A hyperelastic model for simulating cells in flow

In the emerging field of 3D bioprinting, cell damage due to large deformations is considered a main cause for cell death and loss of functionality inside the printed construct. Those deformations, in turn, strongly depend on the mechano-elastic response of the cell to the hydrodynamic stresses experienced during printing. In this work, we present a numerical model to simulate the deformation of biological cells in arbitrary three-dimensional flows. We consider cells as an elastic continuum according to the hyperelastic Mooney-Rivlin model. We then employ force calculations on a tetrahedralized volume mesh. To calibrate our model, we perform a series of FluidFM(R) compression experiments with REF52 cells demonstrating that all three parameters of the Mooney-Rivlin model are required for a good description of the experimental data at very large deformations up to 80%. In addition, we validate the model by comparing to previous AFM experiments on bovine endothelial cells and artificial hydrogel particles. To investigate cell deformation in flow, we incorporate our model into Lattice Boltzmann simulations via an Immersed-Boundary algorithm. In linear shear flows, our model shows excellent agreement with analytical calculations and previous simulation data.Comment: 15 pages, 9 figures, Supplementary information included. Unfortunately, the journal version misses an important contributor. The correct author list is the one given in this document. Biomech Model Mechanobiol (2020

Flow and hydrodynamic shear stress inside a printing needle during biofabrication

We present a simple but accurate algorithm to calculate the flow and shear rate profile of shear thinning fluids, as typically used in biofabrication applications, with an arbitrary viscosity-shear rate relationship in a cylindrical nozzle. By interpolating the viscosity with a set of power-law functions, we obtain a mathematically exact piecewise solution to the incompressible Navier-Stokes equation. The algorithm is validated with known solutions for a simplified Carreau-Yasuda fluid, full numerical simulations for a realistic chitosan hydrogel as well as experimental velocity profiles of alginate and chitosan solutions in a microfluidic channel. We implement the algorithm in an easy-to-use Python tool, included as Supplementary Material, to calculate the velocity and shear rate profile during the printing process, depending on the shear thinning behavior of the bioink and printing parameters such as pressure and nozzle size. We confirm that the shear stress varies in an exactly linear fashion, starting from zero at the nozzle center to the maximum shear stress at the wall, independent of the shear thinning properties of the bioink. Finally, we demonstrate how our method can be inverted to obtain rheological bioink parameters in-situ directly before or even during printing from experimentally measured flow rate versus pressure data

Sixty years of placebo-controlled antipsychotic drug trials in acute schizophrenia: Systematic review, Bayesian meta-analysis, and meta-regression of efficacy predictors.

Objective Antipsychotic drug efficacy may have decreased over the decades. We, therefore, present a comprehensive meta-analysis of all placebo-controlled trials in acute schizophrenia, we investigate which trial characteristics have changed over the years and which ones are moderators of drug-placebo efficacy differences. Method We searched multiple electronic databases, ClinicalTrials.gov and the FDA website. The outcomes were overall efficacy (primary outcome), responder rates, drop-out rates, positive, negative and depressive symptoms, quality of life, functioning, and major side-effects. Multiple potential moderators of overall efficacy were analyzed by meta-regression. Results 167 double-blind randomized controlled trials with 28102 participants were included. The standardized mean difference (SMD) for overall efficacy was 0.47 (95% CrI 0.42,0.51), but accounting for small trial effects/publication bias reduced the SMD to 0.38. 51% in the antipsychotic group versus 30% in placebo had at least a ‘minimal’ response, and 23% versus 14% had a ‘good’ response. Positive symptoms improved more than negative symptoms and depression. There were also small-to medium sized improvements in quality of life and functioning (SMDs 0.35 and 0.34). In the analysis of response predictors, 17 of 26 trial characteristics analyzed changed over the decades. But in a multivariable meta-regression, only industry-sponsorship and increasing placebo response were significant moderators of effect sizes. Importantly, drug response remained stable over time. Conclusions Approximately two times more patients improved under antipsychotics compared to placebo, but only a minority experienced a good response. Industry sponsorship reduced, rather than increased effect sizes. The decrease of effect sizes over the years was caused by increasing placebo response, not by decreasing drug response. Meta-analyses need to take this confounder into account. In drug development, somewhat smaller sample sizes but better selected patients may overcome a possible vicious circle of increasing sample sizes, more variability and smaller effect sizes.</p