Bioengineering最新文献

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.

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.

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.

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.

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.

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.

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.

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.

The registration of preoperative and follow-up brain MRI, which is crucial in illustrating patients' responses to treatments and providing guidance for postoperative therapy, presents significant challenges. These challenges stem from the considerable deformation of brain tissue and the areas of non-correspondence due to surgical intervention and postoperative changes. We propose a stepwise corrected attention registration network grounded in convolutional neural networks (CNNs). This methodology leverages preoperative and follow-up MRI scans as fixed images and moving images, respectively, and employs a multi-level registration strategy that establishes a precise and holistic correspondence between images, from coarse to fine. Furthermore, our model introduces a corrected attention module into the multi-level registration network that can generate an attention map at the local level through the deformation fields of the upper-level registration network and pathological areas of preoperative images segmented by a mature algorithm in BraTS, serving to strengthen the registration accuracy of non-correspondence areas. A comparison between our scheme and the leading approach identified in the MICCAI's BraTS-Reg challenge indicates a 7.5% enhancement in the target registration error (TRE) metric and improved visualization of non-correspondence areas. These results illustrate the better performance of our stepwise corrected attention registration network in not only enhancing the registration accuracy but also achieving a more logical representation of non-correspondence areas. Thus, this work contributes significantly to the optimization of the registration of brain MRI between preoperative and follow-up scans.

Background: Severe Acute Respiratory Syndrome CoronaVirus-2 (SARS-CoV-2) infection can cause feared consequences, such as affecting microcirculatory activity. The combined use of HRV analysis, genetic algorithms, and machine learning classifiers can be helpful in better understanding the characteristics of microcirculation that are mainly affected by COVID-19 infection.

Methods: This study aimed to verify the presence of microcirculation alterations in patients with COVID-19 infection, performing Heart Rate Variability (HRV) parameters analysis extracted from PhotoPlethysmoGraphy (PPG) signals. The dataset included 97 subjects divided into two groups: healthy (50 subjects) and patients affected by mild-severity COVID-19 (47 subjects). A total of 26 parameters were extracted by the HRV analysis and were investigated using genetic algorithms with three different subject selection methods and five different machine learning classifiers.

Results: Three parameters: meanRR, alpha1, and sd2/sd1 were considered significant, combining the results obtained by the genetic algorithm. Finally, machine learning classifications were performed by training classifiers with only those three features. The best result was achieved by the binary Decision Tree classifier, achieving accuracy of 82%, specificity (or precision) of 86%, and sensitivity of 79%.

Conclusions: The study's results highlight the ability to use HRV parameters extraction from PPG signals, combined with genetic algorithms and machine learning classifiers, to determine which features are most helpful in discriminating between healthy and mild-severity COVID-19-affected subjects.