Retinoic acid receptor beta protects striatopallidal medium spiny neurons from mitochondrial dysfunction and neurodegeneration

Retinoic acid is a powerful regulator of brain development, however its postnatal functions only start to be elucidated. We show that retinoic acid receptor beta (RAR beta), is involved in neuroprotection of striatopallidal medium spiny neurons (spMSNs), the cell type affected in different neuropsychiatric disorders and particularly prone to degenerate in Huntington disease (HD). Accordingly, the number of spMSNs was reduced in the striatum of adult Rar beta(-/-) mice, which may result from mitochondrial dysfunction and neurodegeneration. Mitochondria morphology was abnormal in mutant mice whereas in cultured striatal Rar beta(-/-) neurons mitochondria displayed exacerbated depolarization, and fragmentation followed by cell death in response to glutamate or thapsigargininduced calcium increase. In vivo, Rar beta(-/-)spMSNs were also more vulnerable to the mitochondrial toxin 3-nitropropionic acid (3NP), known to induce HD symptoms in human and rodents. In contrary, an RAR beta agonist, AC261066, decreased glutamate-induced toxicity in primary striatal neurons in vitro, and diminished mitochondrial dysfunction, spMSN cell death and motor deficits induced in wild type mice by 3NP. We demonstrate that the striatopallidal pathway is compromised in Rar beta(-/-) mice and associated with HD-like motor abnormalities. Importantly, similar motor abnormalities and selective reduction of spMSNs were induced by striatal or spMSNspecific inactivation of RAR beta, further supporting a neuroprotective role of RAR beta in postnatal striatum

An experimental system for detection and localization of hemorrhage using ultra-wideband microwaves with deep learning

Stroke is a leading cause of mortality and disability. Emergent diagnosis and intervention are critical, and predicated upon initial brain imaging; however, existing clinical imaging modalities are generally costly, immobile, and demand highly specialized operation and interpretation. Low-energy microwaves have been explored as low-cost, small form factor, fast, and safe probes of tissue dielectric properties, with both imaging and diagnostic potential. Nevertheless, challenges inherent to microwave reconstruction have impeded progress, hence microwave imaging (MWI) remains an elusive scientific aim. Herein, we introduce a dedicated experimental framework comprising a robotic navigation system to translate blood-mimicking phantoms within an anatomically realistic human head model. An 8-element ultra-wideband (UWB) array of modified antipodal Vivaldi antennas was developed and driven by a two-port vector network analyzer spanning 0.6-9.0 GHz at an operating power of 1 mw. Complex scattering parameters were measured, and dielectric signatures of hemorrhage were learned using a dedicated deep neural network for prediction of hemorrhage classes and localization. An overall sensitivity and specificity for detection >0.99 was observed, with Rayliegh mean localization error of 1.65 mm. The study establishes the feasibility of a robust experimental model and deep learning solution for UWB microwave stroke detection