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Khazaei, M.

Publications and source records attributed to Khazaei, M..

2 recordsLinked to original sources

Development and Characterization of Self-Tracing Neural Progenitor Cells for Mapping Their Synaptic Integration into Endogenous Neural Networks

Neural progenitor cell (NPC) transplantation holds immense promise for neurodegenerative and traumatic central nervous system (CNS) pathologies. However, it is crucial to define which neural circuits and pathways are targeted with transplanted NPCs under different conditions. A major roadblock lies in the limited ability to accurately trace integration of grafted cells into the host neural network. Conventional tracers suffer from drawbacks like low trans-synaptic efficiency, toxicity, and difficulty in efficiently and specifically targeting transplanted cells. To address these critical limitations, we have developed self-tracing NPCs genetically engineered to express both anterograde (WGA-mCherry) and retrograde (GFP-TTC) trans-synaptic tracers. These self-tracing NPCs maintain their intrinsic properties, differentiate into electrically active neurons, and integrate into host circuitry in vitro. Importantly, co-culture with primary rat neurons revealed successful trans-synaptic tracing of grafted human neurons, evidenced by single-positive WGA+ or TTC+ rat cells. In vivo, NPCs transplanted into a rodent spinal cord injury model retained tracer expression for 12 weeks, enabling visualization of grafted cells within the spinal cord. Co-labeling with WGA and TTC provided evidence that these NPCs forms neurons which integrated into local circuits. Our novel self-tracing NPC platform offers a powerful tool to overcome trans-synaptic tracing challenges. This approach provides the opportunity to gain critical insights into graft integration and neural circuit remodeling, paving the way for better-designed transplantation strategies and improved therapeutic outcomes in a broad spectrum of CNS disorders.

neuroscience↗

Effect of levodopa on human brain connectome in Parkinsons disease

ObjectiveLevodopa-based drugs are widely used for mitigating the complications induced by PD. Despite the positive effects, several issues regarding the way that levodopa changes brain activities have remained unclear. MethodsA combined strategy using EEG data and graph theory was used for investigating how levodopa changed connectome and processing hubs of the brain during resting-state. Obtained results were subjected to ANOVA test and multiple-comparison post-hoc correction procedure. ResultsResults showed that graph topology of PD patients was not significantly different with the healthy group during eyes-closed condition while in eyes-open condition statistical significant differences were found. The main effect of levodopa medication was observed for gamma-band activity of the brain in which levodopa changed the brain connectome toward a star-like topology. Considering the beta subband of EEG data, graph leaf number increased following levodopa medication in PD patients. Enhanced brain connectivity in gamma band and reduced beta band connections in basal ganglia were also observed after levodopa medication. Furthermore, source localization using dipole fitting showed that levodopa prescription suppressed the activity of collateral trigone. ConclusionOur combined EEG and graph analysis showed that levodopa medication changed the brain connectome, especially in the high-frequency range of EEG (beta and gamma). HighlightsO_LINo differences were found between graph features between ON and OFF PD cases in eyes-closed. C_LIO_LILevodopa enhanced connectivity in gamma band in PD patients. C_LIO_LILevodopa inhibited brain connectivity in beta band. C_LI

neuroscience↗