Phenotypic Screen with the Human Secretome Identifies FGF16 as Inducing Proliferation of iPSC-Derived Cardiac Progenitor Cells.

Paracrine factors can induce cardiac regeneration and repair post myocardial infarction by stimulating proliferation of cardiac cells and inducing the anti-fibrotic, antiapoptotic, and immunomodulatory effects of angiogenesis. Here, we screened a human secretome library, consisting of 923 growth factors, cytokines, and proteins with unknown function, in a phenotypic screen with human cardiac progenitor cells. The primary readout in the screen was proliferation measured by nuclear count. From this screen, we identified FGF1, FGF4, FGF9, FGF16, FGF18, and seven additional proteins that induce proliferation of cardiac progenitor cells. FGF9 and FGF16 belong to the same FGF subfamily, share high sequence identity, and are described to have similar receptor preferences. Interestingly, FGF16 was shown to be specific for proliferation of cardiac progenitor cells, whereas FGF9 also proliferated human cardiac fibroblasts. Biosensor analysis of receptor preferences and quantification of receptor abundances suggested that FGF16 and FGF9 bind to different FGF receptors on the cardiac progenitor cells and cardiac fibroblasts. FGF16 also proliferated naïve cardiac progenitor cells isolated from mouse heart and human cardiomyocytes derived from induced pluripotent cells. Taken together, the data suggest that FGF16 could be a suitable paracrine factor to induce cardiac regeneration and repair.

Fully Nonlinear ${SP}_{3}$ Approximation Based Fluorescence Optical Tomography.

In fluorescence optical tomography, many works in the literature focus on the linear reconstruction problem to obtain the fluorescent yield or the linearized reconstruction problem to obtain the absorption coefficient. The nonlinear reconstruction problem, to reconstruct the fluorophore absorption coefficient, is of interest in imaging studies as it presents the possibility of better reconstructions owing to a more appropriate model. Accurate and computationally efficient forward models are also critical in the reconstruction process. The approximation to the radiative transfer equation (RTE) is gaining importance for tomographic reconstructions owing to its computational advantages over the full RTE while being more accurate and applicable than the commonly used diffusion approximation. This paper presents Gauss-Newton-based fully nonlinear reconstruction for the approximated fluorescence optical tomography problem with respect to shape as well as the conventional finite-element method-based representations. The contribution of this paper is the Frechet derivative calculations for this problem and demonstration of reconstructions in both representations. For the shape reconstructions, radial-basis-function represented level-set-based shape representations are used. We present reconstructions for tumor-mimicking test objects in scattering and absorption dominant settings, respectively, for moderately noisy data sets in order to demonstrate the viability of the formulation. Comparisons are presented between the nonlinear and linearized reconstruction schemes in an element wise setting to illustrate the benefits of using the former especially for absorption dominant media.

Radial-basis-function level-set-based regularized Gauss-Newton-filter reconstruction scheme for dynamic shape tomography.

The dynamic reconstruction problem in tomographic imaging is encountered in several applications, such as species determination, the study of blood flow through arteries/veins, motion compensation in medical imaging, and process tomography. The reconstruction method of choice is the Kalman filter and its variants, which, however, are faced by issues of filter tuning. In addition, since the time-propagation models of physical parameters are typically very complex, most of the time, a random walk model is considered. For geometric deformations, affine models are typically used. In our work, with the objectives of minimizing tuning issues and reconstructing time-varying geometrically deforming features of interest with affine in addition to pointwise-normal scaling motions, a novel level-set-based reconstruction scheme for ray tomography is proposed for shape and electromagnetic parameters using a regularized Gauss-Newton-filter-based scheme. We use an implicit Hermite-interpolation-based radial basis function representation of the zero level set corresponding to the boundary curve. Another important contribution of the paper is an evaluation of the shape-related Frechet derivatives that does not need to evaluate the pointwise Jacobian (the ray-path matrix in our ray-tomography problem). Numerical results validating the formulation are presented for a straight ray-based tomographic reconstruction. To the best of our knowledge, this paper presents the first tomographic reconstruction results in these settings.