Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (19)
  • Open Access

    REVIEW

    Pioneering a new era in Parkinson’s disease management through adipose-derived mesenchymal stem cell therapy

    MOHAMMAD-SADEGH LOTFI, FATEMEH B. RASSOULI*

    BIOCELL, Vol.48, No.10, pp. 1419-1428, 2024, DOI:10.32604/biocell.2024.053597 - 02 October 2024

    Abstract Parkinson’s disease (PD) is one of the fastest-growing neurodegenerative disorders worldwide. So far, PD treatments only offer little clinical relief and cannot reverse or stop the disease progression. Stem cell (SC) therapy is a rapidly evolving technology that holds significant promise for enhancing current therapeutic approaches. Adipose-derived mesenchymal SCs (AD-MSCs) have many features such as easy harvest with minimal invasive techniques, high plasticity, non-immunogenicity, and no ethical issues, which have made them suitable choices for clinical applications in regenerative research. AD-MSCs are ideal tools to treat PD, as they have the potential to differentiate into… More >

  • Open Access

    REVIEW

    The pathogenesis of Parkinson’s disease and crosstalk with other diseases

    TINGTING LIU#, DINGYOU GUO#, JIANSHE WEI*

    BIOCELL, Vol.48, No.8, pp. 1155-1179, 2024, DOI:10.32604/biocell.2024.051518 - 02 August 2024

    Abstract In China, Parkinson’s disease (PD) is the second most prevalent central nervous system (CNS) degenerative illness affecting middle-aged and older persons. Movement disorders including resting tremor, bradykinesia, myotonia, postural instability, and gait instability are the predominant clinical symptoms. The two main types of PD are sporadic and familial, with sporadic PD being the more prevalent of the two. The environment, genetics, mitochondrial dysfunction, oxidative stress, inflammation, protein aggregation and misfolding, loss of trophic factors, cell death, and gut microbiota may all have a role in the etiology of PD. PD is inversely connected with other More >

  • Open Access

    ARTICLE

    Developing a Model for Parkinson’s Disease Detection Using Machine Learning Algorithms

    Naif Al Mudawi*

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4945-4962, 2024, DOI:10.32604/cmc.2024.048967 - 20 June 2024

    Abstract Parkinson’s disease (PD) is a chronic neurological condition that progresses over time. People start to have trouble speaking, writing, walking, or performing other basic skills as dopamine-generating neurons in some brain regions are injured or die. The patient’s symptoms become more severe due to the worsening of their signs over time. In this study, we applied state-of-the-art machine learning algorithms to diagnose Parkinson’s disease and identify related risk factors. The research worked on the publicly available dataset on PD, and the dataset consists of a set of significant characteristics of PD. We aim to apply… More >

  • Open Access

    REVIEW

    Pathogenic genes associated with Parkinson’s disease: molecular mechanism overview

    TINGTING LIU1,#, YIWEI HAO2,#, LIFENG ZHAO2,*

    BIOCELL, Vol.48, No.5, pp. 707-729, 2024, DOI:10.32604/biocell.2024.049130 - 06 May 2024

    Abstract Parkinson’s disease (PD) is a common neurodegenerative disease in the elderly, accounting for more than 1% of the population aged 65 years. Monogenic inheritance is relatively rare in PD, accounting for approximately 5% to 10% of PD patients, and there is a growing body of evidence suggesting that multiple genetic risk factors play a significant role in the pathogenesis of PD. Several groups have identified and reported a number of genes carrying mutations associated with affected family members. Mutated genes associated with PD are also candidates for idiopathic PD, and these genes may also carry… More >

  • Open Access

    REVIEW

    Exercise and exerkine upregulation: Brain-derived neurotrophic factor as a potential non-pharmacological therapeutic strategy for Parkinson’s disease

    VIRAAJ VISHNU PRASAD, JENNIFER SALLY SAMSON, VENKATACHALAM DEEPA PARVATHI*

    BIOCELL, Vol.48, No.5, pp. 693-706, 2024, DOI:10.32604/biocell.2024.048776 - 06 May 2024

    Abstract Physical activity and exercise have several beneficial roles in enhancing both physiological and psychological well-being of an individual. In addition to aiding the regulation of aerobic and anaerobic metabolism, exercise can stimulate the synthesis of exerkine hormones in the circulatory system. Among several exerkines that have been investigated for their therapeutic potential, Brain-derived neurotrophic factor (BDNF) is considered the most promising candidate, especially in the management of neurodegenerative diseases. Owing to the ability of physical activity to enhance BDNF synthesis, several experimental studies conducted so far have validated this hypothesis and produced satisfactory results at More > Graphic Abstract

    Exercise and exerkine upregulation: Brain-derived neurotrophic factor as a potential non-pharmacological therapeutic strategy for Parkinson’s disease

  • Open Access

    ARTICLE

    Privacy Preserved Brain Disorder Diagnosis Using Federated Learning

    Ali Altalbe1,2,*, Abdul Rehman Javed3

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2187-2200, 2023, DOI:10.32604/csse.2023.040624 - 28 July 2023

    Abstract Federated learning has recently attracted significant attention as a cutting-edge technology that enables Artificial Intelligence (AI) algorithms to utilize global learning across the data of numerous individuals while safeguarding user data privacy. Recent advanced healthcare technologies have enabled the early diagnosis of various cognitive ailments like Parkinson’s. Adequate user data is frequently used to train machine learning models for healthcare systems to track the health status of patients. The healthcare industry faces two significant challenges: security and privacy issues and the personalization of cloud-trained AI models. This paper proposes a Deep Neural Network (DNN) based More >

  • Open Access

    ARTICLE

    Parkinson’s Disease Classification Using Random Forest Kerb Feature Selection

    E. Bharath1,*, T. Rajagopalan2

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 1417-1433, 2023, DOI:10.32604/iasc.2023.032102 - 05 January 2023

    Abstract Parkinson’s disease (PD) is a neurodegenerative disease cause by a deficiency of dopamine. Investigators have identified the voice as the underlying symptom of PD. Advanced vocal disorder studies provide adequate treatment and support for accurate PD detection. Machine learning (ML) models have recently helped to solve problems in the classification of chronic diseases. This work aims to analyze the effect of selecting features on ML efficiency on a voice-based PD detection system. It includes PD classification models of Random forest, decision Tree, neural network, logistic regression and support vector machine. The feature selection is made… More >

  • Open Access

    ARTICLE

    Sensor-Based Gait Analysis for Parkinson’s Disease Prediction

    Sathya Bama B*, Bevish Jinila Y

    Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2085-2097, 2023, DOI:10.32604/iasc.2023.028481 - 05 January 2023

    Abstract Parkinson’s disease is identified as one of the key neurodegenerative disorders occurring due to the damages present in the central nervous system. The cause of such brain damage seems to be fully explained in many research studies, but the understanding of its functionality remains to be impractical. Specifically, the development of a quantitative disease prediction model has evolved in recent decades. Moreover, accelerometer sensor-based gait analysis is accepted as an important tool for recognizing the walking behavior of the patients during the early prediction and diagnosis of Parkinson’s disease. This type of minimal infrastructure equipment… More >

  • Open Access

    ARTICLE

    Integrated Privacy Preserving Healthcare System Using Posture-Based Classifier in Cloud

    C. Santhosh Kumar1, K. Vishnu Kumar2,*

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2893-2907, 2023, DOI:10.32604/iasc.2023.029669 - 17 August 2022

    Abstract Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system. Online patient data processing from remote places may lead to severe privacy problems. Moreover, the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud servers. Solve the privacy problem. The proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption schemes. It can help maintain the privacy preservation and confidentiality of patients’ medical data during diagnosis of Parkinson’s disease. In addition, the energy More >

  • Open Access

    ARTICLE

    Colliding Bodies Optimization with Machine Learning Based Parkinson’s Disease Diagnosis

    Ashit Kumar Dutta1,*, Nazik M. A. Zakari2, Yasser Albagory3, Abdul Rahaman Wahab Sait4

    Computer Systems Science and Engineering, Vol.44, No.3, pp. 2195-2207, 2023, DOI:10.32604/csse.2023.026461 - 01 August 2022

    Abstract Parkinson’s disease (PD) is one of the primary vital degenerative diseases that affect the Central Nervous System among elderly patients. It affect their quality of life drastically and millions of seniors are diagnosed with PD every year worldwide. Several models have been presented earlier to detect the PD using various types of measurement data like speech, gait patterns, etc. Early identification of PD is important owing to the fact that the patient can offer important details which helps in slowing down the progress of PD. The recently-emerging Deep Learning (DL) models can leverage the past… More >

Displaying 1-10 on page 1 of 19. Per Page