Motion artifact reduction in photoplethysmogram signals based on Periodic Component Factorization.

Continuous blood pressure measurement based on pulse transit time (PTT) is a deeply research topic over recent decades. Advanced algorithms have been proposed by scholars to give satisfactory estimation in stationary position. Nevertheless, pulse transit time (PTT) is shown to be strongly affected by hand movement and the estimation of blood pressure is no longer accurate under strenuous exercise. Because of this, a novel algorithm called Periodic Component Factorization (PCF), which is an extension of Independent Component Analysis (ICA), for better removal of motion artifact (MA) from photoplethysmography (PPG) signals is proposed in this paper. Compared to FastICA algorithm based on nongaussianity such as kurtosis and skewness, PCF is able to extract dependent source components from noisy signals when the PPG signal shows quasi-periodicity or periodicity. This newly proposed algorithm undoubtedly shows its practicality and effectiveness in removing motion artifact of PPG signals.

Continuous systolic and diastolic blood pressure estimation utilizing long short-term memory network.

A novel blood pressure estimation method based on long short-term memory neural network, one of the recurrent neural networks being commonly used nowadays, is proposed in this paper for better chronic diseases monitoring. Along with the neural network, a newly proposed ambulatory blood pressure (ABP) processing technique called Two-stage Zero-order Holding (TZH) algorithm has also been presented in the paper. The proposed methodology has the advantages over traditional blood pressure estimation algorithms which are based on Pulse Transit time (PTT). The paper addresses the effectiveness of the algorithm by computing the Root-Mean-Squared Errors (RMSE) between the BP estimated and the ground truth. Our algorithm shows precise systolic blood pressure and diastolic blood pressure estimation with the average RMSE values in 2.751 mmHg and 1.604 mmHg respectively across the sample used. Experimental results suggest that BP estimation based on LSTM has great potential to be embedded into monitoring system for better accuracy and generalization.

Delivery of RNA interference.

Over the last few years, RNA Interference (RNAi), a naturally occurring mechanism of gene regulation conserved in plant and mammalian cells, has opened numerous novel opportunities for basic research across the field of biology. While RNAi has helped accelerate discovery and understanding of gene functions, it also has great potential as a therapeutic and potentially prophylactic modality. Challenging diseases failing conventional therapeutics could become treatable by specific silencing of key pathogenic genes. More specifically, therapeutic targets previously deemed "undruggable" by small molecules, are now coming within reach of RNAi based therapy. For RNAi to be effective and elicit gene silencing response, the double-stranded RNA molecules must be delivered to the target cell. Unfortunately, delivery of these RNA duplexes has been challenging, halting rapid development of RNAi-based therapies. In this review we present current advancements in the field of siRNA delivery methods, including the pros and cons of each method.