Search for: All records

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.

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.

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.

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.

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.

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.

Metastatic cancer in bones is incurable, which causes significant mobility and mortality to the patients. In this work, we investigate the role of interstitial fluid flow on cancer cells' growth within the interconnected pores of human bone. In-vitro experiments were carried out in a bio-reactor which includes bone-like scaffold specimens. A pump is used to maintain a laminar flow condition inside the bioreactor to resemble fluid flow in bones. The scaffold specimens are harvested after 23 days in the bioreactor. The scaffold specimen is scanned with Micro-CT under the resolution of 70 micrometers. We created a full-scale 3D computational model of the scaffold based on the micro-CT data using the open-source software Seg3D and Meshmixer. Based on the geometrical models, we generated the computational grids using the commercial software Gridgen. We performed Computational Fluid Dynamics (CFD) simulations with the immersed boundary method (Gilmanov, Le, Sotiropoulos, JCP 300, 1, 2015) to investigate the flow patterns inside the pores of the scaffolds. The results reveal a non-uniform flow distribution in the vicinity of the scaffold. The flow velocity and the shear stress distributions inside the scaffold are shown to be convoluted and very sensitive to the pore sizes. Our future work will further quantify these distributions and correlate them to cancer cells' growth observed in the experiments.

Donor–acceptor (D–A) type semiconducting polymers have shown great potential for the application of deformable and stretchable electronics in recent decades. However, due to their heterogeneous structure with rigid backbones and long solubilizing side chains, the fundamental understanding of their molecular picture upon mechanical deformation still lacks investigation. Here, the molecular orientation of diketopyrrolopyrrole (DPP)‐based D–A polymer thin films is probed under tensile deformation via both experimental measurements and molecular modeling. The detailed morphological analysis demonstrates highly aligned polymer crystallites upon deformation, while the degree of backbone alignment is limited within the crystalline domain. Besides, the aromatic ring on polymer backbones rotates parallel to the strain direction despite the relatively low overall chain anisotropy. The effect of side‐chain length on the DPP chain alignment is observed to be less noticeable. These observations are distinct from traditional linear‐chain semicrystalline polymers like polyethylene due to distinct characteristics of backbone/side‐chain combination and the crystallographic characteristics in DPP polymers. Furthermore, a stable and isotropic charge carrier mobility is obtained from fabricated organic field‐effect transistors. This study deconvolutes the alignment of different components within the thin‐film microstructure and highlights that crystallite rotation and chain slippage are the primary deformation mechanisms for semiconducting polymers.