Formation of elongated fascicle-inspired 3D tissues consisting of high-density, aligned cells using sacrificial outer molding.

The majority of muscles, nerves, and tendons are composed of fiber-like fascicle morphology. Each fascicle has a) elongated cells highly aligned with the length of the construct, b) a high volumetric cell density, and c) a high length-to-width ratio with a diameter small enough to facilitate perfusion. Fiber-like fascicles are important building blocks for forming tissues of various sizes and cross-sectional shapes, yet no effective technology is currently available for producing long and thin fascicle-like constructs with aligned, high-density cells. Here we present a method for molding cell-laden hydrogels that generate cylindrical tissue structures that are ~100 µm in diameter with an extremely high length to diameter ratio (>100 : 1). Using this method we have successfully created skeletal muscle tissue with a high volumetric density (~50%) and perfect cell alignment along the axis. A new molding technique, sacrificial outer molding, allows us to i) create a long and thin cylindrical cavity of the desired size in a sacrificial mold that is solid at a low temperature, ii) release gelling agents from the sacrificial mold material after the cell-laden hydrogel is injected into fiber cavities, iii) generate a uniform axial tension between anchor points at both ends that promotes cell alignment and maturation, and iv) perfuse the tissue effectively by exposing it to media after melting the sacrificial outer mold at 37 °C. The effects of key parameters and conditions, including initial cavity diameter, axial tension, and concentrations of the hydrogel and gelling agent upon tissue compaction, volumetric cell density, and cell alignment are presented.

Active motion artifact reduction for wearable sensors using laguerre expansion and signal separation.

This paper presents an active noise cancellation technique that utilizes an accelerometry measurement to recover corrupted wearable health monitoring signals. The technique presented here requires no calibration for each patient and is computationally efficient. Also, a method of determining when the desired signal is correlated with the motion reference is presented with a means of partial signal recovery. The Laguerre basis function provides robustness against calibration as well as improvement in computational efficiency, and symmetric decorrelation accommodates the management of correlated signals. Experimentation shows that the system can produce up to an 85% reduction in wearable sensor error for accelerations up to 2G.

Adaptive left ventricular ejection time estimation using multiple peripheral pressure waveforms.

An adaptive approach is proposed for the problem of left ventricular ejection time (LVET) estimation using peripheral pressure waveform signals. The proposed algorithm, which makes use of 2 peripheral pressure measurements, makes it possible to adaptively estimate the LVET in response to different cardiovascular physiologic states. The algorithm builds on features obtained from global and branch-specific characterization of the cardiovascular circulation as well as waveform features to dramatically improve the accuracy of LVET estimation. The performance of the proposed approach is evaluated with respect to its heart-rate-based conventional counterpart, which shows approximately 40% improvement of estimation accuracy in terms of R2values □ from 0.6655 for the conventional waveform-based approach to 0.9222 for the proposed approach.

Active noise cancellation using MEMS accelerometers for motion-tolerant wearable bio-sensors.

An active noise cancellation method using a MEMS accelerometer is developed for recovering corrupted wearable sensor signals due to body motion. The method is developed for a finger ring PPG sensor, the signal of which is susceptive to the hand motion of the wearer. A MEMS accelerometer (ACC) imbedded in the PPG sensor detects the hand acceleration, and is used for recovering the corrupted PPG signal. The correlation between the acceleration and the distorted PPG signal is analyzed, and a low-order FIR model relating the signal distortion to the hand acceleration is obtained. The model parameters are identified in real time with a recursive least square method. Experiments show that the active noise cancellation method can recover ring PPG sensor signals corrupted with 2G of acceleration in the longitudinal direction of the digital artery.