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Kotta, N. M.

Publications and source records attributed to Kotta, N. M..

2 recordsLinked to original sources

Impact of bioactive molecule inclusion in lyophilized silk scaffolds varies between in vivo and in vitro assessments

Biomaterials can influence the coordinated efforts required to achieve tissue rehabilitation. Sponge-like silk fibroin scaffolds that include bioactive molecules have been shown to influence tissue repair. However, the mechanisms by which scaffold formulations elicit desired in vivo responses is unclear. Here, acellular silk scaffolds consisting of type I collagen, heparin, and/or vascular endothelial growth factor (VEGF) were used to investigate material fabrication and composition parameters that drive scaffold degradation, cell infiltration, and adipose tissue deposition in vivo. In subcutaneous implants, scaffold degradation was assessed, and results show that the percentage of cells infiltrating the scaffold increased when scaffold formulations contained bioactive molecules. To gain further insight, calculated in vitro enzymatic degradation rates increased with higher enzyme concentrations and theoretical cleavage sites. However, the addition of type I collagen and heparin to the scaffold at relevant concentrations did not change degradation rates, compared to silk alone. These in vitro results are contrary to observations in vivo, where bioactive molecules influence local protein deposition, immune cell infiltration rates, and vascularization. Thus, quantitative in vitro and in vivo evaluations aid in determining the mechanisms by which biomaterials influence tissue repair and support intentional biomaterial design for clinical applications. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=157 SRC="FIGDIR/small/493207v1_ufig1.gif" ALT="Figure 1"> View larger version (55K): org.highwire.dtl.DTLVardef@7b403forg.highwire.dtl.DTLVardef@1b6ed79org.highwire.dtl.DTLVardef@a0b439org.highwire.dtl.DTLVardef@9843f3_HPS_FORMAT_FIGEXP M_FIG C_FIG This work examines the role of scaffold fabrication and bioactive molecule inclusion on the enzymatic degradation of silk fibroin-based lyophilized sponges. Specifically, the roles of collagen I, heparin, and vascular endothelial growth factor are analyzed to determine the impact of formulation on rate of degradation. In addition, scaffolds are either pre-fabricated, where these bioactive molecules are included in the polymer solution prior to casting the scaffold or the bioactive molecules are introduced following scaffold formation through passive adsorption to the silk fibroin scaffold surface. Scaffolds are enzymatically degraded in vitro, and kinetic rate constants are calculated for the different formulations. In vivo, cellularity, adipose tissue accumulation, and scaffold area are assessed over time. Additionally, immunohistochemistry is used to visualize VEGF Receptor 2, CD 68, and -smooth muscle actin over time.

bioengineering↗

Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA) for Adipose Tissue Segmentation

ObjectiveWe quantify adipose tissue deposition at surgical sites as a function of biomaterial implantation. Impact StatementTo our knowledge, this study is the first investigation to apply convolutional neural network (CNN) models to identify and segment adipose tissue in histological images from silk fibroin biomaterial implants. IntroductionWhen designing biomaterials for the treatment of various soft tissue injuries and diseases, one must consider the extent of adipose tissue deposition. In this work, we implant silk fibroin biomaterials in a rodent subcutaneous injury model. Current strategies for quantifying adipose tissue after biomaterial implantation are often tedious and prone to human bias during analysis. MethodsWe used CNN models with novel spatial histogram layer(s) that can more accurately identify and segment regions of adipose tissue in hematoxylin and eosin (H&E) and Massons Trichrome stained images, allowing for determination of the optimal biomaterial formulation. We compared the method, Jointly Optimized Spatial Histogram UNET Architecture (JOSHUA), to the baseline UNET model and an extension of the baseline model, Attention UNET, as well as to versions of the models with a supplemental "attention"-inspired mechanism (JOSHUA+ and UNET+). ResultsThe inclusion of histogram layer(s) in our models shows improved performance through qualitative and quantitative evaluation. ConclusionOur results demonstrate that the proposed methods, JOSHUA and JOSHUA+, are highly beneficial for adipose tissue identification and localization. The new histological dataset and code for our experiments are publicly available.

bioengineering↗