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Introduction: Alzheimer’s disease (AD) is one of the most prevalent neurodegenerative disorders, characterized by progressive memory loss and cognitive decline, ultimately leading to dementia. Currently, there is no definitive cure, and available treatments focus only on alleviating symptoms and slowing disease progression. In recent years, nanomedicine has emerged as a potential approach for treating various diseases, including AD. Nanotechnology offer innovative solutions to key challenges in AD treatment, such as poor drug solubility in biological fluids, low bioavailability, limited ability to cross the blood-brain barrier (BBB), and rapid drug metabolism. This systematic review describes the potential applications and benefits of nanoparticles in the fight against AD. Materials and Methods: To achieve the study’s objectives, a comprehensive literature search was conducted across reputable databases, covering publications from 1990 to November 2024. The search included keywords related to AD, its diagnosis, and treatment. Results: The findings suggest that nanoparticles can enhance the effectiveness of existing AD treatments by improving drug solubility, increasing bioavailability, and facilitating drug transport across the BBB. These properties suggest that nanoparticles could be promising tools for more effective AD management. Conclusion: Advances in nanomedicine present significant opportunities for the creation of innovative therapeutic approaches for AD. By improving drug delivery and treatment efficacy, nanoparticles could contribute to early detection, better disease management, and potential long-term treatment.
 

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Introduction: Several difficulties of students with attention deficit/ hyperactivity disorder (ADHD) are related to executive dysfunctions, which may improve by neurofeedback training. This study was aimed to investigate the effect of neurofeedback training on sustain attention and working memory in male elementary school students with ADHD. Materials and Methods: This study was a quasi-experimental investigation with a pretest-posttest design. The statistical population were included male elementary school students with ADHA that referred to counseling centers of Varamin city in 2015. Thirty students were selected using sampling method and randomly divided into two groups. The experimental group educated twelve 60-minute-sessions of neurofeedback training. To assess sustain attention and working memory, CPT and N-back computerized tests were performed, respectively. Results: There was a significant difference between the averages of sustains attention and working memory of experimental and control groups in the posttest stage. The neurofeedback training significantly increased sustain attention and working memory in male elementary school students with ADHD. Conclusion: The findings suggest use of neurofeedback training to improve cognitive dysfunction in children with ADHD.

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Introduction: Cognitive control deficits are seen in many psychological and brain disorders. The dual mechanism of control (DMC) theory assumes two proactive (PC) and reactive control (RC) modes for cognitive control and uses the AX version of continuous performance test (AX-CPT) as the main research paradigm. This test determines deficits in cognitive control modes in various disorders. Midbrain dopamine signal plays an important role in the pathophysiology of various disorders with cognitive control deficits and plays a key role in DMC theory. Materials and Methods: The present study was a computer simulation to investigate the effect of midbrain dopamine signal manipulation on cognitive control deficits. Simulation is based on LEABRA cognitive architecture and using the PBWM model as a model of working memory and PVLV model as a model of midbrain dopaminergic system. This investigation has been implemented in the emergent computer simulation software where the AX-CPT is presented to the model and the model performance was measured. Simulation results are calculated in three proactive control behavioral index (PCB), PC, and RC indices. Results: With increasing gain of phasic dopamine signal from 0 to 100 percent, a 15 to 45 percent changes occurred in the trend of curves. Increasing up to 15 percent, the error indices sharply decreased, PC was reduced, RC is increased, and PCB tends from PC to RC. Increasing from 15 to 45 percent, PC was increased, RC was reduced, and PCB tends from RC to PC. These trends were damped between 45 to 100 percent enhancement. Indices related to the average reaction time showed a reversed pattern of error indices. Conclusion: The results of the error indices by decreasing phasic dopamine level indicate an increase in PC deficit and RC improvement as well as a greater tendency toward RC. These results are consistent with the hypodopaminergic pattern and DMC mechanism deficits in attention deficit hyperactivity disorder, depression, negative symptoms of schizophrenia as well as chronic addiction of cocaine and alcohol and Parkinson's disease.

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Introduction: Alzheimer's disease is a brain disorder that gradually destroys cognitive function and eventually the ability to carry out daily routine tasks. Early diagnosis of this disease has attracted the attention of many physicians and scholars, and several methods have been used to detect it in early phases. Evaluation of artificial neural networks is low-cost with no side effect method that is used for diagnosing and predicting Alzheimer's disease in subjects with mild cognitive impairment based on electroencephalogram signals. Materials and Methods: for this systematic review, keywords "Alzheimer's", "Artificial Neural Network" and "EEG" were searched in IEEE, PubMed central, ScienceDirect, and Google Scholar databases between 2000 to 2019. Then, they were selected for critical evaluation based on the most relevance to the subject under study. Results: The search result in these databases was 100 articles. Excluding unrelated articles, only 30 articles were studied. In the present study, different types of artificial neural networks were described, Next, the accuracy of the classification obtained by these methods was investigated. The results have shown that some methods, despite being less used in research or have simple architecture, have the highest accuracy for classification. In many studies, artificial neural networks have also been considered in comparison with other classification methods and the results show the superiority of these methods. Conclusion: Artificial neural networks can be used as a tool for early detection of Alzheimer's disease. This approach can be evaluated for its classification accuracy, speed, architecture, and common use. Some networks are accurate at classifying and understanding data, but are slow or require specific hardware/software environments. Some other networks work better with simple architectures than complex networks. Furthermore, changing the architecture of conventional networks or combining them with other methods resulted in significantly different results. Increasing accuracy of classification of these networks in the diagnosis of mild cognitive impairment could help to predict Alzheimer's disease appropriately.

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Introduction: Math problem solving requires improving both details and generalities perception by the brain's parietal cortex and in turn, achieving this ability requires the development of cognitive abilities. The purpose of this study was to improve cognitive abilities in math problem-solving through combined neurofeedback and transcranial electrical stimulation therapy. Materials and Methods: This study was a quantitative study of the single case type with the ABAB design. The statistical population was the ninth-grade high school students referring to two counseling centers in Karaj. Among them, 5 weak students in math problem-solving were selected purposefully and voluntarily. The entry criteria were age between 14 and 16 years, a math score below 13.5 from 20 in the previous semester. Furthermore, participants should not have a diagnosis of learning disorders and coexistence, medical treatment, and a math reinforcement course. The measurement tools were the fourth edition of the Wechsler IQ test, transcranial electrical stimulation devices, neurofeedback, and math exam scores between two academic semesters. The combined intervention of electrical stimulation and neurofeedback was performed for 50 minutes, two months, and twice a week with the aim of promoting alpha and theta waves and suppressing beta three waves in the parietal cortex. Percentage improvement formulas, Cohen's effect size, and visual analysis were used to analyze the data. Results: The results showed that the combined treatment was effective in the mentioned brain waves. On the other hand, cognitive factors in Wechsler's intelligence scale, including active memory, processing speed, perceptual reasoning, and verbal comprehension, as well as students' math exam scores showed a significant improvement. Conclusion: The findings showed that transcranial stimulation of the parietal cortex and neuro-feedback brain training are able to increase the learning ability of students who have problems understanding the details and generalities of mathematics. Therefore, this non-invasive combined method can be used as an approach to improve the cognitive abilities of students who are weak in solving mathematical problems.

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Introduction: Epidemiological studies have consistently identified diabetes as a major risk factor for cognitive dysfunction. With the increasing global prevalence of diabetes, its impact on cognitive health is expected to become a significant public health concern in the coming years. This review investigates the effects of type 1 diabetes on spatial and verbal memory function. Materials and Methods: This study follows a systematic review methodology, involving the collection, classification, and synthesis of research findings related to spatial and verbal memory deficits in patients with type 1 diabetes. Relevant articles were identified using Google Scholar, PubMed, ScienceDirect, NoorMags, and SID databases, covering the period 2010 to 2023. The search terms included "spatial memory," "verbal memory," and "type 1 diabetes." An initial search yielded 42 articles, of which 14 were excluded due to irrelevance, leaving 28 studies for analysis. Results: These studies indicate structural abnormalities in the frontal and temporal cortices, as well as subcortical gray matter, in subjects with type 1 diabetes. The brain and neural tissues are primarily dependent on glucose as an energy source, and hence, any change in carbohydrate metabolism can directly affect the brain's function, including memory. Conclusion: Deficits in spatial and verbal memory among patients with type 1 diabetes can be attributed to the direct effects of altered glucose metabolism on the brain, as well as diabetes-related cardiovascular complications. Since the presence and progression of memory impairment significantly worsen the quality of life of diabetic patients, multidisciplinary studies are necessary to investigate this issue and develop targeted interventions.