Unlocking cardiac motion: assessing software and machine learning for single-cell and cardioid kinematic insights.

The heart coordinates its functional parameters for optimal beat-to-beat mechanical activity. Reliable detection and quantification of these parameters still represent a hot topic in cardiovascular research. Nowadays, computer vision allows the development of open-source algorithms to measure cellular kinematics. However, the analysis software can vary based on analyzed specimens. In this study, we compared different software performances in in-silico model, in-vitro mouse adult ventricular cardiomyocytes and cardioids. We acquired in-vitro high-resolution videos during suprathreshold stimulation at 0.5-1-2 Hz, adapting the protocol for the cardioids. Moreover, we exposed the samples to inotropic and depolarizing substances. We analyzed in-silico and in-vitro videos by (i) MUSCLEMOTION, the gold standard among open-source software; (ii) CONTRACTIONWAVE, a recently developed tracking software; and (iii) ViKiE, an in-house customized video kinematic evaluation software. We enriched the study with three machine-learning algorithms to test the robustness of the motion-tracking approaches. Our results revealed that all software produced comparable estimations of cardiac mechanical parameters. For instance, in cardioids, beat duration measurements at 0.5 Hz were 1053.58 ms (MUSCLEMOTION), 1043.59 ms (CONTRACTIONWAVE), and 937.11 ms (ViKiE). ViKiE exhibited higher sensitivity in exposed samples due to its localized kinematic analysis, while MUSCLEMOTION and CONTRACTIONWAVE offered temporal correlation, combining global assessment with time-efficient analysis. Finally, machine learning reveals greater accuracy when trained with MUSCLEMOTION dataset in comparison with the other software (accuracy > 83%). In conclusion, our findings provide valuable insights for the accurate selection and integration of software tools into the kinematic analysis pipeline, tailored to the experimental protocol.

Bioengineering a cryogel-derived bioartificial liver using particle image velocimetry defined fluid dynamics.

Bioartificial Liver (BAL) devices are extracorporeal systems designed to support or recover hepatic function in patients with liver failure. The design of an effective BAL remains an open challenge since it requires a complex co-optimisation of cell colonisation, biomaterial scaffold and BAL fluid dynamics. Building on previous evidence of suitability as a blood perfusion device for detoxification, the current study investigated the use of RGD-containing p(HEMA)-alginate cryogels as BAL scaffolds. Cryogels were modified with alginate to reduce protein fouling and functionalised with an RGD-containing peptide to increase hepatocyte adhesion. A novel approach for characterisation of the internal flow through the porous matrix was developed by employing Particle Image Velocimetry (PIV) to visualise flow inside cryogels. Based on PIV results, which showed the laminar nature of flow inside cryogel pores, a multi-layered bioreactor composed of spaced cryogel discs was designed to improve blood/hepatocyte mass exchange. The stacked bioreactor showed a significantly higher production of albumin and urea compared to the column version, with improved cell colonisation and proliferation over time. The cell-free cryogel-based device was tested for safety in a bile-duct ligation model of liver cirrhosis. Thus, a stacked bioreactor prototype was developed based on a surface-engineered cryogel design with optimised fluid dynamics for BAL use.

Catch-and-Release of Target Cells Using Aptamer-Conjugated Electroactive Zwitterionic Oligopeptide SAM.

Nucleic acid aptamers possess attractive features such as specific molecular recognition, high-affinity binding, and rapid acquisition and replication, which could be feasible components for separating specific cells from other cell types. This study demonstrates that aptamers conjugated to an oligopeptide self-assembled monolayer (SAM) can be used to selectively trap human hepatic cancer cells from cell mixtures containing normal human hepatocytes or human fibroblasts. Molecular dynamics calculations have been performed to understand how the configurations of the aptamers are related to the experimental results of selective cell capture. We further demonstrate that the captured hepatic cancer cells can be detached and collected along with electrochemical desorption of the oligopeptide SAM, and by repeating these catch-and-release processes, target cells can be enriched. This combination of capture with aptamers and detachment with electrochemical reactions is a promising tool in various research fields ranging from basic cancer research to tissue engineering applications.