Abstract Deep learning (DL) shows notable success in biomedical studies. However, most DL algorithms work as a black box, exclude biomedical experts, and need extensive data. We introduce the Self-Enhancing Multi-Photon Artificial Intelligence (SEMPAI), that integrates hypothesis-driven priors in a data-driven DL approach for research on multiphoton microscopy (MPM) of muscle fibers. SEMPAI utilizes meta-learning to optimize prior integration, data representation, and neural network architecture simultaneously. This allows hypothesis testing and provides interpretable feedback about the origin of biological information in MPM images. SEMPAI performs joint learning of several tasks to enable prediction for small datasets. The method is applied on an extensive multi-study dataset resulting in the largest joint analysis of pathologies and function for single muscle fibers. SEMPAI outperforms state-of-the-art biomarkers in six of seven predictive tasks, including those with scarce data. SEMPAI’s DL models with integrated priors are superior to those without priors and to prior-only machine learning approaches.
Electroactive hydrogels can be used to influence cell response and maturation by electrical stimulation. However, hydrogel formulations which are 3D printable, electroactive, cytocompatible, and allow cell adhesion, remain a challenge in the design of such stimuli-responsive biomaterials for tissue engineering. Here, a combination of pyrrole with a high gelatin-content oxidized alginate-gelatin (ADA-GEL) hydrogel is reported, offering 3D-printability of hydrogel precursors to prepare cytocompatible and electrically conductive hydrogel scaffolds. By oxidation of pyrrole, electroactive polypyrrole:polystyrenesulfonate (PPy:PSS) is synthesized inside the ADA-GEL matrix. The hydrogels are assessed regarding their electrical/mechanical properties, 3D-printability, and cytocompatibility. It is possible to prepare open-porous scaffolds via bioplotting which are electrically conductive and have a higher cell seeding efficiency in scaffold depth in comparison to flat 2D hydrogels, which is confirmed via multiphoton fluorescence microscopy. The formation of an interpenetrating polypyrrole matrix in the hydrogel matrix increases the conductivity and stiffness of the hydrogels, maintaining the capacity of the gels to promote cell adhesion and proliferation. The results demonstrate that a 3D-printable ADA-GEL can be rendered conductive (ADA-GEL-PPy:PSS), and that such hydrogel formulations have promise for cell therapies, in vitro cell culture, and electrical-stimulation assisted tissue engineering.
Electrical stimulation of mammalian cells in vitro, including stem or neuronal cells and myocytes, has been a widely established method for assessing physiological or pathophysiological mechanisms in a variety of fields of research. Conventional infrastructure used in in vitro electrical stimulation experiments is typically proprietary, noncustomizable, costly, and requires a high level of skill to use and maintain. The emergence of rapid prototyping technology makes it possible to quickly engineer alternatives to conventional stimulation infrastructure that are open source, customizable, low cost, and user friendly. In this study, we describe the rapid prototype of a three-dimensional (3D)-printed reusable growth chamber with integrated electrodes for electrical stimulation and parallel microscopic evaluation of cultured cells that can easily be reconstructed within a few hours using 3D desktop printing and off-the-shelf components. The chamber is light weight (∼8 g), small (76 × 26 × 6 mm), and extremely low cost (
Deep learning (DL) shows notable success in biomedical studies. However, most DL algorithms work as black boxes, exclude biomedical experts, and need extensive data. This is especially problematic for fundamental research in the laboratory, where often only small and sparse data are available and the objective is knowledge discovery rather than automation. Furthermore, basic research is usually hypothesis-driven and extensive prior knowledge (priors) exists. To address this, the Self-Enhancing Multi-Photon Artificial Intelligence (SEMPAI) that is designed for multiphoton microscopy (MPM)-based laboratory research is presented. It utilizes meta-learning to optimize prior (and hypothesis) integration, data representation, and neural network architecture simultaneously. By this, the method allows hypothesis testing with DL and provides interpretable feedback about the origin of biological information in 3D images. SEMPAI performs multi-task learning of several related tasks to enable prediction for small datasets. SEMPAI is applied on an extensive MPM database of single muscle fibers from a decade of experiments, resulting in the largest joint analysis of pathologies and function for single muscle fibers to date. It outperforms state-of-the-art biomarkers in six of seven prediction tasks, including those with scarce data. SEMPAI's DL models with integrated priors are superior to those without priors and to prior-only approaches.
The mechanical properties of hydrogels, as well as native and engineered tissues are key parameters frequently assessed in biomaterial science and tissue engineering research. However, a lack of standardized methods and user-independent data analysis has impacted the research community for many decades and contributes to poor reproducibility and comparability of datasets, representing a significant issue often neglected in publications. In this study, we provide a software package, MechAnalyze, facilitating the standardized and automated analysis of force-displacement data generated in unconfined compression tests. Using comparative studies of datasets analyzed manually and with MechAnalyze, we demonstrate that the software reliably determines the compressive moduli, failure stress and failure strain of hydrogels, as well as engineered and native tissues, while providing an intuitive user interface that requires minimal user input. MechAnalyze provides a fast and user-independent data analysis method and advances process standardization, reproducibility, and comparability of data for the mechanical characterization of biomaterials as well as native and engineered tissues. Mechanical properties of hydrogels are crucial parameters in the development of new materials for tissue engineering. However, manual assessment is tedious, not standardized and suffers under user-to-user bias. Hence, the here presented stand-alone software package provides analysis and statistics of force–displacement and material geometry data to determine the compressive moduli, failure stress, and failure strain in a standardized, robust, and automated fashion. MechAnalyze will substantially support biomechanical testing of hydrogels as well as engineered and native tissues and will thus, be of appreciable value to a broad target group in regenerative medicine, tissue engineering, but also life sciences and biomedicine.
Biofabrication can be a tool to three-dimensionally (3D) print muscle cells embedded inside hydrogel biomaterials, ultimately aiming to mimic the complexity of the native muscle tissue and to create in-vitro muscle analogues for advanced repair therapies and drug testing. However, to 3D print muscle analogues of high cell alignment and synchronous contraction, the effect of biofabrication process parameters on myoblast growth has to be understood. A suitable biomaterial matrix is required to provide 3D printability as well as matrix degradation to create space for cell proliferation, matrix remodelling capacity, and cell differentiation. We demonstrate that by the proper selection of nozzle size and extrusion pressure, the shear stress during extrusion-bioprinting of mouse myoblast cells (C2C12) can achieve cell orientation when using oxidized alginate-gelatin (ADA-GEL) hydrogel bionk. The cells grow in the direction of printing, migrate to the hydrogel surface over time, and differentiate into ordered myotube segments in areas of high cell density. Together, our results show that ADA-GEL hydrogel can be a simple and cost-efficient biodegradable bioink that allows the successful 3D bioprinting and cultivation of C2C12 cells in-vitro to study muscle engineering.
Capsular contracture remains a challenge in plastic surgery and represents one of the most common postoperative complications following alloplastic breast reconstruction. The impact of the surface structure of silicone implants on the foreign body reaction and the behaviour of connective tissue-producing cells has already been discussed. The aim of this study was to investigate different pore sizes of silicone surfaces and their influence on human fibroblasts in an in vitro model. Four different textures (no, fine, medium and coarse texture) produced with the salt-loss technique, have been assessed in an in vitro model. Human fibroblasts were seeded onto silicone sheets and evaluated after 1, 4 and 7 days microscopically, with viability assay and gene expression analysis. Comparing the growth behaviour and adhesion of the fibroblasts on the four different textures, a dense cell layer, good adhesion and bridge-building ability of the cells could be observed for the fine and medium texture. Cell number and viability of the cells were increasing during the time course of experiments on every texture. TGFß1 was lowest expressed on the fine and medium texture indicating a trend for decreased fibrotic activity. For silicone surfaces produced with the salt-loss technique, we were able to show an antifibrotic effect of smaller sized pores. These findings underline the hypothesis of a key role of the implant surface and the pore size and pore structure in preventing capsular contracture.
Abstract Two‐dimensional (2D) cancer models have been the standard for drug development over the past few years, but they frequently do not resemble in vivo properties adequately. 3D models are superior in many aspects and are, therefore, more similar to human pathophysiology. Over the past years, the emerging field of biofabrication has made significant advances, resulting in even more sophisticated 3D models. With this study, a hydrogel is created for biofabrication that is suitable for mimicking the tumor microenvironment in vitro and is further tested as a new vascularized melanoma model in vivo. The alginate/hyaluronic acid/gelatin bioink shows good shape‐fidelity, high cell survival rates, and enables successful cultivation of melanoma cells and adipose‐derived stem cells as well as cell differentiation in vitro. In vivo, in the arteriovenous loop model, it proves to be a unique method to study melanoma progression, tumor vascularization, and ultimately and reliably metastases in an isolated and controlled environment. These results show that this 3D model is very application‐oriented for molecular research and therapy development.
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