dementia

 Climate change is a major public health challenge, particularly for vulnerable populations, such as the elderly and people with dementia. With global warming, understanding the relationship between high temperatures and health outcomes, such as mortality, hospital admissions, and hospitalizations due to dementia, becomes increasingly important. This narrative review aims to collect and analyze existing evidence on the impact of rising temperatures on dementia.

This narrative review study was conducted using the SANRA checklist. Key terms, including "dementia," "temperature," and their equivalents, were searched in databases, such as PubMed, ProQuest, Web of Science, and Google Scholar, without time restrictions. Screening was also performed based on inclusion and exclusion criteria and study objectives.

Evidence suggests that rising temperatures are associated with increased mortality, hospital admissions, and emergency department visits, especially among the elderly. In addition, exposure to heat during both day and night significantly increases the risk of dementia-related mortality. In fact, both extreme heat events and continuous exposure to high temperatures adversely affect the health of people with dementia.

Given the direct relationship between rising temperatures and dementia, it is imperative that policymakers take necessary steps to protect the elderly from the dangers posed by rising temperatures. These measures can not only reduce healthcare costs but also improve the quality of life for older adults.

Computational modeling plays a pivotal role in bridging the gap between cognitive neuroscience and clinical neurology, particularly in the context of neurodegenerative diseases like Alzheimer's disease (AD). This study explores the application of computational models to understand cognitive systems and the pathological processes leading to cognitive decline in AD.

We proposed a set of computational approaches, including neural networks and dynamical systems modeling, to simulate neural activity, synaptic plasticity, and interactions between genetic and environmental factors. Data integration from neuroimaging, genomics, and behavioral studies was crucial in enhancing the accuracy and predictive capabilities of these models.

The computational models provided significant insights into the mechanisms of cognition, memory formation, and their deterioration in AD. Our models identified potential biomarkers and informed strategies for therapeutic intervention, demonstrating the importance of a multi-disciplinary approach to understanding and treating cognitive decline.

Computational modeling is essential for promoting our understanding of AD and other cognitive disorders. Future research should focus on refining these models and fostering greater interdisciplinary collaboration to develop more accurate and comprehensive simulations.

Considering the importance of issuing correct and valid legal medical expert opinions about the financial incapability due to dementia, This study aims to examine the level of agreement between the expert opinions issued by the provincial legal medicine commission offices and the central legal medicine commission office and the effect of the use of diagnostic criteria such as the psychological test (MMSE), the existence of the results of neuroimaging, external consultations and the presence of a neurologist in expert meetings has been done on the level of this agreement.

The present study was conducted on 122 cases of people claiming financial incapability due to dementia referred to the central medical commission from September 2022 to September 2023. To examine the level of agreement (reliability) of expert opinions issued by Cohen's kappa coefficient, to analyze data from descriptive statistics methods and to investigate the relationship between some diagnostic criteria on the level of agreement, logistic regression was used using SPSS 19 software.

There is a good agreement with the Kappa coefficient of 0.626 between the expert opinions issued regarding the financial incapability or the financial capability in the last commission held in the provinces and the Central Commission, and there is a good agreement with the Kappa coefficient of 0.681 between the expert opinions issued regarding the time of onset of financial incapability. In 76.2% of cases, there was no test MMSE, 77.8% of external consultations, 24.6% of neuroimaging or related results. The three variables have a significant and positive effect on the level of agreements made.

Although the level of agreement in the expert opinions issued between the last commission held in the province and the central commission is good, the agreement between the interrater should be relied on as a reliable source when the interrater is made according to specific and standard diagnostic criteria. Therefore, the use of standard and same instruction, the use of new psychometric tools for more accurate diagnosis of the mental capacity of patients, and the continuous training of psychiatrists and psychologists are necessary.

Parkinson's disease (PD) is a significant neurodegenerative disorder affecting a substantial portion of the global population each year. It arises primarily from the destruction of dopamine-secreting cells, leading to an imbalance between the brain's two key neurotransmitters: dopamine and acetylcholine. While various genetic and environmental factors have been implicated in its development, the disease currently has no definitive cure. Treatment options include surgery or medications that aim to increase dopamine levels in the brain. In recent decades, nanoparticles have garnered extensive attention for their potential in treating various diseases, including PD. Traditional treatments face challenges such as low bioavailability, poor permeability across the blood-brain barrier, limited solubility in biological fluids, rapid metabolism, and inadequate biological distribution. Nanomedicine provides a promising solution to address these limitations, enhancing drug delivery and therapeutic efficacy for PD.

In this review, articles published between 1990 and August 2024 were sourced from reputable databases, including Scopus, ISC, PubMed, and Google Scholar, using keywords related to PD and its treatment. In the initial search, 106 articles were identified as relevant to the study's objectives. Following the application of inclusion and exclusion criteria and the removal of duplicates, 75 articles were selected for the final analysis.

The findings indicate that nanoparticles enhance the effectiveness of existing drugs by improving their solubility, bioavailability, and ability to cross the blood-brain barrier. Moreover, nanoparticles can be labeled to enable targeted therapies at specific sites. Furthermore, they offer the potential to explore the therapeutic effects of various biological compounds that have not yet been utilized for treating PD.

Nanoparticles enable the development of new treatments and strategies for combating PD by enhancing the bioavailability of existing effective compounds and reducing their side effects.

Neurodegenerative diseases such as Alzheimer’s and Parkinson’s are among the most common neurological disorders in older adults, characterized by the gradual loss of brain neurons. Despite considerable scientific advancements, a definitive cure for these diseases remains elusive. Preclinical study models play a vital role in understanding underlying mechanisms and developing novel therapies. This article provides a comprehensive review of animal, pharmaceutical, and genetic models, each simulating different aspects of behavior, pathology, and molecular biology related to these diseases. Furthermore, the use of artificial intelligence (AI) and programming languages in data analysis and creating personalized statistical models has opened new avenues for predicting disease progression and designing targeted treatments. Transgenic animal models are employed to study the effects of genetic mutations, while pharmaceutical models use neurotoxins such as amyloid-beta and 6-hydroxydopamine to replicate pathophysiological pathways. Combining these approaches with AI tools offers a more accurate representation of disease complexities.

To gain a comprehensive understanding of these diseases, it is crucial to develop advanced models that integrate biological, pharmaceutical, and AI methodologies. These models can enhance the accuracy of disease simulations, identify novel therapeutic pathways, and improve patients' quality of life.

Mild Cognitive Impairment (MCI) is a cognitive profile of aging that requires attention and treatment. Among the types of cognitive rehabilitation therapies, non-invasive brain stimulation and cognitive training can be highlighted as interventions for mild cognitive impairment.

The present study aimed to evaluate the effectiveness of cognitive training interventions, cranial electrical stimulation, and their combination in attention and delayed memory of individuals with mild cognitive impairment. 

This research employs a single-case experimental design involving comparing multiple treatments with follow-up. The study population consists of elderly individuals with mild cognitive impairment attending the memory clinic at Modarres Hospital in Isfahan in 2023. According to the research design, two patients were selected through purposive sampling, and the three treatment modalities were implemented in the following sequence: cranial electrical stimulation, a combined treatment that includes cognitive training along with transcranial electrical stimulation, and cognitive training alone.

The results of the follow-up phase for the first and second participants regarding the attention variable were as follows: PND1 =100; RCI1 =19.05; PND2 =100; RCI2 =19.05. For the delayed memory variable, the first and second participants' results were: PND1 =100; RCI1 =13; PND2 =100; RCI2 =15. Therefore, the treatment arrangement of cranial electrical stimulation, combined therapy, and cognitive training has a significant positive effect on attention and delayed memory.

Cranial electrical stimulation influences neurotransmitters and neural networks, creating a foundation for cognitive training effects by establishing new synapses and alterations in dendritic configuration. Consequently, their synergistic effects contribute to the improvement of cognitive behaviors.

As explained in the first part, the acquisition of firsthand experiences is an essential element of qualitative research for uncovering and enriching study findings. However, individuals with cognitive disorders are often excluded due to various challenges. In these cases, researchers may rely on caregivers or close associates to understand their feelings and views. This approach not only reduces these individuals to mere objects but also presents ethical dilemmas, thereby impacting the depth and richness of study findings (1). Given that the methods of data collection significantly impacts the results of studies (2), it is imperative for qualitative researchers to strive towards directly gathering information from the individuals themselves. Understanding the experiences of people with cognitive impairments is crucial for providing evidence-based services. However, these patients often encounter challenges such as speech difficulties (3), ‘pseudo-reminiscences’ (4-6), mood fluctuations and behavioral problems (7), and other issues. Therefore, researchers should take into consideration certain factors when conducting interviews with these individuals as in-depth interviews aim to gain a deeper understanding of participants' life experiences (8,9). These interviews require specialized skills across various stages, encompassing the development of interview guides, participant recruitment, obtaining consent, conducting effective interviews, data analysis and interpretation, and proficient communication and dissemination of research findings. This section delves into the key aspects of each of these phases. During interviews, it is important to prioritize the well-being of both the researcher and the participant while reflecting on and ensuring the depth and richness of the interview content (6).

Alzheimer’s disease (AD) is the leading cause of dementia, and there is growing evidence that exercise programs may help improve some symptoms of the disease. This study aimed to compare the effects of endurance and resistance training on acetylcholine and interleukin-1 beta levels in AD-model male rats.

Twenty-eight male Wistar rats were selected and randomly assigned to four groups: healthy control, AD control, AD + resistance training, and AD + endurance training. AD was induced using an 8 mg dose of trimethyltin chloride. The endurance training consisted of swimming in water at 30–33°C, five days a week, for sessions gradually increasing from 15 to 60 minutes over 12 weeks. Resistance training involved rats climbing a one-meter ladder with weights attached to their tails, consisting of 26 steps at an 85-degree incline, also performed five days a week for twelve weeks. After the exercise period, blood levels of acetylcholine and interleukin-1 beta were measured using the ELISA method.

The results showed that acetylcholine levels in the AD control group were significantly lower than in both the AD + endurance group and the healthy control group. Following the exercise intervention, interleukin-1 beta levels in the healthy control group were significantly lower than in the AD control, AD + resistance, and AD + endurance groups. However, there was no significant difference in interleukin-1 beta levels between the endurance and resistance training groups.

These findings suggest that exercise may be beneficial in reducing inflammation associated with AD, with endurance training showing slightly greater effectiveness than resistance training in this context. This suggests that specific types of exercise might play a role in managing the inflammatory state of AD, potentially offering therapeutic benefits.

The primary aim of this article is to critically assess and compare conventional diagnostic methods for Alzheimer's disease (AD), with a particular focus on the promising capabilities of biomarkers and brain mapping techniques. As the incidence of AD rises globally, novel diagnostic strategies are needed to improve upon traditional methods, which often lack predictive accuracy and precision. This study provides an in-depth review of advanced diagnostic tools, including Artificial Intelligence (AI) applications (e.g., machine learning and deep learning), brain mapping techniques (e.g., electroencephalography and Magnetic Resonance Imaging), and biomarkers (e.g., tau protein and beta-amyloid), which can be identified through innovative visual and manual techniques. Additionally, the research explores the potential of identifying precursor proteins in the blood of patients with AD before symptom onset, presenting a significant opportunity for early intervention that could greatly impact treatment outcomes.

The findings underscore the potential of combining brain mapping methods with manual analysis to facilitate transformational advancements in the diagnostics of AD. This combined approach enhances the detection of structural and functional brain changes associated with AD, contributing to more accurate and earlier diagnoses. Furthermore, brain-derived proteins are present at significantly higher levels in cerebrospinal fluid than in blood, where they are diluted by abundant plasma proteins, such as albumin and immunoglobulins. This observation raises questions about the reliability of current clinical diagnostic practices and emphasizes the importance of validating new diagnostic markers with AI-based manual techniques against neuropathological standards. Finally, the study concludes that AI, when used in conjunction with cognitive assessments, biomarkers, brain mapping approaches, and molecular testing, can substantially enhance diagnostic accuracy and reliability, which are essential for managing and treating patients with AD effectively.

Early detection and reliable differentiation of the Alzheimer’s diseases from normal aging dementia provide optimal rehabilitation. MRI is a convenient imaging method for interpreting dementia caused by brain atrophy. Visual interpretation of brain MRI for atrophy is a qualitative procedure which un able to discriminate Alzheimer atrophy from aging brain atrophy. In recent years, Quantitative texture analysis of the medical imaging represent important biological information from pixels of the digital imaging for differential diseases discrimination. Quantitative texture analysis of the brain atrophy is not yet available for routine clinical use. The aim of this study is to evaluate performance of the applied automated texture analysis methods in discrimination Alzheimer versus normal aging by brain MRI.

In this approach, a total of 26 brain MRI (13 Alzheimer and 13 normal aging) images were analyzed By MaZda software. About 26 suitable regions of interest (ROI) were selected from hippocampal on MR images. Up to 270 texture features parameters were computed per ROI. The sets of 10 features parameters as a best differential descriptor are selected by applying Fisher and or POE+ACC algorithms. Under two standard / nonstandard states, both sets of features were discriminated by PCA, LDA and NDA. The confuse matrix applied for determination sensitivity, specificity and accuracy of applied texture analysis methods. The ROC cure analysis was used for examining the discrimination performance of the applied texture analysis methods.

In comparison with PCA and LDA, in general, NDA has the best result for discriminating Alzheimer from normal aging dementia with sensitivity 100%, specificity of 100% and accuracy 100%.

our results indicate that the computerize brain atrophy discrimination in MR image can be an auxiliary tool in diagnosing Alzheimer's in early stages.