Tissue Engineering Part A最新文献

Severe coronary artery disease is often treated with a coronary artery bypass graft using an autologous blood vessel. When this is not available, a commercially available synthetic graft can be used as an alternative but is associated with high failure rates and complications. Therefore, the research focus has shifted toward the development of biodegradable, regenerative vascular grafts that can convert into neoarteries. We previously developed an electrospun tropoelastin (TE)-polyglycerol sebacate (PGS) vascular graft that rapidly regenerated into a neoartery, with a cellular composition and extracellular matrix approximating the native aorta. We noted, however, that the TE-PGS graft underwent dilation until sufficient neotissue had been regenerated. This study investigated the mechanisms behind the observed dilation following TE-PGS vascular graft implantation in mice. We saw more pronounced dilation at the graft middle compared with the graft proximal and graft distal regions at 8 weeks postimplantation. Histological analysis revealed less degradation at the graft middle, although the remaining graft material appeared pitted, suggesting compromised structural and mechanical integrity. We also observed delayed cellular infiltration and extracellular matrix (ECM) deposition at the graft middle, corresponding with the area's reduced ability to resist dilation. In contrast, the graft proximal region exhibited greater degradation and significantly enhanced cellular infiltration and ECM regeneration. The nonuniform dilation was attributed to the combined effect of the regional differences in graft degradation and arterial regeneration. Consideration of these findings is crucial for graft optimization prior to its use in clinical applications.

Manual grading of cartilage histology images for investigating the extent and severity of osteoarthritis (OA) involves critical examination of the cell characteristics, which makes this task tiresome, tedious, and error prone. This results in wide interobserver variation, causing ambiguities in OA grade prediction. Such drawbacks of manual assessment can be overcome by implementing artificial intelligence-based automated image classification techniques such as deep learning (DL). Hence, we present the feasibility of training a deep neural network with cartilage histology images, which can grade the extent and severity of knee OA based on modified Mankin scoring system. The grading system used here for automating OA grading was simplified and modified based on the microscopic observations from the histology images, where three parameters (Safranin-O staining intensity, chondrocyte distribution and arrangement, and morphology) were considered for evaluating the OA progression. The histology images were tiled, labeled, and grouped together based on the developed grading system (Grade 0-3). Four different DL architectures were tried for image classification and the best performing model was selected by fivefold validation method. With a validation accuracy of ∼84%, 0.632 Cohen's kappa score, and an excellent receiver operating characteristic (ROC)-area under the ROC curve ranging between 0.89 and 0.99, DenseNet121 was selected among the four models as the best performing model, and was used for inferencing on new data. Final grades obtained from the models were in accordance with the grades provided by the medical experts. We hereby demonstrate that a DL architecture can be taught to interpret the degree of cartilage degradation, with excellent discriminatory ability across all four classes of OA severity. Unlike other works where radiographic images have been considered for grading of OA, we have considered histology images, which is a fundamental approach for grading OA extent and severity. This would bring a paradigm shift in histology-based assessment of OA, making this automated approach to be explored as an option for OA grading standardization. Ethical approval number-IAH-BMR-018/10-19.

In recent years, there has been a significant expansion in the realm of processing microscopy images, thanks to the advent of machine learning techniques. These techniques offer diverse applications for image processing. Currently, numerous methods are used for processing microscopy images in the field of biology, ranging from conventional machine learning algorithms to sophisticated deep learning artificial neural networks with millions of parameters. However, a comprehensive grasp of the intricacies of these methods usually necessitates proficiency in programming and advanced mathematics. In our comprehensive review, we explore various widely used deep learning approaches tailored for the processing of microscopy images. Our emphasis is on algorithms that have gained popularity in the field of biology and have been adapted to cater to users lacking programming expertise. In essence, our target audience comprises biologists interested in exploring the potential of deep learning algorithms, even without programming skills. Throughout the review, we elucidate each algorithm's fundamental concepts and capabilities without delving into mathematical and programming complexities. Crucially, all the highlighted algorithms are accessible on open platforms without requiring code, and we provide detailed descriptions and links within our review. It's essential to recognize that addressing each specific problem demands an individualized approach. Consequently, our focus is not on comparing algorithms but on delineating the problems they are adept at solving. In practical scenarios, researchers typically select multiple algorithms suited to their tasks and experimentally determine the most effective one. It is worth noting that microscopy extends beyond the realm of biology; its applications span diverse fields such as geology and material science. Although our review predominantly centers on biomedical applications, the algorithms and principles outlined here are equally applicable to other scientific domains. Furthermore, a number of the proposed solutions can be modified for use in entirely distinct computer vision cases.

Pancreatic ductal adenocarcinoma (PDAC) represents one of the only cancers with an increasing incidence rate and is often associated with intra- and peri-tumoral scarring, referred to as desmoplasia. This scarring is highly heterogeneous in extracellular matrix (ECM) architecture and plays complex roles in both tumor biology and clinical outcomes that are not yet fully understood. Using hematoxylin and eosin (H&E), a routine histological stain utilized in existing clinical workflows, we quantified ECM architecture in 85 patient samples to assess relationships between desmoplastic architecture and clinical outcomes such as survival time and disease recurrence. By utilizing unsupervised machine learning to summarize a latent space across 147 local (e.g., fiber length, solidity) and global (e.g., fiber branching, porosity) H&E-based features, we identified a continuum of histological architectures that were associated with differences in both survival and recurrence. Furthermore, we mapped H&E architectures to a CO-Detection by indEXing (CODEX) reference atlas, revealing localized cell- and protein-based niches associated with outcome-positive versus outcome-negative scarring in the tumor microenvironment. Overall, our study utilizes standard H&E staining to uncover clinically relevant associations between desmoplastic organization and PDAC outcomes, offering a translatable pipeline to support prognostic decision-making and a blueprint of spatial-biological factors for modeling by tissue engineering methods.

The fusion index is a key indicator for quantifying the differentiation of a myoblast population, which is often calculated manually. In addition to being time-consuming, manual quantification is also error prone and subjective. Several software tools have been proposed for addressing these limitations but suffer from various drawbacks, including unintuitive interfaces and limited performance. In this study, we describe MyoFInDer, a Python-based program for the automated computation of the fusion index of skeletal muscle. At the core of MyoFInDer is a powerful artificial intelligence-based image segmentation model. MyoFInDer also determines the total nuclei count and the percentage of stained area and allows for manual verification and correction. MyoFInDer can reliably determine the fusion index, with a high correlation to manual counting. Compared with other tools, MyoFInDer stands out as it minimizes the interoperator variability, minimizes process time and displays the best correlation to manual counting. Therefore, it is a suitable choice for calculating fusion index in an automated way, and gives researchers access to the high performance and flexibility of a modern artificial intelligence model. As a free and open-source project, MyoFInDer can be modified or extended to meet specific needs.

Oral squamous cell carcinoma (OSCC) is a highly unpredictable disease with devastating mortality rates that have not changed over the past decades, in the face of advancements in treatments and biomarkers, which have improved survival for other cancers. Delays in diagnosis are frequent, leading to more disfiguring treatments and poor outcomes for patients. The clinical challenge lies in identifying those patients at the highest risk of developing OSCC. Oral epithelial dysplasia (OED) is a precursor of OSCC with highly variable behavior across patients. There is no reliable clinical, pathological, histological, or molecular biomarker to determine individual risk in OED patients. Similarly, there are no robust biomarkers to predict treatment outcomes or mortality in OSCC patients. This review aims to highlight advancements in artificial intelligence (AI)-based methods to develop predictive biomarkers of OED transformation to OSCC or predictive biomarkers of OSCC mortality and treatment response. Biomarkers such as S100A7 demonstrate promising appraisal for the risk of malignant transformation of OED. Machine learning-enhanced multiplex immunohistochemistry workflows examine immune cell patterns and organization within the tumor immune microenvironment to generate outcome predictions in immunotherapy. Deep learning (DL) is an AI-based method using an extended neural network or related architecture with multiple "hidden" layers of simulated neurons to combine simple visual features into complex patterns. DL-based digital pathology is currently being developed to assess OED and OSCC outcomes. The integration of machine learning in epigenomics aims to examine the epigenetic modification of diseases and improve our ability to detect, classify, and predict outcomes associated with epigenetic marks. Collectively, these tools showcase promising advancements in discovery and technology, which may provide a potential solution to addressing the current limitations in predicting OED transformation and OSCC behavior, both of which are clinical challenges that must be addressed in order to improve OSCC survival.

Biomaterials often have subtle properties that ultimately drive their bespoke performance. Given this nuanced structure-function behavior, the standard scientific approach of one experiment at a time or design of experiment methods is largely inefficient for the discovery of complex biomaterials. More recently, high-throughput experimentation coupled with machine learning methods has matured beyond expert users allowing scientists and engineers from diverse backgrounds to access these powerful data science tools. As a result, we now have the opportunity to strategically utilize all available data from high-throughput experiments to train efficacious models and map the structure-function behavior of biomaterials for their discovery. Herein, we discuss this necessary shift to data-driven determination of structure-function properties of biomaterials as we highlight how machine learning is leveraged in identifying physicochemical cues for biomaterials in tissue engineering, gene delivery, drug delivery, protein stabilization, and antifouling materials. We also discuss data-mining approaches that are coupled with machine learning to map biomaterial functions that reduce the load on experimental approaches for faster biomaterial discovery. Ultimately, harnessing the prowess of machine learning will lead to accelerated discovery and development of optimal biomaterial designs.

In this study, we present a versatile, scaffold-free approach to create ring-shaped engineered vascular tissue segments using human mesenchymal stem cell-derived smooth muscle cells (hMSC-SMCs) and endothelial cells (ECs). We hypothesized that incorporation of ECs would increase hMSC-SMC differentiation without compromising tissue ring strength or fusion to form tissue tubes. Undifferentiated hMSCs and ECs were co-seeded into custom ring-shaped agarose wells using four different concentrations of ECs: 0%, 10%, 20%, and 30%. Co-seeded EC and hMSC rings were cultured in SMC differentiation medium for a total of 22 days. Tissue rings were then harvested for histology, Western blotting, wire myography, and uniaxial tensile testing to examine their structural and functional properties. Differentiated hMSC tissue rings comprising 20% and 30% ECs exhibited significantly greater SMC contractile protein expression, endothelin-1 (ET-1)-meditated contraction, and force at failure compared with the 0% EC rings. On average, the 0%, 10%, 20%, and 30% EC rings exhibited a contractile force of 0.745 ± 0.117, 0.830 ± 0.358, 1.31 ± 0.353, and 1.67 ± 0.351 mN (mean ± standard deviation [SD]) in response to ET-1, respectively. Additionally, the mean maximum force at failure for the 0%, 10%, 20%, and 30% EC rings was 88.5 ± 36. , 121 ± 59.1, 147 ± 43.1, and 206 ±  0.8 mN (mean ± SD), respectively. Based on these results, 30% EC rings were fused together to form tissue-engineered blood vessels (TEBVs) and compared with 0% EC TEBV controls. The addition of 30% ECs in TEBVs did not affect ring fusion but did result in significantly greater SMC protein expression (calponin and smoothelin). In summary, co-seeding hMSCs with ECs to form tissue rings resulted in greater contraction, strength, and hMSC-SMC differentiation compared with hMSCs alone and indicates a method to create a functional 3D human vascular cell coculture model.