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Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by a selective degeneration of dopaminergic neurons in the ventral midbrain of humans called substantia nigra. This results in an irreversible and debilitating motor dysfunction. Though both genetic and idiopathic factors are implicated in the disease etiology, idiopathic PD comprise the majority of clinical cases and is caused due to environmental toxicants and oxidative stress.Activation of Fyn kinase has been implicated to be an early signalling event that primes both neuroin ammatory and neurodegenerative events associated with dopaminergic cell death. Fyn kinase is activated by dephosphorylation at the negatively regulating tyrosine site by tyrosine phosphatases. However, the tyrosine phosphatase that dephosphorylates and activates Fyn kinase is unidenti ed. One of the tyrosine phosphatases -PTEN (Phosphatase and Tensin homolog deleted on chromosome 10) a lipid and protein tyrosine phosphatase pathological roles in causing Parkinson's disease has been previously studied in experimental models. We sought to study if PTEN would be the upstream regulator of Fyn activation in PD models. Our ndings demonstrate for the rst time that PTEN is a very early stress-sensor in response to oxidative stress and neurodegenerative toxicants in in vitro models of PD. Pharmacological inhibition of PTEN attenuates Fyn kinase and rescues dopaminergic neurons from neurotoxicants induced cytotoxicity. Our ndings also identify PTEN's additional and novel roles in contributing to mitochondrial dysfunction and neuroin ammatory pathways, both of which contribute to neurodegenerative processes. Taken together, we have identi ed PTEN as a disease course altering pharmacological target that may be further validated for the development of novel therapeutic strategies.
Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by a selective degeneration of dopaminergic neurons in the ventral midbrain of humans called substantia nigra. This results in an irreversible and debilitating motor dysfunction. Though both genetic and idiopathic factors are implicated in the disease etiology, idiopathic PD comprise the majority of clinical cases and is caused due to environmental toxicants and oxidative stress.Activation of Fyn kinase has been implicated to be an early signalling event that primes both neuroin ammatory and neurodegenerative events associated with dopaminergic cell death. Fyn kinase is activated by dephosphorylation at the negatively regulating tyrosine site by tyrosine phosphatases. However, the tyrosine phosphatase that dephosphorylates and activates Fyn kinase is unidenti ed. One of the tyrosine phosphatases -PTEN (Phosphatase and Tensin homolog deleted on chromosome 10) a lipid and protein tyrosine phosphatase pathological roles in causing Parkinson's disease has been previously studied in experimental models. We sought to study if PTEN would be the upstream regulator of Fyn activation in PD models. Our ndings demonstrate for the rst time that PTEN is a very early stress-sensor in response to oxidative stress and neurodegenerative toxicants in in vitro models of PD. Pharmacological inhibition of PTEN attenuates Fyn kinase and rescues dopaminergic neurons from neurotoxicants induced cytotoxicity. Our ndings also identify PTEN's additional and novel roles in contributing to mitochondrial dysfunction and neuroin ammatory pathways, both of which contribute to neurodegenerative processes. Taken together, we have identi ed PTEN as a disease course altering pharmacological target that may be further validated for the development of novel therapeutic strategies.
Parkinson’s disease is a common chronic disease that affects a large number of people. In the real world, however, Parkinson’s disease can result in a loss of physical performance, which is classified as a movement disorder by clinicians. Parkinson’s disease is currently diagnosed primarily through clinical symptoms, which are highly dependent on clinician experience. As a result, there is a need for effective early detection methods. Traditional machine learning algorithms filter out many inherently relevant features in the process of dimensionality reduction and feature classification, lowering the classification model’s performance. To solve this problem and ensure high correlation between features while reducing dimensionality to achieve the goal of improving classification performance, this paper proposes a recurrent neural network classification model based on self attention and motion perception. Using a combination of self-attention mechanism and recurrent neural network, as well as wearable inertial sensors, the model classifies and trains the five brain area features extracted from MRI and DTI images (cerebral gray matter, white matter, cerebrospinal fluid density, and so on). Clinical and exercise data can be combined to produce characteristic parameters that can be used to describe movement sluggishness. The experimental results show that the model proposed in this paper improves the recognition performance of Parkinson’s disease, which is better than the compared methods by 2.45% to 12.07%.
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