ABSTRACT
The appearance of misfolded and aggregated proteins is a pathological hallmark of numerous neurodegenerative diseases including Alzheimer's disease and Parkinson's disease. Sleep disruption is proposed to contribute to these pathological processes and is a common early feature among neurodegenerative disorders. Synucleinopathies are a subclass of neurodegenerative conditions defined by the presence of α-synuclein aggregates, which may not only enhance cell death, but also contribute to disease progression by seeding the formation of additional aggregates in neighboring cells. The mechanisms driving intercellular transmission of aggregates remains unclear. We propose that disruption of sleep-active glymphatic function, caused by loss of precise perivascular AQP4 localization, inhibits α-synuclein clearance and facilitates α-synuclein propagation and seeding. We examined human post-mortem frontal cortex and found that neocortical α-synuclein pathology was associated with AQP4 mis-localization throughout the gray matter. Using a transgenic mouse model lacking the adapter protein α-syntrophin, we observed that loss of perivascular AQP4 localization impairs the glymphatic clearance of α-synuclein from intersititial to cerebrospinal fluid. Using a mouse model of α-synuclein propogation, using pre-formed fibril injection, we observed that loss of perivascular AQP4 localization increased α-synuclein aggregates. Our results indicate α-synuclein clearance and propagation are mediated by glymphatic function and that AQP4 mis-localization observed in the presence of human synucleinopathy may contribute to the development and propagation of Lewy body pathology in conditions such as Lewy Body Dementia and Parkinson's disease.
ABSTRACT
Photoplethysmography (PPG) has become ubiquitous with the development of smart watches and the mobile healthcare market. However, PPG is vulnerable to various types of noises that are ever present in uncontrolled environments, and the key to obtaining meaningful signals depends on successful denoising of PPG. In this context, algorithms have been developed to denoise PPG, but many were validated in controlled settings or are reliant on multiple steps that must all work correctly. This paper proposes a novel PPG denoising algorithm based on bidirectional recurrent denoising auto-encoder (BRDAE) that requires minimal pre-processing steps and have the benefit of waveform feature accentuation beyond simple denoising. The BRDAE was trained and validated on a dataset with artificially augmented noise, and was tested on a large open database of PPG signals collected from patients enrolled in intensive care units as well as from PPG data collected intermittently during the daily routine of nine subjects over 24 h. Denoising with the trained BRDAE improved signal-to-noise ratio of the noise-augmented data by 7.9 dB during validation. In the test datasets, the denoised PPG showed statistically significant improvement in heart rate detection as compared with the original PPG in terms of correlation to reference and root-mean-squared error. These results indicate that the proposed method is an effective solution for denoising the PPG signal, and promises values beyond traditional denoising by providing PPG feature accentuation for pulse waveform analysis.