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Abstract
The Foreign body response (FBR) is a major unresolved challenge that compromises medical implant integration and function by inflammation and fibrotic encapsulation. Mice implanted with polymeric scaffolds coupled to intravital non-linear multiphoton microscopy acquisition enable multiparametric, longitudinal investigation of the FBR evolution and interference strategies. However, follow-up analyses based on visual localization and manual segmentation are extremely time-consuming, subject to human error, and do not allow for automated parameter extraction. We developed an integrated computational pipeline based on an innovative and versatile variant of the U-Net neural network to segment and quantify cellular and extracellular structures of interest, which is maintained across different objectives without impairing accuracy. This software for automatically detecting the elements of the FBR shows promise to unravel the complexity of this pathophysiological process.

Abstract
The foreign body response (FBR) is a clinically relevant issue that can cause malfunction of implanted medical devices by fibrotic encapsulation. Whereas inflammatory aspects of the FBR have been established, underlying fibroblast-dependent mechanisms remain unclear. We here combine multiphoton microscopy with ad hoc reporter mice expressing α–smooth muscle actin (αSMA) protein to determine the locoregional fibroblast dynamics, activation, and fibrotic encapsulation of polymeric materials. Fibroblasts invaded as individual cells and established a multicellular network, which transited to a two-compartment fibrotic response displaying an αSMA cold external capsule and a long-lasting, inner αSMA hot environment. The recruitment of fibroblasts and extent of fibrosis were only incompletely inhibited after depletion of macrophages, implicating coexistence of macrophage-dependent and macrophage-independent mediators. Furthermore, neither altering material type or porosity modulated αSMA+ cell recruitment and distribution. This identifies fibroblast activation and network formation toward a two-compartment FBR as a conserved, self-organizing process partially independent of macrophages. Fibroblast recruitment in the foreign body response is a conserved, self-organizing process partially independent of macrophages.

Abstract
The investigation of novel hydrogel systems allows for the study of relationships between biomaterials, cells, and other factors within osteochondral tissue engineering. Three-dimensional (3D) printing is a popular research method that can allow for further interrogation of these questions via the fabrication of 3D hydrogel environments that mimic tissue-specific, complex architectures. However, the adaptation of promising hydrogel biomaterial systems into 3D-printable bioinks remains a challenge. Here, we delineated an approach to that process. First, we characterized a novel methacryloylated gelatin composite hydrogel system and assessed how calcium phosphate and glycosaminoglycan additives upregulated bone- and cartilage-like matrix deposition and certain genetic markers of differentiation within human mesenchymal stem cells (hMSCs), such as RUNX2 and SOX9. Then, new assays were developed and utilized to study the effects of xanthan gum and nanofibrillated cellulose, which allowed for cohesive fiber deposition, reliable droplet formation, and non-fracturing digital light processing (DLP)-printed constructs within extrusion, inkjet, and DLP techniques, respectively. Finally, these bioinks were used to 3D print constructs containing viable encapsulated hMSCs over a 7 d period, where DLP printed constructs facilitated the highest observed increase in cell number over 7 d (∼2.4×). The results presented here describe the promotion of osteochondral phenotypes via these novel composite hydrogel formulations, establish their ability to bioprint viable, cell-encapsulating constructs using three different 3D printing methods on multiple bioprinters, and document how a library of modular bioink additives affected those physicochemical properties important to printability.

Abstract
Metastatic, castration-resistant prostate cancer (mCRPC) is an advanced prostate cancer with limited therapeutic options and poor patient outcomes. To investigate whether multitargeted kinase inhibitors (KIs) represent an opportunity for mCRPC drug development, we applied machine learning{textendash}based functional screening and identified two KIs, PP121 and SC-1, which demonstrated strong suppression of CRPC growth in vitro and in vivo. Furthermore, we show the marked ability of these KIs to improve on standard-of-care chemotherapy in both tumor response and survival, suggesting that combining multitargeted KIs with chemotherapy represents a promising avenue for mCRPC treatment. Overall, our findings demonstrate the application of a multidisciplinary strategy that blends bench science with machine-learning approaches for rapidly identifying KIs that result in desired phenotypic effects.Castration-resistant prostate cancer (CRPC) is an advanced subtype of prostate cancer with limited therapeutic options. Here, we applied a systems-based modeling approach called kinome regularization (KiR) to identify multitargeted kinase inhibitors (KIs) that abrogate CRPC growth. Two predicted KIs, PP121 and SC-1, suppressed CRPC growth in two-dimensional in vitro experiments and in vivo subcutaneous xenografts. An ex vivo bone mimetic environment and in vivo tibia xenografts revealed resistance to these KIs in bone. Combining PP121 or SC-1 with docetaxel, standard-of-care chemotherapy for late-stage CRPC, significantly reduced tibia tumor growth in vivo, decreased growth factor signaling, and vastly extended overall survival, compared to either docetaxel monotherapy. These results highlight the utility of computational modeling in forming physiologically relevant predictions and provide evidence for the role of multitargeted KIs as chemosensitizers for late-stage, metastatic CRPC.All study data are included in the article and/or supporting information.

Abstract
Radium 223 (223Ra) is an α-emitter approved for the treatment of bone metastatic prostate cancer (PCa), which exerts direct cytotoxicity towards PCa cells near the bone interface, whereas cells positioned in the core respond poorly, due to short α-particle penetrance. β1 integrin (β1I) interference has been shown to increase radiosensitivity and significantly enhance external beam radiation efficiency. We hypothesized that targeting β1I would improve 223Ra outcome. We tested the effect of combining 223Ra and anti-β1I antibody treatment in PC3 and C4-2B PCa cell models expressing high and low β1I levels, respectively. In vivo tumor growth was evaluated through bioluminescence. Cellular and molecular determinants of response were analyzed by ex vivo three-dimensional imaging of bone lesions, proteomic analysis and further confirmed by computational modeling and in vitro functional analysis in tissue-engineered bone mimetic systems. Interference with β1I combined with 223Ra reduced PC3 cell growth in bone and significantly improved overall mouse survival, while no change was achieved in C4-2B tumors. Anti-β1I treatment decreased PC3 tumor cell mitosis index and spatially expanded 223Ra lethal effects two-fold, in vivo and in silico. Regression was paralleled by decreased expression of radio-resistance mediators. Targeting β1I significantly improves 223Ra outcome and points towards combinatorial application in PCa tumors with high β1I expression.