Cryopreservation of 3D Bioprinted Scaffolds with Temperature-Controlled-Cryoprinting.

Temperature-Controlled-Cryoprinting (TCC) is a new 3D bioprinting technology that allows for the fabrication and cryopreservation of complex and large cell-laden scaffolds. During TCC, bioink is deposited on a freezing plate that descends further into a cooling bath, keeping the temperature at the nozzle constant. To demonstrate the effectiveness of TCC, we used it to fabricate and cryopreserve cell-laden 3D alginate-based scaffolds with high cell viability and no size limitations. Our results show that Vero cells in a 3D TCC bioprinted scaffold can survive cryopreservation with a viability of 71%, and cell viability does not decrease as higher layers are printed. In contrast, previous methods had either low cell viability or decreasing efficacy for tall or thick scaffolds. We used an optimal temperature profile for freezing during 3D printing using the two-step interrupted cryopreservation method and evaluated drops in cell viability during the various stages of TCC. Our findings suggest that TCC has significant potential for advancing 3D cell culture and tissue engineering.

Computational Fontan Analysis: Preserving Accuracy While Expediting Workflow.

Background: Postoperative outcomes of the Fontan operation have been linked to geometry of the cavopulmonary pathway, including graft shape after implantation. Computational fluid dynamics (CFD) simulations are used to explore different surgical options. The objective of this study is to perform a systematic in vitro validation for investigating the accuracy and efficiency of CFD simulation to predict Fontan hemodynamics. Methods: CFD simulations were performed to measure indexed power loss (iPL) and hepatic flow distribution (HFD) in 10 patient-specific Fontan models, with varying mesh and numerical solvers. The results were compared with a novel in vitro flow loop setup with 3D printed Fontan models. A high-resolution differential pressure sensor was used to measure the pressure drop for validating iPL predictions. Microparticles with particle filtering system were used to measure HFD. The computational time was measured for a representative Fontan model with different mesh sizes and numerical solvers. Results: When compared to in vitro setup, variations in CFD mesh sizes had significant effect on HFD (P = .0002) but no significant impact on iPL (P = .069). Numerical solvers had no significant impact in both iPL (P = .50) and HFD (P = .55). A transient solver with 0.5 mm mesh size requires computational time 100 times more than a steady solver with 2.5 mm mesh size to generate similar results. Conclusions: The predictive value of CFD for Fontan planning can be validated against an in vitro flow loop. The prediction accuracy can be affected by the mesh size, model shape complexity, and flow competition.

Non-invasive Prediction of Peak Systolic Pressure Drop across Coarctation of Aorta using Computational Fluid Dynamics.

This paper proposes a novel method to noninvasively measure the peak systolic pressure difference (PSPD) across coarctation of the aorta for diagnosing the severity of coarctation. Traditional non-invasive estimates of pressure drop from the ultrasound can underestimate the severity and invasive measurements by cardiac catheterization can carry risks for patients. To address the issues, we employ computational fluid dynamics (CFD) computation to accurately predict the PSPD across a coarctation based on cardiac magnetic resonance (CMR) imaging data and cuff pressure measurements from one arm. The boundary conditions of a patient-specific aorta model are specified at the inlet of the ascending aorta by using the time-dependent blood velocity, and the outlets of descending aorta and supra aortic branches by using a 3-element Windkessel model. To estimate the parameters of the Windkessel model, steady flow simulations were performed using the time-averaged flow rates in the ascending aorta, descending aorta, and two of the three supra aortic branches. The mean cuff pressure from one arm was specified at the outlet of one of the supra aortic branches. The CFD predicted PSPDs of 5 patients (n=5) were compared with the invasively measured pressure drops obtained by catheterization. The PSPDs were accurately predicted (mean µ=0.3mmHg, standard deviation σ =4.3mmHg) in coarctation of the aorta using completely non-invasive flow and cuff pressure data. The results of our study indicate that the proposed method could potentially replace invasive measurements for estimating the severity of coarctations.Clinical relevance-Peak systolic pressure drop is an indicator of the severity of coarctation of the aorta. It can be predicted without any additional risks to patients using non-invasive cuff pressure and flow data from CMR.

Setting Up All-Atom Molecular Dynamics Simulations to Study the Interactions of Peripheral Membrane Proteins with Model Lipid Bilayers.

All-atom molecular dynamics (MD) simulations enable the study of biological systems at atomic detail, complement the understanding gained from experiment, and can also motivate experimental techniques to further examine a given biological process. This method is based on statistical mechanics; it predicts the trajectory of atoms over time by solving Newton's Laws of motion taking into account all forces. Here, we describe the use of this methodology to study the interaction between peripheral membrane proteins and a lipid bilayer. Specifically, we provide step-by-step instructions to set up MD simulations to study the binding and interaction of the amphipathic helix of Osh4, a lipid transport protein, and Thanatin, an antimicrobial peptide (AMP), with model lipid bilayers using both fully detailed lipid tails and the highly mobile membrane-mimetic (HMMM) method to enhance conformational sampling.

Effect of Membrane Lipid Packing on Stable Binding of the ALPS Peptide.

The amphipathic lipid packing sensor (ALPS) motif, originally discovered on the ArfGAP1 membrane-binding protein, binds to pre-existing large packing defects in a membrane (spontaneous or due to membrane curvature), though a more precise relationship between the ALPS peptide and packing defect characteristics of a membrane remains unclear. We developed an image processing technique for identifying packing defects to quantify the relationship between the ALPS peptide of the Osh4 protein in yeast and packing defects on a membrane model using molecular dynamics simulations. We used the highly mobile membrane mimetic (HMMM) model to create very large packing defects and expedite the binding time scale. Most prominently, we show that the probability of the ALPS peptide moving toward the membrane increases when it is near a large packing defect. Deviations from this trend exist for very large packing defects (≳115 Å2), which we propose is due to an overwhelming hydrophobic effect and a reduced electrostatic effect when large portions of the nonpolar core are exposed and the peptide is oriented unfavorably. Furthermore, we compared our HMMM results to similar simulations using all-atom lipid membranes. The binding time scales of the ALPS peptide can be reduced by roughly 1 order of magnitude when HMMM is used, while still maintaining many of the important physical characteristics of the binding process observed when using an all-atom lipid membrane.