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Готові списки джерел за темами / Neuro-degenerative disorders

Добірка наукової літератури з теми "Neuro-degenerative disorders"

Автор: Grafiati

Опубліковано: 6 вересня 2022

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Статті в журналах
Дисертації
Частини книг
Тези доповідей конференцій

Статті в журналах з теми "Neuro-degenerative disorders":

1

Husain, Masud, and Christopher Kennard. "Neuro-ophthalmology of degenerative neurological disorders." Current Opinion in Ophthalmology 6, no. 6 (December 1995): 41–47. http://dx.doi.org/10.1097/00055735-199512000-00007.

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2

C. Deocaris, Custer, Wen-Jing Lu, Sunil C. Kaul, and Renu Wadhwa. "Druggability of Mortalin for Cancer and Neuro-Degenerative Disorders." Current Pharmaceutical Design 19, no. 3 (November 1, 2012): 418–29. http://dx.doi.org/10.2174/1381612811306030418.

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3

C. Deocaris, Custer, Wen-Jing Lu, Sunil C. Kaul, and Renu Wadhwa. "Druggability of Mortalin for Cancer and Neuro-Degenerative Disorders." Current Pharmaceutical Design 19, no. 3 (January 1, 2013): 418–29. http://dx.doi.org/10.2174/138161213804143680.

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4

Valente, André, Paulo Oliveira, Svetlana Khaiboullina, András Palotás, and Albert Rizvanov. "Biological Insight, High-Throughput Datasets and the Nature of Neuro-Degenerative Disorders." Current Drug Metabolism 14, no. 7 (August 1, 2013): 814–18. http://dx.doi.org/10.2174/13892002113149990100.

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5

Mohammadian Rad, Nastaran, Twan van Laarhoven, Cesare Furlanello, and Elena Marchiori. "Novelty Detection using Deep Normative Modeling for IMU-Based Abnormal Movement Monitoring in Parkinson’s Disease and Autism Spectrum Disorders." Sensors 18, no. 10 (October 19, 2018): 3533. http://dx.doi.org/10.3390/s18103533.

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Detecting and monitoring of abnormal movement behaviors in patients with Parkinson’s Disease (PD) and individuals with Autism Spectrum Disorders (ASD) are beneficial for adjusting care and medical treatment in order to improve the patient’s quality of life. Supervised methods commonly used in the literature need annotation of data, which is a time-consuming and costly process. In this paper, we propose deep normative modeling as a probabilistic novelty detection method, in which we model the distribution of normal human movements recorded by wearable sensors and try to detect abnormal movements in patients with PD and ASD in a novelty detection framework. In the proposed deep normative model, a movement disorder behavior is treated as an extreme of the normal range or, equivalently, as a deviation from the normal movements. Our experiments on three benchmark datasets indicate the effectiveness of the proposed method, which outperforms one-class SVM and the reconstruction-based novelty detection approaches. Our contribution opens the door toward modeling normal human movements during daily activities using wearable sensors and eventually real-time abnormal movement detection in neuro-developmental and neuro-degenerative disorders.

6

Piazzi, Manuela, Alberto Bavelloni, Vittoria Cenni, Irene Faenza, and William L. Blalock. "Revisiting the Role of GSK3, A Modulator of Innate Immunity, in Idiopathic Inclusion Body Myositis." Cells 10, no. 11 (November 21, 2021): 3255. http://dx.doi.org/10.3390/cells10113255.

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Idiopathic or sporadic inclusion body myositis (IBM) is the leading age-related (onset >50 years of age) autoimmune muscular pathology, resulting in significant debilitation in affected individuals. Once viewed as primarily a degenerative disorder, it is now evident that much like several other neuro-muscular degenerative disorders, IBM has a major autoinflammatory component resulting in chronic inflammation-induced muscle destruction. Thus, IBM is now considered primarily an inflammatory pathology. To date, there is no effective treatment for sporadic inclusion body myositis, and little is understood about the pathology at the molecular level, which would offer the best hopes of at least slowing down the degenerative process. Among the previously examined potential molecular players in IBM is glycogen synthase kinase (GSK)-3, whose role in promoting TAU phosphorylation and inclusion bodies in Alzheimer’s disease is well known. This review looks to re-examine the role of GSK3 in IBM, not strictly as a promoter of TAU and Abeta inclusions, but as a novel player in the innate immune system, discussing some of the recent roles discovered for this well-studied kinase in inflammatory-mediated pathology.

7

Balasubramanian, Kishore, NP Ananthamoorthy, and K. Ramya. "Prediction of neuro-degenerative disorders using sunflower optimisation algorithm and Kernel extreme learning machine: A case-study with Parkinson’s and Alzheimer’s disease." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 236, no. 3 (December 20, 2021): 438–53. http://dx.doi.org/10.1177/09544119211060989.

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Parkinson’s and Alzheimer’s Disease are believed to be most prevalent and common in older people. Several data-mining approaches are employed on the neuro-degenerative data in predicting the disease. A novel method has been built and developed to diagnose Alzheimer’s (AD) and Parkinson’s (PD) in early stages, which includes image acquisition, pre-processing, feature extraction and selection, followed by classification. The challenge lies in selecting the optimal feature subset for classification. In this work, the Sunflower Optimisation Algorithm (SFO) is employed to select the optimal feature set, which is then fed to the Kernel Extreme Learning Machine (KELM) for classification. The method is tested on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and local dataset for AD, the University of California, Irvine (UCI) machine learning repository and the Istanbul dataset for PD. Experimental outcomes have demonstrated a high accuracy level in both AD and PD diagnosis. For AD diagnosis, the highest classification rate is obtained for the AD versus NC classification using the ADNI dataset (99.32%) and local dataset (98.65%). For PD diagnosis, the highest accuracy of 99.52% and 99.45% is achieved on the UCI and Istanbul datasets, respectively. To show the robustness of the method, the method is compared with other similar methods of feature selection and classification with 10-fold cross-validation (CV) and with unseen data. The method proposed has an excellent prospect, bringing greater convenience to clinicians in making a better solid decision in clinical diagnosis of neuro-degenerative diseases.

8

Tejeswinee, K., Gracia Jacob Shomona, and R. Athilakshmi. "Feature Selection Techniques for Prediction of Neuro-Degenerative Disorders: A Case-Study with Alzheimer’s And Parkinson’s Disease." Procedia Computer Science 115 (2017): 188–94. http://dx.doi.org/10.1016/j.procs.2017.09.125.

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9

Elmi, Gul Rehman, Kalsoom Saleem, Mirza Muhammad Faran Ashraf Baig, Muhammad Naeem Aamir, Minglian Wang, Xiuli Gao, Muhammad Abbas, and Masood Ur Rehman. "Recent Advances of Magnetic Gold Hybrids and Nanocomposites, and Their Potential Biological Applications." Magnetochemistry 8, no. 4 (April 1, 2022): 38. http://dx.doi.org/10.3390/magnetochemistry8040038.

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Magnetic gold nanoparticles (mGNP) have become a great interest of research for nanomaterial scientists because of their significant magnetic and plasmonic properties applicable in biomedical applications. Various synthetic approaches and surface modification techniques have been used for mGNP including the most common being the coprecipitation, thermal decomposition, and microemulsion methods in addition to the Brust Schiffrin technique, which involves the reduction of metal precursors in a two-phase system (water and toluene) in the presence of alkanethiol. The hybrid magnetic–plasmonic nanoparticles based on iron core and gold shell are being considered as potential theranostic agents. In this critical review, in addition to future works, we have summarized recent developments for synthesis and surface modification of mGNP with their applications in modern biomedical science such as drug and gene delivery, bioimaging, biosensing, and neuro-regeneration, neuro-degenerative and arthritic disorders. This review includes techniques and biological applications of mGNP majorly based on research from the previous six years.

10

Adkins, Austin M., Laurie L. Wellman, and Larry D. Sanford. "Controllable and Uncontrollable Stress Differentially Impact Fear Conditioned Alterations in Sleep and Neuroimmune Signaling in Mice." Life 12, no. 9 (August 26, 2022): 1320. http://dx.doi.org/10.3390/life12091320.

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Stress induces neuroinflammation and disrupts sleep, which together can promote a number of stress-related disorders. Fear memories associated with stress can resurface and reproduce symptoms. Our previous studies have demonstrated sleep outcomes can be modified by stressor controllability following stress and fear memory recall. However, it is unknown how stressor controllability alters neuroinflammatory signaling and its association with sleep following fear memory recall. Mice were implanted with telemetry transmitters and experienced escapable or inescapable footshock and then were re-exposed to the shuttlebox context one week later. Gene expression was assessed with Nanostring® panels using RNA extracted from the basolateral amygdala and hippocampus. Freezing and temperature were examined as behavioral measures of fear. Increased sleep after escapable stress was associated with a down-regulation in neuro-inflammatory and neuro-degenerative related genes, while decreased sleep after inescapable stress was associated with an up-regulation in these genes. Behavioral measures of fear were virtually identical. Sleep and neuroimmune responses appear to be integrated during fear conditioning and reproduced by fear memory recall. The established roles of disrupted sleep and neuroinflammation in stress-related disorders indicate that these differences may serve as informative indices of how fear memory can lead to psychopathology.

Більше джерел

Дисертації з теми "Neuro-degenerative disorders":

1

Ostertag, Cécilia. "Analyse des pathologies neuro-dégénératives par apprentissage profond." Thesis, La Rochelle, 2022. http://www.theses.fr/2022LAROS003.

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Le suivi et l'établissement de pronostics sur l'état cognitif des personnes affectées par une maladie neurologique sont cruciaux, car ils permettent de fournir un traitement approprié à chaque patient, et cela le plus tôt possible. Ces patients sont donc suivis régulièrement pendant plusieurs années, dans le cadre d'études longitudinales. À chaque visite médicale, une grande quantité de données est acquise : présence de facteurs de risque associés à la maladie, imagerie médicale (IRM ou PET-scan), résultats de tests cognitifs, prélèvements de molécules identifiées comme biomarqueurs de la maladie, etc. Ces différentes modalités apportent des informations sur la progression de la maladie, certaines complémentaires et d'autres redondantes. De nombreux modèles d'apprentissage profond ont été appliqués avec succès aux données biomédicales, notamment pour des problématiques de segmentation d'organes ou de diagnostic de maladies. Ces travaux de thèse s'intéressent à la conception d'un modèle de type "réseau de neurones profond" pour la prédiction du déclin cognitif de patients à l'aide de données multimodales. Ainsi, nous proposons une architecture composée de sous-modules adaptés à chaque modalité : réseau convolutif 3D pour les IRM de cerveau, et couches entièrement connectées pour les données cliniques quantitatives et qualitatives. Pour évaluer l'évolution du patient, ce modèle prend en entrée les données de deux visites médicales quelconques. Ces deux visites sont comparées grâce à une architecture siamoise. Après avoir entraîné et validé ce modèle en utilisant comme cas d'application la maladie d'Alzheimer, nous nous intéressons au transfert de connaissance avec d'autres maladies neuro-dégénératives, et nous utilisons avec succès le transfert d'apprentissage pour appliquer notre modèle dans le cas de la maladie de Parkinson. Enfin, nous discutons des choix que nous avons pris pour la prise en compte de l'aspect temporel du problème, aussi bien lors de la création de la vérité terrain en fonction de l'évolution au long terme d'un score cognitif, que pour le choix d'utiliser des paires de visites au lieu de plus longues séquences
Monitoring and predicting the cognitive state of a subject affected by a neuro-degenerative disorder is crucial to provide appropriate treatment as soon as possible. Thus, these patients are followed for several years, as part of longitudinal medical studies. During each visit, a large quantity of data is acquired : risk factors linked to the pathology, medical imagery (MRI or PET scans for example), cognitive tests results, sampling of molecules that have been identified as bio-markers, etc. These various modalities give information about the disease's progression, some of them are complementary and others can be redundant. Several deep learning models have been applied to bio-medical data, notably for organ segmentation or pathology diagnosis. This PhD is focused on the conception of a deep neural network model for cognitive decline prediction, using multimodal data, here both structural brain MRI images and clinical data. In this thesis we propose an architecture made of sub-modules tailored to each modality : 3D convolutional network for the brain MRI, and fully connected layers for the quantitative and qualitative clinical data. To predict the patient's evolution, this model takes as input data from two medical visits for each patient. These visits are compared using a siamese architecture. After training and validating this model with Alzheimer's disease as our use case, we look into knowledge transfer to other neuro-degenerative pathologies, and we use transfer learning to adapt our model to Parkinson's disease. Finally, we discuss the choices we made to take into account the temporal aspect of our problem, both during the ground truth creation using the long-term evolution of a cognitive score, and for the choice of using pairs of visits as input instead of longer sequences

Частини книг з теми "Neuro-degenerative disorders":

1

Athilakshmi, R., Shomona Gracia Jacob, and R. Rajavel. "Protein Sequence Based Anomaly Detection for Neuro-Degenerative Disorders Through Deep Learning Techniques." In Advances in Intelligent Systems and Computing, 547–54. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1882-5_48.

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2

Venkataramana, Lokeswari, Shomona Gracia Jacob, S. Saraswathi, and R. Athilakshmi. "Clinical Decision Support System for Neuro-Degenerative Disorders: An Optimal Feature Selective Classifier and Identification of Predictor Markers." In Advances in Intelligent Systems and Computing, 10–20. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16657-1_2.

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3

Jayaram, Saravanan, Praveen Thaggikuppe Krishnamurthy, Meghana Joshi, and Vishnu Kumar. "Nrf2 as a Potential Therapeutic Target for Treatment of Huntington’s Disease." In From Pathophysiology to Treatment of Huntington's Disease [Working Title]. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.103177.

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Oxidative stress-induced neuronal damage plays a significant role in pathogenesis of several neuro-degenerative disorders including Huntington’s disease. In Huntington’s disease, oxidative stress-induced neuronal damage is reported to be mediated by PGC-1α and microglial cells. This development led to various clinical trials that tested the efficacy of several exogenous antioxidants such as vitamin E, vitamin C, etc. to prevent the oxidative stress-induced cell damage in several neuro-degenerative disorders. But these randomized clinical trials did not find any significant beneficial effects of exogenous antioxidants in neuro-degenerative disorders. This forced scientists to search endogenous targets that would enhance the production of antioxidants. Nrf2 is one such ideal target that increases the transcription of genes involved in production of antioxidants. Nrf2 is a transcription factor that controls the expression of antioxidant genes that defend cells against oxidative stress. This chapter focuses on the role of oxidative stress in Huntington’s disease and explores the therapeutic benefits of Nrf2 activators.

4

dAlessio, Patrizia, Rita Ostan, Miriam Capri, and Claudio Franceschi. "On the Way to Longevity: How to Combat Neuro-Degenerative Disease." In Senescence and Senescence-Related Disorders. InTech, 2013. http://dx.doi.org/10.5772/54914.

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5

Heer, Hemraj, Vishav Prabhjot Kaur, Tania Bajaj, Arti Singh, Priyanka Bajaj, and Charan Singh. "Impact of Nano-Formulations of Natural Compounds in the Management of Neuro degenerative Diseases." In Recent Advances in the Treatment of Neurodegenerative Disorders, 178–207. BENTHAM SCIENCE PUBLISHERS, 2021. http://dx.doi.org/10.2174/9781681087726121010013.

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6

Ranjan, Vandana, Aisha, and Kalpna Verma. "Brief Description of Public Health and Burden of Neurodegenerative Diseases." In Neurodegenerative Diseases - Multifactorial Degenerative Processes, Biomarkers and Therapeutic Approaches (First Edition), 261–72. BENTHAM SCIENCE PUBLISHERS, 2022. http://dx.doi.org/10.2174/9789815040913122010016.

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Анотація:

Physical and mental well-being is treasure for mankind in a competitive and progressive global scenario. For a country, result oriented tasks can be accomplished only with its healthy population. Along with many diseases of global concern, neurological disorders have drawn concern globally as these are sharing an increasing proportion in global burden of diseases. Further cases of neurodegenerative disorders, majorly affecting aged population, have been recently reported to record a considerable increase which has complicated the health and care-giving (old age homes) services as part of public health. Many public health policies have been laid down by many developed and developing countries in accordance of WHO guidelines which in turn based on GBD studies, made till date. Major share of neurodegenerative disorders is contributed by Alzheimer’s Disease, Parkinson’s Disease, Amyotrophic Lateral Sclerosis & Multiple Sclerosis. The recent past has witnessed growing number of deaths and disability adjusted life years, DALY, caused by neurodegenerative diseases. Public health services and related government policies are not enough, according to WHO, to properly address the current situation. Lack of public awareness towards neurological disorders of all kind, is one of the major challenges to Figure out actual data; for prevalence of neuro-disorders.

Тези доповідей конференцій з теми "Neuro-degenerative disorders":

1

Jacob, Shomona Gracia, and R. Athilakshmi. "Extraction of Protein Sequence features for Prediction of Neuro-degenerative Brain Disorders." In ICIA-16: International Conference on Informatics and Analytics. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2980258.2980312.

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