Publications

Introduction: Cyclic strain can regulate smooth muscle cell (SMC) gene and protein expression in mesenchymal progenitor cells (MPCs). We tested the hypothesis that the differentiation of BMPCs to endothelial cells (ECs) and SMCs may be controlled by the appropriate type, magnitude, and frequency of mechanical stimulation. Methods: Bone marrow MPCs were seeded onto collagen I substrates and exposed to three stimuli – cyclic strain (CS), cyclic pressure (CP), steady shear stress (SS) – at 3 magnitudes and 2 frequencies for 5 days. Morphology and proliferation were analyzed by Scion Image. Protein expression was examined via immunohistochemistry. RT‐PCR was used to quantify genes relevant to various cell phenotypes and matrix proteins. Results: MPCs responded differentially via changes in morphology, proliferation, gene, and protein expression. Cell size correlated negatively with SS and positively with CP. Proliferation was enhanced by CP, but decreased in for SS. CS caused a sequential increase in the number of SMC proteins. Although gene expression for EC and SMC genes was elevated in CP and SS, respectively, no protein expression was found. Most ECM genes were up‐regulated under CP, while only collagen I was increased in SS. Increases in TGFβ gene expression correlated with SMC and collagen I gene expression. Conclusions: We conclude that the differentiation of MPCs towards SMCs and ECs may be regulated, in part, by the specific mechanical stimulus applied. This work was funded by the NIH BRP grant # R01 HL069368, NIH training grant # T32 EB001026, Ruth L. Kirschstein predoctoral fellowship #F31 EB004791, and American Heart Association predoctoral fellowship #0415437U.

<h2>ABSTRACT</h2><h3>Background</h3> An abdominal aortic aneurysm (AAA) is an irreversible dilation of the terminal aorta, posing significant rupture risks. Current guidelines for surgical intervention rely on aneurysm diameter thresholds (5.0 cm for women, 5.5 cm for men). However, ruptures frequently occur below these thresholds, highlighting limitations in diameter-based approaches. Machine learning (ML) techniques that integrate clinical, biomechanical, and morphological data may enhance predictive accuracy, but can suffer from limited interpretability due to their inherent complexity. <h3>Objective</h3> This study aims to create an interpretable surrogate model for understanding a clinician's decision to perform AAA repair surgery. This was done by transforming complex machine learning (ML) outputs into a logistic regression framework using Shapley feature contributions, thereby increasing interpretability in clinical decision-making. <h3>Methods</h3> In a previous study, CT images were used to perform biomechanical and morphological analyses of AAA. The resulting dataset included various clinical variables (demographics, comorbidities, and pharmaceutical use). A total of 512 patients with either stable or repair outcomes were included in this study, with the training set (n = 410) and testing set (n = 102) split using an 80:20 ratio. Custom Python scripts were developed to train and evaluate logistic regression and ensemble boosted tree models on the dataset to classify repair outcomes from stable ones. Odds ratios were computed for the logistic regression, and Shapley Additive exPlanations values were calculated to interpret feature importance in the ensemble boosted tree model. <h3>Results</h3> The logistic regression model demonstrated an average cross-validation accuracy of 91% ± 3%, specificity of 77% ± 11%, sensitivity of 61% ± 11%, and an area under the receiver operating characteristics curve (AUROC) of 79% ± 5%. In comparison, the XGBoost model achieved an average accuracy of 95% ± 2%, specificity of 93% ± 7%, sensitivity of 74% ± 11%, and an AUROC of 86% ± 5%. <h3>Conclusions</h3> The utility of providing a probability for an event to occur can be potentially beneficial to clinicians based on the surrogate logistic function, as they are familiar with the risk score derived from odds ratios. The feature weights can be provided to promote a better understanding of which variables contribute to decision-making. Future work to understand clinicians' attitudes towards decision-support tools may lead to greater adoption of well-validated artificial intelligence tools.

Urethral dysfunction is a common complication of diabetes mellitus, spinal cord injury, vaginal childbirth, and pelvic trauma. Stress urinary incontinence (SUI) is the involuntary loss of urine due to the inability of the urethral sphincter to maintain a tight seal during the storage phase, and is a disease that physically and emotionally affects 25 million American women annually [1]. There are currently several treatments for SUI including surgery, Kegel exercises, and electrical stimulation, each accompanied by limited effectiveness and/or complications [2–3]. We believe that regenerative medicine techniques applied to the native urethra may aid in improving the function and support of the diseased urethra. Thus, we have begun the development of a tissue engineered urethral wrap (TEUW) for placement as a cuff around the native urethra and integration with the host tissue. The goal of this work was to determine optimized culture conditions for TEUWs and to determine if their use in vivo improves urethral function.

Abstract Background The extracellular matrix (ECM) structural deficiencies in chronic wounds prevent the wounds from healing through natural physiological processes. Electrospun biocompatible polymers offer a platform to produce microstructure wound dressing materials that mimic the ECM containing various bioactives to address the deficiencies in the chronic wound healing process. Quantitative characterization of the electrospun fiber microstructure could provide valuable information on using fiber constructs to facilitate wound healing. This work employed a validated image analysis tool to quantitatively explain various parameters for the microstructure of six electrospun fiber constructs, D1{Polycaprolactone (PCL), Polyvinyl alcohol (PVA), Keratin}, D2{PCL, PVA, keratin, Co-enzyme Q10 (CoQ10)}, D3 (PCL, PVA, keratin, mupirocin), D4 (PCL, PVA, keratin, CoQ10, mupirocin, valsartan), D5 {PVA, Hyaluronic acid (HA)}, and D6 (PVA), using scanning electron microscopy imaging modality. Results The fiber intersection density (FID) parameter was quantified in the formulations, e.g., 0.272% for D5 and 0.416% for D4. Orientation histograms for D1 and D6 are characteristic of isotropic materials, while orientations for D2 and D3 indicate anisotropy with 2 preferred orientations in each formulation. D4 and D5 present orientations characteristic of transversely isotropic materials. The tortuosity for D2 and D4 indicates almost straight fiber segments, in contrast with undulated fiber segments in all other formulations. Furthermore, the mean fiber diameter was quantified, e.g., 1.414 and 1.630 mm for D3 and D4, respectively. Conclusion Co-electrospun PVA/PCL microfibers offer great potential for controlled delivery of bioactives needed to accelerate the healing of chronic wounds. This image-based analysis technology quantitatively characterized different formulations of electrospun fiber scaffolds. This analysis sets the stage for future study that utilizes microstructural information in finite element biomechanical modeling, to investigate possible influence of structure-based mechanical factors on the ECM restorative potential of wound dressings. Adjustment of electrospinning conditions could produce fabricated constructs like the native ECM structural components with a functional role in wound healing.

<h2>Abstract</h2> Microfabrication technology, more commonly applied to the manufacture of integrated circuits, can be used to build devices useful for mechanical delivery of drugs and genes. Microprobes fabricated using silicon micromachining have been used to deliver DNA into cells as an alternative to bombardment and microinjection. This idea can be extended to intravascular stents with integrated microprobes capable of piercing compressed plaque and delivering anti-restenosis therapies into coronary arteries. Preliminary experiments using filleted rabbit arteries have demonstrated transection of the internal elastic lamina. New nonplanar microfabrication technologies are necessary for creating practical devices with cylindrical symmetry; a promising possibility is to use microfabricated structures of anodic metal oxides.

The stress-strain curve of biological soft tissues helps characterize their mechanical behavior. The yield point on this curve is when a specimen breaches its elastic range due to irreversible microstructural damage. The yield point is easily found using the offset yield method in traditional engineering materials. However, correctly identifying the yield point in soft tissues can be subjective due to its nonlinear material behavior. The typical method for yield point identification is visual inspection, which is investigator-dependent and does not lend itself to automation of the analysis pipeline. An automated algorithm to identify the yield point objectively assesses soft tissues' biomechanical properties. This study aimed to analyze data from uniaxial extension testing on biological soft tissue specimens and create a machine learning (ML) model to determine a tissue sample's yield point. We present a trained machine learning model from 279 uniaxial extension curves from testing aneurysmal/nonaneurysmal and longitudinal/circumferential oriented tissue specimens that multiple experts labeled through an adjudication process. The ML model showed a median error of 5% in its estimated yield stress compared to the expert picks. The study found that an ML model could accurately identify the yield point (as defined) in various aortic tissues. Future studies will be performed to validate this approach by visually inspecting when damage occurs and adjusting the model using the ML-based approach.

Macromolecular components of the vascular extracellular matrix (ECM), particularly elastic fibers and collagen fibers, are critical for the proper physiological function of arteries. When the unique biomechanical combination of these fibers is disrupted, or in the ultimate extreme where fibers are completely lost, arterial disease can emerge. Bioengineers in the realms of vascular tissue engineering and regenerative medicine must therefore ideally consider how to create tissue engineered vascular grafts containing the right balance of these fibers and how to develop regenerative treatments for situations such as an aneurysm where fibers have been lost. Previous work has demonstrated that the primary cells responsible for vascular ECM production during development, arterial smooth muscle cells (SMCs), can be induced to make new elastic fibers when exposed to secreted factors from adipose-derived stromal cells. To further dissect how this signal is transmitted, in this study, the factors were partitioned into extracellular vesicle (EV)-rich and EV-depleted fractions as well as unseparated controls. EVs were validated using electron microscopy, dynamic light scattering, and protein quantification before testing for biological effects on SMCs. In 2D culture, EVs promoted SMC proliferation and migration. After 30 days of 3D fibrin construct culture, EVs promoted SMC transcription of the elastic microfibril gene FBN1 as well as SMC deposition of insoluble elastin and collagen. Uniaxial biomechanical properties of strand fibrin constructs were no different after 30 days of EV treatment versus controls. In summary, it is apparent that some of the positive effects of adipose-derived stromal cells on SMC elastogenesis are mediated by EVs, indicating a potential use for these EVs in a regenerative therapy to restore the biomechanical function of vascular ECM in arterial disease.