mohammad javad gholamzadeh

Several laboratory markers derived from a complete blood count (CBC) have been proposed as potential indicators for assessing the risk of cerebral venous thrombosis (CVT). However, limited and conflicting evidence exists regarding this association. This study aimed to evaluate the role of CBC parameters in CVT development and their link to disease characteristics.

This case-control study included patients diagnosed with CVT between March 2018 and March 2021. All participants with CVT were registered in the organized registry system at the Neurology Research Center of Shiraz University of Medical Sciences, Shiraz, Iran (CVT registry code: 9001013381). The control group consisted of age- and sex-matched individuals without systemic diseases. CBC results from the control group and the first recorded hospital CBC of the patient group were collected.

The study included 295 patients with CVT [49 with idiopathic CVT (iCVT) and 246 with secondary CVT (sCVT)] and 341 healthy individuals. Among the CVT group, 72.54% were women. Patients with CVT had higher red cell distribution width (RDW) and lower red blood cell (RBC) count, hemoglobin (Hb) levels, and hematocrit compared to the non-CVT group. In iCVT cases, male gender, RBC count, Hb levels, and hematocrit were notably higher compared to sCVT cases. Logistic regression analysis showed that female gender, smoking, and higher hematocrit values were associated with increased probability of iCVT.

The study suggests that certain CBC parameters may serve as potential markers for assessing CVT risk and differentiating between iCVT and sCVT cases. Validation and further research are needed to explore the underlying mechanisms.

Various plant species of genus Epipremnum have already been reportedto have different types of pharmacological activities. However, another plant of the samegenus Epipremnum aureum has not been scientifically exposed to a significant extent todate. Although it contains many bioactives, it has only been studied for antidepressantactivity. The present study aims to evaluate the nootropic potential of standardized extractof Epipremnum aureum against scopolamine-induced amnesia in experimental animals. The nootropic potential of Epipremnum aureum was evaluated using an elevatedplus maze and Morris water maze apparatus. A dose of 400mg/kg and 600mg/kg was usedto access the nootropic activity. Scopolamine (0.4 mg/kg) was used to induce amnesia inmice. Additionally, the anti-acetylcholinesterase activity of the extract was evaluated bymeasuring the level of acetylcholinesterase in the mice brain. Epipremnum aureum was found to increase memory and reverse the amnesicaction of scopolamine in a dose-dependent manner. In elevated plus maze and Morriswater maze, Epipremnum aureum decreased the transfer latency as compared to the controlgroup. Further biochemical investigation revealed an increased level of acetylcholine anddecreased level of TBARS resulting in reversing the effect of scopolamine in amnesic mice. Epipremnum aureum showed positive results in reversing the amnesiaaction of scopolamine which may be the probable mechanism for its memory retentionactivity. Based on the experimental outcome, the present study provides a piece of scientificevidence for the nootropic potential of Epipremnum aureum in experimental animals.

This paper contains an analysis and comparison of different classifiers on different datasetsof Psychiatric Disorders- Personality Disorder, Depression, Anxiety, Schizophreniaand Alzheimer's disease. Psychiatric disorders are also referred to as mental disorders,abnormalities of the mind that result in persistent behavior which can seriously cause dayto day function and life. Stochastic in AI refers to if there is any uncertainty or randomnessinvolved in results and are used during optimization; Using this process also helps toprovide precise results. The study of stochastic process in AI uses mathematical knowledgeand techniques from probability, set theory, calculus, linear algebra and mathematicalanalysis like Fourier analysis, real analysis, and functional analysis. this technique is usedto construct neural network for making artificial intelligent mode for processing andminimizing human effort. This paper contains classifiers like SVM, MLP, LR, KNN, DT,and RF. Several types of attributes are used and have been trained by Weka tool, MATLAB,and Python. The results show that the SVM classifier showed the best performance for allthe attributes and disorders researched in this paper.

Obstructive sleep apnea (OSA) is associated with arousals due to thecessation of breathing during sleep. On the other hand, sleep spindles, an EEG wave mainlyseen in stage 2 of non-REM sleep (N2), are responsible for many functions including themaintenance of sleep. We aimed to investigate the association between sleep spindles andOSA and compare the additional polysomnography (PSG) metrics in a group of patientswith OSA. Fifty consecutive patients with moderate and severe OSA wererecruited. Association of apnea-hypopnea index (AHI) with spindles in N2 and arousalswere evaluated. Other PSG metrics were compared in the moderate versus severe group. Body mass and snore indices were significantly more in the severe group (p=0.002and p<0.001, respectively). Arousals were more frequently seen in severe OSA cases(p=0.064). Sleep spindle index did not have any relationship with AHI and the numberof arousals. However, arousals were weakly correlated with AHI (Spearman’s rho= 0.293,p=0.039) and snore index (Spearman’s rho= 0.365, p=0.010). Severity of OSA did not show a clear correlation with spindle density in N2.Further studies with larger samples and a control group are needed to prove a relationshipbetween sleep spindles and OSA.

Sleep disorders are very common in today’s world as we all are living a relatively competitive life; where we undervalue our mental health. There are some sleep disorders that share almost similar symptoms yet various pathological underpinnings leading to clinical misjudgments; thereby resulting in the inappropriate treatments. The present study has attempted to investigate possible correlation between various types of sleep predicaments. To do so; we used multiple statistical analysis algorithms as well as prediction models on our database and performed manual testing to draw our conclusion. Our analyses revealed that sleep disorders; and namely sleep apnea-hypopnea syndrome; tend to present with related comorbidities

Schizophrenia (SZ) is a mental illness that impairs a person's mental capacity, emotionaldispositions, and personal and social quality of life. Manual SZ patient screening is timeconsuming,expensive, and prone to human mistakes. As a result, a autonomous, relativelyaccurate, and reasonably economical system for diagnosing schizophrenia patients isrequired. Machine learning methods are capable of learning subtle hidden patterns fromhigh dimensional imaging data and achieve significant correlations for the classificationof Schizophrenia. In this study, the diverse types of symptoms of the affected person areselected which have the weights assigned by cross-correlations and the model classifiesthe probability of schizophrenia in the person based on the highest weighted symptomspresent in the report of the patient using machine learning classifiers. The classificationis made by various classifiers in which the Support Vector Machine (SVM) gives thebest result. In the neuroscience domain, it has been one of the most popular machinelearningtools. SVM with Radial Basis Function kernel helps to distinguish betweenpatients and healthy controls with significant accuracy of 76% without normalization andPrincipal Component Analysis (PCA). The K nearest neighbor’s algorithm also with nonormalization and PCA showed an accuracy of 73% in predicting SZ which is remarkablyclose to the SVM given the small size dataset.

This study is aimed at profiling cognitive functions in patients with age-relatedmacular degeneration (AMD). This cross-sectional investigation enrolled 45 patients with AMD and 45 age- andsex-matched controls. The overall cognitive performance in AMD sufferers versus controlsubjects was assessed using the Persian version of Addenbrooke’s Cognitive Examinationbattery (ACE-R). Subjects’ sleep quality was also evaluated using the Pittsburgh SleepQuality Index (PSQI). The mean global assessment and subscale scores were statisticallycompared between groups. The mean global scores for ACE-R in AMD and control groups (80.4±12.3 and86 ± 9.6, respectively) were found to be statistically different (p=0.018). On the other hand,there was no significant difference (p=0.793) between the AMD and control groups interms of PSQI scores (9.7±2.8 and 9.8±2.8, respectively). AMD patients seem to have cognitively underperformed in memoryand verbal fluency domains compared to the control group. Evidence on cognitiveimpairments in patients with AMD may possibly herald neurocognitive insufficienciesand have common pathological mechanisms with dementias.