Sherifa Ahmed Hamed, Mohamed Ahmed Abd-ElHamed, Amira Mohamed Oseily, Mohamed Kamal Sabra Mohamed
{"title":"Assessment of auditory perceptual functions in patients with Parkinson's disease.","authors":"Sherifa Ahmed Hamed, Mohamed Ahmed Abd-ElHamed, Amira Mohamed Oseily, Mohamed Kamal Sabra Mohamed","doi":"10.62347/PZQE5280","DOIUrl":"10.62347/PZQE5280","url":null,"abstract":"<p><strong>Background: </strong>Hearing impairments are manifestations of Parkinson's disease (PD). We aimed to assess central auditory processing (CAP) functions with PD and their predictors.</p><p><strong>Methods: </strong>This was a cross-sectional study. It included 35 patients (male = 21; female = 14). The severity of PD was assessed using modified Hoehn and Yahr Scale. The severities of depression and cognitive manifestations were assessed using Beck Depression Inventory II (BDI-II) and Mini Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Participants underwent audiometry and testing of CAP using dichotic digit (DDT), duration pattern (DPT) and speech in noise (SPIN) tests.</p><p><strong>Results: </strong>Patients had mean age at presentation of 56.66 ± 11.05 yrs and mean duration of PD of 4.77 ± 2.73 yrs. Among were ~69% of patients were in early stages of the disease. Compared to controls (n = 25), patients had poor cognition [MMSE: 20.98 ± 2.36, P = 0.001; MoCA: 18.41 ± 3.00, P = 0.001], hearing impairment at high frequencies (4000 HZ), higher speech reception threshold (SRT) (P = 0.001) and worse performance in DDT (P = 0.0001), DPT (P = 0.0001) and SPIN (P = 0.001). These impairments were independently correlated with cognitive deficits (DDT: P = 0.036; DPT: P = 0.050, SPIN: P = 0.023).</p><p><strong>Conclusions: </strong>CAP dysfunctions occur in early stages of PD. They include impairments in auditory discrimination, spatial perception, binaural integration, temporal ordering or sequencing, and selective attention. The DDT, DPT and SPIN are useful battery measures for testing CAP with PD. Dopamine deficiencies in PD at different auditory pathway levels including the brainstem and cortico-subcortical levels and neurodegenerative diffuse PD pathology can be the causes of CAP impairments.</p>","PeriodicalId":72170,"journal":{"name":"American journal of neurodegenerative disease","volume":"14 3","pages":"82-99"},"PeriodicalIF":0.0,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12267191/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676654","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":"Spatial memory deficits in Parkinson's disease: neural mechanisms and assessment.","authors":"Sara García-Navarra, Tania Llana, Marta Méndez","doi":"10.62347/CKGV8650","DOIUrl":"10.62347/CKGV8650","url":null,"abstract":"<p><p>Parkinson's disease (PD) is a progressive neurodegenerative disorder that primarily affects motor function. However, PD may also result in substantial cognitive impairments, including spatial memory deficits. Spatial memory, defined as the ability to encode, store, and retrieve information about environmental spatial orientation, is a critical component of daily functioning. A comprehensive understanding of the neural mechanisms underlying these deficits is imperative for the development of targeted interventions. This narrative review explores the neural basis of spatial memory deficits in PD, summarizing evidence from neuroimaging and neurophysiological studies. In addition, it examines current assessment methods and their clinical applications. Spatial memory is primarily governed by the hippocampus and interconnected cortical and subcortical structures, including the basal ganglia, the prefrontal cortex, and the anterior cingulate cortex. In PD, dopaminergic degeneration in the substantia nigra leads to functional disruptions in these networks. The basal ganglia, particularly the striatum, play a crucial role in procedural aspects of spatial navigation, while the hippocampus is essential for allocentric mapping. The utilization of functional neuroimaging techniques has yielded evidence of altered activity in these regions, which is concomitant with spatial memory deficits. Traditional neuropsychological assessments, laboratory-based tasks, and recent advancements, including virtual reality-based tasks, have been employed in the evaluation of spatial memory. The identification of spatial memory deficits in PD is of significant diagnostic and therapeutic importance. Future research should focus on integrating multimodal assessment tools to enhance diagnostic accuracy and explore novel therapeutic approaches targeting spatial memory dysfunction. The cause of spatial memory deficits in PD is multifactorial, arising from complex interactions between dopaminergic depletion and dysfunction in hippocampal-cortical networks. Advancements in assessment methodologies and targeted interventions hold considerable potential for enhancing spatial cognitive outcomes in patients diagnosed with PD. However, further research is required to refine diagnostic tools and develop effective rehabilitation strategies that are targeted at spatial memory impairments in PD.</p>","PeriodicalId":72170,"journal":{"name":"American journal of neurodegenerative disease","volume":"14 3","pages":"67-81"},"PeriodicalIF":0.0,"publicationDate":"2025-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12267192/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144676655","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":"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}
Nilufar Ghorbani, Sam Mirfendereski, Neda Hosseini Moshkenani
{"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}