Tissue Patches Research Articles

CSGO: A Deep Learning Pipeline for Whole-Cell Segmentation in Hematoxylin and Eosin Stained Tissues

Accurate whole-cell segmentation is essential in various biomedical applications, particularly in studying the tumor microenvironment (TME). Despite advancements in machine learning for nuclei segmentation in hematoxylin and eosin (H&E) stained images, there remains a need for effective whole-cell segmentation methods. This study aims to develop a deep learning-based pipeline to automatically segment cells in H&E-stained tissues, thereby advancing the capabilities of pathological image analysis. The Cell Segmentation with Globally Optimized boundaries (CSGO) framework integrates nuclei and membrane segmentation algorithms, followed by post-processing using an energy-based watershed method. Specifically, we employed the You Only Look Once (Yolo) object detection algorithm for nuclei segmentation and U-Net for membrane segmentation. The membrane detection model was trained on a dataset of 7 hepatocellular carcinomas and 11 normal liver tissue patches. The cell segmentation performance was extensively evaluated on five external datasets, including liver, lung, and oral disease cases. CSGO demonstrated superior performance over the state-of-the-art method Cellpose, achieving higher F1 scores ranging from 0.37 to 0.53 at an intersection over union (IoU) threshold of 0.5 in four of the five external datasets, compared to that of Cellpose from 0.21 to 0.36. These results underscore the robustness and accuracy of our approach in various tissue types. A web-based application is available at https://ai.swmed.edu/projects/csgo, providing a user-friendly platform for researchers to apply our method to their own datasets. Our method exhibits remarkable versatility in whole-cell segmentation across diverse cancer subtypes, serving as an accurate and reliable tool to facilitate TME studies. The advancements presented in this study have the potential to significantly enhance the precision and efficiency of pathological image analysis, contributing to better understanding and treatment of cancer.

Low conduction velocity is a key factor for localisation of reentrant drivers for atrial fibrillation

Abstract Background Atrial Fibrillation (AF) is a prevalent cardiac arrhythmia globally, associated with heightened risks of stroke, dementia, and heart failure. Catheter ablation, the sole curative therapy for AF, yields suboptimal success rates for persistent AF cases. Understanding the mechanisms and dynamics of AF remains challenging, hindering treatment advancements. Novel ablation strategies aimed at targeting substrates harbouring AF reentrant drivers (RD), such as low-voltage ablation and rotor-based ablation, have yet to demonstrate superiority over the gold standard ablation strategy – pulmonary vein isolation. Purpose The purpose of the study is to integrate in-silico 3D left atrial (LA) simulations and explainable artificial intelligence (AI) to pinpoint key factors for RD localisation in persistent AF, aiming to improve the mechanistic understanding of AF and help develop a more effective ablation strategy. Methods 3D LA models were derived from magnetic resonance images of 41 patients and combined with atrial cell electrophysiology models. Persistent AF was initiated in each patient-specific LA model with fast pacing, and five functional features (action potential duration (APD), conduction velocity (CV), wavelength, APD spatial gradient, CV spatial gradient, and wavelength spatial gradient) and three structural features (fibrotic density, fibrotic entropy, and image intensity ratio) were evaluated. A random forest AI model was trained to classify phase singularities corresponding to the RDs (PS-RD) in five distinct tissue classes (total LA tissue, healthy tissue, fibrotic border zone, dense fibrotic tissue, and PS-RD tissue) based on the functional and structural features. Shapley additive explanations (SHAP) analysis was then used to find the most influential feature in the AI model’s decision process. Results Simulations of the patient-specific LA models revealed that RDs were typically localised in regions with the lowest atrial CV, which often coincided with patches of dense fibrotic tissue. In a five-fold cross-validation, the random forest AI model achieved high accuracy in classifying PS-RD regions: a mean ROC AUC of 0.99 ± 0.010, a mean F1 score of 0.86 ± 0.034, a mean recall of 0.95 ± 0.058, and a mean precision score of 0.79 ± 0.085. The further SHAP analysis revealed that low CV was the key determinant in classifying the PS-RD regions. Conclusion Our study combined AF computational modelling and explainable AI to identify the key factors in RD localisation. Both the computational and AI models concluded that low CV was the most important determinant in localising RDs. These findings provide mechanistic evidence for the efficacy of ablation strategies that target low CV atrial areas for persistent AF patients.

Engineering anisotropic tissue analogues: harnessing synergistic potential of extrusion-based bioprinting and extracellular matrix-based bioink

In the realm of tissue engineering, replicating the intricate alignment of cells and the extracellular matrix (ECM) found in native tissue has long been a challenge. Most recent studies have relied on complex multi-step processes to approximate native tissue alignment. To address this challenge, we introduce a novel, single-step method for constructing highly aligned fibrous structures within multi-modular three-dimensional conglomerates. Our approach harnesses the synergistic potential of extrusion-based bioprinting and the fibrillogenesis kinetics of collagen-rich decellularized ECM. We have identified three key parameters governing ECM microfiber alignment during extrusion-based bioprinting: applied shear stress, stretching or extensional force, and post-print deformation. By carefully manipulating these parameters, we have successfully created highly aligned fibrous structures within multi-modular three-dimensional conglomerates. Our technique offers an efficient solution and has been validated by computational modeling. Comprehensive analyses confirm the efficacy across various scenarios, including encapsulated, top-seeded, and migratory cells. Notably, we have demonstrated the versatility and effectiveness of our approach by bioprinting highly aligned cardiac tissue patches, which show further maturation evidenced by the expression of Troponin-T and Myo-D differentiation factor needed for contractility and myotube formation, respectively. In summary, our streamlined approach offers a robust solution for creating anisotropic tissue analogues with precise ECM organization.

Two distinct phenotypes of calcium oxalate stone formers could imply different long-term risks for renal function.

Endoscopic and biopsy findings have identified two distinct phenotypes among individuals with calcium oxalate (CaOx) kidney stones. The first type has normal renal papillae but shows interstitial mineral deposition, known as Randall's plaque. The other phenotype presents with collecting duct plugging and a higher incidence of loss of papilla tissue mass. With Randall's plaque, renal papilla injury involves the loss of small patches of calcified tissue (Randall's plaque detaching with the stone), which likely results in damage to only a few nephrons. In contrast, collecting duct mineral plugs are very large, causing obstruction to tubular flow. Since each terminal collecting duct drains thousands of nephrons, ductal plugs could lead to the degeneration of many nephrons and a significant loss of renal glomeruli. New visualization techniques for immune cells in papillary biopsies have revealed that the Randall's plaque phenotype is marked by the accumulation of macrophages around the plaque regions. In contrast, preliminary data on the plugging phenotype shows collecting duct damage with mineral plugsand increased T-lymphocytes throughout the papilla. These regions also show tubulitis, i.e., T-cell infiltration into nearby collecting duct epithelium. This suggests that while some CaOx stone formers may have some papillary inflammation but with minimal damage to nephrons, others suffer from obstruction to flow for many nephrons that may also include destructive inflammation in the renal tissue. We propose that CaOx stone formers with the plugging phenotype will have a higher long-term risk for loss of renal function.

Adaptive integration of history variables in constrained mixture models for organ-scale growth and remodeling.

In the last decades, many computational models have been developed to predict soft tissue growth and remodeling (G&R). The constrained mixture theory describes fundamental mechanobiological processes in soft tissue G&R and has been widely adopted in cardiovascular models of G&R. However, even after two decades of work, large organ-scale models are rare, mainly due to high computational costs (model evaluation and memory consumption), especially in long-range simulations. We propose two strategies to adaptively integrate history variables in constrained mixture models to enable large organ-scale simulations of G&R. Both strategies exploit that the influence of deposited tissue on the current mixture decreases over time through degradation. One strategy is independent of external loading, allowing the estimation of the computational resources ahead of the simulation. The other adapts the history snapshots based on the local mechanobiological environment so that the additional integration errors can be controlled and kept negligibly small, even in G&R scenarios with severe perturbations. We analyze the adaptively integrated constrained mixture model on a tissue patch for a parameter study and show the performance under different G&R scenarios. To confirm that adaptive strategies enable large organ-scale examples, we show simulations of different hypertension conditions with a real-world example of a biventricular heart discretized with a finite element mesh. In our example, adaptive integrations sped up simulations by a factor of three and reduced memory requirements to one-sixth. The reduction of the computational costs gets even more pronounced for simulations over longer periods. Adaptive integration of the history variables allows studying more finely resolved models and longer G&R periods while computational costs are drastically reduced and largely constant in time.

Two distinct phenotypes of calcium oxalate stone formers could imply different long-term risks for renal function.

Endoscopic and biopsy findings have identified two distinct phenotypes among individuals with calcium oxalate (CaOx) kidney stones. One phenotype exhibits normal renal papillae but shows interstitial mineral deposition, known as Randall's plaque. The other phenotype presents with collecting duct plugging and a higher incidence of loss of papilla tissue mass. With Randall's plaque, renal papilla injury involves the loss of small patches of calcified tissue (Randall's plaque detaching with the stone), which likely results in damage to only a few nephrons. In contrast, collecting duct mineral plugs are very large, causing obstruction to tubular flow. Since each terminal collecting duct drains thousands of nephrons, ductal plugs could lead to the degeneration of many nephrons and a significant loss of renal glomeruli. New visualization techniques for immune cells in papillary biopsies have revealed that the Randall's plaque phenotype is marked by the accumulation of macrophages around the plaque regions. In contrast, preliminary data on the plugging phenotype shows collecting duct damage with mineral plugs, increased T-lymphocytes throughout the papilla, and tubulitis, characterized by T-cell infiltration into nearby collecting duct epithelium. This suggests that while some CaOx stone formers may have some papillary inflammation but with minimal damage to nephrons, others suffer from obstruction to flow for many nephrons that may also include destructive inflammation in the renal tissue. We propose that the long-term risks for loss of renal function will be greater for CaOx stone formers with the plugging phenotype.

Diagnosis and Treatment of an Unusual Maxillary Gingiva Malignant Melanoma Arising Around Dental Implant.

Oral mucosal malignant melanoma (OMMM) arises from malignant melanocytes, and the most affected age, sex, and site are 20 to 83 years, male, and the mucosa of hard palate and maxillary gingiva, respectively. Despite several cases of OMMM have been published in the literature, cases of malignant melanoma arising around the dental implant are rarely reported. A 59-year-old male was admitted to our Department with the complain of gingival black pigmentations following dental implant treatment for 3 years. Intraoral examination revealed a painless black lobulated mass around maxillary gingiva and alveolar fossa of dental implant (first molar). CBCT revealed bone destruction around the implant. Positron emission tomography/computed tomography confirmed the presence of tumoral lesion, which was diagnosed as right maxillary gingiva malignant melanoma (T4aN0M0). Partial maxillectomy+buccal fat pad transfer+free tissue patch repair were carried out. Pathologic analyses confirmed the diagnosis of malignant melanoma. The postoperative course was uneventfully, the patient is undergoing follow-up without any evidence of recurrence. Our report showed that an ill-fitting dental implant may cause OMMM. Excisional biopsy with sufficient surgical margins allows complete removal and final diagnosis of OMMM. Early diagnosis and treatment are recommended.

Autologous Pericranium Graft for Necrotizing Scleritis.

The purpose of this study was to describe the clinical outcome of scleral staphyloma secondary to necrotizing scleritis treated with autologous pericranial grafting. A 63-year-old woman with necrotizing scleritis and choroidal protrusion underwent homologous scleral grafting, which failed. The patient's medical management was optimized for control of her systemic inflammation, and she underwent a second surgery consisting of autologous pericranium grafted to the parietal region. A 3- × 2-cm tissue patch was harvested and sutured over the staphyloma borders with 8/0 polyglactin sutures. Complete re-epithelialization was observed at 3 weeks, the graft was well integrated, and no signs of inflammation were observed at 18 months. The pericranium offers a viable alternative for scleral thinning because of necrotizing scleritis.

Latent Diffusion Models with Image-Derived Annotations for Enhanced AI-Assisted Cancer Diagnosis in Histopathology.

Artificial Intelligence (AI)-based image analysis has immense potential to support diagnostic histopathology, including cancer diagnostics. However, developing supervised AI methods requires large-scale annotated datasets. A potentially powerful solution is to augment training data with synthetic data. Latent diffusion models, which can generate high-quality, diverse synthetic images, are promising. However, the most common implementations rely on detailed textual descriptions, which are not generally available in this domain. This work proposes a method that constructs structured textual prompts from automatically extracted image features. We experiment with the PCam dataset, composed of tissue patches only loosely annotated as healthy or cancerous. We show that including image-derived features in the prompt, as opposed to only healthy and cancerous labels, improves the Fréchet Inception Distance (FID) by 88.6. We also show that pathologists find it challenging to detect synthetic images, with a median sensitivity/specificity of 0.55/0.55. Finally, we show that synthetic data effectively train AI models.

AP collagen peptides (APCPs) promote hair growth by activating the GSK-3β/β-catenin pathway and improve hair condition.

AP collagen peptides (APCPs) are enzymatically decomposed collagen peptides that contain tri-peptides such as glycine-proline-hydroxyproline. We found that APCPs increased the proliferation of both human dermal papilla cells (hDPCs) and human outer root sheath cells (hORSCs). APCPs also stimulated the secretion of several growth factors, including IGFBP-6, PDGF-AB, PIGF and VEGF in hDPCs. Moreover, APCPs enhanced the phosphorylation of Akt(Ser473), GSK-3β(Ser9) and β-catenin(Ser675), indicating the activation of the GSK-3β/β-catenin signalling pathway. Exvivo culture of human hair follicles (hHFs) tissue and invivo patch assay revealed that APCPs promoted the elongation of hHFs and the induction of new hair shafts. In a mouse model, APCPs significantly promoted the transition from telogen to anagen phase and prolonged anagen phase, resulting in increased hair growth. APCPs also improved the thickness, amino acid content (cystine and methionine) and roughness of mouse hair. Taken together, these findings demonstrate that APCPs accelerate hair growth and contribute to overall hair health. Therefore, APCPs have the potential to be utilized as a food supplement and ingredient for preventing hair loss and maintaining hair health.

Focused ultrasound enables selective actuation and Newton-level force output of untethered soft robots

Untethered miniature soft robots have significant application potentials in biomedical and industrial fields due to their space accessibility and safe human interaction. However, the lack of selective and forceful actuation is still challenging in revolutionizing and unleashing their versatility. Here, we propose a focused ultrasound-controlled phase transition strategy for achieving millimeter-level spatially selective actuation and Newton-level force of soft robots, which harnesses ultrasound-induced heating to trigger the phase transition inside the robot, enabling powerful actuation through inflation. The millimeter-level spatial resolution empowers single robot to perform multiple tasks according to specific requirements. As a concept-of-demonstration, we designed soft robot for liquid cargo delivery and biopsy robot for tissue acquisition and patching. Additionally, an autonomous control system is integrated with ultrasound imaging to enable automatic acoustic field alignment and control. The proposed method advances the spatiotemporal response capability of untethered miniature soft robots, holding promise for broadening their versatility and adaptability.

Combined HRAS and NRAS ablation induces a RASopathy phenotype in mice

BackgroundHRASKO/NRASKO double knockout mice exhibit exceedingly high rates of perinatal lethality due to respiratory failure caused by a significant lung maturation delay. The few animals that reach adulthood have a normal lifespan, but present areas of atelectasis mixed with patches of emphysema and normal tissue in the lung.MethodsEight double knockout and eight control mice were analyzed using micro-X-ray computerized tomography and a Small Animal Physiological Monitoring system. Tissues and samples from these mice were analyzed using standard histological and Molecular Biology methods and the significance of the results analyzed using a Student´s T-test.ResultsThe very few double knockout mice surviving up to adulthood display clear craniofacial abnormalities reminiscent of those seen in RASopathy mouse models, as well as thrombocytopenia, bleeding anomalies, and reduced platelet activation induced by thrombin. These surviving mice also present heart and spleen hyperplasia, and elevated numbers of myeloid-derived suppressor cells in the spleen. Mechanistically, we observed that these phenotypic alterations are accompanied by increased KRAS-GTP levels in heart, platelets and primary mouse embryonic fibroblasts from these animals.ConclusionsOur data uncovers a new, previously unidentified mechanism capable of triggering a RASopathy phenotype in mice as a result of the combined removal of HRAS and NRAS.

Efficacy of Beta vulgaris Essential Oils Against Potato Dry Rot Disease and Deoxynivalenol (DON) Mycotoxin

AbstractPotato dry rot is a global problem caused by Fusarium species. Symptoms of dry rot include wrinkled brown to black patches of tissue. Fusarium spp. infection in potato tubers results in the formation of mycotoxins. Fungi that cause dry rot are commonly Fusarium sambucinum and Fusarium solani. This article was made to determine the effect of the essential oils of the Beta vulagaris plant on Fusarium species, which is the causative agent of dry rot in potatoes. In the study, Beta vulgaris plant essential oil content was determined by Gas Chromatography (GC–MC). In addition, the amount of deoxynivalenol (DON) mycotoxin produced by Fusarium species was determined by enzyme-linked immunosorbent assay (ELISA) analyses. Inhibition rates of these essential oils (1, 5, 10, 20, 50, 100, 250, 500, 1000 µl) on Fusarium sambicunum (1.3, 3.0, 5.2, 7.3, 9.1, 12.7, 22.3, 27.1, 29.1%) were found. Similarly, inhibition rates on Fusarium solani (1.1, 2.8, 4.3, 6.7, 8.8, 10.5, 19.4, 24.7, 27.3%) were found. In addition, the results showed that the amount of deoxynivalenol DON in 25 potato tubers ranged from 44.1–172.6 ppb. Infections of potato tubers caused by certain Fusarium species are typically accompanied by mycotoxin production, thus posing a potential risk to human health and food safety. In this study, it was determined that the essential oils of Beta vulgaris plant were effective against Fusarium spp., which are the cause of dry rot disease that may occur in potatoes.

Joint Super-resolution and Tissue Patch Classification for Whole Slide Histological Images

Joint Super-resolution and Tissue Patch Classification for Whole Slide Histological Images

LinG3D: visualizing the spatio-temporal dynamics of clonal evolution.

Cancers are spatially heterogenous, thus their clonal evolution, especially following anti-cancer treatments, depends on where the mutated cells are located within the tumor tissue. For example, cells exposed to different concentrations of drugs, such as cells located near the vessels in contrast to those residing far from the vasculature, can undergo a different evolutionary path. However, classical representations of cell lineage trees do not account for this spatial component of emerging cancer clones. Here, we propose routines to trace spatial and temporal clonal evolution in computer simulations of the tumor evolution models. The LinG3D (Lineage Graphs in 3D) is an open-source collection of routines (in MATLAB, Python, and R) that enables spatio-temporal visualization of clonal evolution in a two-dimensional tumor slice from computer simulations of the tumor evolution models. These routines draw traces of tumor clones in both time and space, and may include a projection of a selected microenvironmental factor, such as the drug or oxygen distribution within the tumor, if such a microenvironmental factor is used in the tumor evolution model. The utility of LinG3D has been demonstrated through examples of simulated tumors with different number of clones and, additionally, in experimental colony growth assay. This routine package extends the classical lineage trees, that show cellular clone relationships in time, by adding the space component to show the locations of cellular clones within the 2D tumor tissue patch from computer simulations of tumor evolution models.

Development of a Flexible Sensor-Integrated Tissue Patch to Monitor Early Organ Rejection Processes Using Impedance Spectroscopy.

Heart failure represents a primary cause of hospitalization and mortality in both developed and developing countries, often necessitating heart transplantation as the only viable recovery path. Despite advances in transplantation medicine, organ rejection remains a significant post-operative challenge, traditionally monitored through invasive endomyocardial biopsies (EMB). This study introduces a rapid prototyping approach to organ rejection monitoring via a sensor-integrated flexible patch, employing electrical impedance spectroscopy (EIS) for the non-invasive, continuous assessment of resistive and capacitive changes indicative of tissue rejection processes. Utilizing titanium-dioxide-coated electrodes for contactless impedance sensing, this method aims to mitigate the limitations associated with EMB, including procedural risks and the psychological burden on patients. The biosensor's design features, including electrode passivation and three-dimensional microelectrode protrusions, facilitate effective monitoring of cardiac rejection by aligning with the heart's curvature and responding to muscle contractions. Evaluation of sensor performance utilized SPICE simulations, scanning electron microscopy, and cyclic voltammetry, alongside experimental validation using chicken heart tissue to simulate healthy and rejected states. The study highlights the potential of EIS in reducing the need for invasive biopsy procedures and offering a promising avenue for early detection and monitoring of organ rejection, with implications for patient care and healthcare resource utilization.

Abstract PO1-26-01: Leveraging Clinicopathological Factors and Deep Learning-Based Morphometrics for PDX Engraftment Success Prediction in Breast Cancer

Abstract Background: Patient-derived xenografts (PDXs) are pivotal in cancer research. Despite histopathological insights into factors driving PDX success, the role of artificial intelligence (AI) in predicting PDX engraftment remains unexplored. We aimed to bridge this gap by integrating clinicopathological data and AI-based morphometric analysis to predict PDX success in breast cancer. Methods: PDXs were generated from tumor tissues derived from breast cancer patients who had undergone surgical intervention. Clinicopathological information including subtypes, pathological diagnosis, modified Bloom-Richardson system histologic grades, treatment with neoadjuvant chemotherapy (NAC), Miller Payne grade, residual caner burden score, invasive tumor size, lymphovascular invasion status, AJCC 8th T and N stages and the percentage of tumor infiltrating lymphocytes were collected and analyzed. For the image analysis component, whole-slide images (WSIs) of hematoxylin and eosin–stained tissue samples from 64 surgically resected breast cancer patients were used as a training set for an AI model under the supervision of 2 pathologists to extract morphometric features. The model transformed image tiles into patches of morphologically similar patterns, and categorized them into adipose tissue, background, necrosis, invasive carcinoma, carcinoma in situ, stroma, and terminal ductal lobular unit. This trained model was subsequently applied to the WSIs employed in the establishment of PDXs. The classified patches and their relative ratios within the invasive tumor boundary were compiled. The consolidated data from clinicopathological and image analyses were subjected to logistic regression to discern correlates of successful PDX engraftment. Results: Out of the 311 patient tumor samples used for generating PDXs, (131 post-chemotherapy, 180 chemo-naïve), 47 PDXs were successfully established (15.1%). Logistic regression revealed several factors for successful engraftment including NAC treatment with an odds ratio of 6.71 (28.2% vs 5.6%, 95% CI: 2.53 - 17.80, p < 0.001), higher histologic grades (25.3% vs 2.2%, OR = 5.80, 95% CI: 1.49 - 22.63, p = 0.01), triple negative breast cancer compared to hormone receptor-positive cancers (32.0% vs 3.3%, OR = 10.99, 95% CI: 1.26 - 95.89, p = 0.03), and tumors of larger size (OR = 1.34, 95% CI: 1.02 - 1.76, p = 0.03). Interestingly, the percentage of specific tissue patch types within tumor did not significantly impact the likelihood of successful PDX engraftment. However, in our logistic regression analysis based solely on morphometric features, presence of necrosis within the tumor notably enhanced PDX establishment. Specifically, each percent increase in necrosis within tumor boosted the odds of successful PDX creation by 0.01% (OR = 1.0001, 95% CI: 1.00003 - 1.00024, p = 0.01). Conclusions: PDXs are often successfully established from clinically aggressive breast cancers, particularly those with NAC treatment, higher histologic grades, TNBC subtype, and larger tumor size. While morphometric features contribute to the prediction of PDX engraftment success, their importance is surpassed by these clinicopathological factors. However, presence of necrosis emerged as a key morphometric predictor of successful PDX engraftment. Keywords: breast cancer; patient-derived xenograft (PDX); Breast Cancer Morphometrics; Cancer Predictive Modeling. Citation Format: Jongwon Lee, GeonHee Lee, Gyungyub Gong, Hee Jin Lee. Leveraging Clinicopathological Factors and Deep Learning-Based Morphometrics for PDX Engraftment Success Prediction in Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-26-01.

SimuCell3D: three-dimensional simulation of tissue mechanics with cell polarization

The three-dimensional (3D) organization of cells determines tissue function and integrity, and changes markedly in development and disease. Cell-based simulations have long been used to define the underlying mechanical principles. However, high computational costs have so far limited simulations to either simplified cell geometries or small tissue patches. Here, we present SimuCell3D, an efficient open-source program to simulate large tissues in three dimensions with subcellular resolution, growth, proliferation, extracellular matrix, fluid cavities, nuclei and non-uniform mechanical properties, as found in polarized epithelia. Spheroids, vesicles, sheets, tubes and other tissue geometries can readily be imported from microscopy images and simulated to infer biomechanical parameters. Doing so, we show that 3D cell shapes in layered and pseudostratified epithelia are largely governed by a competition between surface tension and intercellular adhesion. SimuCell3D enables the large-scale in silico study of 3D tissue organization in development and disease at a great level of detail.

BIOLOGICAL PROSTHESES FOR CARDIOVASCULAR SURGERY – A HALF-CENTURY HISTORY AND DEVELOPMENT PROSPECTS

HighlightsSurgical correction of cardiovascular deformities is inextricably linked with the use of prostheses. The development and clinical testing of biological prostheses has been conducted under the guidance of the Member of Russian Academy of Sciences Leonid Barbarash at the Kuzbass Cardiology Center for more than 50 years. The uniqueness of the developed products lies in the use of a new biological tissue preservative – ethylene glycol diglycidyl ether, and an integrated approach involving the development and manufacture of products, study of long-term replacement outcome, causes of prosthetic dysfunction and genetic aspects of the use of bioprostheses in patients. AbstractThis article highlights the main achievements in the development of biological prostheses for cardiovascular surgery under the leadership of the Member of Russian Academy of Sciences L.S. Barbarash since the 1980s. Throughout this period, Leonid Barbarash and his team have developed and mass-produced various designs of bioprosthetic heart valves and tissue preservation methods, arterial grafts, and patches. The article presents the results of developing next-generation vascular biodegradable grafts capable of complete remodeling and stimulating endothelialization after the implantation into the vascular bed. Moreover, we conducted a series of genetic studies evaluating the significance of gene polymorphisms of various pathogenetic pathways involved in the pathological calcification of native heart valves and their prostheses.

General Semi-Solid Freeze Casting for Uniform Large-Scale Isotropic Porous Scaffolds: An Application for Extensive Oral Mucosal Reconstruction.

Ice-templated porous biomaterials possess transformative potential in regenerative medicine; yet, scaling up ice-templating processes for broader applications-owing to inconsistent pore formation-remains challenging. This study reports an innovative semi-solid freeze-casting technique that draws inspiration from semi-solid metal processing (SSMP) combined with ice cream-production routines. This versatile approach allows for the large-scale assembly of various materials, from polymers to inorganic particles, into isotropic 3D scaffolds featuring uniformly equiaxed pores throughout the centimeter scale. Through (cryo-)electron microscopy, X-ray tomography, and finite element modeling, the structural evolution of ice grains/pores is elucidated, demonstrating how the method increases the initial ice nucleus density by pre-fabricating a semi-frozen slurry, which facilitates a transition from columnar to equiaxed grain structures. For a practical demonstration, as-prepared scaffolds are integrated into a bilayer tissue patch using biodegradable waterborne polyurethane (WPU) for large-scale oral mucosal reconstruction in minipigs. Systematic analyses, including histology and RNA sequencing, prove that the patch modulates the healing process toward near-scarless mucosal remodeling via innate and adaptive immunomodulation and activation of pro-healing genes converging on matrix synthesis and epithelialization. This study not only advances the field of ice-templating fabrication but sets a promising precedent for scaffold-based large-scaletissue regeneration.