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Abstract One of the most fundamental characteristics of a biomaterial tailored for bone repair and regeneration is its ability to promote bone regeneration and healing of large defects. This work reports producing a functionalized and hieratically porous bone scaffold that significantly supports cell adhesion and proliferation by providing bone mimicry structure and controlled release of protein. The Slit Guidance Ligand 3 (SLIT3) protein was previously tested to promote bone formation and control the resorption process in natural bone healing. In this study, our goal was to design a nanocomposite bone scaffold to be functionalized with SLIT3 protein and then evaluate the uptake and release profile from surface into culture media to support bone marrow-derived mesenchymal stem cells (MSC) 3D culture. Indirect 3D printing of a polylactic-co-glycolic acid (PLGA), hydroxyapatite nanoparticles, and polydopamine coated (PLGA-HANPs-PDA) was utilized to obtain a hierarchically porous and SLIT3 protein-releasing scaffold. The produced scaffold was evaluated and optimized using chemical, architectural, mechanical, and biological characterization techniques. Optimal physicochemical properties resulted in a unique microstructure with an average pore size of 178.06 ± 45 µm, 63% porosity, and stable and homogenous chemical composition. Mechanical testing demonstrated a compression strength up to 1.5 MPa at 75% strain, with a compression modulus of 0.58 ± 0.05 MPa. Preliminary biological experiments showed that the scaffold exhibited gradual SLIT3 protein release, biodegradability, and reliable biocompatibility for MSC cell culture. Finally, we showed for first time the bioactivity of SLIT3 protein within PLGA-HANPs-PDA scaffold to promote attachment and growth of mesenchymal stem cell (MSCs) seeded in bone mimicry scaffold matrix. The collected findings will serve as a bedrock for thorough and targeted in vitro studies to evaluate anticipated osteogenesis the MSCs. 

Abstract The objective of this study is to investigate the rolling dynamics of leukocytes in microchannel flows using a hybrid continuum-particle approach. Leukocytes play an essential role in the immune system, and their margination behavior has been extensively studied both experimentally and numerically. In this study, we have developed a series of numerical experiments using a hybrid DPD-CFD solver with the membrane stiffness of the modeled leukocytes as the primary investigation subject. Our results show that increasing the stiffness of the cell's membrane influences its deformability and trajectory in microchannel flows. The results obtained from this study could be valuable in designing next-generation micro-carriers for targeted drug delivery systems, which mimic the margination behavior of leukocytes. 

Abstract This work presents the development of a novel approach to model cancer cell dynamics in microcirculation. The proposed numerical model is based on a hybrid continuum-particle approach. The cancer cell model includes the cell membrane, nucleus, cytoplasm and the cytoskeleton. The Dissipative Particle Dynamics method was employed to simulate the mechanical components. The blood plasma is modeled as a Newtonian incompressible fluid. A Fluid-Structure Interaction coupling, leveraging the Immersed Boundary Method is developed to simulate the cell's response to flow dynamics. The model is applied to resolve the transport of cancer cells with realistic morphologies in microcirculatory flows. Our results suggest that the controlling of oscillatory flows can be utilized to induce specific morphological shapes and the surrounding fluid patterns, allowing full manipulation and control of the cell. Furthermore, the intracellular and extracellular dynamics response of the cancer cell is intrinsically linked to their shape, in which certain morphologies displayed strong resistance to the fluid-induced forces and the ability to migrate in various directions. Our computational framework provides new capabilities for designing bioengineering devices for cell manipulation and separation. 

Abstract The Lyme disease spirocheteBorrelia burgdorfericauses an infection with diverse clinical outcomes, which can include arthritis as well as cardiac and neurological manifestations.B. burgdorferiexpresses different outer surface lipoproteins at different stages in its infectious cycle, many of which are adhesins. Utilizing atomic force microscopy (AFM), we obtained topography images and force–distance curves of wild-typeB. burgdorferiand mutant strains encoding different complements of surface lipoproteins. AFM data show that a reduced number of genome-encoded lipoproteins correlates with decreased binding probability, weakens unbinding force, and negatively affects cell elasticity. 

Abstract Beyond the most common oncogenes activated by mutation (mut-drivers), there likely exists a variety of low-frequency mut-drivers, each of which is a possible frontier for targeted therapy. To identify new and understudied mut-drivers, we developed a machine learning (ML) model that integrates curated clinical cancer data and posttranslational modification (PTM) proteomics databases. We applied the approach to 62,746 patient cancers spanning 84 cancer types and predicted 3,964 oncogenic mutations across 1,148 genes, many of which disrupt PTMs of known and unknown function. The list of putative mut-drivers includes established drivers and others with poorly understood roles in cancer. This ML model is available as a web application. As a case study, we focused the approach on nonreceptor tyrosine kinases (NRTK) and found a recurrent mutation in activated CDC42 kinase-1 (ACK1) that disrupts the Mig6 homology region (MHR) and ubiquitin-association (UBA) domains on the ACK1 C-terminus. By studying these domains in cultured cells, we found that disruption of the MHR domain helps activate the kinase while disruption of the UBA increases kinase stability by blocking its lysosomal degradation. This ACK1 mutation is analogous to lymphoma-associated mutations in its sister kinase, TNK1, which also disrupt a C-terminal inhibitory motif and UBA domain. This study establishes a mut-driver discovery tool for the research community and identifies a mechanism of ACK1 hyperactivation shared among ACK family kinases. Implications:This research identifies a potentially targetable activating mutation in ACK1 and other possible oncogenic mutations, including PTM-disrupting mutations, for further study. 

Abstract We performed the transport of a breast cancer cell (MB231-TGFb) in a microvessel using high-resolution simulations. Using open-source imaging software Slicer3D and Meshmixer, the 3D surface mesh forming the cell membrane was reconstructed from confocal microscopic images. The Dissipative Particle Dynamics method is used to model the cell membrane. The extracellular fluid flow is modeled with the Immersed Boundary Method to solve the governing equations of the blood plasma. The unsteady flow is applied at the inlet of the microchannel with an oscillatory pattern. Our results showed that the extracellular flow patterns are highly dependent on the waveform profile. The oscillatory flow showed the creation of vortices that influence the cellular deformations in the microchannel. These results could have implications on the destination of the cancer cells during transport in physiological flows. 

Abstract Prostate cancer bone metastasis is the leading cause of cancer-related mortality in men in the United States, causing severe damage to skeletal tissue. The treatment of advanced-stage prostate cancer is always challenging due to limited drug treatment options, resulting in low survival rates. There is a scarcity of knowledge regarding the mechanisms associated with the effects of biomechanical cues by the interstitial fluid flow on prostate cancer cell growth and migration. We have designed a novel bioreactor system to demonstrate the impact of interstitial fluid flow on the migration of prostate cancer cells to the bone during extravasation. First, we demonstrated that a high flow rate induces apoptosis in PC3 cells via TGF-β1 mediated signaling; thus, physiological flow rate conditions are optimum for cell growth. Next, to understand the role of interstitial fluid flow in prostate cancer migration, we evaluated the migration rate of cells under static and dynamic conditions in the presence or absence of bone. We report that CXCR4 levels were not significantly changed under static and dynamic conditions, indicating that CXCR4 activation in PC3 cells is not influenced by flow conditions but by the bone, where CXCR4 levels were upregulated. The bone-upregulated CXCR4 levels led to increased MMP-9 levels resulting in a high migration rate in the presence of bone. In addition, upregulated levels ofα_vβ₃integrins under fluid flow conditions contributed to an overall increase in the migration rate of PC3 cells. Overall, this study demonstrates the potential role of interstitial fluid flow in prostate cancer invasion. Understanding the critical role of interstitial fluid flow in promoting prostate cancer cell progression will enhance current therapies for advanced-stage prostate cancer and provide improved treatment options for patients. 

Abstract Sensitivity analysis is a popular feature selection approach employed to identify the important features in a dataset. In sensitivity analysis, each input feature is perturbed one-at-a-time and the response of the machine learning model is examined to determine the feature's rank. Note that the existing perturbation techniques may lead to inaccurate feature ranking due to their sensitivity to perturbation parameters. This study proposes a novel approach that involves the perturbation of input features using a complex-step. The implementation of complex-step perturbation in the framework of deep neural networks as a feature selection method is provided in this paper, and its efficacy in determining important features for real-world datasets is demonstrated. Furthermore, the filter-based feature selection methods are employed, and the results obtained from the proposed method are compared. While the results obtained for the classification task indicated that the proposed method outperformed other feature ranking methods, in the case of the regression task, it was found to perform more or less similar to that of other feature ranking methods. 

Abstract Evaluating the exact first derivative of a feedforward neural network (FFNN) output with respect to the input feature is pivotal for performing the sensitivity analysis of the trained neural network with respect to the inputs. In this paper, a novel method is presented that computes the analytical quality first derivative of a trained feedforward neural network output with respect to the input features without the need for backpropagation. To this end, the complex step derivative approximation is illustrated, and its implementation in the framework of the feedforward neural network is described. Artificial datasets are generated, and the efficacy of the proposed method for both regression and classification tasks is demonstrated. The results obtained for the regression task indicated that the proposed method is capable of obtaining analytical quality derivatives, and in the case of the classification task, the least relevant features could be identified. 

Abstract The morphological stability of an organic photovoltaic (OPV) device is greatly affected by the dynamics of donors and acceptors occurring near the device's operational temperature. These dynamics can be quantified by the glass transition temperature (T_g) of conjugated polymers (CPs). Because flexible side chains possess much faster dynamics, the cleavage of the alkyl side chains will reduce chain dynamics, leading to a higherT_g. In this work, theT_gs for CPs are systematically studied with controlled side chain cleavage. Isothermal annealing of polythiophenes featuring thermally cleavable side chains at 140 °C, is found to remove more than 95% of alkyl side chains in 24 h, and raise the backboneT_gfrom 23 to 75 °C. Coarse grain molecular dynamics simulations are used to understand theT_gdependence on side chain cleavage. X‐ray scattering indicates that the relative degree of crystallization remains constantduring isothermal annealing process. The effective conjugation length is not influenced by thermal cleavage; however, the density of chromophore is doubled after the complete removal of alkyl side chains. The combined effect of enhancingT_gand conserving crystalline structures during the thermal cleavage process can provide a pathway to improving the stability of optoelectronic properties in future OPV devices.