No abstract

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by the gradual loss of dopaminergic neurons in the substantia nigra and the accumulation of α-synuclein aggregates called Lewy bodies. Here, nanodecoys were designed from a rabies virus polypeptide with a 29 amino acid (RVG29)-modified red blood cell membrane (RBCm) to encapsulate curcumin nanocrystals (Cur-NCs), which could effectively protect dopaminergic neurons. The RVG29-RBCm/Cur-NCs nanodecoys effectively escaped from reticuloendothelial system (RES) uptake, enabled prolonged blood circulation, and enhanced blood–brain barrier (BBB) crossing after systemic administration. Cur-NCs loaded inside the nanodecoys exhibited the recovery of dopamine levels, inhibition of α-synuclein aggregation, and reversal of mitochondrial dysfunction in PD mice. These findings indicate the promising potential of biomimetic nanodecoys in treating PD and other neurodegenerative diseases.

We propose a machine learning based approach to design few-mode DRAs by using neural networks to optimize the pump wavelengths, powers and mode content in order to obtain flat gain spectrum with low mode-dependent gain (MDG). Based on the proposed intelligent inverse design method, amplification optimization for the random fiber laser based two-mode DRA can be achieved with gain flatness of 1.0 dB and MDG of 0.6 dB at 14.5 dB on-off gain level. For backward pumping four-mode DRA, gain flatness of 0.46 dB and MDG of 0.3 dB can be achieved at 12.5 dB on-off gain.