian Journal of Neurological Sciences
Volume:6 Issue: 20, Jan 2020

Patients with ischemic stroke and underlying Atrial Fibrillation (AF) have a high risk of recurrent embolic events. New Oral Anticoagulant (NOAC) is highly effective and reduces the risk of recurrence in AF-associated Ischemic Stroke (AFAIS).

This study aimed to determine the prescription pattern of NOAC and its determinant factors in patients with non-valvular AFAIS.

This research was a cross-sectional descriptive study and the participants were referred to an academic hospital in the north of Iran from 2017 to 2018. The study variables included demographic variables such as the use of new anticoagulants, age, sex, place of residence, income level, education, the history of stroke and myocardial Infarction (MI), medication, and stroke severity based on The National Institutes of Health Stroke Scale criteria. The patient’s functional status based on the modified Rankin Scale (mRS) was extracted from the patients’ medical records. The data analysis was conducted by SPSS V. 19, using the Chi-square test and t-test, as well as the logistic regression model.

In this study, 363 patients with ischemic stroke with the origin of non-valvular AF and the mean age of 67.87 years were studied. Of them, 191 (52.6%) patients were women, and 30.6% were prescribed rivaroxaban at the time of discharge. The results showed that women were more likely to use rivaroxaban than men (P=0.001, OR=0.422). The history of stroke (P=0.004, OR=2.17) and the stroke severity (P=0.05, OR=2.19) was associated with an increase in NOAC prescription.

The results of this study showed that the administration of NOAC in this population was low and associated with gender and the severity and the history of stroke.

Clinical course of Clinically Isolated Syndrome (CIS) is variable, and identifying patients who will eventually develop Multiple Sclerosis (MS) is essential.

To assess the conversion rate of CIS to Clinically Definite Multiple Sclerosis (CDMS) and its predictors in southern Iran.

A total of 143 CIS patients registered to Fars Multiple Sclerosis Society (FMSS) were enrolled in the study from 2006 until 2012, and all of them were followed for 5 years. Also, their demographic and MRI data were recorded. The obtained data were analyzed by univariate and multivariable Cox regression models in SPSS v. 17. P<0.05 was considered statistically significant.

About 26.6% of patients progressed to MS after a mean duration of 3.4±1.1 years. The conversion rate was 27.6% in patients presented with optic neuritis, and 25.6% in patients presented with spinal cord problems. Although it was not statistically significant (P=0.23), the mean age of the patients who converted to MS was lower at the onset of the presentation (27.6 vs. 29.4 years). In patients who had 3 or more MRI lesions, the conversion rate was 49.2%; however, it was only 9.8% in subjects who had fewer than 3 lesions (OR=8.95, 95% CI=3.69–21.7, P <0.001). Women had higher conversion rate though it was not statistically significant (OR=2.09, 95% CI=0.57–7.64, P=0.26).

Our results supported this supposition that the number of MRI lesions at baseline can be used as a predictor of CIS conversion to MS.

Multiple Sclerosis (MS) is the most frequent non-traumatic neurological disease capable of causing disability in young adults. Detection of MS lesions with magnetic resonance imaging (MRI) is the most common technique. However, manual interpretation of vast amounts of data is often tedious and error-prone. Furthermore, changes in lesions are often subtle and extremely unrepresentative.

To develop an automated non-subjective method for the detection and quantification of MS lesions.

This paper focuses on the automatic detection and classification of MS lesions in brain MRI images. Two datasets, one simulated and the other one recorded in hospital, are utilized in this work. A novel hybrid algorithm combining image processing and machine learning techniques is implemented. To this end, first, intricate morphological patterns are extracted from MRI images via texture analysis. Then, statistical textures-based features are extracted. Afterward, two supervised machine learning algorithms, i.e., the Hidden Markov Model (HMM) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are employed within a hybrid platform. The hybrid system makes decisions based on ensemble learning. The stacking technique is used to apply predictions from both models o train a perceptron as a decisive model.

Experimental results on both datasets indicate that the proposed hybrid method outperforms HMM and ANFIS classifiers with reducing false positives. Furthermore, the performance of the proposed method compared with the state-of-the-art methods, was approved.

Remarkable results of the proposed method motivate advanced detection systems employing other MRI sequences and their combination.

Early simple, low-cost diagnosis of schizophrenia may accelerate the beginning of the treatment process. Here, utilizing the projective tools, including fractal images, are some of the diagnostic aids.

This study aimed to compare the preferences, descriptions, and response latency to fractal images between schizophrenic and healthy individuals.

In this case-control study, the statistical population included all schizophrenic patients hospitalized in Shafa Hospital in Rasht City, Guilan, Iran, in summer 2018 and matched healthy individuals considering the gender and age. Twelve fractal images were shown to schizophrenic patients and healthy people, and their psychological projections to these pictures were recorded.

For the image called extraviganze, the latency time to elicit the descriptions in patients was noticeably more than that in the healthy group (t=2.465, df=58, P=0.017). Meanwhile, the patients’ interest in dark and dreadful fractals such as fractal beings, North, and Apophys eyes was significantly higher than that in the healthy group (P<0.05). However, people with schizophrenia refrained from bright, light fractals with a regular geometric/graphical structure such as Gridspace and redf-shift images (P<0.05). The people with schizophrenia provide less appropriate associations and more irrelevant descriptions, especially about the abstractive and complex fractals, compared to the healthy group.

The latency to elicit descriptions for fractal images in people with schizophrenia is longer than that in the healthy group, and they have more pauses, irrelevant, and incoherent speech when describing more abstractive images. People with schizophrenia prefer darker, more dreadful images and avoid clear, luminous, and fractal images with a regular geometric/graphical pattern.

There is a relationship between the primary dysmenorrhea and psychological variables.

The purpose of this study was to investigate the effectiveness of positive thinking and self-compassion training on cognitive flexibility and cognitive failure in girls with primary dysmenorrhea.

This is a quasi-experimental study carried out on multiple groups with pretest-posttest design. The sample research included 63 females with primary dysmenorrhea. They were recruited among students in Rasht City, Iran, district one education administration (first and second grade of high school), and were assigned within two experimental groups (under positive thinking and self-compassion training) and one control group (without training). Data collection was carried out by using a screening questionnaire of symptoms before premenstrual, Cognitive Flexibility Inventory (CFI), and cognitive failure inventory of Broadbent and his colleagues (1982). Analysis of univariate covariance was used to analyze the data.

The findings showed that positive thinking and self-compassion training were effective in reducing the cognitive failure of women with primary dysmenorrhea (P<0.005), and selfcompassion training had more effect on reducing cognitive failure than positive thinking. Still, positive thinking and self-compassion did not affect cognitive flexibility.

Psychological interventions, including positive thinking and self-compassion training, especially the latter, can improve cognition among girls with primary dysmenorrhea and improve their mental health.

Psychiatrists use different scales to evaluate post-stroke depression; however, some concerns have raised about their low specificity.

This study aimed to assess the validity and reliability of the Persian version of the Post-Stroke Depression Scale (PSDS) in Iran.

In this analytical cross-sectional study, 155 patients with stroke who were referred to neurology clinics in Rasht City, Iran, were interviewed by a psychiatric assistant (Gold Standard DSM-5 interview was used to separate the depressed from the non-depressed). The participants were then assessed by the PSDS and the hospital anxiety and depression scale (HADS). Moreover, a Receiver Operating Characteristic (ROC) curve with the standard Gold DSM-5 interview was used to determine the ability of the scales and to categorize depression. Eventually, the data were analyzed in SPSS v. 19.

Data analysis indicates that the factor structure of HADS is one-dimensional, and exploratory and confirmatory analysis supported the fit for the one-factor model as the best fitting model. Bartlett test (The Chi-square=408.217, df=28, P<0.001) showed significant relationships between variables. The internal consistency of HADS was 0.638 for depression and 0.617 for anxiety. The test-retest reliability is equal to for 60 subjects were randomly re-evaluated within one to two weeks, reported that r=0.783, for anxiety and r=0.741 for depression. Finally, based on the ROC curve, the cut-off point of 9 was chosen, and the different severity of depression was distinguished by 9, 14, and 20.

The Persian version of PSDS possesses appropriate psychometric properties among the Iranian population.

Ginseng has long been used as a tonic and panacea, a dietary supplement, or a therapeutic agent in different countries. Among many common side effects for this herbal, the affective disorder is one of the rare ones.

We present a case of mania with psychotic features. The patient was an 18-year-old male who consumed Asian red ginseng for five months to treat his overweight. His physical examination was normal except for mild mental retardation. Mental status examination revealed increased psychomotor activity, anxious mood, unstable affect, irritability, aggression, pressured speech, grandiosity, auditory hallucinations, and persecutory delusions. After the admission, he was ordered to stop ginseng taking and supportive care and treatment with risperidone, lorazepam, and valproate sodium started. After 15 days, all symptoms were treated.

Despite the widespread use of herbal and dietary supplements, physicians and health care providers should be concerned about the side effects of these products, such as mania and psychosis.