ECM stiffness modulates the proliferation but not the motility of primary corneal keratocytes in response to PDGF-BB.

During corneal wound healing, keratocytes present within the corneal stroma become activated into a repair phenotype upon the release of growth factors, such as transforming growth factor-beta 1 (TGF-ß1) and platelet-derived growth factor-BB (PDGF-BB). The process of injury and repair can lead to changes in the mechanical properties of the tissue, and previous work has shown that the TGF-ß1-mediated myofibroblast differentiation of corneal keratocytes depends on substratum stiffness. It is still unclear, however, if changes in stiffness can modulate keratocyte behavior in response to other growth factors, such as PDGF-BB. Here, we used a polyacrylamide (PA) gel system to determine whether changes in stiffness influence the proliferation and motility of primary corneal keratocytes treated with PDGF-BB. In the presence of PDGF-BB, cells on stiffer substrata exhibited a more elongated morphology and had higher rates of proliferation than cells in a more compliant microenvironment. Using a freeze-injury to assay cell motility, however, we did not observe any stiffness-dependent differences in the migration of keratocytes treated with PDGF-BB. Taken together, these data highlight the importance of biophysical cues during corneal wound healing and suggest that keratocytes respond differently to changes in ECM stiffness in the presence of different growth factors.

Emergency department-reported injuries associated with mechanical home exercise equipment in the USA.

The goal of this study was to generate national estimates of injuries associated with mechanical home exercise equipment, and to describe these injuries across all ages. Emergency department (ED)-treated injuries associated with mechanical home exercise equipment were identified from 2007 to 2011 from the National Electronic Injury Surveillance System. Text narratives provided exercise equipment type (treadmill, elliptical, stationary bicycle, unspecified/other exercise machine). Approximately 70 302 (95% CI 59 086 to 81 519) mechanical exercise equipment-related injuries presented to US EDs nationally during 2007-2011, of which 66% were attributed to treadmills. Most injuries among children (≤4 years) were lacerations (34%) or soft tissue injuries (48%); among adults (≥25 years) injuries were often sprains/strains (30%). Injured older adults (≥65 years) had greater odds of being admitted, held for observation, or transferred to another hospital, compared with younger ages (OR: 2.58; 95% CI 1.45 to 4.60). Mechanical exercise equipment is a common cause of injury across ages. Injury awareness and prevention are important complements to active lifestyles.

SCorP: Statistics-Informed Dense Correspondence Prediction Directly from Unsegmented Medical Images.

Statistical shape modeling (SSM) is a powerful computational framework for quantifying and analyzing the geometric variability of anatomical structures, facilitating advancements in medical research, diagnostics, and treatment planning. Traditional methods for shape modeling from imaging data demand significant manual and computational resources. Additionally, these methods necessitate repeating the entire modeling pipeline to derive shape descriptors (e.g., surface-based point correspondences) for new data. While deep learning approaches have shown promise in streamlining the construction of SSMs on new data, they still rely on traditional techniques to supervise the training of the deep networks. Moreover, the predominant linearity assumption of traditional approaches restricts their efficacy, a limitation also inherited by deep learning models trained using optimized/established correspondences. Consequently, representing complex anatomies becomes challenging. To address these limitations, we introduce SCorP, a novel framework capable of predicting surface-based correspondences directly from unsegmented images. By leveraging the shape prior learned directly from surface meshes in an unsupervised manner, the proposed model eliminates the need for an optimized shape model for training supervision. The strong shape prior acts as a teacher and regularizes the feature learning of the student network to guide it in learning image-based features that are predictive of surface correspondences. The proposed model streamlines the training and inference phases by removing the supervision for the correspondence prediction task while alleviating the linearity assumption. Experiments on the LGE MRI left atrium dataset and Abdomen CT-1K liver datasets demonstrate that the proposed technique enhances the accuracy and robustness of image-driven SSM, providing a compelling alternative to current fully supervised methods.

Corneal keratocytes, fibroblasts, and myofibroblasts exhibit distinct transcriptional profiles in vitro.

Purpose: After stromal injury to the cornea, the release of growth factors and pro-inflammatory cytokines promotes the activation of quiescent keratocytes into a migratory fibroblast and/or fibrotic myofibroblast phenotype. Persistence of the myofibroblast phenotype can lead to corneal fibrosis and scarring, which are leading causes of blindness worldwide. This study aims to establish comprehensive transcriptional profiles for cultured corneal keratocytes, fibroblasts, and myofibroblasts to gain insights into the mechanisms through which these phenotypic changes occur. Methods: Primary rabbit corneal keratocytes were cultured in either defined serum-free media (SF), fetal bovine serum (FBS) containing media, or in the presence of TGF-ß1 to induce keratocyte, fibroblast, or myofibroblast phenotypes, respectively. Bulk RNA sequencing followed by bioinformatic analyses was performed to identify significant differentially expressed genes (DEGs) and enriched biological pathways for each phenotype. Results: Genes commonly associated with keratocytes, fibroblasts, or myofibroblasts showed high relative expression in SF, FBS, or TGF-ß1 culture conditions, respectively. Differential expression and functional analyses revealed novel DEGs for each cell type, as well as enriched pathways indicative of differences in proliferation, apoptosis, extracellular matrix (ECM) synthesis, cell-ECM interactions, cytokine signaling, and cell mechanics. Conclusions: Overall, these data demonstrate distinct transcriptional differences among cultured corneal keratocytes, fibroblasts, and myofibroblasts. We have identified genes and signaling pathways that may play important roles in keratocyte differentiation, including many related to mechanotransduction and ECM biology. Our findings have revealed novel molecular markers for each cell type, as well as possible targets for modulating cell behavior and promoting physiological corneal wound healing.

Treatment with both TGF-ß1 and PDGF-BB disrupts the stiffness-dependent myofibroblast differentiation of corneal keratocytes.

During corneal wound healing, stromal keratocytes transform into a repair phenotype that is driven by the release of cytokines, like transforming growth factor-beta 1 (TGF-ß1) and platelet-derived growth factor-BB (PDGF-BB). Previous work has shown that TGF-ß1 promotes the myofibroblast differentiation of corneal keratocytes in a manner that depends on PDGF signaling. In addition, changes in mechanical properties are known to regulate the TGF-ß1-mediated differentiation of cultured keratocytes. While PDGF signaling acts synergistically with TGF-ß1 during myofibroblast differentiation, how treatment with multiple growth factors affects stiffness-dependent differences in keratocyte behavior is unknown. Here, we treated primary corneal keratocytes with PDGF-BB and TGF-ß1 and cultured them on polyacrylamide (PA) substrata of different stiffnesses. In the presence of TGF-ß1 alone, the cells underwent stiffness-dependent myofibroblast differentiation. On stiff substrata, the cells developed robust stress fibers, exhibited high levels of ⍺-SMA staining, formed large focal adhesions (FAs), and exerted elevated contractile forces, whereas cells in a compliant microenvironment showed low levels of ⍺-SMA immunofluorescence, formed smaller focal adhesions, and exerted decreased contractile forces. When the cultured keratocytes were treated simultaneously with PDGF-BB however, increased levels of ⍺-SMA staining and stress fiber formation were observed on compliant substrata, even though the cells did not exhibit elevated contractility or focal adhesion size. Pharmacological inhibition of PDGF signaling disrupted the myofibroblast differentiation of cells cultured on substrata of all stiffnesses. These results indicate that treatment with PDGF-BB can decouple molecular markers of myofibroblast differentiation from the elevated contractile phenotype otherwise associated with these cells, suggesting that crosstalk in the mechanotransductive signaling pathways downstream of TGF-ß1 and PDGF-BB can regulate the stiffness-dependent differentiation of cultured keratocytes. Statement of Significance: In vitro experiments have shown that changes in ECM stiffness can regulate the differentiation of myofibroblasts. Typically, these assays involve the use of individual growth factors, but it is unclear how stiffness-dependent differences in cell behavior are affected by multiple cytokines. Here, we used primary corneal keratocytes to show that treatment with both TGF-ß1 and PDGF-BB disrupts the dependency of myofibroblast differentiation on substratum stiffness. In the presence of both growth factors, keratocytes on soft substrates exhibited elevated ⍺-SMA immunofluorescence without a corresponding increase in contractility or focal adhesion formation. This result suggests that molecular markers of myofibroblast differentiation can be dissociated from the elevated contractile behavior associated with the myofibroblast phenotype, suggesting potential crosstalk in mechanotransductive signaling pathways downstream of TGF-ß1 and PDGF-BB.

Mesh2SSM: From Surface Meshes to Statistical Shape Models of Anatomy.

Statistical shape modeling is the computational process of discovering significant shape parameters from segmented anatomies captured by medical images (such as MRI and CT scans), which can fully describe subject-specific anatomy in the context of a population. The presence of substantial non-linear variability in human anatomy often makes the traditional shape modeling process challenging. Deep learning techniques can learn complex non-linear representations of shapes and generate statistical shape models that are more faithful to the underlying population-level variability. However, existing deep learning models still have limitations and require established/optimized shape models for training. We propose Mesh2SSM, a new approach that leverages unsupervised, permutation-invariant representation learning to estimate how to deform a template point cloud to subject-specific meshes, forming a correspondence-based shape model. Mesh2SSM can also learn a population-specific template, reducing any bias due to template selection. The proposed method operates directly on meshes and is computationally efficient, making it an attractive alternative to traditional and deep learning-based SSM approaches.

ADASSM: Adversarial Data Augmentation in Statistical Shape Models From Images.

Statistical shape models (SSM) have been well-established as an excellent tool for identifying variations in the morphology of anatomy across the underlying population. Shape models use consistent shape representation across all the samples in a given cohort, which helps to compare shapes and identify the variations that can detect pathologies and help in formulating treatment plans. In medical imaging, computing these shape representations from CT/MRI scans requires time-intensive preprocessing operations, including but not limited to anatomy segmentation annotations, registration, and texture denoising. Deep learning models have demonstrated exceptional capabilities in learning shape representations directly from volumetric images, giving rise to highly effective and efficient Image-to-SSM networks. Nevertheless, these models are data-hungry and due to the limited availability of medical data, deep learning models tend to overfit. Offline data augmentation techniques, that use kernel density estimation based (KDE) methods for generating shape-augmented samples, have successfully aided Image-to-SSM networks in achieving comparable accuracy to traditional SSM methods. However, these augmentation methods focus on shape augmentation, whereas deep learning models exhibit image-based texture bias resulting in sub-optimal models. This paper introduces a novel strategy for on-the-fly data augmentation for the Image-to-SSM framework by leveraging data-dependent noise generation or texture augmentation. The proposed framework is trained as an adversary to the Image-to-SSM network, augmenting diverse and challenging noisy samples. Our approach achieves improved accuracy by encouraging the model to focus on the underlying geometry rather than relying solely on pixel values.

Statistical Shape Modeling of Biventricular Anatomy with Shared Boundaries.

Statistical shape modeling (SSM) is a valuable and powerful tool to generate a detailed representation of complex anatomy that enables quantitative analysis and the comparison of shapes and their variations. SSM applies mathematics, statistics, and computing to parse the shape into a quantitative representation (such as correspondence points or landmarks) that will help answer various questions about the anatomical variations across the population. Complex anatomical structures have many diverse parts with varying interactions or intricate architecture. For example, the heart is a four-chambered anatomy with several shared boundaries between chambers. Coordinated and efficient contraction of the chambers of the heart is necessary to adequately perfuse end organs throughout the body. Subtle shape changes within these shared boundaries of the heart can indicate potential pathological changes that lead to uncoordinated contraction and poor end-organ perfusion. Early detection and robust quantification could provide insight into ideal treatment techniques and intervention timing. However, existing SSM approaches fall short of explicitly modeling the statistics of shared boundaries. In this paper, we present a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that captures morphological and alignment changes of individual anatomies and their shared boundary surfaces throughout the population. We demonstrate the effectiveness of the proposed methods using a biventricular heart dataset by developing shape models that consistently parameterize the cardiac biventricular structure and the interventricular septum (shared boundary surface) across the population data.

Signaling Downstream of Focal Adhesions Regulates Stiffness-Dependent Differences in the TGF-ß1-Mediated Myofibroblast Differentiation of Corneal Keratocytes.

Following injury and refractive surgery, corneal wound healing can initiate a protracted fibrotic response that interferes with ocular function. This fibrosis is related, in part, to the myofibroblast differentiation of corneal keratocytes in response to transforming growth factor beta 1 (TGF-ß1). Previous studies have shown that changes in the mechanical properties of the extracellular matrix (ECM) can regulate this process, but the mechanotransductive pathways that govern stiffness-dependent changes in keratocyte differentiation remain unclear. Here, we used a polyacrylamide (PA) gel system to investigate how mechanosensing via focal adhesions (FAs) regulates the stiffness-dependent myofibroblast differentiation of primary corneal keratocytes treated with TGF-ß1. Soft (1 kPa) and stiff (10 kPa) PA substrata were fabricated on glass coverslips, plated with corneal keratocytes, and cultured in defined serum free media with or without exogenous TGF-ß1. In some experiments, an inhibitor of focal adhesion kinase (FAK) activation was also added to the media. Cells were fixed and stained for F-actin, as well as markers for myofibroblast differentiation (α-SMA), actomyosin contractility phosphorylated myosin light chain (pMLC), focal adhesions (vinculin), or Smad activity (pSmad3). We also used traction force microscopy (TFM) to quantify cellular traction stresses. Treatment with TGF-ß1 elicited stiffness-dependent differences in the number, size, and subcellular distribution of FAs, but not in the nuclear localization of pSmad3. On stiff substrata, cells exhibited large FAs distributed throughout the entire cell body, while on soft gels, the FAs were smaller, fewer in number, and localized primarily to the distal tips of thin cellular extensions. Larger and increased numbers of FAs correlated with elevated traction stresses, increased levels of α-SMA immunofluorescence, and more prominent and broadly distributed pMLC staining. Inhibition of FAK disrupted stiffness-dependent differences in keratocyte contractility, FA patterning, and myofibroblast differentiation in the presence of TGF-ß1. Taken together, these data suggest that signaling downstream of FAs has important implications for the stiffness-dependent myofibroblast differentiation of corneal keratocytes.

Statistical shape modeling of multi-organ anatomies with shared boundaries.

Introduction: Statistical shape modeling (SSM) is a valuable and powerful tool to generate a detailed representation of complex anatomy that enables quantitative analysis of shapes and their variations. SSM applies mathematics, statistics, and computing to parse the shape into some quantitative representation (such as correspondence points or landmarks) which can be used to study the covariance patterns of the shapes and answer various questions about the anatomical variations across the population. Complex anatomical structures have many diverse parts with varying interactions or intricate architecture. For example, the heart is a four-chambered organ with several shared boundaries between chambers. Subtle shape changes within the shared boundaries of the heart can indicate potential pathologic changes such as right ventricular overload. Early detection and robust quantification could provide insight into ideal treatment techniques and intervention timing. However, existing SSM methods do not explicitly handle shared boundaries which aid in a better understanding of the anatomy of interest. If shared boundaries are not explicitly modeled, it restricts the capability of the shape model to identify the pathological shape changes occurring at the shared boundary. Hence, this paper presents a general and flexible data-driven approach for building statistical shape models of multi-organ anatomies with shared boundaries that explicitly model contact surfaces. Methods: This work focuses on particle-based shape modeling (PSM), a state-of-art SSM approach for building shape models by optimizing the position of correspondence particles. The proposed PSM strategy for handling shared boundaries entails (a) detecting and extracting the shared boundary surface and contour (outline of the surface mesh/isoline) of the meshes of the two organs, (b) followed by a formulation for a correspondence-based optimization algorithm to build a multi-organ anatomy statistical shape model that captures morphological and alignment changes of individual organs and their shared boundary surfaces throughout the population. Results: We demonstrate the shared boundary pipeline using a toy dataset of parameterized shapes and a clinical dataset of the biventricular heart models. The shared boundary model for the cardiac biventricular data achieves consistent parameterization of the shared surface (interventricular septum) and identifies the curvature of the interventricular septum as pathological shape differences.

Benchmarking off-the-shelf statistical shape modeling tools in clinical applications.

Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Technological advancements of in vivo imaging have led to the development of open-source computational tools that automate the modeling of anatomical shapes and their population-level variability. However, little work has been done on the evaluation and validation of such tools in clinical applications that rely on morphometric quantifications(e.g., implant design and lesion screening). Here, we systematically assess the outcome of widely used, state-of-the-art SSM tools, namely ShapeWorks, Deformetrica, and SPHARM-PDM. We use both quantitative and qualitative metrics to evaluate shape models from different tools. We propose validation frameworks for anatomical landmark/measurement inference and lesion screening. We also present a lesion screening method to objectively characterize subtle abnormal shape changes with respect to learned population-level statistics of controls. Results demonstrate that SSM tools display different levels of consistencies, where ShapeWorks and Deformetrica models are more consistent compared to models from SPHARM-PDM due to the groupwise approach of estimating surface correspondences. Furthermore, ShapeWorks and Deformetrica shape models are found to capture clinically relevant population-level variability compared to SPHARM-PDM models.

All Roads Lead to Rome: Diverse Etiologies of Tricuspid Regurgitation Create a Predictable Constellation of Right Ventricular Shape Changes.

Introduction: Myriad disorders cause right ventricular (RV) dilation and lead to tricuspid regurgitation (TR). Because the thin-walled, flexible RV is mechanically coupled to the pulmonary circulation and the left ventricular septum, it distorts with any disturbance in the cardiopulmonary system. TR, therefore, can result from pulmonary hypertension, left heart failure, or intrinsic RV dysfunction; but once it occurs, TR initiates a cycle of worsening RV volume overload, potentially progressing to right heart failure. Characteristic three-dimensional RV shape-changes from this process, and changes particular to individual TR causes, have not been defined in detail. Methods: Cardiac MRI was obtained in 6 healthy volunteers, 41 patients with ≥ moderate TR, and 31 control patients with cardiac disease without TR. The mean shape of each group was constructed using a three-dimensional statistical shape model via the particle-based shape modeling approach. Changes in shape were examined across pulmonary hypertension and congestive heart failure subgroups using principal component analysis (PCA). A logistic regression approach based on these PCA modes identified patients with TR using RV shape alone. Results: Mean RV shape in patients with TR exhibited free wall bulging, narrowing of the base, and blunting of the RV apex compared to controls (p < 0.05). Using four primary PCA modes, a logistic regression algorithm identified patients with TR correctly with 82% recall and 87% precision. In patients with pulmonary hypertension without TR, RV shape was narrower and more streamlined than in healthy volunteers. However, in RVs with TR and pulmonary hypertension, overall RV shape continued to demonstrate the free wall bulging characteristic of TR. In the subgroup of patients with congestive heart failure without TR, this intermediate state of RV muscular hypertrophy was not present. Conclusion: The multiple causes of TR examined in this study changed RV shape in similar ways. Logistic regression classification based on these shape changes reliably identified patients with TR regardless of etiology. Furthermore, pulmonary hypertension without TR had unique shape features, described here as the "well compensated" RV. These results suggest shape modeling as a promising tool for defining severity of RV disease and risk of decompensation, particularly in patients with pulmonary hypertension.