American journal of neurodegenerative disease最新文献

{"title":"Advances in nanotechnology for targeted drug delivery in neurodegenerative diseases.","authors":"Diana Rafieezadeh, Golkamand Sabeti, Amirreza Khalaji, Hossein Mohammadi","doi":"10.62347/QHVI3317","DOIUrl":"10.62347/QHVI3317","url":null,"abstract":"<p><p>Neurodegenerative diseases, including Alzheimer's, Parkinson's, and multiple sclerosis, are a growing healthcare challenge due to their impact on quality of life and the difficulty in treating them. These disorders are associated with brain lesions and barriers, such as the blood-brain barrier (BBB), that impede effective treatment. Nanotechnology, especially functionalized nanoparticles (NPs), is emerging as a promising tool for overcoming these barriers. Nanoparticles, such as liposomes, polymeric micelles, and gold nanoparticles (AuNPs), show potential for targeted drug and gene delivery to the brain, enhancing bioavailability, circulation time, and treatment efficacy. Nanocarrier-based systems have demonstrated success in protecting nucleic acids from degradation, improving BBB penetration, and delivering genetic material to target specific brain areas. Exosomes and artificial vesicles also hold promise for their size and biocompatibility. Gold nanoparticles are gaining attention for their neuroprotective and anti-inflammatory properties, particularly in treating Alzheimer's, Parkinson's, and stroke. These systems can modify gene expression and address the underlying mechanisms of these diseases. In addition to drug delivery, noninvasive strategies like intranasal administration are being explored to enhance patient adherence. However, challenges remain, including regulatory hurdles and the need for further research to optimize these technologies. As research advances, the synergy between materials science, bioengineering, and medicine will pave the way for more effective treatments for neurodegenerative diseases. The aim of this study is to explore the potential of functionalized NPs in overcoming the BBB and improving targeted drug delivery for the treatment of neurodegenerative diseases.</p>","PeriodicalId":72170,"journal":{"name":"American journal of neurodegenerative disease","volume":"14 2","pages":"51-57"},"PeriodicalIF":0.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12089748/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Do multiple reconstructive surgeries increase loss of cervical lordosis in patients with multilevel degenerative cervical diseases? A retrospective cohort study.","authors":"Tao Liu, Zhongzheng Zhi, Fuchao Zhou, Weicheng Pan, Rongcheng Zhang, Zhimin He, Shuiqiang Qiu","doi":"10.62347/MKUX5540","DOIUrl":"10.62347/MKUX5540","url":null,"abstract":"<p><strong>Study design: </strong>A retrospective cohort study.</p><p><strong>Background and objective: </strong>There are no data on changes in cervical sagittal alignment and curvature after second and third surgeries in patients with multilevel cervical degenerative diseases (CDD). This study aimed to explore these changes following multiple decompression and reconstruction surgeries.</p><p><strong>Methods: </strong>145 patients with multilevel CDD were enrolled based on medical records extracted from 2015 to 2023. They were divided into three groups according to the number of surgeries. 63 patients underwent first decompression and reconstruction surgery (Group 1), 53 patients underwent second surgery (Group 2) and 29 patients underwent third surgery (Group 3). Clinical parameters (Japanese Orthopedic Association (JOA) score for neural functional recovery, visual analogue scale (VAS) and neck disability index (NDI) for neck pain) and radiologic parameters (T1 slope (T1S), cervical lordosis (C2-7CL), C2-7 sagittal vertical axis (C2-7SVA)) were reviewed and analyzed.</p><p><strong>Results: </strong>The mean period between final surgery and last follow-up was more than 12 months. There were significant differences among 3 groups in terms of operation time, blood loss and hospital stay (P < 0.001). Functional scores changed significantly after decompression surgeries (P < 0.001) in 3 groups. Radiographic parameters increased after surgery in group 1 (P < 0.001), while C2-7CL and T1S decreased after second and third surgery in group 2 and group 3 (P < 0.001). Comparing with group 1, there were significant differences showed in terms of C2-7CL, T1S, NDI and VAS in group 2 and group 3 (P < 0.05), NDI and VAS were significantly larger in group3 compare with group 2 (P < 0.05).</p><p><strong>Conclusion: </strong>Multiple surgeries may exacerbate cervical lordosis loss and increase axial pain, necessitating cautious surgical planning for multilevel CDD.</p>","PeriodicalId":72170,"journal":{"name":"American journal of neurodegenerative disease","volume":"14 2","pages":"58-66"},"PeriodicalIF":0.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12089749/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144121570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determining the normal range of the dimensions and volume of the pituitary gland of children using a 3D magnetic resonance imaging (MRI) protocol in Imam Hossein Hospital of Isfahan in 2021 to 2024.","authors":"Nilufar Ghorbani, Sam Mirfendereski, Neda Hosseini Moshkenani","doi":"10.62347/CXAQ5541","DOIUrl":"10.62347/CXAQ5541","url":null,"abstract":"<p><strong>Background: </strong>Understanding the morphological changes and dimensions of the pituitary gland is crucial for accurate diagnosis and personalized treatment in pediatric patients. Advanced imaging techniques, such as 3D magnetic resonance imaging (MRI), enhance our ability to address knowledge gaps and improve clinical practices in pediatric endocrinology. This study aims to determine normative pituitary gland dimensions and volumes in pediatric patients at Imam Hossein Hospital in Isfahan using advanced 3D MRI protocols.</p><p><strong>Methods: </strong>Conducted as a prospective cross-sectional study, this research focused on children under 15 years without specific conditions. A total of 412 participants were selected through simple random sampling, and data were analyzed using SPSS version 20 to rigorously assess measurements and extract insights beneficial for pediatric endocrinology.</p><p><strong>Results: </strong>The study included participants aged 0 to 15 years, with a higher representation of boys (63.83%) compared to girls (36.17%). Significant differences were observed in height and volume based on gender and age group. Scatterplots illustrated variations in the pituitary gland's volume, width, height, and anterior-posterior diameter according to age and gender.</p><p><strong>Conclusion: </strong>This research provides valuable insights into pediatric endocrinology, facilitating accurate diagnosis and treatment of pituitary disorders in children.</p>","PeriodicalId":72170,"journal":{"name":"American journal of neurodegenerative disease","volume":"14 1","pages":"42-50"},"PeriodicalIF":0.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929036/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of nuclear medicine in neurodegenerative diseases: a narrative review.","authors":"Farshad Riahi, Shahin Fesharaki","doi":"10.62347/SOGE3962","DOIUrl":"10.62347/SOGE3962","url":null,"abstract":"<p><p>Neurodegenerative diseases, such as Alzheimer's, Parkinson's, and Lewy body dementia, are associated with the accumulation of brain proteins, leading to neuroinflammation, disruption of cellular clearance mechanisms, and neuronal death. Nuclear medicine, utilizing technologies like PET and SPECT, plays a crucial role in diagnosing and managing these disorders. Recent advancements in nuclear medicine have enhanced the understanding of disease pathophysiology and facilitated the development of tailored therapeutics. This study aims to address gaps in understanding nuclear medicine's potential to improve early diagnosis, monitor disease progression, and evaluate therapeutic effectiveness. In this review, we analyzed 28 papers and summarized their findings. PET radioligands have revolutionized the in vivo measurement of pathological targets in neurological diseases, offering new insights into the pathophysiology of neurodegenerative conditions. Amyloid PET has emerged as a reliable diagnostic imaging tool, accurately identifying cerebral amyloid-beta accumulation and enabling early differential diagnosis in clinical settings. Furthermore, radiopharmaceuticals such as [18F]Flortaucipir, [18F]FDOPA, and TSPO ligands provide significant advancements in the diagnosis and treatment of neurodegenerative disorders.</p>","PeriodicalId":72170,"journal":{"name":"American journal of neurodegenerative disease","volume":"14 1","pages":"34-41"},"PeriodicalIF":0.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929035/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Curvature estimation techniques for advancing neurodegenerative disease analysis: a systematic review of machine learning and deep learning approaches.","authors":"Seyed-Ali Sadegh-Zadeh, Nasrin Sadeghzadeh, Bahareh Sedighi, Elaheh Rahpeyma, Mahdiyeh Nilgounbakht, Mohammad Amin Barati","doi":"10.62347/DZNQ2482","DOIUrl":"10.62347/DZNQ2482","url":null,"abstract":"<p><p>Neurodegenerative diseases present complex challenges that demand advanced analytical techniques to decode intricate brain structures and their changes over time. Curvature estimation within datasets has emerged as a critical tool in areas like neuroimaging and pattern recognition, with significant applications in diagnosing and understanding neurodegenerative diseases. This systematic review assesses state-of-the-art curvature estimation methodologies, covering classical mathematical techniques, machine learning, deep learning, and hybrid methods. Analysing 105 research papers from 2010 to 2023, we explore how each approach enhances our understanding of structural variations in neurodegenerative pathology. Our findings highlight a shift from classical methods to machine learning and deep learning, with neural network regression and convolutional neural networks gaining traction due to their precision in handling complex geometries and data-driven modelling. Hybrid methods further demonstrate the potential to merge classical and modern techniques for robust curvature estimation. This comprehensive review aims to equip researchers and clinicians with insights into effective curvature estimation methods, supporting the development of enhanced diagnostic tools and interventions for neurodegenerative diseases.</p>","PeriodicalId":72170,"journal":{"name":"American journal of neurodegenerative disease","volume":"14 1","pages":"1-33"},"PeriodicalIF":0.0,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11929037/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing neural computation: experimental validation and optimization of dendritic learning in feedforward tree networks.","authors":"Seyed-Ali Sadegh-Zadeh, Pooya Hazegh","doi":"10.62347/FIQW7087","DOIUrl":"10.62347/FIQW7087","url":null,"abstract":"<p><strong>Objectives: </strong>This study aims to explore the capabilities of dendritic learning within feedforward tree networks (FFTN) in comparison to traditional synaptic plasticity models, particularly in the context of digit recognition tasks using the MNIST dataset.</p><p><strong>Methods: </strong>We employed FFTNs with nonlinear dendritic segment amplification and Hebbian learning rules to enhance computational efficiency. The MNIST dataset, consisting of 70,000 images of handwritten digits, was used for training and testing. Key performance metrics, including accuracy, precision, recall, and F1-score, were analysed.</p><p><strong>Results: </strong>The dendritic models significantly outperformed synaptic plasticity-based models across all metrics. Specifically, the dendritic learning framework achieved a test accuracy of 91%, compared to 88% for synaptic models, demonstrating superior performance in digit classification.</p><p><strong>Conclusions: </strong>Dendritic learning offers a more powerful computational framework by closely mimicking biological neural processes, providing enhanced learning efficiency and scalability. These findings have important implications for advancing both artificial intelligence systems and computational neuroscience.</p>","PeriodicalId":72170,"journal":{"name":"American journal of neurodegenerative disease","volume":"13 5","pages":"49-69"},"PeriodicalIF":0.0,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11751443/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143029701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural reshaping: the plasticity of human brain and artificial intelligence in the learning process.","authors":"Seyed-Ali Sadegh-Zadeh, Mahboobe Bahrami, Ommolbanin Soleimani, Sahar Ahmadi","doi":"10.62347/NHKD7661","DOIUrl":"10.62347/NHKD7661","url":null,"abstract":"<p><p>This study explores the concept of neural reshaping and the mechanisms through which both human and artificial intelligence adapt and learn.</p><p><strong>Objectives: </strong>To investigate the parallels and distinctions between human brain plasticity and artificial neural network plasticity, with a focus on their learning processes.</p><p><strong>Methods: </strong>A comparative analysis was conducted using literature reviews and machine learning experiments, specifically employing a multi-layer perceptron neural network to examine regression and classification problems.</p><p><strong>Results: </strong>Experimental findings demonstrate that machine learning models, similar to human neuroplasticity, enhance performance through iterative learning and optimization, drawing parallels in strengthening and adjusting connections.</p><p><strong>Conclusions: </strong>Understanding the shared principles and limitations of neural and artificial plasticity can drive advancements in AI design and cognitive neuroscience, paving the way for future interdisciplinary innovations.</p>","PeriodicalId":72170,"journal":{"name":"American journal of neurodegenerative disease","volume":"13 5","pages":"34-48"},"PeriodicalIF":0.0,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11751442/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143029756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of dimensionality reduction techniques for EEG-based emotional state classification.","authors":"Seyed-Ali Sadegh-Zadeh, Nasrin Sadeghzadeh, Ommolbanin Soleimani, Saeed Shiry Ghidary, Sobhan Movahedi, Seyed-Yaser Mousavi","doi":"10.62347/ZWRY8401","DOIUrl":"10.62347/ZWRY8401","url":null,"abstract":"<p><strong>Objectives: </strong>The aim of this study is to evaluate the impact of various dimensionality reduction methods, including principal component analysis (PCA), Laplacian score, and Chi-square feature selection, on the classification performance of an electroencephalogram (EEG) dataset.</p><p><strong>Methods: </strong>We applied dimensionality reduction techniques, including PCA, Laplacian score, and Chi-square feature selection, and assessed their impact on the classification performance of EEG data using linear regression, K-nearest neighbour (KNN), and Naive Bayes classifiers. The models were evaluated in terms of their classification accuracy and computational efficiency.</p><p><strong>Results: </strong>Our findings suggest that all dimensionality reduction strategies generally improved or maintained classification accuracy while reducing the computational load. Notably, PCA and Autofeat techniques led to increased accuracy for the models.</p><p><strong>Conclusions: </strong>The use of dimensionality reduction techniques can enhance EEG data classification by reducing computational demands without compromising accuracy. These results demonstrate the potential for these techniques to be applied in scenarios where both computational efficiency and high accuracy are desired. The code used in this study is available at https://github.com/movahedso/Emotion-analysis.</p>","PeriodicalId":72170,"journal":{"name":"American journal of neurodegenerative disease","volume":"13 4","pages":"23-33"},"PeriodicalIF":0.0,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11578865/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142712066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exceptionally giant neglected sacral chordoma in a post-poliotic residual paralysis patient - a rare case scenario.","authors":"Prabodh Kantiwal, Aakarsh Aggarwal, Sandeep K Yadav, Nitesh Gahlot, Abhay Elhence","doi":"10.62347/EKNJ6411","DOIUrl":"10.62347/EKNJ6411","url":null,"abstract":"<p><p>Chordoma is a rare malignant tumour with an incidence of 0.1 case per 1 lakh population per year. The sacrococcygeal region is the most common site to be involved. Herein, we are reporting a case of sacral chordoma, who is a 32-year-old male patient, a known case of post-polio residual paralysis on the left lower limb, who presented with complaint of pain in the lower back and gluteal region for 2 years with swelling in the gluteal region for 1 year, which was gradually increasing in size for 1 year with associated weight loss. MRI revealed an ill-defined lytic expansile altered signal intensity lesion involving S3 to S5 and coccygeal vertebral bodies measuring 13.2 × 16.2 × 14 cm (ap × tr × cc) with adjacent large lobulated heterogeneous soft tissue component and showed multiple coarse calcifications. The lesion anteriorly displaced and abutted the rectum and was deriving its blood supply from branches of bilateral internal iliac arteries. The patient was planned and underwent wide-margin resection (middle sacrectomy with R0 margins with preservation of both S2 and right S3 nerve roots). Histologic Grade was reported to be G2, moderately differentiated, high grade. Pathologic stage classification was reported as pT3a. Postoperatively patient had the same neurological status and was discharged on advice to do full weight bearing walking and self-intermittent catheterisation and laxatives. He was on routine follow up and improved well symptomatically.</p>","PeriodicalId":72170,"journal":{"name":"American journal of neurodegenerative disease","volume":"13 3","pages":"13-22"},"PeriodicalIF":0.0,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11411203/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of willingness to obtain of Covid 19 vaccine in patients with multiple sclerosis.","authors":"Masoud Ghiasian, Maryam Farhadian, Alireza Salehzadeh","doi":"10.62347/OCCZ6431","DOIUrl":"10.62347/OCCZ6431","url":null,"abstract":"<p><strong>Introduction: </strong>Assessing vaccine willingness and understanding sources of vaccine hesitancy in individuals with multiple sclerosis (MS) helps healthcare providers approach patients more effectively while respecting their autonomy to encourage coronavirus disease 2019 (COVID-19) vaccination.</p><p><strong>Materials and methods: </strong>A descriptive-analytical cross-sectional study using a researcher-made checklist was conducted on MS patients referred to Neshat Clinic of Hamadan during the years 2020-2021. The checklist contained questions about demographic information, MS phenotype, duration of illness, expanded disability status scale (EDSS) score, and COVID-19 vaccination status. The expanded disability status scale (EDSS) is the most commonly used instrument for measuring disability in patients with multiple sclerosis (MS). The EDSS scale ranges from 0 to 10 in increments of 0.5 units, denoting advanced points of disability.</p><p><strong>Results: </strong>Based on the results, 20 individuals (10%) were in the vaccine non-acceptance group, while 181 individuals (90%) were in the vaccine acceptance group. A significant number of relapsing and remitting (RR) type MS patients (90.7%) and all primary progressive (PP) type MS patients (100%) accepted the vaccine. In comparison, vaccine non-acceptance in the secondary progressive (SP) group was relatively higher (20.7%) compared to other types of MS, and this difference was significant (P < 0.05). Additionally, there was a statistically significant relationship between the history of COVID-19 and vaccine acceptance (P < 0.05).</p><p><strong>Conclusion: </strong>The study results demonstrated a high rate of COVID-19 vaccine acceptance among MS patients. MS phenotype, previous infection experiences, and other influences allow for COVID-19 vaccine acceptance among MS patients. This information can improve health programs and communication strategies for COVID-19 and future possible infectious disease vaccination in individuals with MS.</p>","PeriodicalId":72170,"journal":{"name":"American journal of neurodegenerative disease","volume":"13 2","pages":"7-12"},"PeriodicalIF":0.0,"publicationDate":"2024-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11250120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141636032","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}