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The Emerging Role of Exosome Nanoparticles in Regenerative Medicine
Published in Harishkumar Madhyastha, Durgesh Nandini Chauhan, Nanopharmaceuticals in Regenerative Medicine, 2022
Zahra Sadat Hashemi, Mahlegha Ghavami, Saeed Khalili, Seyed Morteza Naghib
Prior studies have showed that the dysregulation of miRNAs is another factor that should be taken into account in SCI. Owing to the vital role of miR-133b in the differentiation of neurons and the outgrowth of neuritis (after the administration of miR-133b-modified MSCs), miR-133b containing exosomes considerably improved the functional recovery of hindlimb in SCI rat model. Besides, miR-133b treatment has decreased the lesion volume and enhanced the axonal regeneration following the SCI. Furthermore, it is suggested to activate some signalling pathways involved in axonal regeneration (Li et al. 2020).
Demyelinating Neuropathy
Published in Maher Kurdi, Neuromuscular Pathology Made Easy, 2021
Segmental demyelination is clinically known as demyelinating neuropathy. It is characterized by focal degeneration of myelin sheath with axonal sparing. In rare and severe cases, the axons may be affected and accompanied with axonal degeneration. Demyelination mechanically occurs either due to macrophage antibody-mediated attack or marked axonal atrophy. It should be determined whether the process is fundamentally axonal or demyelinating, as this will affect the treatment and prognosis. Electrodiagnostic tests can occasionally distinguish axonal degeneration from demyelination but will not specify the type of demyelination. The distinction is usually not obvious as Schwann cells and axons are interdependent. As we described in the previous chapter, axonal degeneration can be followed by secondary demyelination. Pathologists must distinguish this type of demyelination through histopathological and ultrastructural examinations. Bear in mind that in primary demyelination, the axons are always preserved.
The patient with acute neurological problems
Published in Peate Ian, Dutton Helen, Acute Nursing Care, 2020
Axons transmit information away from the cell body. Axons vary in length from less than 1mm to over a metre. The proximal portion of the axon is called the initial segment and is the origin of the action potential required for nerve transmission. Action potentials are electrical signals that travel along the surface of a neurone. The signal is maintained (or propagated) by the movement of ions (electrolytes) across the membrane of the neurone.
Understanding intrinsic survival and regenerative pathways through in vivo and in vitro studies: implications for optic nerve regeneration
Published in Expert Review of Ophthalmology, 2021
For axonal regeneration, the intrinsic regenerative pathways must be activated. Neurotrophic factors including the nerve growth factor (NGF) family members bind to the trk receptors and activate the PI3K/Akt/mTOR pathway (Figure 2). NGF eye drops have been used for patients with retinitis pigmentosa [88]. NT-4 also activates the mTOR pathways which contributed to myelination [89], attenuating neuroinflammation [90], and mediating neurogenesis [91]. Our studies of tissue cultures of retinas have shown that NT-4 is the most neuroprotective and regenerative agent [75–79]. Previous studies indicate that citicoline, TUDCA and NT-4 show neuroprotective effect after optic nerve damage in vivo [92–94]. Thus, we combined citicoline, TUDCA, and NT-4 and applied it tropically to rat eyes after optic nerve crush [81]. The triplet group had the most neuroprotective and regenerative effect for damaged RGCs after optic nerve injury [81]. Therefore, the combined topical instillation of neurotrophic factors may be effective for chronic retinal diseases including diabetic retinopathy. We plan to investigate the efficacy of the topical application of a combination of neurotrophic factors in diabetic animal models in the near future.
Molecular mechanisms governing axonal transport: a C. elegans perspective
Published in Journal of Neurogenetics, 2020
Amruta Vasudevan, Sandhya P. Koushika
Axonal transport is a critical process, central to neuronal function and maintenance. In vitro studies have provided a wealth of information about single and ensemble motor behaviours in different cytoskeletal geometries (Holzbaur & Goldman, 2010). Super resolution imaging techniques, such as single molecule localization microscopy, allow researchers to examine complex in vivo cytoskeletal geometries. Recent studies have succeeded in resolving individual microtubules in axons of cultured hippocampal neurons, using anti-tubulin nanobodies to stain microtubules, and a novel optical nanoscopy technique, named motor-PAINT, to assess the stability and orientation of individual microtubules (Mikhaylova et al., 2015; Tas et al., 2017). Such techniques, when applied to model organisms, can pave the way for investigating mechanisms by which motor-cargo complexes exhibit a preference for specific microtubules in vivo, for instance, to understand the role of post-translational modifications of microtubules in track selection by motors. Advanced microscopy techniques such as STORM (He et al., 2016; Stewart & Shen, 2015) and Expansion microscopy (Yu et al., 2020) have already begun to provide insights into the cytoskeletal architecture and synaptic organization of C. elegans neurons. These techniques allow investigators to translate the precision of in vitro measurements to in vivo systems.
Learning-based classification of valence emotion from electroencephalography
Published in International Journal of Neuroscience, 2019
Artificial neural network works as a biological neuron. The three important parameters of the biological neuron are synapses, cell body and axon. The neurons are connected with synapses, constantly receiving the signals to reach to the cell body and if the resulting sum of the signals surpasses a certain threshold, a response is sent through the axon. Similarly, a perceptron takes the weighted sum of inputs and based on some activation functions such as sigmoid Gaussian, etc., it transforms the input into output. The weights (Wi) and learning rate (m) are randomly initialized between 0 and 1. For each training instance the activation function is calculated and then the learning rule is applied to find the error between the actual and the predicted output. The weights are updated if the output of the network is not correct otherwise no change in the weight and bias (B). The two-layer feed-forward network, with the sigmoid activation function, was used in this study. It is formulated as: