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Open AccessEditorial

Microalgae Biotechnology: Methods and Applications

by Xianmin Wang, Songlin Ma and Fantao Kong

Bioengineering 2024, 11(10), 965; https://doi.org/10.3390/bioengineering11100965 (registering DOI) - 26 Sep 2024

Microalgae are regarded as sustainable and promising chassis for biotechnology due to their efficient photosynthesis and ability to convert CO₂ into valuable products [...] Full article

(This article belongs to the Section Biochemical Engineering)

7 pages, 920 KiB

Open AccessArticle

Dynamics of Treatment Response to Faricimab for Diabetic Macular Edema

by Katrin Fasler, Daniel R. Muth, Mariano Cozzi, Anders Kvanta, Magdalena Rejdak, Frank Blaser and Sandrine A. Zweifel

Bioengineering 2024, 11(10), 964; https://doi.org/10.3390/bioengineering11100964 - 26 Sep 2024

Abstract

This study analyzes the dynamics of short-term treatment response to the first intravitreal faricimab injection in eyes with diabetic macular edema (DME). This retrospective, single-center, clinical trial was conducted at the Department of Ophthalmology, University Hospital Zurich. Patients with treatment-naïve and pretreated DME [...] Read more.

This study analyzes the dynamics of short-term treatment response to the first intravitreal faricimab injection in eyes with diabetic macular edema (DME). This retrospective, single-center, clinical trial was conducted at the Department of Ophthalmology, University Hospital Zurich. Patients with treatment-naïve and pretreated DME were included. Patient chart data and imaging were analyzed. Safety and efficacy (corrected visual acuity (CVA), central subfield thickness (CST), and signs of intraocular inflammation (IOI)) of the first faricimab intravitreal therapy (IVT) were evaluated weekly until 4 weeks after injection. Forty-three eyes (81% pretreated) of 31 patients were included. Four weeks after the first faricimab IVT, CVA remained stable and median CST (µm) decreased significantly (p < 0.001) from 325.0 (293.5–399.0) at baseline to 304.0 (286.5–358.0). CVA at week 4 was only associated with baseline CVA (p < 0.001). CST was the only predictive variable (p = 0.002) between baseline and week 4 CST. Weekly safety assessments did not show any sign of clinically significant IOI. This study suggests faricimab is an effective treatment for (pretreated) DME, showing structural benefit 1 month following the first injection without short-term safety signals. Full article

(This article belongs to the Special Issue Recent Advances and Trends in Ophthalmic Diseases Treatment)

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16 pages, 2605 KiB

Open AccessArticle

Applying a Deep Learning Model for Total Kidney Volume Measurement in Autosomal Dominant Polycystic Kidney Disease

by Jia-Lien Hsu, Anandakumar Singaravelan, Chih-Yun Lai, Zhi-Lin Li, Chia-Nan Lin, Wen-Shuo Wu, Tze-Wah Kao and Pei-Lun Chu

Bioengineering 2024, 11(10), 963; https://doi.org/10.3390/bioengineering11100963 - 26 Sep 2024

Abstract

Background: Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary renal disease leading to end-stage renal disease. Total kidney volume (TKV) measurement has been considered as a surrogate in the evaluation of disease severity and prognostic predictor of ADPKD. However, the [...] Read more.

Background: Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary renal disease leading to end-stage renal disease. Total kidney volume (TKV) measurement has been considered as a surrogate in the evaluation of disease severity and prognostic predictor of ADPKD. However, the traditional manual measurement of TKV by medical professionals is labor-intensive, time-consuming, and human error prone. Materials and methods: In this investigation, we conducted TKV measurements utilizing magnetic resonance imaging (MRI) data. The dataset consisted of 30 patients with ADPKD and 10 healthy individuals. To calculate TKV, we trained models using both coronal- and axial-section MRI images. The process involved extracting images in Digital Imaging and Communications in Medicine (DICOM) format, followed by augmentation and labeling. We employed a U-net model for image segmentation, generating mask images of the target areas. Subsequent post-processing steps and TKV estimation were performed based on the outputs obtained from these mask images. Results: The average TKV, as assessed by medical professionals from the testing dataset, was 1501.84 ± 965.85 mL with axial-section images and 1740.31 ± 1172.21 mL with coronal-section images, respectively (p = 0.73). Utilizing the deep learning model, the mean TKV derived from axial- and coronal-section images was 1536.33 ± 958.68 mL and 1636.25 ± 964.67 mL, respectively (p = 0.85). The discrepancy in mean TKV between medical professionals and the deep learning model was 44.23 ± 58.69 mL with axial-section images (p = 0.8) and 329.12 ± 352.56 mL with coronal-section images (p = 0.9), respectively. The average variability in TKV measurement was 21.6% with the coronal-section model and 3.95% with the axial-section model. The axial-section model demonstrated a mean Dice Similarity Coefficient (DSC) of 0.89 ± 0.27 and an average patient-wise Jaccard coefficient of 0.86 ± 0.27, while the mean DSC and Jaccard coefficient of the coronal-section model were 0.82 ± 0.29 and 0.77 ± 0.31, respectively. Conclusion: The integration of deep learning into image processing and interpretation is becoming increasingly prevalent in clinical practice. In our pilot study, we conducted a comparative analysis of the performance of a deep learning model alongside corresponding axial- and coronal-section models, a comparison that has been less explored in prior research. Our findings suggest that our deep learning model for TKV measurement performs comparably to medical professionals. However, we observed that varying image orientations could introduce measurement bias. Specifically, our AI model exhibited superior performance with axial-section images compared to coronal-section images. Full article

(This article belongs to the Special Issue Deciphering Medicine: The Role of Explainable Artificial Intelligence in Healthcare Innovations)

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11 pages, 1098 KiB

Open AccessArticle

Pediatric Intensive Care Unit Length of Stay Prediction by Machine Learning

by Hammad A. Ganatra, Samir Q. Latifi and Orkun Baloglu

Bioengineering 2024, 11(10), 962; https://doi.org/10.3390/bioengineering11100962 - 26 Sep 2024

Abstract

Purpose: To develop and validate machine learning models for predicting the length of stay (LOS) in the Pediatric Intensive Care Unit (PICU) using data from the Virtual Pediatric Systems (VPS) database. Methods: A retrospective study was conducted utilizing machine learning (ML) [...] Read more.

Purpose: To develop and validate machine learning models for predicting the length of stay (LOS) in the Pediatric Intensive Care Unit (PICU) using data from the Virtual Pediatric Systems (VPS) database. Methods: A retrospective study was conducted utilizing machine learning (ML) algorithms to analyze and predict PICU LOS based on historical patient data from the VPS database. The study included data from over 100 North American PICUs spanning the years 2015–2020. After excluding entries with missing variables and those indicating recovery from cardiac surgery, the dataset comprised 123,354 patient encounters. Various ML models, including Support Vector Machine, Stochastic Gradient Descent Classifier, K-Nearest Neighbors, Decision Tree, Gradient Boosting, CatBoost, and Recurrent Neural Networks (RNNs), were evaluated for their accuracy in predicting PICU LOS at thresholds of 24 h, 36 h, 48 h, 72 h, 5 days, and 7 days. Results: Gradient Boosting, CatBoost, and RNN models demonstrated the highest accuracy, particularly at the 36 h and 48 h thresholds, with accuracy rates between 70 and 73%. These results far outperform traditional statistical and existing prediction methods that report accuracy of only around 50%, which is effectively unusable in the practical setting. These models also exhibited balanced performance between sensitivity (up to 74%) and specificity (up to 82%) at these thresholds. Conclusions: ML models, particularly Gradient Boosting, CatBoost, and RNNs, show moderate effectiveness in predicting PICU LOS with accuracy slightly over 70%, outperforming previously reported human predictions. This suggests potential utility in enhancing resource and staffing management in PICUs. However, further improvements through training on specialized databases can potentially achieve better accuracy and clinical applicability. Full article

(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities)

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15 pages, 1321 KiB

Open AccessCommentary

The Use of Mesenchymal Stem/Stromal Cell-Derived Extracellular Vesicles in the Treatment of Osteoarthritis: Insights from Preclinical Studies

by Mitch Jones, Elena Jones and Dimitrios Kouroupis

Bioengineering 2024, 11(10), 961; https://doi.org/10.3390/bioengineering11100961 - 26 Sep 2024

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Abstract

Osteoarthritis (OA) is a prominent cause of disability, and has severe social and economic ramifications across the globe. The main driver of OA’s pervasiveness is the fact that no current medical interventions exist to reverse or even attenuate the degeneration of cartilage within [...] Read more.

Osteoarthritis (OA) is a prominent cause of disability, and has severe social and economic ramifications across the globe. The main driver of OA’s pervasiveness is the fact that no current medical interventions exist to reverse or even attenuate the degeneration of cartilage within the articular joint. Crucial for cell-to-cell communication, extracellular vesicles (EVs) contribute to OA progression through the delivery of bioactive molecules in the inflammatory microenvironment. By repurposing this acellular means of signal transmission, therapeutic drugs may be administered to degenerated cartilage tissue in the hopes of encouraging regeneration. Positive outcomes are apparent in in vivo studies on this subject; however, for this therapy to prove itself in the clinical world, efforts towards standardizing the characterization, application, biological contents, and dosage are essential. Full article

(This article belongs to the Section Regenerative Engineering)

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28 pages, 21118 KiB

Open AccessArticle

Galectin-3/Gelatin Electrospun Scaffolds Modulate Collagen Synthesis in Skin Healing but Do Not Improve Wound Closure Kinetics

by Karrington A. McLeod, Madeleine Di Gregorio, Dylan Tinney, Justin Carmichael, David Zuanazzi, Walter L. Siqueira, Amin Rizkalla and Douglas W. Hamilton

Bioengineering 2024, 11(10), 960; https://doi.org/10.3390/bioengineering11100960 - 25 Sep 2024

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Abstract

Chronic wounds remain trapped in a pro-inflammatory state, with strategies targeted at inducing re-epithelialization and the proliferative phase of healing desirable. As a member of the lectin family, galectin-3 is implicated in the regulation of macrophage phenotype and epithelial migration. We investigated if [...] Read more.

Chronic wounds remain trapped in a pro-inflammatory state, with strategies targeted at inducing re-epithelialization and the proliferative phase of healing desirable. As a member of the lectin family, galectin-3 is implicated in the regulation of macrophage phenotype and epithelial migration. We investigated if local delivery of galectin-3 enhanced skin healing in a full-thickness excisional C57BL/6 mouse model. An electrospun gelatin scaffold loaded with galectin-3 was developed and compared to topical delivery of galectin-3. Electrospun gelatin/galectin-3 scaffolds had an average fiber diameter of 200 nm, with 83% scaffold porosity approximately and an average pore diameter of 1.15 μm. The developed scaffolds supported dermal fibroblast adhesion, matrix deposition, and proliferation in vitro. In vivo treatment of 6 mm full-thickness excisional wounds with gelatin/galectin-3 scaffolds did not influence wound closure, re-epithelialization, or macrophage phenotypes, but increased collagen synthesis. In comparison, topical delivery of galectin-3 [6.7 µg/mL] significantly increased arginase-I cell density at day 7 versus untreated and gelatin/galectin-3 scaffolds (p < 0.05). A preliminary assessment of increasing the concentration of topical galectin-3 demonstrated that at day 7, galectin-3 [12.5 µg/mL] significantly increased both epithelial migration and collagen content in a concentration-dependent manner. In conclusion, local delivery of galectin 3 shows potential efficacy in modulating skin healing in a concentration-dependent manner. Full article

(This article belongs to the Special Issue Biomaterials and Technology for Skin Wound Healing)

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19 pages, 860 KiB

Open AccessArticle

An Edge-Enhanced Network for Polyp Segmentation

by Yao Tong, Ziqi Chen, Zuojian Zhou, Yun Hu, Xin Li and Xuebin Qiao

Bioengineering 2024, 11(10), 959; https://doi.org/10.3390/bioengineering11100959 - 25 Sep 2024

Viewed by 86

Abstract

Colorectal cancer remains a leading cause of cancer-related deaths worldwide, with early detection and removal of polyps being critical in preventing disease progression. Automated polyp segmentation, particularly in colonoscopy images, is a challenging task due to the variability in polyp appearance and the [...] Read more.

Colorectal cancer remains a leading cause of cancer-related deaths worldwide, with early detection and removal of polyps being critical in preventing disease progression. Automated polyp segmentation, particularly in colonoscopy images, is a challenging task due to the variability in polyp appearance and the low contrast between polyps and surrounding tissues. In this work, we propose an edge-enhanced network (EENet) designed to address these challenges by integrating two novel modules: the covariance edge-enhanced attention (CEEA) and cross-scale edge enhancement (CSEE) modules. The CEEA module leverages covariance-based attention to enhance boundary detection, while the CSEE module bridges multi-scale features to preserve fine-grained edge details. To further improve the accuracy of polyp segmentation, we introduce a hybrid loss function that combines cross-entropy loss with edge-aware loss. Extensive experiments show that the EENet achieves a Dice score of 0.9208 and an IoU of 0.8664 on the Kvasir-SEG dataset, surpassing state-of-the-art models such as Polyp-PVT and PraNet. Furthermore, it records a Dice score of 0.9316 and an IoU of 0.8817 on the CVC-ClinicDB dataset, demonstrating its strong potential for clinical application in polyp segmentation. Ablation studies further validate the contribution of the CEEA and CSEE modules. Full article

(This article belongs to the Special Issue Applications of Computational Modeling in Biomedical Image and Signal Processing)

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16 pages, 1376 KiB

Open AccessArticle

Exploiting K-Space in Magnetic Resonance Imaging Diagnosis: Dual-Path Attention Fusion for K-Space Global and Image Local Features

by Congchao Bian, Can Hu and Ning Cao

Bioengineering 2024, 11(10), 958; https://doi.org/10.3390/bioengineering11100958 - 25 Sep 2024

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Abstract

Magnetic resonance imaging (MRI) diagnosis, enhanced by deep learning methods, plays a crucial role in medical image processing, facilitating precise clinical diagnosis and optimal treatment planning. Current methodologies predominantly focus on feature extraction from the image domain, which often results in the loss [...] Read more.

Magnetic resonance imaging (MRI) diagnosis, enhanced by deep learning methods, plays a crucial role in medical image processing, facilitating precise clinical diagnosis and optimal treatment planning. Current methodologies predominantly focus on feature extraction from the image domain, which often results in the loss of global features during down-sampling processes. However, the unique global representational capacity of MRI K-space is often overlooked. In this paper, we present a novel MRI K-space-based global feature extraction and dual-path attention fusion network. Our proposed method extracts global features from MRI K-space data and fuses them with local features from the image domain using a dual-path attention mechanism, thereby achieving accurate MRI segmentation for diagnosis. Specifically, our method consists of four main components: an image-domain feature extraction module, a K-space domain feature extraction module, a dual-path attention feature fusion module, and a decoder. We conducted ablation studies and comprehensive comparisons on the Brain Tumor Segmentation (BraTS) MRI dataset to validate the effectiveness of each module. The results demonstrate that our method exhibits superior performance in segmentation diagnostics, outperforming state-of-the-art methods with improvements of up to 63.82% in the HD95 distance evaluation metric. Furthermore, we performed generalization testing and complexity analysis on the Automated Cardiac Diagnosis Challenge (ACDC) MRI cardiac segmentation dataset. The findings indicate robust performance across different datasets, highlighting strong generalizability and favorable algorithmic complexity. Collectively, these results suggest that our proposed method holds significant potential for practical clinical applications. Full article

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16 pages, 2517 KiB

Open AccessArticle

Between Two Worlds: Investigating the Intersection of Human Expertise and Machine Learning in the Case of Coronary Artery Disease Diagnosis

by Ioannis D. Apostolopoulos, Nikolaos I. Papandrianos, Dimitrios J. Apostolopoulos and Elpiniki Papageorgiou

Bioengineering 2024, 11(10), 957; https://doi.org/10.3390/bioengineering11100957 - 25 Sep 2024

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Abstract

Coronary artery disease (CAD) presents a significant global health burden, with early and accurate diagnostics crucial for effective management and treatment strategies. This study evaluates the efficacy of human evaluators compared to a Random Forest (RF) machine learning model in predicting CAD risk. [...] Read more.

Coronary artery disease (CAD) presents a significant global health burden, with early and accurate diagnostics crucial for effective management and treatment strategies. This study evaluates the efficacy of human evaluators compared to a Random Forest (RF) machine learning model in predicting CAD risk. It investigates the impact of incorporating human clinical judgments into the RF model’s predictive capabilities. We recruited 606 patients from the Department of Nuclear Medicine at the University Hospital of Patras, Greece, from 16 February 2018 to 28 February 2022. Clinical data inputs included age, sex, comprehensive cardiovascular history (including prior myocardial infarction and revascularisation), CAD predisposing factors (such as hypertension, dyslipidemia, smoking, diabetes, and peripheral arteriopathy), baseline ECG abnormalities, and symptomatic descriptions ranging from asymptomatic states to angina-like symptoms and dyspnea on exertion. The diagnostic accuracies of human evaluators and the RF model (when trained with datasets inclusive of human judges’ assessments) were comparable at 79% and 80.17%, respectively. However, the performance of the RF model notably declined to 73.76% when human clinical judgments were excluded from its training dataset. These results highlight a potential synergistic relationship between human expertise and advanced algorithmic predictions, suggesting a hybrid approach as a promising direction for enhancing CAD diagnostics. Full article

(This article belongs to the Special Issue Artificial Intelligence in Biomedical Diagnosis and Prognosis: Second Edition)

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10 pages, 241 KiB

Open AccessArticle

Effect of Task Constraints on Neurobiological Systems Involved in Postural Control in Individuals with and without Chronic Ankle Instability

by Yuki A. Sugimoto, Patrick O. McKeon, Christopher K. Rhea, Carl G. Mattacola and Scott E. Ross

Bioengineering 2024, 11(10), 956; https://doi.org/10.3390/bioengineering11100956 - 25 Sep 2024

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Abstract

The purpose of this study is to investigate the effect of task constraints on the neurobiological systems while maintaining postural control under various sensory feedback manipulations in individuals with and without Chronic Ankle Instability (CAI). Forty-two physically active individuals, with and without CAI, [...] Read more.

The purpose of this study is to investigate the effect of task constraints on the neurobiological systems while maintaining postural control under various sensory feedback manipulations in individuals with and without Chronic Ankle Instability (CAI). Forty-two physically active individuals, with and without CAI, were enrolled in a case-control study conducted at a biomechanics research laboratory. All participants underwent the Sensory Organization Test (SOT), which assesses individuals’ ability to integrate somatosensory, visual, and vestibular feedback to maintain postural control in double-, uninjured-, and injured-limb stances under six different conditions in which variations in the sway-referenced support surface (platform) and visual surroundings, with and without vision, are manipulated to affect somatosensory and visual feedback. Center-of-Pressure (COP) path length was computed from raw data collected during trials of each SOT condition. Sample Entropy (SampEN) values were extracted from the COP path length time series to examine neurobiological systems complexity, with lower SampEN values indicating more predictable and periodic (rigid) neurobiological systems, while higher SampEN values indicate more unpredictable and random systems. The results show that specific task constraints affect the neurobiological systems. Specifically, individuals with CAI demonstrated reduced complexity (decreased SampEN values) in the neurobiological systems during the uninjured-limb stance when all sensory feedback was intact and during both uninjured- and injured-limb stances when they were forced to rely on vestibular feedback. These results highlight the interplay between sensory feedback and task constraints in individuals with CAI and suggest potential adaptations in the neurobiological systems involved in postural control. Full article

(This article belongs to the Section Biomechanics and Sports Medicine)

16 pages, 4402 KiB

Open AccessArticle

Computational Model for Early-Stage Aortic Valve Calcification Shows Hemodynamic Biomarkers

by Asad Mirza, Chia-Pei Denise Hsu, Andres Rodriguez, Paulina Alvarez, Lihua Lou, Matty Sey, Arvind Agarwal, Sharan Ramaswamy and Joshua Hutcheson

Bioengineering 2024, 11(10), 955; https://doi.org/10.3390/bioengineering11100955 - 24 Sep 2024

Viewed by 269

Abstract

Heart disease is a leading cause of mortality, with calcific aortic valve disease (CAVD) being the most prevalent subset. Being able to predict this disease in its early stages is important for monitoring patients before they need aortic valve replacement surgery. Thus, this [...] Read more.

Heart disease is a leading cause of mortality, with calcific aortic valve disease (CAVD) being the most prevalent subset. Being able to predict this disease in its early stages is important for monitoring patients before they need aortic valve replacement surgery. Thus, this study explored hydrodynamic, mechanical, and hemodynamic differences in healthy and very mildly calcified porcine small intestinal submucosa (PSIS) bioscaffold valves to determine any notable parameters between groups that could, possibly, be used for disease tracking purposes. Three valve groups were tested: raw PSIS as a control and two calcified groups that were seeded with human valvular interstitial and endothelial cells (VICs/VECs) and cultivated in calcifying media. These two calcified groups were cultured in either static or bioreactor-induced oscillatory flow conditions. Hydrodynamic assessments showed metrics were below thresholds associated for even mild calcification. Young’s modulus, however, was significantly higher in calcified valves when compared to raw PSIS, indicating the morphological changes to the tissue structure. Fluid–structure interaction (FSI) simulations agreed well with hydrodynamic results and, most notably, showed a significant increase in time-averaged wall shear stress (TAWSS) between raw and calcified groups. We conclude that tracking hemodynamics may be a viable biomarker for early-stage CAVD tracking. Full article

(This article belongs to the Special Issue Bioengineering Strategies for Cardiac Tissue)

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24 pages, 2739 KiB

Open AccessReview

Bridging the Gap: Advances and Challenges in Heart Regeneration from In Vitro to In Vivo Applications

by Tatsuya Watanabe, Naoyuki Hatayama, Marissa Guo, Satoshi Yuhara and Toshiharu Shinoka

Bioengineering 2024, 11(10), 954; https://doi.org/10.3390/bioengineering11100954 - 24 Sep 2024

Viewed by 358

Abstract

Cardiovascular diseases, particularly ischemic heart disease, area leading cause of morbidity and mortality worldwide. Myocardial infarction (MI) results in extensive cardiomyocyte loss, inflammation, extracellular matrix (ECM) degradation, fibrosis, and ultimately, adverse ventricular remodeling associated with impaired heart function. While heart transplantation is the [...] Read more.

Cardiovascular diseases, particularly ischemic heart disease, area leading cause of morbidity and mortality worldwide. Myocardial infarction (MI) results in extensive cardiomyocyte loss, inflammation, extracellular matrix (ECM) degradation, fibrosis, and ultimately, adverse ventricular remodeling associated with impaired heart function. While heart transplantation is the only definitive treatment for end-stage heart failure, donor organ scarcity necessitates the development of alternative therapies. In such cases, methods to promote endogenous tissue regeneration by stimulating growth factor secretion and vascular formation alone are insufficient. Techniques for the creation and transplantation of viable tissues are therefore highly sought after. Approaches to cardiac regeneration range from stem cell injections to epicardial patches and interposition grafts. While numerous preclinical trials have demonstrated the positive effects of tissue transplantation on vasculogenesis and functional recovery, long-term graft survival in large animal models is rare. Adequate vascularization is essential for the survival of transplanted tissues, yet pre-formed microvasculature often fails to achieve sufficient engraftment. Recent studies report success in enhancing cell survival rates in vitro via tissue perfusion. However, the transition of these techniques to in vivo models remains challenging, especially in large animals. This review aims to highlight the evolution of cardiac patch and stem cell therapies for the treatment of cardiovascular disease, identify discrepancies between in vitro and in vivo studies, and discuss critical factors for establishing effective myocardial tissue regeneration in vivo. Full article

(This article belongs to the Special Issue New Strategies for Cardiac Tissue Repair and Regeneration)

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29 pages, 4014 KiB

Open AccessArticle

Characterization of Amnion-Derived Membrane for Clinical Wound Applications

by Alison L. Ingraldi, Tim Allen, Joseph N. Tinghitella, William C. Merritt, Timothy Becker and Aaron J. Tabor

Bioengineering 2024, 11(10), 953; https://doi.org/10.3390/bioengineering11100953 - 24 Sep 2024

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Abstract

Human amniotic membrane (hAM), the innermost placental layer, has unique properties that allow for a multitude of clinical applications. It is a common misconception that birth-derived tissue products, such as dual-layered dehydrated amnion–amnion graft (dHAAM), are similar regardless of the manufacturing steps. A [...] Read more.

Human amniotic membrane (hAM), the innermost placental layer, has unique properties that allow for a multitude of clinical applications. It is a common misconception that birth-derived tissue products, such as dual-layered dehydrated amnion–amnion graft (dHAAM), are similar regardless of the manufacturing steps. A commercial dHAAM product, Axolotl Biologix DualGraft™, was assessed for biological and mechanical characteristics. Testing of dHAAM included antimicrobial, cellular biocompatibility, proteomics analysis, suture strength, and tensile, shear, and compressive modulus testing. Results demonstrated that the membrane can be a scaffold for fibroblast growth (cellular biocompatibility), containing an average total of 7678 unique proteins, 82,296 peptides, and 96,808 peptide ion variants that may be antimicrobial. Suture strength results showed an average pull force of 0.2 N per dHAAM sample (equating to a pull strength of 8.5 MPa). Tensile modulus data revealed variation, with wet samples showing 5× lower stiffness than dry samples. The compressive modulus and shear modulus displayed differences between donors (lots). This study emphasizes the need for standardized processing protocols to ensure consistency across dHAAM products and future research to explore comparative analysis with other amniotic membrane products. These findings provide baseline data supporting the potential of amniotic membranes in clinical applications. Full article

(This article belongs to the Special Issue Bioinspired and Biogenic Materials for Biomedical and Bioengineering Applications)

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Bioengineering, Volume 11, Issue 10 (October 2024) – 13 articles

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