1,720,972 research outputs found
On the biomechanical function of scaffolds for engineering load-bearing soft tissues
No full textReplacement or regeneration of load-bearing soft tissues has long been the impetus for the development bioactive materials. While maturing, current efforts continue to be confounded by our lack of understanding of the intricate multi-scale hierarchical arrangements and interactions typically found in native tissues. The current state of the art in biomaterial processing enables a degree of controllable microstructure that can be used for the development of model systems to deduce fundamental biological implications of matrix morphologies on cell function. Furthermore, the development of computational frameworks which allow for the simulation of experimentally derived observations represents a positive departure from what has mostly been an empirically driven field, enabling a deeper understanding of the highly complex biological mechanisms we wish to ultimately emulate. Ongoing research is actively pursuing new materials and processing methods to control material structure down to the micro-scale to sustain or improve cell viability, guide tissue growth, and provide mechanical integrity, all while exhibiting the capacity to degrade in a controlled manner. The purpose of this review is not to focus solely on material processing but to assess the ability of these techniques to produce mechanically sound tissue surrogates, highlight the unique structural characteristics produced in these materials, and discuss how this translates to distinct macroscopic biomechanical behaviors
Micro Scale Based Mechanical Models for Electrospun Poly (Ester Urethane) Urea Scaffolds
No full textMicro scale based mechanical models can provide a tool to guide tissue engineering scaffold design and to investigate on how the cellular mechanical and metabolic response are related to local micro-structural deformations. The present study proposes a novel approach to automatically collect micro-architectural data from SEM images of electrospun poly (ester urethane) urea (PEUU) and to recreate statistically equivalent scaffold mechanical models. Sets of contiguous SEM images for each of the three mandrel velocities (1.5, 4.5, 9.0 m/s) were analyzed. A combination of thresholding and morphological procedures enabled fibers overlaps to be detected. The algorithm precision was tested on regular grids of known characteristics. A modified Delanauy network was generated starting from the detected 2D fiber overlap coordinates. The following micro-architectural data were extracted from the generated network: (1) fiber overlap number and position, (2) connectivity distribution, (3) fiber angle distribution. Appropriate representative volume element (RVE) size was determined. A finite element model of the meso scale system (250 x 250 μm) was constructed respecting the micro-architectural data characterized by the image analysis. FEM and image analysis results revealed the capacity of the approach to characterize the material mechanical behavior at both the fiber and the global levels
Micro Scale Based Mechanical Models for Electrospun Poly (Ester Urethane) Urea Scaffolds.
No full textA novel approach to fully characterize fiber network morphology of planar fibrous tissues and scaffolds
No full textUnderstanding how scaffold structure influences cell morphology, metabolism, phenotypic expression, and predicting mechanical behaviors have increasingly become crucial goals in the development of engineered tissue scaffolds. A novel image-based analysis algorithm that provides an automatic tool to characterize engineered tissue fiber network topology is presented. Micro architectural descriptors that unambiguously define the fiber network topology were detected, which include fiber orientation distribution, connectivity, intersection spatial density, and diameter. Algorithm performance was tested using actual sample scanning electron microscopy (SEM) images of (1) electrospun poly(ester urethane)urea (ES-PEUU) scaffolds, (2) rabbit MSCs seeded collagen gel scaffolds, and (3) decellularized rat carotid arteries. Qualitative and quantitative validation was performed comparing fiber network topology manually detected by human operators (n=5) with the one automatically detected by the algorithm. R2 correlation values defining the correlation between manual detected and algorithm detected results for the fiber angle distribution and for the fiber connectivity distribution were 0.86 and 0.93 respectively. Algorithm detected fiber intersections and fiber diameter values were inside the (mean ± standard deviation) range detected by human operators. The algorithm’s ability to automatically identify and quantify the complete fiber network morphology regardless of the scaffold typology and of the scale of the problem was proven analyzing three different scaffold models. While the presented validation shows strong consistency between the human operators and the algorithm analysis results the automatic procedure guaranties objectivity and a significant reduction of the analysis time
Micro - Architectural Data Extraction for Electrospun Poly (Ester Urethane) Urea Scaffolds for Biomechanical Modeling.
No full textProblem: Soft tissue engineered applications have raised the need for accurate descriptions of tissue microstructure and their contributions to global mechanical behavior [1]. Accurate material image analysis is crucial to model engineered tissue biomechanics. The present study proposes a novel method to automatically collect micro-architectural data from electron micrographs (SEM) of electrospun poly (ester urethane) urea (PEUU).
Methods: Sets of contiguous SEM images for electrospun PEUU scaffolds made using three mandrel collection tangential velocities (1.5, 4.5, 9.0 m/s) were analyzed. A combination of thresholding and morphological procedures enabled overlaps of fibers to be detected. The algorithm detection precision was tested on regular grids of known characteristics. A modified Delanauy network was generated starting from the detected 2D fiber overlap coordinates. The following micro-architectural data were extracted from the generated network: (1) fiber overlap number and position, (2) connectivity distribution, (3) fiber angle distribution. Appropriate representative volume element (RVE) size was determined performing the image analysis over material areas of different sizes.
Results: The number of overlaps, the total number of connections and the estimated porosity all decreased as the mandrel velocity was raised. Fiber orientation results were consistent with previous findings [1]. The RVE size increased as the mandrel velocity increased consistent with a higher degree of structural organization and fiber alignment. The number of fiber overlaps was predicted for a given mandrel velocity and scaffold area.
Conclusions: The detected fiber overlaps, connectivity and angle distribution showed consistency with the known relationship between mandrel velocity and fiber alignment. The extracted data are considered to be highly relevant for electrospun PEUU scaffolds and collagenous tissue biomechanical modeling. The proposed approach enables a detailed analysis of the micro-architecture to be performed on electrospun PEUU scaffolds.
References: 1) Courtney et al. Biomaterials 2006. 27, 3631-3638.
Acknowledgements: The authors would like to acknowledge financial support from the NIH grant R01 HL-068816.
Disclosures: None of the authors have financial interests related to the topic of the abstract
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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